Cancer Diagnostics Using Non-Coding Transcripts

BACKGROUND OF THE INVENTION

Cancer is the uncontrolled growth of abnormal cells anywhere in a body. The abnormal cells are termed cancer cells, malignant cells, or tumor cells. Many cancers and the abnormal cells that compose the cancer tissue are further identified by the name of the tissue that the abnormal cells originated from (for example, breast cancer, lung cancer, colon cancer, prostate cancer, pancreatic cancer, thyroid cancer). Cancer is not confined to humans; animals and other living organisms can get cancer. Cancer cells can proliferate uncontrollably and form a mass of cancer cells. Cancer cells can break away from this original mass of cells, travel through the blood and lymph systems, and lodge in other organs where they can again repeat the uncontrolled growth cycle. This process of cancer cells leaving an area and growing in another body area is often termed metastatic spread or metastatic disease. For example, if breast cancer cells spread to a bone (or anywhere else), it can mean that the individual has metastatic breast cancer.

Standard clinical parameters such as tumor size, grade, lymph node involvement and tumor-node-metastasis (TNM) staging (American Joint Committee on Cancer http://www.cancerstaging.org) may correlate with outcome and serve to stratify patients with respect to (neo)adjuvant chemotherapy, immunotherapy, antibody therapy and/or radiotherapy regimens. Incorporation of molecular markers in clinical practice may define tumor subtypes that are more likely to respond to targeted therapy. However, stage-matched tumors grouped by histological or molecular subtypes may respond differently to the same treatment regimen. Additional key genetic and epigenetic alterations may exist with important etiological contributions. A more detailed understanding of the molecular mechanisms and regulatory pathways at work in cancer cells and the tumor microenvironment (TME) could dramatically improve the design of novel anti-tumor drugs and inform the selection of optimal therapeutic strategies. The development and implementation of diagnostic, prognostic and therapeutic biomarkers to characterize the biology of each tumor may assist clinicians in making important decisions with regard to individual patient care and treatment. Thus, disclosed herein are methods, compositions and systems for the analysis of coding and/or non-coding targets for the diagnosis, prognosis, and monitoring of a cancer.

This background information is provided for the purpose of making known information believed by the applicant to be of possible relevance to the present invention. No admission is necessarily intended, nor should be construed, that any of the preceding information constitutes prior art against the present invention.

SUMMARY OF THE INVENTION

To aid in the understanding of the present invention, a list of commonly used abbreviations is provided in Table 1. Disclosed herein are compositions, systems, and methods for diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject. In some instances, the method comprises (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a coding target and a non-coding target, wherein the non-coding target is a non-coding RNA transcript selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

In some instances, the method comprises (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a coding target and a non-coding target, wherein the non-coding target is not selected from the group consisting of a miRNA and an intronic sequence; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

Alternatively, the method comprises (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a coding target and a non-coding target, wherein the non-coding target is not selected from the group consisting of a miRNA, an intronic sequence, and a UTR sequence; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

In other instances, the method comprises (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein (i) the plurality of targets consist essentially of a non-coding target or a non-exonic transcript; (ii) the non-coding target is selected from the group consisting of a UTR sequence, an intronic sequence, or a non-coding RNA transcript, and (iii) the non-coding RNA transcript is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the method further comprises assaying an expression level of a coding target.

In some instances, the method comprises (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a non-coding target, wherein the non-coding target is a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the method further comprises assaying an expression level of a coding target.

Alternatively, the method comprises (a) providing a sample from a subject; (b) conducting a reaction to determine an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets are identified based on a classifier; and (c) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

The method may comprise (a) providing a sample from a subject; (b) conducting a reaction to determine an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets are identified based on at least one probe selection region (PSR); and (c) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

In other instances, the method comprises (a) providing a sample from a subject; (b) conducting a reaction to determine an expression level in a sample from the subject for a plurality of targets, wherein at least about 10% of the plurality of targets are non-coding targets; and (c) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets.

Further disclosed herein in some embodiments is a method of analyzing a cancer in an individual in need thereof, comprising: (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets selected from Table 6; and (b) comparing the expression profile from the sample to an expression profile of a control or standard. In some embodiments, the method further comprises providing diagnostic or prognostic information to the individual about the cardiovascular disorder based on the comparison.

Further disclosed herein in some embodiments is a method of diagnosing cancer in an individual in need thereof, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets selected from Table 6; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) diagnosing a cancer in the individual if the expression profile of the sample (i) deviates from the control or standard from a healthy individual or population of healthy individuals, or (ii) matches the control or standard from an individual or population of individuals who have or have had the cancer.

Further disclosed herein in some embodiments is a method of predicting whether an individual is susceptible to developing a cancer, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets selected from Table 6; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) predicting the susceptibility of the individual for developing a cancer based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

Further disclosed herein in some embodiments is a method of predicting an individual's response to a treatment regimen for a cancer, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets selected from Table 6; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) predicting the individual's response to a treatment regimen based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

Disclosed herein in some embodiments is a method of prescribing a treatment regimen for a cancer to an individual in need thereof, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets selected from Table 6; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) prescribing a treatment regimen based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

In some embodiments, the methods disclosed herein further comprise diagnosing the individual with a cancer if the expression profile of the sample (a) deviates from the control or standard from a healthy individual or population of healthy individuals, or (b) matches the control or standard from an individual or population of individuals who have or have had the cancer.

The methods disclosed herein can further comprise predicting the susceptibility of the individual for developing a cancer based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. In some instances, the methods disclosed herein further comprise prescribing a treatment regimen based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. Alternatively, or additionally, the methods disclosed herein further comprise altering a treatment regimen prescribed or administered to the individual based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

In some instances, the methods disclosed herein further comprise predicting the individual's response to a treatment regimen based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. In some instances, the deviation is the expression level of one or more targets from the sample is greater than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. Alternatively, or additionally, the deviation is the expression level of one or more targets from the sample is at least about 30% greater than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. In some embodiments, the deviation is the expression level of one or more targets from the sample is less than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. In some instances, the deviation is the expression level of one or more targets from the sample is at least about 30% less than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals.

The methods disclosed herein can further comprise using a machine to isolate the target or the probe from the sample. Alternatively, or additionally, the methods disclosed herein further comprise contacting the sample with a label that specifically binds to the target, the probe, or a combination thereof. In some embodiments, the methods disclosed herein further comprise contacting the sample with a label that specifically binds to a target selected from Table 6. In some embodiments, the methods disclosed herein further comprise amplifying the target, the probe, or any combination thereof. The methods disclosed herein can further comprise sequencing the target, the probe, or any combination thereof. In some instances, the method further comprises quantifying the expression level of the plurality of targets. In some embodiments, the method further comprises labeling the plurality of targets.

In some instances, the methods disclosed herein further comprise converting the expression levels of the target sequences into a likelihood score that indicates the probability that a biological sample is from a patient who will a clinical outcome. In some instances, the clinical outcome is an exhibition of: (a) no evidence of disease; (b) no disease progression; (c) disease progression; (d) metastasis; (e) no metastasis; (f) systemic cancer; or (g) biochemical recurrence.

In some embodiments, the methods disclosed herein further comprise quantifying the expression level of the plurality of targets. In some instances, the method further comprises labeling the plurality of targets. In some instances, the target sequences are differentially expressed in the cancer. In some embodiments, the differential expression is dependent on aggressiveness. The expression profile can be determined by a method selected from the group consisting of RT-PCR, Northern blotting, ligase chain reaction, array hybridization, and a combination thereof. Alternatively, the expression profile is determined by RNA-Seq.

In some instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 50%. In other instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 60%. The methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 65%. Alternatively, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 70%. In some instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 75%. In other instances, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 80%. The methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 85%. Alternatively, the methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 90%. The methods disclosed herein can diagnose, prognose, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 95%.

In some instances, assaying the expression level of a plurality of targets comprises the use of a probe set. Assaying the expression level of a plurality of targets can comprise the use of a probe selection region (PSR). Alternatively, or additionally, assaying the expression level of a plurality of targets can comprise the use of an ICE block. In some embodiments, obtaining the expression level comprises the use of a classifier. The classifier may comprise a probe selection region (PSR). In some instances, the classifier comprises the use of an algorithm. The algorithm can comprise a machine learning algorithm. In some instances, obtaining the expression level also comprise sequencing the plurality of targets. In some embodiments, obtaining the expression level may also comprise amplifying the plurality of targets. In some embodiments, obtaining the expression level may also comprise quantifying the plurality of targets.

In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the malignancy or malignant potential of the cancer or tumor. Alternatively, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the stage of the cancer. The diagnosing, predicting, and/or monitoring the status or outcome of a cancer can comprise determining the tumor grade. Alternatively, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises assessing the risk of developing a cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes assessing the risk of cancer recurrence. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining the efficacy of treatment.

In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen.

Further disclosed herein is a kit for analyzing a cancer, comprising (a) a probe set comprising a plurality of target sequences, wherein the plurality of target sequences comprises at least one target sequence listed in Table 6; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the target sequences in a sample. In some embodiments, the kit further comprises a computer model or algorithm for correlating the expression level or expression profile with disease state or outcome. In some embodiments, the kit further comprises a computer model or algorithm for designating a treatment modality for the individual. In some embodiments, the kit further comprises a computer model or algorithm for normalizing expression level or expression profile of the target sequences. In some embodiments, the kit further comprises a computer model or algorithm comprising a robust multichip average (RMA), probe logarithmic intensity error estimation (PLIER), non-linear fit (NLFIT) quantile-based, nonlinear normalization, or a combination thereof.

Further disclosed herein is a kit for analyzing a cancer, comprising (a) a probe set comprising a plurality of target sequences, wherein the plurality of target sequences hybridizes to one or more targets selected from Table 6; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the target sequences in a sample. In some embodiments, the kit further comprises a computer model or algorithm for correlating the expression level or expression profile with disease state or outcome. In some embodiments, the kit further comprises a computer model or algorithm for designating a treatment modality for the individual. In some embodiments, the kit further comprises a computer model or algorithm for normalizing expression level or expression profile of the target sequences. In some embodiments, the kit further comprises a computer model or algorithm comprising a robust multichip average (RMA), probe logarithmic intensity error estimation (PLIER), non-linear fit (NLFIT) quantile-based, nonlinear normalization, or a combination thereof.

Disclosed herein, in some embodiments, is a classifier for diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The classifier may comprise a classifier as disclosed in Table 17. The classifier can comprise a classifier as disclosed in Table 19. The classifier can comprise the GLM2, KNN12, KNN16, NB20, SVM5, SVM11, SVM20 classifiers or any combination thereof. The classifier can comprise a GLM2 classifier. Alternatively, the classifier comprises a KNN12 classifier. The classifier can comprise a KNN16 classifier. In other instances, the classifier comprises a NB20 classifier. The classifier may comprise a SVM5 classifier. In some instances, the classifier comprises a SVM11 classifier. Alternatively, the classifier comprises a SVM20 classifier. Alternatively, the classifier comprises one or more Inter-Correlated Expression (ICE) blocks disclosed herein. The classifier can comprise one or more probe sets disclosed herein. In some instances, the classifiers disclosed herein have an AUC value of at least about 0.50. In other instances, the classifiers disclosed herein have an AUC value of at least about 0.60. The classifiers disclosed herein can have an AUC value of at least about 0.70.

Further disclosed herein, is an Inter-Correlated Expression (ICE) block for diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The ICE block may comprise one or more ICE Block IDs as disclosed in Tables 22-24. The ICE block can comprise Block ID_—2879, Block ID_—2922, Block ID_—4271, Block ID_—4627, Block ID_—5080, or any combination thereof. Alternatively, the ICE block comprises Block ID_—6592, Block ID_—4226, Block ID_—6930, Block ID_—7113, Block ID_—5470, or any combination thereof. In other instances, the ICE block comprises Block ID_—7716, Block ID_—4271, Block ID_—5000, Block ID_—5986, Block ID_—1146, Block ID_—7640, Block ID_—4308, Block ID_—1532, Block ID_—2922, or any combination thereof. The ICE block can comprise Block ID_—2922. Alternatively, the ICE block comprises Block ID_—5080. In other instances, the ICE block comprises Block ID_—6592. The ICE block can comprise Block ID_—4627. Alternatively, the ICE block comprises Block ID_—7113. In some instances, the ICE block comprises Block ID_—5470. In other instances, the ICE block comprises Block ID_—5155. The ICE block can comprise Block ID_—6371. Alternatively, the ICE block comprises Block ID_—2879.

Further disclosed herein, is a probe set for diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The probe set may comprise a plurality of probes, wherein (i) the probes in the set are capable of detecting an expression level of at least one non-coding target; and (ii) the expression level determines the cancer status of the subject with at least about 40% specificity. In some embodiments, the probe set further comprises a probe capable of detecting an expression level of at least one coding target.

Further disclosed herein, is a probe set for diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The probe set may comprise a plurality of probes, wherein (i) the probes in the set are capable of detecting an expression level of at least one non-coding target; and (ii) the expression level determines the cancer status of the subject with at least about 40% accuracy. In some embodiments, the probe set further comprises a probe capable of detecting an expression level of at least one coding target.

Further disclosed herein, is a probe selection region (PSR) for diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The PSR can comprise any of the probe sets disclosed herein. Alternatively, the PSR comprises any of the probe sets as disclosed in Tables 4, 15, 17, 19, 22-24, and 27-30 (see ‘Probe set ID’ column). In some instances, the probe set comprises probe set ID 2518027. Alternatively, the probe set comprises probe set ID 3046448; 3046449; 3046450; 3046457; 3046459; 3046460; 3046461; 3046462; 3046465; 3956596; 3956601; 3956603; 3103704; 3103705; 3103706; 3103707; 3103708; 3103710; 3103712; 3103713; 3103714; 3103715; 3103717; 3103718; 3103720; 3103721; 3103725; 3103726; 2719689; 2719692; 2719694; 2719695; 2719696; 2642733; 2642735; 2642738; 2642739; 2642740; 2642741; 2642744; 2642745; 2642746; 2642747; 2642748; 2642750; 2642753; 3970026; 3970034; 3970036; 3970039; 2608321; 2608324; 2608326; 2608331; 2608332; 2536222; 2536226; 2536228; 2536229; 2536231; 2536232; 2536233; 2536234; 2536235; 2536236; 2536237; 2536238; 2536240; 2536241; 2536243; 2536245; 2536248; 2536249; 2536252; 2536253; 2536256; 2536260; 2536261; 2536262; 3670638; 3670639; 3670641; 3670644; 3670645; 3670650; 3670659; 3670660; 3670661; 3670666, a complement thereof, a reverse complement thereof, or any combination thereof.

Further disclosed herein in some embodiments is a system for analyzing a cancer, comprising: (a) a probe set comprising a plurality of target sequences, wherein (i) the plurality of target sequences hybridizes to one or more targets selected from Table 6; or (ii) the plurality of target sequences comprises one or more target sequences selected SEQ ID NOs: 1-903; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the target hybridized to the probe in a sample from a subject suffering from a cancer.

In some instances, the plurality of targets disclosed herein comprises at least 5 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 10 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 15 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 20 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 30 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 35 targets selected from Table 6. In some embodiments, the plurality of targets comprises at least 40 targets selected from Table 6.

In some instances, the systems disclosed herein further comprise an electronic memory for capturing and storing an expression profile. The systems disclosed herein can further comprise a computer-processing device, optionally connected to a computer network. Alternatively, or additionally, the systems disclosed herein further comprise a software module executed by the computer-processing device to analyze an expression profile. In some instances, the systems disclosed herein further comprise a software module executed by the computer-processing device to compare the expression profile to a standard or control. The systems disclosed herein can further comprise a software module executed by the computer-processing device to determine the expression level of the target. The systems disclosed herein can further comprise a machine to isolate the target or the probe from the sample. In some instances systems disclosed herein further comprises a machine to sequence the target or the probe. Alternatively, or additionally, the systems disclosed herein further comprise a machine to amplify the target or the probe. The systems disclosed herein can further comprise a label that specifically binds to the target, the probe, or a combination thereof. In some embodiments, the systems disclosed herein further comprise a software module executed by the computer-processing device to transmit an analysis of the expression profile to the individual or a medical professional treating the individual. In some embodiments, the systems disclosed herein further comprise a software module executed by the computer-processing device to transmit a diagnosis or prognosis to the individual or a medical professional treating the individual. In some instances, the systems disclosed herein further comprise a sequencer for sequencing the plurality of targets. In other instances, the systems disclosed herein further comprise an instrument for amplifying the plurality of targets. In some embodiments, the systems disclosed herein further comprise a label for labeling the plurality of targets.

In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some instances, the cancer is a bladder cancer.

In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence.

The non-coding target can be selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some embodiments, the non-coding target is selected from an intronic sequence, a sequence within the UTR, or a non-coding RNA transcript. In some embodiments, the non-coding target is an intronic sequence or partially overlaps with an intronic sequence. In some embodiments, the non-coding target is a UTR sequence or partially overlaps with a UTR sequence.

In some embodiments, the non-coding target is a non-coding RNA transcript. In some embodiments, the non-coding RNA transcript is selected from the group consisting of PASR, TASR, aTASR, TSSa-RNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the non-coding RNA transcript is non-polyadenylated.

In some instances, the coding target is selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence.

In some instances, the plurality of targets comprises at least about 2 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. Alternatively, or additionally, the plurality of targets comprises at least about 3 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The plurality of targets can comprise at least about 5 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The plurality of targets can comprise at least about 10 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The plurality of targets can comprise at least about 15 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The plurality of targets can comprise at least about 20 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The plurality of targets can comprise at least about 25 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the plurality of targets comprises at least about 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, or 425 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In other instances, the plurality of targets comprises at least about 450, 475, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, or 900 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference in their entireties to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Venn Diagram of the distribution of coding (a), non-coding (b) and non-exonic (c) PSRs found differentially expressed in normal versus primary tumor tissue (N vs P), primary versus metastatic Tissue (P vs M), and normal versus metastatic tissue (N vs M), respectively.

FIG. 2. Annotation of non-exonic PSRs and distribution of non-coding transcripts found to be differentially expressed between normal and primary tumour (a, d), primary tumour and metastatic tissue (b,e) and normal versus metastatic tissue (c,f). Those PSRs in the NC TRANSCRIPT slice of each pie chart are assessed for their overlap with non-coding transcripts to generate the categorization shown at the right for each pairwise comparison. AS: Antisense.

FIG. 3. MDS plots of the distribution of primary tumour samples with (circle) and without (square) metastatic events compared to metastatic (triangle) and normal (+) tissues for coding (a), non-coding (b) and non-exonic (c) probe sets.

FIG. 4. Kaplan-Meier plots of the two groups of primary tumor samples classified by KNN (more ‘normal-like’ vs. ‘metastatic-like’) using the biochemical recurrence (BCR) end point for coding (a), non-coding (b) and non-exonic (c).

FIG. 5. MDS plots of the distribution of primary tumour samples with Gleason score of 6 (circle), 7 (triangle), 8 and 9 (square) compared to metastatic (+) and normal (x) tissues for coding (a), non-coding (b) and non-exonic (c) PSRs.

FIG. 6. Illustration of (a) protein-coding and (b) non protein-coding gene structures.

FIG. 7. Illustration of the categorization of probe selection regions.

FIG. 8. List of potential probe selection regions.

FIG. 9. BCR KMM plot in MSKCC for different KNN models based on PSR genomic subsets

FIG. 10. Illustration of syntenic blocks.

FIG. 11. Venn Diagram distribution of differentially expressed transcripts across pairwise comparison. N vs P: Normal Adjacent versus Primary tumor comparison. P vs M: Primary Tumor versus Metastatic sample comparison. N vs M: Normal adjacent versus Metastatic Sample comparison.

FIG. 12. Heat map of genes with two or more transcripts differentially expressed across any pairwise comparison. Transcript names are provided as annotated in Ensembl. Heatmap is colored according to median expression values for Normal (N), Primary (P) and metastatic (M) samples. ‘*’ indicates that the transcript is protein-coding. Background indicates the expression value considered as background level based on control probe sets on the HuEx array.

FIG. 13. Heat map of genes with one or more transcripts differentially expressed across any pairwise comparison for which all transcripts were assessed. Transcript names are provided as annotated in Ensembl. Gene names are annotated based on their gene symbol. Heatmap is colored according to median expression values for Normal (N), Primary (P) and metastatic (M) samples. ‘*’ indicates that the transcript is protein-coding. ‘+’ indicates significant differential expression of a given transcript or gene. Background indicates the expression value considered as background level based on control probe sets on the HuEx array.

FIG. 14. Kaplan Meier plots of the two groups of primary tumor samples classified by KNN (“normal-like” vs “metastatic-like”) using the BCR endpoint for (a) Transcripts (represented by transcript-specific PSRs), (b) Kaftan nomogram and (c) Genes.

FIG. 15. Illustration of filtered and kept TS-PSRs. A) TS-PSR of a gene having only one transcript annotated. B) TS-PSRs for only one transcript of a gene with two or more transcripts. c) A gene for which at least two of its transcripts has a TS-PSR.

FIG. 16. Genomic Annotation and Distribution of the PSRs found differentially expressed within chr2q31.3 region.

FIG. 17. KM curve for a PSR (Probe set ID 2518027) for the BCR endpoint. P-value=0.00.

FIG. 18. Distribution of PSRs differentially expressed between low risk (GS<7) and high risk (GS>7) samples.

FIG. 19. (a) Box plots showing DIGS-RF12 segregating the Gleason 3+4 samples from the Gleason 4+3 samples. (b) KM plot of BCR-Free survival based on the groups predicted by DIGS-RF12.

FIG. 20. Genes with transcript-specific PSRs differentially expressed based on MSKCC data. (a) Gene CHRAC1. (b) Gene IMPDH1

FIG. 21. Depicts the ROC curves at 4 years (a) Survival ROC curves at 4 years for the training set for GC and GCC for patients with progression. (b) Survival ROC curves at 4 years for the testing set for GC and GCC for patients with progression.

FIG. 22. Discrimination Box plots for GC and GCC. Box plots depict the distribution of classifier scores between patients with and without progression. Boxes extend between the 25th and 75th percentiles (lower and upper quartiles, respectively), and the notch represents the 50th percentile (median). Whiskers extend indicating 95% confidence intervals.

FIG. 23. Calibration plots for GC and GCC. Calibration plots segregate the classifier scores into quintiles. For each quintile, mean score is plotted against the total proportion of patients who experienced progression. Perfect calibration, represented by the dashed 45-degree line, implies that the mean score is roughly equivalent to the proportion of patients who experienced progression (e.g. if the mean score is 0.20, then approximately 20% of patients in that quintile group experienced progression). Triangles represent the grouped patients, plotted by mean classifier score of that group against the observed frequency of progression. Compared to a poor model, a classifier that is a good discriminator will have a greater distance between the groups. The 95% confidence intervals are plotted for each group. Intercept indicates whether the predictions are systemically too high or too low, and an optimal slope approximately equals 1; slopes <1 indicate overfitting of the classifier.

FIG. 24. Cumulative incidence of disease progression for GC and GCC. Cumulative incidence curves were constructed using competing risks analysis to accommodate censoring due to death and other events that bias Kaplan-Meier estimates of incidence.

FIG. 25. Illustration of probe selection methods

FIG. 26. ROC curves (A) and KM plots (B) for NB20. (A) ROC curves are shown separately for training (trn) and testing (tst) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Kaplan Meier curves on the training (trn) and testing (tst) sets for two groups of patients (GC=Low and GC=High) based on PAM clustering.

FIG. 27. ROC curves (A) and KM plots (B) for KNN12. (A) ROC curves are shown separately for training (trn) and testing (tst) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Kaplan Meier curves on the training (trn) and testing (tst) sets for two groups of patients (GC=Low and GC=High) based on PAM clustering.

FIG. 28. ROC curves (A) and KM plots (B) for GLM2. (A) ROC curves are shown separately for training (trn) and testing (tst) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Kaplan Meier curves on the training (trn) and testing (tst) sets for two groups of patients (GC=Low and GC=High) based on PAM clustering.

FIG. 29. ROC curves (A) and KM plots (B) for a PSR intronic to gene MECOM (probe set ID 2704702). (A) ROC curves are shown separately for training (trn) and testing (tst) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Kaplan Meier curves on the training (trn) and testing (tst) sets for two groups of patients (GC=Low and GC=High) based on PAM clustering.

FIG. 30. ROC curves (A) and box plots (B) for SVM20. (A) ROC curves are shown separately for training (left) and testing (right) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Box plots on the training (left) and testing (right) sets. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+).

FIG. 31. ROC curves (A) and box plots (B) for SVM11. (A) ROC curves are shown separately for training (left) and testing (right) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Box plots on the training (left) and testing (right) sets. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+).

FIG. 32. ROC curves (A) and box plots (B) for SVM5. (A) ROC curves are shown separately for training (left) and testing (right) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Box plots on the training (left) and testing (right) sets. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+).

FIG. 33. ROC curves (A) and box plots (B) for GLM2. (A) ROC curves are shown separately for training (left) and testing (right) sets. 95% confidence intervals for AUC as well as P-values for the significance of the P-values based on the non-parametric Wilcoxon test. (B) Box plots on the training (left) and testing (right) sets. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+).

FIG. 34. Box plot (A) and ROC curve (B) for ICE Block 7716 for GS endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance.

FIG. 35. Box plot (A) and ROC curve (B) for ICE Block 4271 for GS endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance.

FIG. 36. Box plot (A) and ROC curve (B) for ICE Block 5000 for GS endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance.

FIG. 37. Box plot (A) and ROC curve (B) for ICE Block 2922 for GS endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance.

FIG. 38. Box plot (A) and ROC curve (B) for ICE Block 5080 for GS endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (GS6 or GS7+). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance.

FIG. 39. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 6592 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 40. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 4627 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 41. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 7113 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 42. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 5470 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 43. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 5155 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 44. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 6371 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 45. Box plot (A), ROC curve (B) and KM plots (C) for ICE Block 2879 for BCR endpoint. (A) Box plot. Notches represent 95% confidence intervals for the scores associated to a given group (BCR or non-BCR). (B) ROC curve. 95% confidence interval for the AUC is provided as a metric of the statistical significance. (C) Kaplan Meier curve for two groups of patients based on median split into high and low expression groups. Chi-square P-value indicates the statistical significance of the difference between the curves for both groups.

FIG. 46. Discrimination of KNN16 in MSKCC upgrading testing set.

FIG. 47. ROC plot of clinical and pathological factors in comparison to KNN16.

FIG. 48. Heatmap of the 98 selected features in the pooled training and testing set.

FIG. 49. Multidimensional scaling of normal and tumor samples for lung and colorectal cancer. (A) MDS plots of normal (triangle) and cancer (circle) matched lung samples using differentially expressed non-coding RNA features. (B) MDS plots of normal (triangle) and cancer (circle) colorectal samples using differentially expressed non-coding RNA features.

FIG. 50. Multidimensional scaling and expression density curve of tumor samples at different progression stages for lung and colorectal cancer. (A) MDS plots of tumor stage I (triangle) and stages II and III (circle) lung samples using differentially expressed non-coding RNA features. (B) Expression density of the XIST-associated PSR 4012540 for stage II (dotted line) and stage III (solid line) colorectal carcinomas.

Table 1. List of Abbreviations.

Table 2. Summary of the clinical characteristics of the dataset used in Example 1.

Table 3. Definitions of Ensembl ‘Transcript Biotype’ annotations for non-coding transcripts found differentially expressed.

Table 4. Long non-coding RNAs differentially expressed in prostate cancer.

Table 5. Logistic regression analysis for prediction of the probability of clinical recurrence (CR). SVI: Seminal Vesicle Invasion; ECE: Extracapsular Extension; SMS: Surgical Margin Status; LNI: Lymph node Involvement; PreTxPSA: Pre-operative PSA; PGS: Pathological Gleason Score.

Table 6. List of Coding probe selection regions (coding PSRs) and Non-coding probe selection regions (non-coding PSRs).

Table 7. Protein-coding genes with non-coding transcripts differentially expressed. NvsP: Normal Adjacent versus Primary tumor comparison. PvsM: Primary Tumor versus Metastatic sample comparison. NvsM: Normal adjacent versus Metastatic Sample comparison.

Table 8. Transcripts found differentially expressed across all pairwise comparison (top) and across Normal vs Primary Tumor and Primary Tumor vs Metastatic samples comparisons (bottom). (*) indicates upregulation. No (*) indicates downregulation. N.A.: Not Applicable.

Table 9. Multivariable Logistic Regression Analysis of transcripts (represented by Transcript-Specific PSRs) and genes adjusted by Kattan Nomogram. KNN-positive: metastatic-like. *: Greater than 50% probability of BCR used as cut-off OR: Odds Ratio. CI: Confidence Interval.

Table 10. Characteristics of the study population.

Table 11. Multivariable Cox proportional hazards modeling of clinicopathologic features.

Table 12. Classifier performance of clinicopathologic features. In addition, two multivariate clinical classifiers were built using a logistic model (CC1) as well as a Cox model (CC2).

Table 13. Multivariable Cox proportional hazards modeling of GC and clinicopathologic features.

Table 14. Raw clinical data, QC results, training and testing sets and classifier scores for each of the 251 samples.

Table 15. List of probe sets and associated genes that overlap with KNN89 PSRs.

Table 16. Machine Learning algorithms, ranking, standardization methods and number of features included in each classifier. Additionally, the performance based on AUC is included for the training and testing sets.

Table 17. Sequences composing the classifiers. For each sequence, the chromosomal coordinates, associated gene (if not intergenic), type of feature (coding or non-coding), and classifier(s) are listed.

Table 18. Machine Learning algorithms, ranking, standardization methods and number of features included in each classifier. Additionally, the performance based on AUC is included for the training and testing sets.

Table 19. Sequences composing the classifiers. For each sequence, the chromosomal coordinates, associated gene (if not intergenic), type of feature (coding or non-coding), and classifier(s) are listed.

Table 20. Number of ICE blocks found across different comparisons and different correlation thresholds. Numbers in parenthesis indicate the number of ICE blocks found differentially expressed when using a P-value threshold of 0.05.

Table 21. Number of ICE blocks differentially expressed across different compositions of coding and non-coding PSRs, different correlation thresholds and different comparisons. The number of ICE blocks found differentially expressed is obtained by using a P-value threshold of 0.05.

Table 22. ICE blocks found differentially expressed for the Gleason Score comparison when using a strict correlation threshold of 0.9. For each ICE block, the following information is provided: Block ID, Wilcoxon P-value, chromosomal location, number of overlapping genes across the genomic span of the ICE block, overlapping genes, Composition of the ICE block as a percentage of coding and non-coding PSRs, number of PSRs composing the ICE block and Probe set IDs that correspond to the PSRs composing the ICE block.

Table 23. ICE blocks found differentially expressed for the Biochemical Recurrence comparison when using a strict correlation threshold of 0.9. For each ICE block, the following information is provided: Block ID, Wilcoxon P-value, chromosomal location, number of overlapping genes across the genomic span of the ICE block, overlapping genes, Composition of the ICE block as a percentage of coding and non-coding PSRs, number of PSRs composing the ICE block and Probe set IDs that correspond to the PSRs composing the ICE block.

Table 24. Sequences and Probe set IDs associated to the PSRs composing the ICE blocks assessed in FIGS. 33-44.

Table 25. The number of cases and controls in the training and testing set.

Table 26. Features used for modeling a KNN classifier.

Table 27. Differentially expressed non-coding RNA features between normal and tumor lung cancer. For each feature, sequence number ID, probe set IDs and associated gene are listed.

Table 28. Differentially expressed non-coding RNA features between normal and tumor colorectal cancer. For each feature, sequence number ID, probe set IDs and associated gene are listed.

Table 29. Differentially expressed non-coding RNA features between stage I and stage II+III lung cancer. For each feature, sequence number ID, probe set IDs and associated gene are listed.

Table 30. Differentially expressed non-coding RNA features between stage II and stage III colorectal cancer. For each feature, sequence number ID, probe set IDs and associated gene are listed.

DETAILED DESCRIPTION OF THE INVENTION

The present invention discloses systems and methods for diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject using expression-based analysis of coding targets, non-coding targets, and/or non-exonic transcripts. Generally, the method comprises (a) optionally providing a sample from a subject suffering from a cancer; (b) assaying the expression level for a plurality of targets in the sample; and (c) diagnosing, predicting and/or monitoring the status or outcome of the cancer based on the expression level of the plurality of targets.

Assaying the expression level for a plurality of targets in the sample may comprise applying the sample to a microarray. In some instances, assaying the expression level may comprise the use of an algorithm. The algorithm may be used to produce a classifier. Alternatively, the classifier may comprise a probe selection region. Assaying the expression level for a plurality of targets may comprise detecting and/or quantifying the plurality of targets.

In some instances, the plurality of targets may comprise a coding target and a non-coding target and the non-coding target is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. Alternatively, the plurality of targets may comprise a coding target and a non-coding target, wherein the non-coding target does not comprise a miRNA, an intronic sequence, and a UTR sequence. In other instances, the plurality of targets may consist essentially of a non-coding target selected from the group consisting of a UTR sequence, an intronic sequence, or a non-coding RNA transcript, wherein the non-coding RNA transcript comprises a piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, or LSINCTs. The plurality of targets may also comprise a non-coding target, wherein the non-coding target is a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated.

In some instances, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 1-903. In other instances, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target comprises a sequence selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target comprises a sequence selected from SEQ ID NOs.: 262-291.

Further disclosed herein, is a probe set for diagnosing, predicting, and/or monitoring a cancer in a subject. In some instances, the probe set comprises a plurality of probes capable of detecting an expression level of at least one non-coding RNA transcript, wherein the expression level determines the cancer status or outcome of the subject with at least about 45% specificity. In some instances, the probe set comprises a plurality of probes capable of detecting an expression level of at least one non-coding RNA transcript, wherein the expression level determines the cancer status or outcome of the subject with at least about 45% accuracy.

In some instances, characterizing the subject comprises determining whether the subject would respond to an anti-cancer therapy. Alternatively, characterizing the subject comprises identifying the subject as a non-responder to an anti-cancer therapy. Optionally, characterizing the subject comprises identifying the subject as a responder to an anti-cancer therapy.

Before the present invention is described in further detail, it is to be understood that this invention is not limited to the particular methodology, compositions, articles or machines described, as such methods, compositions, articles or machines can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention.

DEFINITIONS

Unless defined otherwise or the context clearly dictates otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In describing the present invention, the following terms may be employed, and are intended to be defined as indicated below.

The term “polynucleotide” as used herein refers to a polymer of greater than one nucleotide in length of ribonucleic acid (RNA), deoxyribonucleic acid (DNA), hybrid RNA/DNA, modified RNA or DNA, or RNA or DNA mimetics, including peptide nucleic acids (PNAs). The polynucleotides may be single- or double-stranded. The term includes polynucleotides composed of naturally-occurring nucleobases, sugars and covalent internucleoside (backbone) linkages as well as polynucleotides having non-naturally-occurring portions which function similarly. Such modified or substituted polynucleotides are well known in the art and for the purposes of the present invention, are referred to as “analogues.”

“Complementary” or “substantially complementary” refers to the ability to hybridize or base pair between nucleotides or nucleic acids, such as, for instance, between a sensor peptide nucleic acid or polynucleotide and a target polynucleotide. Complementary nucleotides are, generally, A and T (or A and U), or C and G. Two single-stranded polynucleotides or PNAs are said to be substantially complementary when the bases of one strand, optimally aligned and compared and with appropriate insertions or deletions, pair with at least about 80% of the bases of the other strand, usually at least about 90% to 95%, and more preferably from about 98 to 100%.

Alternatively, substantial complementarity exists when a polynucleotide may hybridize under selective hybridization conditions to its complement. Typically, selective hybridization may occur when there is at least about 65% complementarity over a stretch of at least 14 to 25 bases, for example at least about 75%, or at least about 90% complementarity. See, M. Kanehisa, Nucleic Acids Res. 12:203 (1984).

“Preferential binding” or “preferential hybridization” refers to the increased propensity of one polynucleotide to bind to its complement in a sample as compared to a noncomplementary polymer in the sample.

Hybridization conditions may typically include salt concentrations of less than about 1M, more usually less than about 500 mM, for example less than about 200 mM. In the case of hybridization between a peptide nucleic acid and a polynucleotide, the hybridization can be done in solutions containing little or no salt. Hybridization temperatures can be as low as 5° C., but are typically greater than 22° C., and more typically greater than about 30° C., for example in excess of about 37° C. Longer fragments may require higher hybridization temperatures for specific hybridization as is known in the art. Other factors may affect the stringency of hybridization, including base composition and length of the complementary strands, presence of organic solvents and extent of base mismatching, and the combination of parameters used is more important than the absolute measure of any one alone. Other hybridization conditions which may be controlled include buffer type and concentration, solution pH, presence and concentration of blocking reagents to decrease background binding such as repeat sequences or blocking protein solutions, detergent type(s) and concentrations, molecules such as polymers which increase the relative concentration of the polynucleotides, metal ion(s) and their concentration(s), chelator(s) and their concentrations, and other conditions known in the art.

“Multiplexing” herein refers to an assay or other analytical method in which multiple analytes can be assayed simultaneously.

A “target sequence” as used herein (also occasionally referred to as a “PSR” or “probe selection region”) refers to a region of the genome against which one or more probes can be designed. Exemplary probe selection regions are depicted in FIGS. 7-8. A “target sequence” may be a coding target or a non-coding target. A “target sequence” may comprise exonic and/or non-exonic sequences. Alternatively, a “target sequence” may comprise an ultraconserved region. An ultraconserved region is generally a sequence that is at least 200 base pairs and is conserved across multiple species. An ultraconserved region may be exonic or non-exonic. Exonic sequences may comprise regions on a protein-coding gene, such as an exon, UTR, or a portion thereof. Non-exonic sequences may comprise regions on a protein-coding, non protein-coding gene, or a portion thereof. For example, non-exonic sequences may comprise intronic regions, promoter regions, intergenic regions, a non-coding transcript, an exon anti-sense region, an intronic anti-sense region, UTR anti-sense region, non-coding transcript anti-sense region, or a portion thereof.

As used herein, a probe is any polynucleotide capable of selectively hybridizing to a target sequence, a complement thereof, a reverse complement thereof, or to an RNA version of the target sequence, the complement thereof, or the reverse complement therof. A probe may comprise ribonucleotides, deoxyribonucleotides, peptide nucleic acids, and combinations thereof. A probe may optionally comprise one or more labels. In some embodiments, a probe may be used to amplify one or both strands of a target sequence or an RNA form thereof, acting as a sole primer in an amplification reaction or as a member of a set of primers.

As used herein, the term “probe set” refers to a set of synthetic oligonucleotide probes. The oligonucleotide probes can be on Exon arrays that interrogate gene expression from one exon. Often, the probe set comprises four probes. Probes of the probe set can anneal to the sense strand of a coding transcript and/or a non-coding transcript. In some instances, the probes of the probe set are located on an array. The probes of the probe set can be located on the array in an antisense orientation. In some instances, a probe set can refer to a probe set as described by Affymetrix (http://www.microarrays.ca/services/exonarray_design_technote.pdf).

As used herein, the term “probe selection region” (“PSR”) is often the smallest unit on an array for expression profiling. In some instances, a PSR is represented by an individual probe set. The PSR can be an exon or overlap with an exon. The PSR can comprise or overlap with at least a portion of a coding transcript. Alternatively, a PSR can comprise or overlap with at least a portion of a non-coding transcript. In some instances, an exon cluster (e.g., a group of overlapping exons) can be divided into multiple PSRs. In some instances, a probe set can refer to a PSR as described by Affymetrix (http://www.microarrays.ca/services/exonarray_design_technote.pdf). In some instances, the terms “PSR”, “probe selection region”, and “probe set” can be used interchangeably to refer to a region on a coding transcript and/or non-coding transcript. In some instances, the region represented by the probe set comprises a sequence that is antisense to the PSR.

In some instances, the probe sets and PSRs can be used to interrogate expression from coding transcripts and/or non-coding transcripts. Probe set IDs as disclosed in Tables 17, 19, 22-24, and 27-30 refer to probe sets as described by Affymetrix (http://www.affymetrix.com/analysis/index.affx).

As used herein, a non-coding target may comprise a nucleotide sequence. The nucleotide sequence is a DNA or RNA sequence. A non-coding target may include a UTR sequence, an intronic sequence, or a non-coding RNA transcript. A non-coding target also includes sequences which partially overlap with a UTR sequence or an intronic sequence. A non-coding target also includes non-exonic transcripts.

As used herein, a non-coding RNA (ncRNA) transcript is an RNA transcript that does not encode a protein. ncRNAs include short ncRNAs and long ncRNAs (lncRNAs). Short ncRNAs are ncRNAs that are generally 18-200 nucleotides (nt) in length. Examples of short ncRNAs include, but are not limited to, microRNAs (miRNAs), piwi-associated RNAs (piRNAs), short interfering RNAs (siRNAs), promoter-associated short RNAs (PASRs), transcription initiation RNAs (tiRNAs), termini-associated short RNAs (TASRs), antisense termini associated short RNAs (aTASRs), small nucleolar RNAs (snoRNAs), transcription start site antisense RNAs (TSSa-RNAs), small nuclear RNAs (snRNAs), retroposon-derived RNAs (RE-RNAs), 3′UTR-derived RNAs (uaRNAs), x-ncRNA, human Y RNA (hY RNA), unusually small RNAs (usRNAs), small NF90-associated RNAs (snaRs), vault RNAs (vtRNAs), small Cajal body-specific RNAs (scaRNAs), and telomere specific small RNAs (tel-sRNAs). LncRNAs are cellular RNAs, exclusive of rRNAs, greater than 200 nucleotides in length and having no obvious protein-coding capacity (Lipovich L, et al., MacroRNA underdogs in a microRNA world: evolutionary, regulatory, and biomedical significance of mammalian long non-protein-coding RNA, Biochim Biophys Acta, 2010, 1799(9): 597-615). LncRNAs include, but are not limited to, large or long intergenic ncRNAs (lincRNAs), transcribed ultraconserved regions (T-UCRs), pseudogenes, GAA-repeat containing RNAs (GRC-RNAs), long intronic ncRNAs, antisense RNAs (aRNAs), promoter-associated long RNAs (PALRs), promoter upstream transcripts (PROMPTs), and long stress-induced non-coding transcripts (LSINCTs).

As used herein, a coding target includes nucleotide sequences that encode for a protein and peptide sequences. The nucleotide sequence is a DNA or RNA sequence. The coding target includes protein-coding sequence. Protein-coding sequences include exon-coding sequences (e.g., exonic sequences).

As used herein, diagnosis of cancer may include the identification of cancer in a subject, determining the malignancy of the cancer, or determining the stage of the cancer.

As used herein, prognosis of cancer may include predicting the clinical outcome of the patient, assessing the risk of cancer recurrence, determining treatment modality, or determining treatment efficacy.

“Having” is an open-ended phrase like “comprising” and “including,” and includes circumstances where additional elements are included and circumstances where they are not.

“Optional” or “optionally” means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where the event or circumstance occurs and instances in which it does not.

As used herein, the term “metastasis” (“Mets”) describes the spread of a cancer from one part of the body to another. A tumor formed by cells that have spread can be called a “metastatic tumor” or a “metastasis.” The metastatic tumor often contains cells that are like those in the original (primary) tumor.

As used herein, the term “progression” describes the course of a disease, such as a cancer, as it becomes worse or spreads in the body.

As used herein, the term “about” refers to approximately +/−10% variation from a given value. It is to be understood that such a variation is always included in any given value provided herein, whether or not it is specifically referred to.

Use of the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Thus, for example, reference to “a polynucleotide” includes a plurality of polynucleotides, reference to “a target” includes a plurality of such targets, reference to “a normalization method” includes a plurality of such methods, and the like. Additionally, use of specific plural references, such as “two,” “three,” etc., read on larger numbers of the same subject, unless the context clearly dictates otherwise.

Terms such as “connected,” “attached,” “linked” and “conjugated” are used interchangeably herein and encompass direct as well as indirect connection, attachment, linkage or conjugation unless the context clearly dictates otherwise.

Where a range of values is recited, it is to be understood that each intervening integer value, and each fraction thereof, between the recited upper and lower limits of that range is also specifically disclosed, along with each subrange between such values. The upper and lower limits of any range can independently be included in or excluded from the range, and each range where either, neither or both limits are included is also encompassed within the invention. Where a value being discussed has inherent limits, for example where a component can be present at a concentration of from 0 to 100%, or where the pH of an aqueous solution can range from 1 to 14, those inherent limits are specifically disclosed. Where a value is explicitly recited, it is to be understood that values, which are about the same quantity or amount as the recited value, are also within the scope of the invention, as are ranges based thereon. Where a combination is disclosed, each sub-combination of the elements of that combination is also specifically disclosed and is within the scope of the invention. Conversely, where different elements or groups of elements are disclosed, combinations thereof are also disclosed. Where any element of an invention is disclosed as having a plurality of alternatives, examples of that invention in which each alternative is excluded singly or in any combination with the other alternatives are also hereby disclosed; more than one element of an invention can have such exclusions, and all combinations of elements having such exclusions are hereby disclosed.

Coding and Non-Coding Targets

The methods disclosed herein often comprise assaying the expression level of a plurality of targets. The plurality of targets may comprise coding targets and/or non-coding targets of a protein-coding gene or a non protein-coding gene. As depicted in FIG. 6A, a protein-coding gene structure may comprise an exon and an intron. The exon may further comprise a coding sequence (CDS) and an untranslated region (UTR). The protein-coding gene may be transcribed to produce a pre-mRNA and the pre-mRNA may be processed to produce a mature mRNA. The mature mRNA may be translated to produce a protein.

As depicted in FIG. 6B, a non protein-coding gene structure may comprise an exon and intron. Usually, the exon region of a non protein-coding gene primarily contains a UTR. The non protein-coding gene may be transcribed to produce a pre-mRNA and the pre-mRNA may be processed to produce a non-coding RNA (ncRNA).

FIG. 7 illustrates potential targets (e.g., probe selection regions) within a protein-coding gene and a non protein-coding gene. A coding target may comprise a coding sequence of an exon. A non-coding target may comprise a UTR sequence of an exon, intron sequence, intergenic sequence, promoter sequence, non-coding transcript, CDS antisense, intronic antisense, UTR antisense, or non-coding transcript antisense. A non-coding transcript may comprise a non-coding RNA (ncRNA).

In some instances, the plurality of targets may be differentially expressed. For example, as shown in FIG. 20A, the CHRAC1-001 transcript specific probe selection region (probe set ID 3118459), the CHRAC1-003 transcript specific probe selection region (probe set ID 3118456) and the CHRAC1-005 transcript specific p probe selection region (probe set ID 3118454) demonstrate that the CHRAC1-001, -003, and -005 transcripts are differentially expressed in the Primary vs Normal and the Primary vs Mets. FIG. 20B provides another example of the differential expression of gene with transcript-specific PSRs.

In some instances, adjacent and differentially expressed PSRs can form a block of differentially expressed PSRs (e.g., syntenic block). For example, as shown in FIG. 10B, a plurality of differentially expressed and adjacent PSRs (based on the bars of the transcriptional profile) may form one syntenic block (as depicted by the rectangle). A syntenic block may comprise one or more genes. The syntenic block as depicted in FIG. 10B corresponds to the three genes, RP11-39404.2, MIR143, MIR145 depicted in FIG. 10A. In some instances, the syntenic block may comprise PSRs specific to a coding target, non-coding targets, or a combination thereof. In some instances, as shown in FIG. 10A-B, the syntenic block comprises PSRs specific to a non-coding target. In some instances, the syntenic blocks may be categorized according to their components. For example, the syntenic block depicted in FIG. 10B would be a non-coding syntenic block differentially expressed which is composed of non-coding targets such as miRNAs, intergenic regions, etc.

In some instances, a plurality of PSRs is differentially expressed. The differentially expressed PSRs may form one or more syntenic blocks. As shown in FIG. 10C, differentially expressed PSRs may form two or more syntenic blocks (as outlined by the boxes). In some instances, the two or more syntenic blocks may correspond to one or more molecules. For example, two or more syntenic blocks could correspond to a non-coding target. Alternatively, two or more syntenic blocks may correspond to a coding target.

In some instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-903. In some instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-441. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 292-321. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 460-480. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 231-261. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 442-457. In some instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 436, 643-721. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 722-801. The non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 879-903. In some instances, the non-coding target is located on chr2q31.3. In some instances, the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 262-291. In some instances, the non-coding target is a lncRNA. The lncRNA can be a vlncRNA or vlincRNA.

In some instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 879-903. In some instances, the non-coding target comprises a sequence that is complementary to a sequence located on chr2q31.3. In some instances, the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 262-291.

In some instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-903. In some instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-441. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 292-321. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 460-480. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 231-261. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 442-457. In some instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 436, 643-721. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 722-801. The coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 879-903. In some instances, the coding target is located on chr2q31.3. In some instances, the coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 262-291.

In some instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 879-903. In some instances, the coding target comprises a sequence that is complementary to a sequence located on chr2q31.3. In some instances, the coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 262-291.

In some instances, the plurality of targets comprises a coding target and/or a non-coding target. The plurality of targets can comprise any of the coding targets and/or non-coding targets disclosed herein. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-441. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 292-321. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 460-480. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 231-261. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 442-457. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 436, 643-721. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 722-801. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 879-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target is located on chr2q31.3. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 262-291.

In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target can comprise a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 879-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to a sequence located on chr2q31.3. In some instances, the plurality of targets comprises a coding target and/or a non-coding target, wherein the coding target and/or the non-coding target comprises a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 262-291.

Alternatively, a non-coding target comprises a UTR sequence, an intronic sequence, or a non-coding RNA transcript. In some instances, a non-coding target comprises sequences which partially overlap with a UTR sequence or an intronic sequence. A non-coding target also includes non-exonic transcripts. Exonic sequences may comprise regions on a protein-coding gene, such as an exon, UTR, or a portion thereof. Non-exonic sequences may comprise regions on a protein-coding, non protein-coding gene, or a portion thereof. For example, non-exonic sequences may comprise intronic regions, promoter regions, intergenic regions, a non-coding transcript, an exon anti-sense region, an intronic anti-sense region, UTR anti-sense region, non-coding transcript anti-sense region, or a portion thereof.

In some instances, the coding target and/or non-coding target is at least about 70% identical to a sequence selected from SEQ ID NOs.: 1-903. Alternatively, the coding target and/or non-coding target is at least about 80% identical to a sequence selected from SEQ ID NOs.: 1-903. In some instances, the coding target and/or non-coding target is at least about 85% identical to a sequence selected from SEQ ID NOs.: 1-903. In some instances, the coding target and/or non-coding target is at least about 90% identical to a sequence selected from SEQ ID NOs.: 1-903. Alternatively, the coding target and/or non-coding target are at least about 95% identical to a sequence selected from SEQ ID NOs.: 1-903.

In some instances, the plurality of targets comprises two or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises three or more sequences selected (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises five or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises six or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises ten or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises fifteen or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises twenty or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises twenty five or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof. In some instances, the plurality of targets comprises thirty or more sequences selected from (a) SEQ ID NOs.: 1-903; (b) SEQ ID NOs.: 1-352; (c) SEQ ID NOs.: 322-352; (d) SEQ ID NOs.: 292-321; (e) SEQ ID NOs.: 231-261; (f) coding target and/or a non-coding target located on chr2q31.3; (g) SEQ ID NOs.: 262-291; (h) SEQ ID NOs.: 353-441; (i) SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, 459; (j) SEQ ID NOs.: 460-480; (k) SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, 481-642; (l) SEQ ID NOs.: 442-457; (m) SEQ ID NOs.: 436, 643-721; (n) SEQ ID NOs.: 722-801; (o) SEQ ID NOs.: 653, 663, 685, 802-878; (p) SEQ ID NOs.: 879-903; (q) a sequence with at least 80% identity to sequences listed in a-p; or (r) a complement thereof.

In some instances, the plurality of targets disclosed herein comprises a target that is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 bases or base pairs in length. In other instances, the plurality of targets disclosed herein comprises a target that is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 kilo bases or kilo base pairs in length. Alternatively, the plurality of targets disclosed herein comprises a target that is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 mega bases or mega base pairs in length. The plurality of targets disclosed herein can comprise a target that is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 giga bases or giga base pairs in length.

In some instances, the non-coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 bases or base pairs in length. In other instances, the non-coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 kilo bases or kilo base pairs in length. Alternatively, the non-coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 mega bases or mega base pairs in length. The non-coding target can be at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 giga bases or giga base pairs in length.

In some instances, the coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 bases or base pairs in length. In other instances, the coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 kilo bases or kilo base pairs in length. Alternatively, the coding target is at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 mega bases or mega base pairs in length. The coding target can be at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 650, 700, 750, 800, 850, 900, 950, or 1000 giga bases or giga base pairs in length.

Non-Coding RNAs

In some instances, the plurality of targets comprises a non-coding RNA. Generally, non-coding RNAs (ncRNAs) are functional transcripts that do not code for proteins. ncRNAs are loosely grouped into two major classes based on transcript size: small ncRNAs and large ncRNAs (lncRNAs).

Small ncRNAs

Small ncRNAs are typically 18 to 200 nucleotides (nt) in size and may be processed from longer precursors. Examples of small ncRNAs include, but are not limited to, microRNAs (miRNAs), piwi-associated RNAs (piRNAs), short interfering RNAs (siRNAs), promoter-associated short RNAs (PASRs), transcription initiation RNAs (tiRNAs), termini-associated short RNAs (TASRs), antisense termini associated short RNAs (aTASRs), small nucleolar RNAs (snoRNAs), transcription start site antisense RNAs (TSSa-RNAs), small nuclear RNAs (snRNAs), retroposon-derived RNAs (RE-RNAs), 3′UTR-derived RNAs (uaRNAs), x-ncRNA, human Y RNA (hY RNA), unusually small RNAs (usRNAs), small NF90-associated RNAs (snaRs), vault RNAs (vtRNAs), small Cajal body-specific RNAs (scaRNAs), and telomere specific small RNAs (tel-sRNAs).

miRNAs

miRNAs can be divided into two subclasses: canonical and non-canonical miRNAs. Canonical miRNAs may initially be transcribed as long RNAs that contain hairpins. The 60-75 nt hairpins can be recognized by the RNA-binding protein Dgcr8 (DiGeorge syndrome critical region 8), which may direct the RNase III enzyme Drosha to cleave the base of the hairpin. Following cleavage by the Drosha-Dgcr8 complex, also called the microprocessor, the released hairpin may be transported to the cytoplasm, where Dicer, another RNase III enzyme, then cleaves it into a single short 18-25 nt dsRNA. Non-canonical miRNAs may bypass processing by the microprocessor by using other endonucleases or by direct transcription of a short hairpin. The resulting pre-miRNAs can then be exported from the nucleus and cleaved once by Dicer.

piRNAs

The piRNAs may differ from the miRNAs and endo-siRNAs in that they often do not require Dicer for their processing. piRNAs may be 25-32 nt in length, and can be expressed in the germline in mammals. They may be defined by their interaction with the Piwi proteins, a distinct family of Argonaute proteins (including Miwi, Miwi2 and Mili in mouse; also known as Piwil1, Piwil4 and Piwil2, respectively). piRNAs can be generated from long single-stranded RNA precursors that are often encoded by complex and repetitive intergenic sequences.

siRNAs

siRNAs can be derived from long dsRNAs in the form of either sense or antisense RNA pairs or as long hairpins, which may then directly be processed by Dicer consecutively along the dsRNA to produce multiple siRNAs. Therefore, canonical miRNAs, non-canonical miRNAs and endo-siRNAs may involve Dicer processing and can be ˜21 nt in length. Furthermore, in all three cases, one strand of the Dicer product may associate with an Argonaute protein (Ago 1-4 in mammals; also known as Eif2c1-4) to form the active RISC (RNA-induced silencing complex). Often, these ribonucleoprotein complexes may be able to bind to and control the levels and translation of their target mRNAs, if the match between the small RNA and its target is perfect, the target is cleaved; if not, the mRNA is destabilized through as yet unresolved mechanisms.

PASRs, tiRNAs, and TSSa-RNAs

PASRs can be broadly defined as short transcripts, generally 20-200 nt long, capped, with 5′ ends that coincide with the transcription start sites (TSSs) of protein and non-coding genes. TiRNAs are predominantly 18 nt in length and generally found downstream of TSSs. TSSa-RNAs can be 20-90 nt long and may be localized within −250 to +50 base pairs of transcription start sites (TSSs). PASRs, tiRNAs, and TSSa-RNAs may strongly associate with highly expressed genes and regions of RNA Polymerase II (RNAPII) binding, may be weakly expressed, and may show bidirectional distributions that mirror RNAPII (Taft J, et al., Evolution, biogenesis and function of promoter-associated RNAs, Cell Cycle, 2009, 8(15):2332-2338).

TASRs and aTASRs

TASRs may be 22-200 nt in length and are found to cluster at 5′ and 3′ termini of annotated genes. aTASRs can be found within 50 bp and antisense to 3′ UTRs of annotated transcripts.

snoRNAs

SnoRNAs represent one of the largest groups of functionally diverse trans-acting ncRNAs currently known in mammalian cells. snoRNAs can range between 60-150 nucleotides in length. From a structural basis, snoRNAs may fall into two categories termed box C/D snoRNAs (SNORDs) and box H/ACA snoRNAs (SNORAs). SNORDs can serve as guides for the 2′-O-ribose methylation of rRNAs or snRNAs, whereas SNORAs may serve as guides for the isomerization of uridine residues into pseudouridine.

snRNAs

snRNAs, historically referred to as U-RNAs, may be less than 200 nt long and may play key roles in pre-mRNA splicing. snRNAs are further divided into two main categories based on shared sequences and associated proteins. Sm-class RNAs can have a 5′ trimethylguanosine cap and bind several Sm proteins. Lsm-RNAs may possess a monomethylphosphate 5′ cap and a uridine rich 3′ end acting as a binding site for Lsm proteins. Sm class of snRNAs (U1, U2, U4 and U5) are synthesized by RNA Pol II. For Sm class, pre-snRNAs are transcribed and 5′ monomethylguanosine capped in the nucleus, exported via multiple factors to the cytoplasm for further processing. After cytoplamic hypermethylation of 5′ cap (trimethylguanosine) and 3′ trimming, the snRNA is translocated back into the nucleus. snRNPs for Sm class snRNAs are also assembled in the cytosol. Lsm snRNA (U6 and other snoRNAs) are transcribed by Pol III and keep the monomethylguanosine 5′ cap and in the nucleus. Lsm snRNAs never leave the nucleus.

lncRNAs

LncRNAs are cellular RNAs, exclusive of rRNAs, greater than 200 nucleotides in length and having no obvious protein-coding capacity (Lipovich L, et al., MacroRNA underdogs in a microRNA world: evolutionary, regulatory, and biomedical significance of mammalian long non-protein-coding RNA, Biochim Biophys Acta, 2010, 1799(9):597-615). LncRNAs include, but are not limited to, large or long intergenic ncRNAs (lincRNAs), transcribed ultraconserved regions (T-UCRs), pseudogenes, GAA-repeat containing RNAs (GRC-RNAs), long intronic ncRNAs, antisense RNAs (aRNAs), promoter-associated long RNAs (PALRs), promoter upstream transcripts (PROMPTs), long stress-induced non-coding transcripts (LSINCTs), very long non-coding RNAs (vlncRNAs), and very long intergenic non-coding RNA (vlincRNAs). vlncRNAs (very long non-coding RNAs) are a type of lncRNAs that are often greater than 5 kb long and for which detailed information is available. vlincRNAs (very long intergenic non-coding RNAs) are generally expressed intergenic regions. In some instances, the vlincRNAs are at least about 30 kb, 40 kb, 50 kb, 60 kb, 70 kb, 80 kb, 90 kb, or 100 kb in length (Kapranov P et al., 2010, BMC Biol, 8:149).

T-UCRs

T-UCRs are transcribed genomic elements longer than 200 base pairs (bp) (range: 200-779 bp) that are absolutely conserved (100% identity with no insertion or deletions) among mouse, rat, and human genomes. T-UCRs may be intergenic (located between genes), intronic, exonic, partially exonic, exon containing, or “multiple” (location varies because of gene splice variants).

Pseudogenes

Pseudogenes are commonly defined as sequences that resemble known genes but cannot produce functional proteins. Pseudogenes can be broadly classified into two categories: processed and nonprocessed. Nonprocessed pseudogenes usually contain introns, and they are often located next to their paralogous parent gene. Processed pseudogenes are thought to originate through retrotransposition; accordingly, they lack introns and a promoter region, but they often contain a polyadenylation signal and are flanked by direct repeats.

Probes/Primers

The present invention provides for a probe set for diagnosing, monitoring and/or predicting a status or outcome of a cancer in a subject comprising a plurality of probes, wherein (i) the probes in the set are capable of detecting an expression level of at least one non-coding target; and (ii) the expression level determines the cancer status of the subject with at least about 40% specificity.

The probe set may comprise one or more polynucleotide probes. Individual polynucleotide probes comprise a nucleotide sequence derived from the nucleotide sequence of the target sequences, complementary sequences thereof, or reverse complement sequences thereof. The nucleotide sequence of the polynucleotide probe is designed such that it corresponds to, is complementary to, or is reverse complementary to the target sequences. The polynucleotide probe can specifically hybridize under either stringent or lowered stringency hybridization conditions to a region of the target sequences, to the complement thereof, or to a nucleic acid sequence (such as a cDNA, RNA) derived therefrom.

The selection of the polynucleotide probe sequences and determination of their uniqueness may be carried out in silico using techniques known in the art, for example, based on a BLASTN search of the polynucleotide sequence in question against gene sequence databases, such as the Human Genome Sequence, UniGene, dbEST or the non-redundant database at NCBI. In one embodiment of the invention, the polynucleotide probe is complementary to a region of a target mRNA derived from a target sequence in the probe set. Computer programs can also be employed to select probe sequences that may not cross hybridize or may not hybridize non-specifically.

FIG. 25 illustrates in an exemplary approach to selecting probes, also referred to herein as biomarkers, useful in diagnosing, predicting, and/or monitoring the status or outcome of a cancer, in accordance with an embodiment of this invention. In some instances, microarray hybridization of RNA, extracted from prostate cancer tissue samples and amplified, may yield a dataset that is then summarized and normalized by the fRMA technique (See McCall et al., “Frozen robust multiarray analysis (fRMA),” Biostatistics Oxford England 11.2 (2010): 242-253). The raw expression values captured by the probes can be summarized and normalized into PSR values. Cross-hybridizing probe sets, highly variable PSRs (e.g., PSRs with variance above the 90th percentile), and probe sets containing less than 4 probes can be removed or filtered. Following fRMA and filtration, the data can be decomposed into its principal components and an analysis of variance model can be used to determine the extent to which a batch effect remains present in the first 10 principal components (see Leek et al. “Tackling the widespread and critical impact of batch effects in high-throughput data,” Nat. Rev. Genetics 11.10 (2010): 733-739).

These remaining probe sets can be further refined by filtration by a T-test between CR (clinical recurrence) and non-CR samples. In some instances, the probe sets with a P-value of >0.01 can be removed or filtered. The remaining probe sets can undergo further selection. Feature selection can be performed by regularized logistic regression using the elastic-net penalty (see Zou & Hastie, “Regularization and variable selection via the elastic net,” Journal of the Royal Stat. Soc.—Series B: Statistical Methodology 67.2 (2005): 301-320). The regularized regression can be bootstrapped over 1000 times using all training data. With each iteration of bootstrapping, probe sets that have non-zero co-efficient following 3-fold cross validation can be tabulated. In some instances, probe sets that were selected in at least 25% of the total runs can be used for model building.

One skilled in the art understands that the nucleotide sequence of the polynucleotide probe need not be identical to its target sequence in order to specifically hybridize thereto. The polynucleotide probes of the present invention, therefore, comprise a nucleotide sequence that is at least about 65% identical to a region of the coding target or non-coding target. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 70% identical a region of the coding target or non-coding target. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 75% identical a region of the coding target or non-coding target. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 80% identical a region of the coding target or non-coding target. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 85% identical a region of the coding target or non-coding target. In another embodiment, the nucleotide sequence of the polynucleotide probe is at least about 90% identical a region of the coding target or non-coding target. In a further embodiment, the nucleotide sequence of the polynucleotide probe is at least about 95% identical to a region of the coding target or non-coding target.

Methods of determining sequence identity are known in the art and can be determined, for example, by using the BLASTN program of the University of Wisconsin Computer Group (GCG) software or provided on the NCBI website. The nucleotide sequence of the polynucleotide probes of the present invention may exhibit variability by differing (e.g. by nucleotide substitution, including transition or transversion) at one, two, three, four or more nucleotides from the sequence of the coding target or non-coding target.

Other criteria known in the art may be employed in the design of the polynucleotide probes of the present invention. For example, the probes can be designed to have <50% G content and/or between about 25% and about 70% G+C content. Strategies to optimize probe hybridization to the target nucleic acid sequence can also be included in the process of probe selection.

Hybridization under particular pH, salt, and temperature conditions can be optimized by taking into account melting temperatures and by using empirical rules that correlate with desired hybridization behaviors. Computer models may be used for predicting the intensity and concentration-dependence of probe hybridization.

The polynucleotide probes of the present invention may range in length from about 15 nucleotides to the full length of the coding target or non-coding target. In one embodiment of the invention, the polynucleotide probes are at least about 15 nucleotides in length. In another embodiment, the polynucleotide probes are at least about 20 nucleotides in length. In a further embodiment, the polynucleotide probes are at least about 25 nucleotides in length. In another embodiment, the polynucleotide probes are between about 15 nucleotides and about 500 nucleotides in length. In other embodiments, the polynucleotide probes are between about 15 nucleotides and about 450 nucleotides, about 15 nucleotides and about 400 nucleotides, about 15 nucleotides and about 350 nucleotides, about 15 nucleotides and about 300 nucleotides, about 15 nucleotides and about 250 nucleotides, about 15 nucleotides and about 200 nucleotides in length. In some embodiments, the probes are at least 15 nucleotides in length. In some embodiments, the probes are at least 15 nucleotides in length. In some embodiments, the probes are at least 20 nucleotides, at least 25 nucleotides, at least 50 nucleotides, at least 75 nucleotides, at least 100 nucleotides, at least 125 nucleotides, at least 150 nucleotides, at least 200 nucleotides, at least 225 nucleotides, at least 250 nucleotides, at least 275 nucleotides, at least 300 nucleotides, at least 325 nucleotides, at least 350 nucleotides, at least 375 nucleotides in length.

The polynucleotide probes of a probe set can comprise RNA, DNA, RNA or DNA mimetics, or combinations thereof, and can be single-stranded or double-stranded. Thus the polynucleotide probes can be composed of naturally-occurring nucleobases, sugars and covalent internucleoside (backbone) linkages as well as polynucleotide probes having non-naturally-occurring portions which function similarly. Such modified or substituted polynucleotide probes may provide desirable properties such as, for example, enhanced affinity for a target gene and increased stability. The probe set may comprise a probe that hybridizes to or corresponds to a coding target and/or a non-coding target. Preferably, the probe set comprises a plurality of probes that hybridizes to or corresponds to a combination of a coding target and non-coding target.

The probe set may comprise a plurality of probes that hybridizes to or corresponds to at least about 5 coding targets and/or non-coding targets. Alternatively, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 10 coding targets and/or non-coding targets. The probe set may comprise a plurality of probes that hybridizes to or corresponds to at least about 15 coding targets and/or non-coding targets. In some instances, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 20 coding targets and/or non-coding targets. Alternatively, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 30 coding targets and/or non-coding targets. The probe set can comprise a plurality of probes that hybridizes to or corresponds to at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 coding targets and/or non-coding targets.

The probe set may comprise a plurality of probes that hybridizes to or corresponds to at least about 5 non-coding targets. Alternatively, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 10 non-coding targets. The probe set may comprise a plurality of probes that hybridizes to or corresponds to at least about 15 non-coding targets. In some instances, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 20 non-coding targets. Alternatively, the probe set comprises a plurality of probes that hybridizes to or corresponds to at least about 30 non-coding targets. The probe set can comprise a plurality of probes that hybridizes to or corresponds to at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 non-coding targets.

The probe set may comprise a plurality of probes, wherein at least about 5% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 8% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 10% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 12% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 15% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 18% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 20% of the plurality of probes hybridize to or correspond to non-coding targets. In some instances, the probe set comprises a plurality of probes, wherein at least about 25% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 30% of the plurality of probes hybridize to or correspond to non-coding targets. Alternatively, the probe set comprises a plurality of probes, wherein at least about 35% of the plurality of probes hybridize to or correspond to non-coding targets. In some instances, the probe set comprises a plurality of probes, wherein at least about 40% of the plurality of probes hybridize to or correspond to non-coding targets. In other instances, the probe set comprises a plurality of probes, wherein at least about 45% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 50% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 55% of the plurality of probes hybridize to or correspond to non-coding targets. Alternatively, the probe set comprises a plurality of probes, wherein at least about 60% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 65% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 70% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 75% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 80% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 85% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 90% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 95% of the plurality of probes hybridize to or correspond to non-coding targets. The probe set may comprise a plurality of probes, wherein at least about 97% of the plurality of probes hybridize to or correspond to non-coding targets.

The probe set can comprise a plurality of probes, wherein less than about 95% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 90% of the plurality of probes hybridize to or correspond to coding targets. Alternatively, the probe set comprises a plurality of probes, wherein less than about 85% of the plurality of probes hybridize to or correspond to coding targets. In some instances, the probe set comprises a plurality of probes, wherein less than about 80% of the plurality of probes hybridize to or correspond to coding targets. In other instances, the probe set comprises a plurality of probes, wherein less than about 75% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 70% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 65% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 60% of the plurality of probes hybridize to or correspond to coding targets. In some instances, the probe set comprises a plurality of probes, wherein less than about 55% of the plurality of probes hybridize to or correspond to coding targets. In other instances, the probe set comprises a plurality of probes, wherein less than about 50% of the plurality of probes hybridize to or correspond to coding targets. Alternatively, the probe set comprises a plurality of probes, wherein less than about 945% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 40% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 35% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 30% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 25% of the plurality of probes hybridize to or correspond to coding targets. In some instances, the probe set comprises a plurality of probes, wherein less than about 20% of the plurality of probes hybridize to or correspond to coding targets. In other instances, the probe set comprises a plurality of probes, wherein less than about 15% of the plurality of probes hybridize to or correspond to coding targets. Alternatively, the probe set comprises a plurality of probes, wherein less than about 12% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 10% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 8% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 5% of the plurality of probes hybridize to or correspond to coding targets. The probe set can comprise a plurality of probes, wherein less than about 3% of the plurality of probes hybridize to or correspond to coding targets.

The probe set may comprise a plurality of probes, wherein (i) the probes in the set are capable of detecting an expression level of at least one non-coding target; and (ii) the expression level determines the cancer status of the subject with at least about 40% specificity. In some embodiments, the probe set further comprises a probe capable of detecting an expression level of at least one coding target. The probe set can comprise any of the probe sets as disclosed in Tables 17, 19, 22-24, and 27-30 (see ‘Probe set ID’ column). In some instances, the probe set comprises probe set ID 2518027. Alternatively, the probe set comprises probe set ID 3046448; 3046449; 3046450; 3046457; 3046459; 3046460; 3046461; 3046462; 3046465; 3956596; 3956601; 3956603; 3103704; 3103705; 3103706; 3103707; 3103708; 3103710; 3103712; 3103713; 3103714; 3103715; 3103717; 3103718; 3103720; 3103721; 3103725; 3103726; 2719689; 2719692; 2719694; 2719695; 2719696; 2642733; 2642735; 2642738; 2642739; 2642740; 2642741; 2642744; 2642745; 2642746; 2642747; 2642748; 2642750; 2642753; 3970026; 3970034; 3970036; 3970039; 2608321; 2608324; 2608326; 2608331; 2608332; 2536222; 2536226; 2536228; 2536229; 2536231; 2536232; 2536233; 2536234; 2536235; 2536236; 2536237; 2536238; 2536240; 2536241; 2536243; 2536245; 2536248; 2536249; 2536252; 2536253; 2536256; 2536260; 2536261; 2536262; 3670638; 3670639; 3670641; 3670644; 3670645; 3670650; 3670659; 3670660; 3670661; 3670666, a complement thereof, a reverse complement thereof, or any combination thereof.

Further disclosed herein, is a classifier for use in diagnosing, predicting, and/or monitoring the outcome or status of a cancer in a subject. The classifier may comprise a classifier as disclosed in Table 17. The classifier can comprise a classifier as disclosed in Table 19. The classifier can comprise the GLM2, KNN12, KNN16, NB20, SVM5, SVM11, SVM20 classifiers or any combination thereof. The classifier can comprise a GLM2 classifier. Alternatively, the classifier comprises a KNN12 classifier. The classifier can comprise a KNN16 classifier. In other instances, the classifier comprises a NB20 classifier. The classifier may comprise a SVM5 classifier. In some instances, the classifier comprises a SVM11 classifier. Alternatively, the classifier comprises a SVM20 classifier. Alternatively, the classifier comprises one or more Inter-Correlated Expression (ICE) blocks disclosed herein. The classifier can comprise one or more probe sets disclosed herein.

The classifier may comprise at least about 5 coding targets and/or non-coding targets. Alternatively, the classifier comprises at least about 10 coding targets and/or non-coding targets. The classifier may comprise at least about 15 coding targets and/or non-coding targets. In some instances, the classifier comprises at least about 20 coding targets and/or non-coding targets. Alternatively, the classifier comprises at least about 30 coding targets and/or non-coding targets. The classifier can comprise at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 coding targets and/or non-coding targets.

The classifier may comprise at least about 5 non-coding targets. Alternatively, the classifier comprises at least about 10 non-coding targets. The classifier may comprise at least about 15 non-coding targets. In some instances, the classifier comprises at least about 20 non-coding targets. Alternatively, the classifier comprises at least about 30 non-coding targets. The classifier can comprise at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 non-coding targets.

The classifier may comprise at least about 5% non-coding targets. The classifier may comprise at least about 8% non-coding targets. The classifier may comprise at least about 10% non-coding targets. The classifier may comprise at least about 12% non-coding targets. The classifier may comprise at least about 15% non-coding targets. The classifier may comprise at least about 18% non-coding targets. The classifier may comprise at least about 20% non-coding targets. In some instances, the classifier comprises at least about 25% non-coding targets. The classifier may comprise at least about 30% non-coding targets. Alternatively, the classifier comprises at least about 35% non-coding targets. In some instances, the classifier comprises at least about 40% non-coding targets. In other instances, the classifier comprises at least about 45% non-coding targets. The classifier may comprise at least about 50% non-coding targets. The classifier may comprise at least about 55% non-coding targets. Alternatively, the classifier comprises at least about 60% non-coding targets. The classifier may comprise at least about 65% non-coding targets. The classifier may comprise at least about 70% non-coding targets. The classifier may comprise at least about 75% non-coding targets. The classifier may comprise at least about 80% non-coding targets. The classifier may comprise at least about 85% non-coding targets. The classifier may comprise at least about 90% non-coding targets. The classifier may comprise at least about 95% non-coding targets. The classifier may comprise at least about 97% non-coding targets.

The classifier can comprise less than about 95% coding targets. The classifier can comprise less than about 90% coding targets. Alternatively, the classifier comprises less than about 85% coding targets. In some instances, the classifier comprises less than about 80% coding targets. In other instances, the classifier comprises less than about 75% coding targets. The classifier can comprise less than about 70% coding targets. The classifier can comprise less than about 65% coding targets. The classifier can comprise less than about 60% coding targets. In some instances, the classifier comprises less than about 55% coding targets. In other instances, the classifier comprises less than about 50% coding targets. Alternatively, the classifier comprises less than about 45% coding targets. The classifier can comprise less than about 40% coding targets. The classifier can comprise less than about 35% coding targets. The classifier can comprise less than about 30% coding targets. The classifier can comprise less than about 25% coding targets. In some instances, the classifier comprises less than about 20% coding targets. In other instances, the classifier comprises less than about 15% coding targets. Alternatively, the classifier comprises less than about 12% coding targets. The classifier can comprise less than about 10% coding targets. The classifier can comprise less than about 8% coding targets. The classifier can comprise less than about 5% coding targets. The classifier can comprise less than about 3% coding targets.

The ICE block may comprise at least about 5 coding targets and/or non-coding targets. Alternatively, the ICE block comprises at least about 10 coding targets and/or non-coding targets. The ICE block may comprise at least about 15 coding targets and/or non-coding targets. In some instances, the ICE block comprises at least about 20 coding targets and/or non-coding targets. Alternatively, the ICE block comprises at least about 30 coding targets and/or non-coding targets. The ICE block can comprise at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 coding targets and/or non-coding targets.

The ICE block may comprise at least about 5 non-coding targets. Alternatively, the ICE block comprises at least about 10 non-coding targets. The ICE block may comprise at least about 15 non-coding targets. In some instances, the ICE block comprises at least about 20 non-coding targets. Alternatively, the ICE block comprises at least about 30 non-coding targets. The ICE block can comprise at least about 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 non-coding targets.

The ICE block may comprise at least about 5% non-coding targets. The ICE block may comprise at least about 8% non-coding targets. The ICE block may comprise at least about 10% non-coding targets. The ICE block may comprise at least about 12% non-coding targets. The ICE block may comprise at least about 15% non-coding targets. The ICE block may comprise at least about 18% non-coding targets. The ICE block may comprise at least about 20% non-coding targets. In some instances, the ICE block comprises at least about 25% non-coding targets. The ICE block may comprise at least about 30% non-coding targets. Alternatively, the ICE block comprises at least about 35% non-coding targets. In some instances, the ICE block comprises at least about 40% non-coding targets. In other instances, the ICE block comprises at least about 45% non-coding targets. The ICE block may comprise at least about 50% non-coding targets. The ICE block may comprise at least about 55% non-coding targets. Alternatively, the ICE block comprises at least about 60% non-coding targets. The ICE block may comprise at least about 65% non-coding targets. The ICE block may comprise at least about 70% non-coding targets. The ICE block may comprise at least about 75% non-coding targets. The ICE block may comprise at least about 80% non-coding targets. The ICE block may comprise at least about 85% non-coding targets. The ICE block may comprise at least about 90% non-coding targets. The ICE block may comprise at least about 95% non-coding targets. The ICE block may comprise at least about 97% non-coding targets.

The ICE block can comprise less than about 95% coding targets. The ICE block can comprise less than about 90% coding targets. Alternatively, the ICE block comprises less than about 85% coding targets. In some instances, the ICE block comprises less than about 80% coding targets. In other instances, the ICE block comprises less than about 75% coding targets. The ICE block can comprise less than about 70% coding targets. The ICE block can comprise less than about 65% coding targets. The ICE block can comprise less than about 60% coding targets. In some instances, the ICE block comprises less than about 55% coding targets. In other instances, the ICE block comprises less than about 50% coding targets. Alternatively, the ICE block comprises less than about 45% coding targets. The ICE block can comprise less than about 40% coding targets. The ICE block can comprise less than about 35% coding targets. The ICE block can comprise less than about 30% coding targets. The ICE block can comprise less than about 25% coding targets. In some instances, the ICE block comprises less than about 20% coding targets. In other instances, the ICE block comprises less than about 15% coding targets. Alternatively, the ICE block comprises less than about 12% coding targets. The ICE block can comprise less than about 10% coding targets. The ICE block can comprise less than about 8% coding targets. The ICE block can comprise less than about 5% coding targets. The ICE block can comprise less than about 3% coding targets.

Further disclosed herein, is a digital Gleason score predictor for prognosing the risk of biochemical recurrence. The digital Gleason score predictor can comprise a classifier. The classifier can comprise at least one non-coding target. In some instances, the classifier further comprises at least one coding-target. In some instances, the digital Gleason score predictor comprises a plurality of targets, wherein the plurality of targets comprise at least one coding target and at least one non-coding target. The non-coding target, coding target and plurality of targets can be any of the targets disclosed herein. The targets can be selected from any of Tables 4, 6-9, 15, 16, 17, 19, 22-24, and 26-30. The targets can comprise a sequence comprising at least a portion of any of SEQ ID NOs.: 1-903. In some instances, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 92%, 95%, 97%, 98%, 99% or 100%. The accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence can be at least about 50%. Alternatively, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 55%. In some instances, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 60%. In other instances, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 65%. The accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence can be at least about 70%. Alternatively, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 75%. In some instances, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 80%. In other instances, the accuracy of the digital Gleason score predictor to predict the risk of biochemical occurrence is at least about 85%.

In some instances, the probe sets, PSRs, ICE blocks, and classifiers disclosed herein are clinically significant. In some instances, the clinical significance of the probe sets, PSRs, ICE blocks, and classifiers is determined by the AUC value. In order to be clinically significant, the AUC value is at least about 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, or 0.95. The clinical significant of the probe sets, PSRs, ICE blocks, and classifiers can be determined by the percent accuracy. For example, a probe set, PSR, ICE block, and/or classifier is determined to be clinically significant if the accuracy of the probe set, PSR, ICE block and/or classifier is at least about 50%, 55%, 60%, 65%, 70%, 72%, 75%, 77%, 80%, 82%, 84%, 86%, 88%, 90%, 92%, 94%, 96%, or 98%. In other instances, the clinical significance of the probe sets, PSRs, ICE blocks, and classifiers is determined by the the median fold difference (MDF) value. In order to be clinically significant, the MDF value is at least about 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.9, or 2.0. In some instances, the MDF value is greater than or equal to 1.1. In other instances, the MDF value is greater than or equal to 1.2. Alternatively, or additionally, the clinical significance of the probe sets, PSRs, ICE blocks, and classifiers is determined by the t-test P-value. In some instances, in order to be clinically significant, the t-test P-value is less than about 0.070, 0.065, 0.060, 0.055, 0.050, 0.045, 0.040, 0.035, 0.030, 0.025, 0.020, 0.015, 0.010, 0.005, 0.004, or 0.003. The t-test P-value can be less than about 0.050. Alternatively, the t-test P-value is less than about 0.010. In some instances, the clinical significance of the probe sets, PSRs, ICE blocks, and classifiers is determined by the clinical outcome. For example, different clinical outcomes can have different minimum or maximum thresholds for AUC values, MDF values, t-test P-values, and accuracy values that would determine whether the probe set, PSR, ICE block, and/or classifier is clinically significant. In another example, a probe set, PSR, ICE block, or classifier can be considered clinically significant if the P-value of the t-test was lower than about 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, or 0.01 in any of the following comparisons: BCR vs non-BCR, CP vs non-CP, PCSM vs non-PCSM. Additionally, a probe set, PSR, ICE block, or classifier is determined to be clinically significant if the P-values of the differences between the KM curves for BCR vs non-BCR, CP vs non-CP, PCSM vs non-PCSM is lower than about 0.08, 0.07, 0.06, 0.05, 0.04, 0.03, 0.02, or 0.01.

The system of the present invention further provides for primers and primer pairs capable of amplifying target sequences defined by the probe set, or fragments or subsequences or complements thereof. The nucleotide sequences of the probe set may be provided in computer-readable media for in silico applications and as a basis for the design of appropriate primers for amplification of one or more target sequences of the probe set.

Primers based on the nucleotide sequences of target sequences can be designed for use in amplification of the target sequences. For use in amplification reactions such as PCR, a pair of primers can be used. The exact composition of the primer sequences is not critical to the invention, but for most applications the primers may hybridize to specific sequences of the probe set under stringent conditions, particularly under conditions of high stringency, as known in the art. The pairs of primers are usually chosen so as to generate an amplification product of at least about 50 nucleotides, more usually at least about 100 nucleotides. Algorithms for the selection of primer sequences are generally known, and are available in commercial software packages. These primers may be used in standard quantitative or qualitative PCR-based assays to assess transcript expression levels of RNAs defined by the probe set. Alternatively, these primers may be used in combination with probes, such as molecular beacons in amplifications using real-time PCR.

In one embodiment, the primers or primer pairs, when used in an amplification reaction, specifically amplify at least a portion of a nucleic acid depicted in one of Table 6 (or subgroups thereof as set forth herein), an RNA form thereof, or a complement to either thereof.

As is known in the art, a nucleoside is a base-sugar combination and a nucleotide is a nucleoside that further includes a phosphate group covalently linked to the sugar portion of the nucleoside. In forming oligonucleotides, the phosphate groups covalently link adjacent nucleosides to one another to form a linear polymeric compound, with the normal linkage or backbone of RNA and DNA being a 3′ to 5′ phosphodiester linkage. Specific examples of polynucleotide probes or primers useful in this invention include oligonucleotides containing modified backbones or non-natural internucleoside linkages. As defined in this specification, oligonucleotides having modified backbones include both those that retain a phosphorus atom in the backbone and those that lack a phosphorus atom in the backbone. For the purposes of the present invention, and as sometimes referenced in the art, modified oligonucleotides that do not have a phosphorus atom in their internucleoside backbone can also be considered to be oligonucleotides.

Exemplary polynucleotide probes or primers having modified oligonucleotide backbones include, for example, those with one or more modified internucleotide linkages that are phosphorothioates, chiral phosphorothioates, phosphorodithioates, phosphotriesters, aminoalkylphosphotriesters, methyl and other alkyl phosphonates including 3′-alkylene phosphonates and chiral phosphonates, phosphinates, phosphoramidates including 3′amino phosphoramidate and aminoalkylphosphoramidates, thionophosphoramidates, thionoalkylphosphonates, thionoalkylphosphotriesters, and boranophosphates having normal 3′-5′ linkages, 2′-5′ linked analogs of these, and those having inverted polarity wherein the adjacent pairs of nucleoside units are linked 3′-5′ to 5′-3′ or 2′-5′ to 5′-2′. Various salts, mixed salts and free acid forms are also included.

Exemplary modified oligonucleotide backbones that do not include a phosphorus atom are formed by short chain alkyl or cycloalkyl internucleoside linkages, mixed heteroatom and alkyl or cycloalkyl internucleoside linkages, or one or more short chain heteroatomic or heterocyclic internucleoside linkages. Such backbones include morpholino linkages (formed in part from the sugar portion of a nucleoside); siloxane backbones; sulfide, sulfoxide and sulphone backbones; formacetyl and thioformacetyl backbones; methylene formacetyl and thioformacetyl backbones; alkene containing backbones; sulphamate backbones; methyleneimino and methylenehydrazino backbones; sulphonate and sulfonamide backbones; amide backbones; and others having mixed N, 0, S and CH₂component parts.

The present invention also contemplates oligonucleotide mimetics in which both the sugar and the internucleoside linkage of the nucleotide units are replaced with novel groups. The base units are maintained for hybridization with an appropriate nucleic acid target compound. An example of such an oligonucleotide mimetic, which has been shown to have excellent hybridization properties, is a peptide nucleic acid (PNA). In PNA compounds, the sugar-backbone of an oligonucleotide is replaced with an amide containing backbone, in particular an aminoethylglycine backbone. The nucleobases are retained and are bound directly or indirectly to aza-nitrogen atoms of the amide portion of the backbone.

The present invention also contemplates polynucleotide probes or primers comprising “locked nucleic acids” (LNAs), which may be novel conformationally restricted oligonucleotide analogues containing a methylene bridge that connects the 2′-O of ribose with the 4′-C. LNA and LNA analogues may display very high duplex thermal stabilities with complementary DNA and RNA, stability towards 3′-exonuclease degradation, and good solubility properties. Synthesis of the LNA analogues of adenine, cytosine, guanine, 5-methylcytosine, thymine and uracil, their oligomerization, and nucleic acid recognition properties have been described. Studies of mismatched sequences show that LNA obey the Watson-Crick base pairing rules with generally improved selectivity compared to the corresponding unmodified reference strands.

LNAs may form duplexes with complementary DNA or RNA or with complementary LNA, with high thermal affinities. The universality of LNA-mediated hybridization has been emphasized by the formation of exceedingly stable LNA:LNA duplexes. LNA:LNA hybridization was shown to be the most thermally stable nucleic acid type duplex system, and the RNA-mimicking character of LNA was established at the duplex level. Introduction of three LNA monomers (T or A) resulted in significantly increased melting points toward DNA complements.

Synthesis of 2′-amino-LNA and 2′-methylamino-LNA has been described and thermal stability of their duplexes with complementary RNA and DNA strands reported. Preparation of phosphorothioate-LNA and 2′-thio-LNA have also been described.

Modified polynucleotide probes or primers may also contain one or more substituted sugar moieties. For example, oligonucleotides may comprise sugars with one of the following substituents at the 2′ position: OH; F; O-, S-, or N-alkyl; O-, S-, or N-alkenyl; 0-, S- or N-alkynyl; or O-alkyl-O-alkyl, wherein the alkyl, alkenyl and alkynyl may be substituted or unsubstituted C₁to C₁₀alkyl or C2 to C₁₀alkenyl and alkynyl. Examples of such groups are: O[(CH₂)_nO]_mCH₃, O(CH₂)_nOCH₃, O(CH₂)_nNH₂, O(CH₂)_nCH₃ONH₂, and O(CH₂)_nON[((CH₂)_nCH₃)]₂, where n and m are from 1 to about 10. Alternatively, the oligonucleotides may comprise one of the following substituents at the 2′ position: C₁to C₁₀lower alkyl, substituted lower alkyl, alkaryl, aralkyl, O-alkaryl or O-aralkyl, SH, SCH₃, OCN, Cl, Br, CN, CF₃, OCF₃, SOCH₃, SO₂CH₃, ONO₂, NO₂, N₃, NH₂, heterocycloalkyl, heterocycloalkaryl, aminoalkylamino, polyalkylamino, substituted silyl, an RNA cleaving group, a reporter group, an intercalator, a group for improving the pharmacokinetic properties of an oligonucleotide, or a group for improving the pharmacodynamic properties of an oligonucleotide, and other substituents having similar properties. Specific examples include 2′-methoxyethoxy (2′-O—CH₂CH₂OCH₃, also known as 2′-O-(2-methoxyethyl) or 2′-MOE), 2′-dimethylaminooxyethoxy (O(CH2)2 ON(CH₃)₂group, also known as 2′-DMA0E), 2′-methoxy (2′-O—CH₃), 2′-aminopropoxy (2′-OCH₂CH₂CH₂NH₂) and 2′-fluoro (2′-F).

Similar modifications may also be made at other positions on the polynucleotide probes or primers, particularly the 3′ position of the sugar on the 3′ terminal nucleotide or in 2′-5′ linked oligonucleotides and the 5′ position of 5′ terminal nucleotide. Polynucleotide probes or primers may also have sugar mimetics such as cyclobutyl moieties in place of the pentofuranosyl sugar.

Polynucleotide probes or primers may also include modifications or substitutions to the nucleobase. As used herein, “unmodified” or “natural” nucleobases include the purine bases adenine (A) and guanine (G), and the pyrimidine bases thymine (T), cytosine (C) and uracil (U).

Modified nucleobases include other synthetic and natural nucleobases such as 5-methylcytosine (5-me-C), 5-hydroxymethyl cytosine, xanthine, hypoxanthine, 2-aminoadenine, 6-methyl and other alkyl derivatives of adenine and guanine, 2-propyl and other alkyl derivatives of adenine and guanine, 2-thiouracil, 2-thiothymine and 2-thiocytosine, 5-halouracil and cytosine, 5-propynyl uracil and cytosine, 6-azo uracil, cytosine and thymine, 5-uracil (pseudouracil), 4-thiouracil, 8-halo, 8-amino, 8-thiol, 8-thioalkyl, 8-hydroxyl and other 8-substituted adenines and guanines, 5-halo particularly 5-bromo, 5-trifluoromethyl and other 5-substituted uracils and cytosines, 7-methylguanine and 7-methyladenine, 8-azaguanine and 8-azaadenine, 7-deazaguanine and 7-deazaadenine and 3-deazaguanine and 3-deazaadenine. Further nucleobases include those disclosed in U.S. Pat. No. 3,687,808; The Concise Encyclopedia Of Polymer Science And Engineering, (1990) pp 858-859, Kroschwitz, J. I., ed. John Wiley & Sons; Englisch et al., Angewandte Chemie, Int. Ed., 30:613 (1991); and Sanghvi, Y. S., (1993) Antisense Research and Applications, pp 289-302, Crooke, S. T. and Lebleu, B., ed., CRC Press. Certain of these nucleobases are particularly useful for increasing the binding affinity of the polynucleotide probes of the invention. These include 5-substituted pyrimidines, 6-azapyrimidines and N-2, N-6 and O-6 substituted purines, including 2-aminopropyladenine, 5-propynyluracil and 5-propynylcytosine. 5-methylcytosine substitutions have been shown to increase nucleic acid duplex stability by 0.6-1.2° C.

One skilled in the art recognizes that it is not necessary for all positions in a given polynucleotide probe or primer to be uniformly modified. The present invention, therefore, contemplates the incorporation of more than one of the aforementioned modifications into a single polynucleotide probe or even at a single nucleoside within the probe or primer.

One skilled in the art also appreciates that the nucleotide sequence of the entire length of the polynucleotide probe or primer does not need to be derived from the target sequence. Thus, for example, the polynucleotide probe may comprise nucleotide sequences at the 5′ and/or 3′ termini that are not derived from the target sequences. Nucleotide sequences which are not derived from the nucleotide sequence of the target sequence may provide additional functionality to the polynucleotide probe. For example, they may provide a restriction enzyme recognition sequence or a “tag” that facilitates detection, isolation, purification or immobilization onto a solid support. Alternatively, the additional nucleotides may provide a self-complementary sequence that allows the primer/probe to adopt a hairpin configuration. Such configurations are necessary for certain probes, for example, molecular beacon and Scorpion probes, which can be used in solution hybridization techniques.

The polynucleotide probes or primers can incorporate moieties useful in detection, isolation, purification, or immobilization, if desired. Such moieties are well-known in the art (see, for example, Ausubel et al., (1997 & updates) Current Protocols in Molecular Biology, Wiley & Sons, New York) and are chosen such that the ability of the probe to hybridize with its target sequence is not affected.

Examples of suitable moieties are detectable labels, such as radioisotopes, fluorophores, chemiluminophores, enzymes, colloidal particles, and fluorescent microparticles, as well as antigens, antibodies, haptens, avidin/streptavidin, biotin, haptens, enzyme cofactors/substrates, enzymes, and the like.

A label can optionally be attached to or incorporated into a probe or primer polynucleotide to allow detection and/or quantitation of a target polynucleotide representing the target sequence of interest. The target polynucleotide may be the expressed target sequence RNA itself, a cDNA copy thereof, or an amplification product derived therefrom, and may be the positive or negative strand, so long as it can be specifically detected in the assay being used. Similarly, an antibody may be labeled.

In certain multiplex formats, labels used for detecting different targets may be distinguishable. The label can be attached directly (e.g., via covalent linkage) or indirectly, e.g., via a bridging molecule or series of molecules (e.g., a molecule or complex that can bind to an assay component, or via members of a binding pair that can be incorporated into assay components, e.g. biotin-avidin or streptavidin). Many labels are commercially available in activated forms which can readily be used for such conjugation (for example through amine acylation), or labels may be attached through known or determinable conjugation schemes, many of which are known in the art.

Labels useful in the invention described herein include any substance which can be detected when bound to or incorporated into the biomolecule of interest. Any effective detection method can be used, including optical, spectroscopic, electrical, piezoelectrical, magnetic, Raman scattering, surface plasmon resonance, colorimetric, calorimetric, etc. A label is typically selected from a chromophore, a lumiphore, a fluorophore, one member of a quenching system, a chromogen, a hapten, an antigen, a magnetic particle, a material exhibiting nonlinear optics, a semiconductor nanocrystal, a metal nanoparticle, an enzyme, an antibody or binding portion or equivalent thereof, an aptamer, and one member of a binding pair, and combinations thereof. Quenching schemes may be used, wherein a quencher and a fluorophore as members of a quenching pair may be used on a probe, such that a change in optical parameters occurs upon binding to the target introduce or quench the signal from the fluorophore. One example of such a system is a molecular beacon. Suitable quencher/fluorophore systems are known in the art. The label may be bound through a variety of intermediate linkages. For example, a polynucleotide may comprise a biotin-binding species, and an optically detectable label may be conjugated to biotin and then bound to the labeled polynucleotide. Similarly, a polynucleotide sensor may comprise an immunological species such as an antibody or fragment, and a secondary antibody containing an optically detectable label may be added.

Chromophores useful in the methods described herein include any substance which can absorb energy and emit light. For multiplexed assays, a plurality of different signaling chromophores can be used with detectably different emission spectra. The chromophore can be a lumophore or a fluorophore. Typical fluorophores include fluorescent dyes, semiconductor nanocrystals, lanthanide chelates, polynucleotide-specific dyes and green fluorescent protein.

Coding schemes may optionally be used, comprising encoded particles and/or encoded tags associated with different polynucleotides of the invention. A variety of different coding schemes are known in the art, including fluorophores, including SCNCs, deposited metals, and RF tags.

Polynucleotides from the described target sequences may be employed as probes for detecting target sequences expression, for ligation amplification schemes, or may be used as primers for amplification schemes of all or a portion of a target sequences. When amplified, either strand produced by amplification may be provided in purified and/or isolated form.

In one embodiment, polynucleotides of the invention include (a) a nucleic acid depicted in Table 6; (b) an RNA form of any one of the nucleic acids depicted in Table 6; (c) a peptide nucleic acid form of any of the nucleic acids depicted in Table 6; (d) a nucleic acid comprising at least 20 consecutive bases of any of (a-c); (e) a nucleic acid comprising at least 25 bases having at least 90% sequenced identity to any of (a-c); and (f) a complement to any of (a-e).

Complements may take any polymeric form capable of base pairing to the species recited in (a)-(e), including nucleic acid such as RNA or DNA, or may be a neutral polymer such as a peptide nucleic acid. Polynucleotides of the invention can be selected from the subsets of the recited nucleic acids described herein, as well as their complements.

In some embodiments, polynucleotides of the invention comprise at least 20 consecutive bases of the nucleic acids as depicted in Table 6 or a complement thereto. The polynucleotides may comprise at least 21, 22, 23, 24, 25, 27, 30, 32, 35 or more consecutive bases of the nucleic acid sequences as depicted in Table 6, as applicable.

The polynucleotides may be provided in a variety of formats, including as solids, in solution, or in an array. The polynucleotides may optionally comprise one or more labels, which may be chemically and/or enzymatically incorporated into the polynucleotide.

In one embodiment, solutions comprising polynucleotide and a solvent are also provided. In some embodiments, the solvent may be water or may be predominantly aqueous. In some embodiments, the solution may comprise at least two, three, four, five, six, seven, eight, nine, ten, twelve, fifteen, seventeen, twenty or more different polynucleotides, including primers and primer pairs, of the invention. Additional substances may be included in the solution, alone or in combination, including one or more labels, additional solvents, buffers, biomolecules, polynucleotides, and one or more enzymes useful for performing methods described herein, including polymerases and ligases. The solution may further comprise a primer or primer pair capable of amplifying a polynucleotide of the invention present in the solution.

In some embodiments, one or more polynucleotides provided herein can be provided on a substrate. The substrate can comprise a wide range of material, either biological, nonbiological, organic, inorganic, or a combination of any of these. For example, the substrate may be a polymerized Langmuir Blodgett film, functionalized glass, Si, Ge, GaAs, GaP, SiO₂, SiN₄, modified silicon, or any one of a wide variety of gels or polymers such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, polystyrene, cross-linked polystyrene, polyacrylic, polylactic acid, polyglycolic acid, poly(lactide coglycolide), polyanhydrides, poly(methyl methacrylate), poly(ethylene-co-vinyl acetate), polysiloxanes, polymeric silica, latexes, dextran polymers, epoxies, polycarbonates, or combinations thereof. Conducting polymers and photoconductive materials can be used.

Substrates can be planar crystalline substrates such as silica based substrates (e.g. glass, quartz, or the like), or crystalline substrates used in, e.g., the semiconductor and microprocessor industries, such as silicon, gallium arsenide, indium doped GaN and the like, and include semiconductor nanocrystals.

The substrate can take the form of an array, a photodiode, an optoelectronic sensor such as an optoelectronic semiconductor chip or optoelectronic thin-film semiconductor, or a biochip. The location(s) of probe(s) on the substrate can be addressable; this can be done in highly dense formats, and the location(s) can be microaddressable or nanoaddressable.

Silica aerogels can also be used as substrates, and can be prepared by methods known in the art. Aerogel substrates may be used as free standing substrates or as a surface coating for another substrate material.

The substrate can take any form and typically is a plate, slide, bead, pellet, disk, particle, microparticle, nanoparticle, strand, precipitate, optionally porous gel, sheets, tube, sphere, container, capillary, pad, slice, film, chip, multiwell plate or dish, optical fiber, etc. The substrate can be any form that is rigid or semi-rigid. The substrate may contain raised or depressed regions on which an assay component is located. The surface of the substrate can be etched using known techniques to provide for desired surface features, for example trenches, v-grooves, mesa structures, or the like.

Surfaces on the substrate can be composed of the same material as the substrate or can be made from a different material, and can be coupled to the substrate by chemical or physical means. Such coupled surfaces may be composed of any of a wide variety of materials, for example, polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glasses, membranes, or any of the above-listed substrate materials. The surface can be optically transparent and can have surface Si—OH functionalities, such as those found on silica surfaces.

The substrate and/or its optional surface can be chosen to provide appropriate characteristics for the synthetic and/or detection methods used. The substrate and/or surface can be transparent to allow the exposure of the substrate by light applied from multiple directions. The substrate and/or surface may be provided with reflective “mirror” structures to increase the recovery of light.

The substrate and/or its surface is generally resistant to, or is treated to resist, the conditions to which it is to be exposed in use, and can be optionally treated to remove any resistant material after exposure to such conditions.

The substrate or a region thereof may be encoded so that the identity of the sensor located in the substrate or region being queried may be determined. Any suitable coding scheme can be used, for example optical codes, RFID tags, magnetic codes, physical codes, fluorescent codes, and combinations of codes.

Preparation of Probes and Primers

The polynucleotide probes or primers of the present invention can be prepared by conventional techniques well-known to those skilled in the art. For example, the polynucleotide probes can be prepared using solid-phase synthesis using commercially available equipment. As is well-known in the art, modified oligonucleotides can also be readily prepared by similar methods. The polynucleotide probes can also be synthesized directly on a solid support according to methods standard in the art. This method of synthesizing polynucleotides is particularly useful when the polynucleotide probes are part of a nucleic acid array.

Polynucleotide probes or primers can be fabricated on or attached to the substrate by any suitable method, for example the methods described in U.S. Pat. No. 5,143,854, PCT Publ. No. WO 92/10092, U.S. patent application Ser. No. 07/624,120, filed Dec. 6, 1990 (now abandoned), Fodor et al., Science, 251: 767-777 (1991), and PCT Publ. No. WO 90/15070). Techniques for the synthesis of these arrays using mechanical synthesis strategies are described in, e.g., PCT Publication No. WO 93/09668 and U.S. Pat. No. 5,384,261. Still further techniques include bead based techniques such as those described in PCT Appl. No. PCT/US93/04145 and pin based methods such as those described in U.S. Pat. No. 5,288,514. Additional flow channel or spotting methods applicable to attachment of sensor polynucleotides to a substrate are described in U.S. patent application Ser. No. 07/980,523, filed Nov. 20, 1992, and U.S. Pat. No. 5,384,261.

Alternatively, the polynucleotide probes of the present invention can be prepared by enzymatic digestion of the naturally occurring target gene, or mRNA or cDNA derived therefrom, by methods known in the art.

Diagnostic Samples

Diagnostic samples for use with the systems and in the methods of the present invention comprise nucleic acids suitable for providing RNA expression information. In principle, the biological sample from which the expressed RNA is obtained and analyzed for target sequence expression can be any material suspected of comprising cancer tissue or cells. The diagnostic sample can be a biological sample used directly in a method of the invention. Alternatively, the diagnostic sample can be a sample prepared from a biological sample.

In one embodiment, the sample or portion of the sample comprising or suspected of comprising cancer tissue or cells can be any source of biological material, including cells, tissue, secretions, or fluid, including bodily fluids. Non-limiting examples of the source of the sample include an aspirate, a needle biopsy, a cytology pellet, a bulk tissue preparation or a section thereof obtained for example by surgery or autopsy, lymph fluid, blood, plasma, serum, tumors, and organs. Alternatively, or additionally, the source of the sample can be urine, bile, excrement, sweat, tears, vaginal fluids, spinal fluid, and stool. In some instances, the sources of the sample are secretions. In some instances, the secretions are exosomes.

The samples may be archival samples, having a known and documented medical outcome, or may be samples from current patients whose ultimate medical outcome is not yet known.

In some embodiments, the sample may be dissected prior to molecular analysis. The sample may be prepared via macrodissection of a bulk tumor specimen or portion thereof, or may be treated via microdissection, for example via Laser Capture Microdissection (LCM).

The sample may initially be provided in a variety of states, as fresh tissue, fresh frozen tissue, fine needle aspirates, and may be fixed or unfixed. Frequently, medical laboratories routinely prepare medical samples in a fixed state, which facilitates tissue storage. A variety of fixatives can be used to fix tissue to stabilize the morphology of cells, and may be used alone or in combination with other agents. Exemplary fixatives include crosslinking agents, alcohols, acetone, Bouin's solution, Zenker solution, Hely solution, osmic acid solution and Carnoy solution.

Crosslinking fixatives can comprise any agent suitable for forming two or more covalent bonds, for example, an aldehyde. Sources of aldehydes typically used for fixation include formaldehyde, paraformaldehyde, glutaraldehyde or formalin. Preferably, the crosslinking agent comprises formaldehyde, which may be included in its native form or in the form of paraformaldehyde or formalin. One of skill in the art would appreciate that for samples in which crosslinking fixatives have been used special preparatory steps may be necessary including for example heating steps and proteinase-k digestion; see methods.

One or more alcohols may be used to fix tissue, alone or in combination with other fixatives. Exemplary alcohols used for fixation include methanol, ethanol and isopropanol.

Formalin fixation is frequently used in medical laboratories. Formalin comprises both an alcohol, typically methanol, and formaldehyde, both of which can act to fix a biological sample.

Whether fixed or unfixed, the biological sample may optionally be embedded in an embedding medium. Exemplary embedding media used in histology including paraffin, Tissue-Tek® V.I.P.™, Paramat, Paramat Extra, Paraplast, Paraplast X-tra, Paraplast Plus, Peel Away Paraffin Embedding Wax, Polyester Wax, Carbowax Polyethylene Glycol, Polyfin™, Tissue Freezing Medium TFMFM, Cryo-Gef™, and OCT Compound (Electron Microscopy Sciences, Hatfield, Pa.). Prior to molecular analysis, the embedding material may be removed via any suitable techniques, as known in the art. For example, where the sample is embedded in wax, the embedding material may be removed by extraction with organic solvent(s), for example xylenes. Kits are commercially available for removing embedding media from tissues. Samples or sections thereof may be subjected to further processing steps as needed, for example serial hydration or dehydration steps.

In some embodiments, the sample is a fixed, wax-embedded biological sample. Frequently, samples from medical laboratories are provided as fixed, wax-embedded samples, most commonly as formalin-fixed, paraffin embedded (FFPE) tissues.

Whatever the source of the biological sample, the target polynucleotide that is ultimately assayed can be prepared synthetically (in the case of control sequences), but typically is purified from the biological source and subjected to one or more preparative steps. The RNA may be purified to remove or diminish one or more undesired components from the biological sample or to concentrate it. Conversely, where the RNA is too concentrated for the particular assay, it may be diluted.

RNA Extraction

RNA can be extracted and purified from biological samples using any suitable technique. A number of techniques are known in the art, and several are commercially available (e.g., FormaPure nucleic acid extraction kit, Agencourt Biosciences, Beverly Mass., High Pure FFPE RNA Micro Kit, Roche Applied Science, Indianapolis, Ind.). RNA can be extracted from frozen tissue sections using TRIzol (Invitrogen, Carlsbad, Calif.) and purified using RNeasy Protect kit (Qiagen, Valencia, Calif.). RNA can be further purified using DNAse I treatment (Ambion, Austin, Tex.) to eliminate any contaminating DNA. RNA concentrations can be made using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Rockland, Del.). RNA can be further purified to eliminate contaminants that interfere with cDNA synthesis by cold sodium acetate precipitation. RNA integrity can be evaluated by running electropherograms, and RNA integrity number (RIN, a correlative measure that indicates intactness of mRNA) can be determined using the RNA 6000 PicoAssay for the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.).

Kits

Kits for performing the desired method(s) are also provided, and comprise a container or housing for holding the components of the kit, one or more vessels containing one or more nucleic acid(s), and optionally one or more vessels containing one or more reagents. The reagents include those described in the composition of matter section above, and those reagents useful for performing the methods described, including amplification reagents, and may include one or more probes, primers or primer pairs, enzymes (including polymerases and ligases), intercalating dyes, labeled probes, and labels that can be incorporated into amplification products.

In some embodiments, the kit comprises primers or primer pairs specific for those subsets and combinations of target sequences described herein. At least two, three, four or five primers or pairs of primers suitable for selectively amplifying the same number of target sequence-specific polynucleotides can be provided in kit form. In some embodiments, the kit comprises from five to fifty primers or pairs of primers suitable for amplifying the same number of target sequence-representative polynucleotides of interest.

In some embodiments, the primers or primer pairs of the kit, when used in an amplification reaction, specifically amplify a non-coding target, coding target, or non-exonic target described herein, at least a portion of a nucleic acid depicted in one of SEQ ID NOs.: 1-903, an RNA form thereof, or a complement to either thereof. The kit may include a plurality of such primers or primer pairs which can specifically amplify a corresponding plurality of different amplify a non-coding target, coding target, or non-exonic transcript described herein, nucleic acids depicted in one of SEQ ID NOs.: 1-903, RNA forms thereof, or complements thereto. At least two, three, four or five primers or pairs of primers suitable for selectively amplifying the same number of target sequence-specific polynucleotides can be provided in kit form. In some embodiments, the kit comprises from five to fifty primers or pairs of primers suitable for amplifying the same number of target sequence-representative polynucleotides of interest.

The reagents may independently be in liquid or solid form. The reagents may be provided in mixtures. Control samples and/or nucleic acids may optionally be provided in the kit. Control samples may include tissue and/or nucleic acids obtained from or representative of tumor samples from patients showing no evidence of disease, as well as tissue and/or nucleic acids obtained from or representative of tumor samples from patients that develop systemic cancer.

The nucleic acids may be provided in an array format, and thus an array or microarray may be included in the kit. The kit optionally may be certified by a government agency for use in prognosing the disease outcome of cancer patients and/or for designating a treatment modality.

Instructions for using the kit to perform one or more methods of the invention can be provided with the container, and can be provided in any fixed medium. The instructions may be located inside or outside the container or housing, and/or may be printed on the interior or exterior of any surface thereof. A kit may be in multiplex form for concurrently detecting and/or quantitating one or more different target polynucleotides representing the expressed target sequences.

Devices

Devices useful for performing methods of the invention are also provided. The devices can comprise means for characterizing the expression level of a target sequence of the invention, for example components for performing one or more methods of nucleic acid extraction, amplification, and/or detection. Such components may include one or more of an amplification chamber (for example a thermal cycler), a plate reader, a spectrophotometer, capillary electrophoresis apparatus, a chip reader, and or robotic sample handling components. These components ultimately can obtain data that reflects the expression level of the target sequences used in the assay being employed.

The devices may include an excitation and/or a detection means. Any instrument that provides a wavelength that can excite a species of interest and is shorter than the emission wavelength(s) to be detected can be used for excitation. Commercially available devices can provide suitable excitation wavelengths as well as suitable detection component.

Exemplary excitation sources include a broadband UV light source such as a deuterium lamp with an appropriate filter, the output of a white light source such as a xenon lamp or a deuterium lamp after passing through a monochromator to extract out the desired wavelength(s), a continuous wave (cw) gas laser, a solid state diode laser, or any of the pulsed lasers. Emitted light can be detected through any suitable device or technique; many suitable approaches are known in the art. For example, a fluorimeter or spectrophotometer may be used to detect whether the test sample emits light of a wavelength characteristic of a label used in an assay.

The devices typically comprise a means for identifying a given sample, and of linking the results obtained to that sample. Such means can include manual labels, barcodes, and other indicators which can be linked to a sample vessel, and/or may optionally be included in the sample itself, for example where an encoded particle is added to the sample. The results may be linked to the sample, for example in a computer memory that contains a sample designation and a record of expression levels obtained from the sample. Linkage of the results to the sample can also include a linkage to a particular sample receptacle in the device, which is also linked to the sample identity.

The devices also comprise a means for correlating the expression levels of the target sequences being studied with a prognosis of disease outcome. Such means may comprise one or more of a variety of correlative techniques, including lookup tables, algorithms, multivariate models, and linear or nonlinear combinations of expression models or algorithms. The expression levels may be converted to one or more likelihood scores, reflecting a likelihood that the patient providing the sample may exhibit a particular disease outcome. The models and/or algorithms can be provided in machine readable format and can optionally further designate a treatment modality for a patient or class of patients.

The device also comprises output means for outputting the disease status, prognosis and/or a treatment modality. Such output means can take any form which transmits the results to a patient and/or a healthcare provider, and may include a monitor, a printed format, or both. The device may use a computer system for performing one or more of the steps provided.

The methods disclosed herein may also comprise the transmission of data/information. For example, data/information derived from the detection and/or quantification of the target may be transmitted to another device and/or instrument. In some instances, the information obtained from an algorithm may also be transmitted to another device and/or instrument. Transmission of the data/information may comprise the transfer of data/information from a first source to a second source. The first and second sources may be in the same approximate location (e.g., within the same room, building, block, campus). Alternatively, first and second sources may be in multiple locations (e.g., multiple cities, states, countries, continents, etc).

Transmission of the data/information may comprise digital transmission or analog transmission. Digital transmission may comprise the physical transfer of data (a digital bit stream) over a point-to-point or point-to-multipoint communication channel. Examples of such channels are copper wires, optical fibres, wireless communication channels, and storage media. The data may be represented as an electromagnetic signal, such as an electrical voltage, radiowave, microwave, or infrared signal.

Analog transmission may comprise the transfer of a continuously varying analog signal. The messages can either be represented by a sequence of pulses by means of a line code (baseband transmission), or by a limited set of continuously varying wave forms (passband transmission), using a digital modulation method. The passband modulation and corresponding demodulation (also known as detection) can be carried out by modern equipment. According to the most common definition of digital signal, both baseband and passband signals representing bit-streams are considered as digital transmission, while an alternative definition only considers the baseband signal as digital, and passband transmission of digital data as a form of digital-to-analog conversion.

Amplification and Hybridization

Following sample collection and nucleic acid extraction, the nucleic acid portion of the sample comprising RNA that is or can be used to prepare the target polynucleotide(s) of interest can be subjected to one or more preparative reactions. These preparative reactions can include in vitro transcription (IVT), labeling, fragmentation, amplification and other reactions. mRNA can first be treated with reverse transcriptase and a primer to create cDNA prior to detection, quantitation and/or amplification; this can be done in vitro with purified mRNA or in situ, e.g., in cells or tissues affixed to a slide.

By “amplification” is meant any process of producing at least one copy of a nucleic acid, in this case an expressed RNA, and in many cases produces multiple copies. An amplification product can be RNA or DNA, and may include a complementary strand to the expressed target sequence. DNA amplification products can be produced initially through reverse translation and then optionally from further amplification reactions. The amplification product may include all or a portion of a target sequence, and may optionally be labeled. A variety of amplification methods are suitable for use, including polymerase-based methods and ligation-based methods. Exemplary amplification techniques include the polymerase chain reaction method (PCR), the lipase chain reaction (LCR), ribozyme-based methods, self sustained sequence replication (3SR), nucleic acid sequence-based amplification (NASBA), the use of Q Beta replicase, reverse transcription, nick translation, and the like.

Asymmetric amplification reactions may be used to preferentially amplify one strand representing the target sequence that is used for detection as the target polynucleotide. In some cases, the presence and/or amount of the amplification product itself may be used to determine the expression level of a given target sequence. In other instances, the amplification product may be used to hybridize to an array or other substrate comprising sensor polynucleotides which are used to detect and/or quantitate target sequence expression.

The first cycle of amplification in polymerase-based methods typically forms a primer extension product complementary to the template strand. If the template is single-stranded RNA, a polymerase with reverse transcriptase activity is used in the first amplification to reverse transcribe the RNA to DNA, and additional amplification cycles can be performed to copy the primer extension products. The primers for a PCR must, of course, be designed to hybridize to regions in their corresponding template that can produce an amplifiable segment; thus, each primer must hybridize so that its 3′ nucleotide is paired to a nucleotide in its complementary template strand that is located 3′ from the 3′ nucleotide of the primer used to replicate that complementary template strand in the PCR.

The target polynucleotide can be amplified by contacting one or more strands of the target polynucleotide with a primer and a polymerase having suitable activity to extend the primer and copy the target polynucleotide to produce a full-length complementary polynucleotide or a smaller portion thereof. Any enzyme having a polymerase activity that can copy the target polynucleotide can be used, including DNA polymerases, RNA polymerases, reverse transcriptases, enzymes having more than one type of polymerase or enzyme activity. The enzyme can be thermolabile or thermostable. Mixtures of enzymes can also be used. Exemplary enzymes include: DNA polymerases such as DNA Polymerase I (“Pol I”), the Klenow fragment of Pol I, T4, T7, Sequenase® T7, Sequenase® Version 2.0 T7, Tub, Taq, Tth, Pfic, Pfu, Tsp, Tfl, Tli and Pyrococcus sp GB-D DNA polymerases; RNA polymerases such as E. coli, SP6, T3 and T7 RNA polymerases; and reverse transcriptases such as AMV, M-MuLV, MMLV, RNAse H MMLV (SuperScript®), SuperScript® II, ThermoScript®, HIV-1, and RAV2 reverse transcriptases. All of these enzymes are commercially available. Exemplary polymerases with multiple specificities include RAV2 and Tli (exo-) polymerases. Exemplary thermostable polymerases include Tub, Taq, Tth, Pfic, Pfu, Tsp, Tfl, Tli and Pyrococcus sp. GB-D DNA polymerases.

Suitable reaction conditions are chosen to permit amplification of the target polynucleotide, including pH, buffer, ionic strength, presence and concentration of one or more salts, presence and concentration of reactants and cofactors such as nucleotides and magnesium and/or other metal ions (e.g., manganese), optional cosolvents, temperature, thermal cycling profile for amplification schemes comprising a polymerase chain reaction, and may depend in part on the polymerase being used as well as the nature of the sample. Cosolvents include formamide (typically at from about 2 to about 10%), glycerol (typically at from about 5 to about 10%), and DMSO (typically at from about 0.9 to about 10%). Techniques may be used in the amplification scheme in order to minimize the production of false positives or artifacts produced during amplification. These include “touchdown” PCR, hot-start techniques, use of nested primers, or designing PCR primers so that they form stem-loop structures in the event of primer-dimer formation and thus are not amplified. Techniques to accelerate PCR can be used, for example centrifugal PCR, which allows for greater convection within the sample, and comprising infrared heating steps for rapid heating and cooling of the sample. One or more cycles of amplification can be performed. An excess of one primer can be used to produce an excess of one primer extension product during PCR; preferably, the primer extension product produced in excess is the amplification product to be detected. A plurality of different primers may be used to amplify different target polynucleotides or different regions of a particular target polynucleotide within the sample.

An amplification reaction can be performed under conditions which allow an optionally labeled sensor polynucleotide to hybridize to the amplification product during at least part of an amplification cycle. When the assay is performed in this manner, real-time detection of this hybridization event can take place by monitoring for light emission or fluorescence during amplification, as known in the art.

Where the amplification product is to be used for hybridization to an array or microarray, a number of suitable commercially available amplification products are available. These include amplification kits available from NuGEN, Inc. (San Carlos, Calif.), including the WTA-Ovation™ System, WT-Ovation™ System v2, WT-Ovation™ Pico System, WT-Ovation™ FFPE Exon Module, WT-Ovation™ FFPE Exon Module RiboAmp and RiboAmp^PlusRNA Amplification Kits (MDS Analytical Technologies (formerly Arcturus) (Mountain View, Calif.), Genisphere, Inc. (Hatfield, Pa.), including the RampUp Plus™ and SenseAmp™ RNA Amplification kits, alone or in combination. Amplified nucleic acids may be subjected to one or more purification reactions after amplification and labeling, for example using magnetic beads (e.g., RNAC1ean magnetic beads, Agencourt Biosciences).

Multiple RNA biomarkers (e.g., RNA targets) can be analyzed using real-time quantitative multiplex RT-PCR platforms and other multiplexing technologies such as GenomeLab GeXP Genetic Analysis System (Beckman Coulter, Foster City, Calif.), SmartCycler® 9600 or GeneXpert® Systems (Cepheid, Sunnyvale, Calif.), ABI 7900 HT Fast Real Time PCR system (Applied Biosystems, Foster City, Calif.), LightCycler® 480 System (Roche Molecular Systems, Pleasanton, Calif.), xMAP 100 System (Luminex, Austin, Tex.) Solexa Genome Analysis System (Illumina, Hayward, Calif.), OpenArray Real Time qPCR (BioTrove, Woburn, Mass.) and BeadXpress System (Illumina, Hayward, Calif.). Alternatively, or additional, coding targets and/or non-coding targets can be analyzed using RNA-Seq. In some instances, coding and/or non-coding targets are analyzed by sequencing.

Detection and/or Quantification of Target Sequences

Any method of detecting and/or quantitating the expression of the encoded target sequences can in principle be used in the invention. The expressed target sequences can be directly detected and/or quantitated, or may be copied and/or amplified to allow detection of amplified copies of the expressed target sequences or its complement.

Methods for detecting and/or quantifying a target can include Northern blotting, sequencing, array or microarray hybridization, by enzymatic cleavage of specific structures (e.g., an Invader® assay, Third Wave Technologies, e.g. as described in U.S. Pat. Nos. 5,846,717, 6,090,543; 6,001,567; 5,985,557; and 5,994,069) and amplification methods (e.g. RT-PCR, including in a TaqMan® assay (PE Biosystems, Foster City, Calif., e.g. as described in U.S. Pat. Nos. 5,962,233 and 5,538,848)), and may be quantitative or semi-quantitative, and may vary depending on the origin, amount and condition of the available biological sample. Combinations of these methods may also be used. For example, nucleic acids may be amplified, labeled and subjected to microarray analysis.

In some instances, assaying the expression level of a plurality of targets comprises amplifying the plurality of targets. Amplifying the plurality of targets can comprise PCR, RT-PCR, qPCR, digital PCR, and nested PCR.

In some instances, the target sequences are detected by sequencing. Sequencing methods may comprise whole genome sequencing or exome sequencing. Sequencing methods such as Maxim-Gilbert, chain-termination, or high-throughput systems may also be used. Additional, suitable sequencing techniques include classic dideoxy sequencing reactions (Sanger method) using labeled terminators or primers and gel separation in slab or capillary, sequencing by synthesis using reversibly terminated labeled nucleotides, pyrosequencing, 454 sequencing, allele specific hybridization to a library of labeled oligonucleotide probes, sequencing by synthesis using allele specific hybridization to a library of labeled clones that is followed by ligation, real time monitoring of the incorporation of labeled nucleotides during a polymerization step, shotgun sequencing and SOLiD sequencing.

Additional methods for detecting and/or quantifying a target sequence can comprise single-molecule sequencing (e.g., Illumina, Helicos, PacBio, ABI SOLID), in situ hybridization, bead-array technologies (e.g., Luminex xMAP, Illumina BeadChips), branched DNA technology (e.g., Panomics, Genisphere), and Ion Torrent™.

In some instances, methods for detecting and/or quantifying a target sequence comprise transcriptome sequencing techniques. Transcription sequencing (e.g., RNA-seq, “Whole Transcriptome Shotgun Sequencing” (“WTSS”)) may comprise the use of high-throughput sequencing technologies to sequence cDNA in order to get information about a sample's RNA content. Transcriptome sequencing can provide information on differential expression of genes, including gene alleles and differently spliced transcripts, non-coding RNAs, post-transcriptional mutations or editing, and gene fusions. Transcriptomes can also be sequenced by methods comprising Sanger sequencing, Serial analysis of gene expression (SAGE), cap analysis gene expression (CAGE), and massively parallel signature sequencing (MPSS). In some instances, transcriptome sequencing can comprise a variety of platforms. A non-limiting list of exemplary platforms include an Illumina Genome Analyzer platform, ABI Solid Sequencing, and Life Science's 454 Sequencing.

Reverse Transcription for ORT-PCR Analysis

Reverse transcription can be performed by any method known in the art. For example, reverse transcription may be performed using the Omniscript kit (Qiagen, Valencia, Calif.), Superscript III kit (Invitrogen, Carlsbad, Calif.), for RT-PCR. Target-specific priming can be performed in order to increase the sensitivity of detection of target sequences and generate target-specific cDNA.

TaqMan® Gene Expression Analysis

TaqMan®RT-PCR can be performed using Applied Biosystems Prism (ABI) 7900 HT instruments in a 5 1.11 volume with target sequence-specific cDNA equivalent to 1 ng total RNA.

Primers and probes concentrations for TaqMan analysis are added to amplify fluorescent amplicons using PCR cycling conditions such as 95° C. for 10 minutes for one cycle, 95° C. for 20 seconds, and 60° C. for 45 seconds for 40 cycles. A reference sample can be assayed to ensure reagent and process stability. Negative controls (e.g., no template) should be assayed to monitor any exogenous nucleic acid contamination.

Classification Arrays

The present invention contemplates that a classifier, ICE block, PSR, probe set or probes derived therefrom may be provided in an array format. In the context of the present invention, an “array” is a spatially or logically organized collection of polynucleotide probes. An array comprising probes specific for a coding target, non-coding target, or a combination thereof may be used. Alternatively, an array comprising probes specific for two or more of transcripts listed in Table 6 or a product derived thereof can be used. Desirably, an array may be specific for at least about 5, 10, 15, 20, 25, 30, 50, 75, 100, 150, 200 or more of transcripts listed in Table 6. The array can be specific for at least about 250, 300, 350, 400 or more transcripts listed in Table 6. Expression of these sequences may be detected alone or in combination with other transcripts. In some embodiments, an array is used which comprises a wide range of sensor probes for prostate-specific expression products, along with appropriate control sequences. In some instances, the array may comprise the Human Exon 1.0 ST Array (HuEx 1.0 ST, Affymetrix, Inc., Santa Clara, Calif.).

Typically the polynucleotide probes are attached to a solid substrate and are ordered so that the location (on the substrate) and the identity of each are known. The polynucleotide probes can be attached to one of a variety of solid substrates capable of withstanding the reagents and conditions necessary for use of the array. Examples include, but are not limited to, polymers, such as (poly)tetrafluoroethylene, (poly)vinylidenedifluoride, polystyrene, polycarbonate, polypropylene and polystyrene; ceramic; silicon; silicon dioxide; modified silicon; (fused) silica, quartz or glass; functionalized glass; paper, such as filter paper; diazotized cellulose; nitrocellulose filter; nylon membrane; and polyacrylamide gel pad. Substrates that are transparent to light are useful for arrays that may be used in an assay that involves optical detection.

Examples of array formats include membrane or filter arrays (for example, nitrocellulose, nylon arrays), plate arrays (for example, multiwell, such as a 24-, 96-, 256-, 384-, 864- or 1536-well, microtitre plate arrays), pin arrays, and bead arrays (for example, in a liquid “slurry”). Arrays on substrates such as glass or ceramic slides are often referred to as chip arrays or “chips.” Such arrays are well known in the art. In one embodiment of the present invention, the Cancer Prognosticarray is a chip.

Annotation of Probe Selection Regions

In some instances, the methods disclosed herein comprise the annotation of one or more probe selection regions (PSRs). In some instances, the PSRs disclosed are annotated into categories (e.g., coding, non-coding). Annotation of the PSRs can utilize a variety of software packages. In some instances, annotation of the PSRs comprises the use of the xmapcore package (Yates et al 2010), which is the human genome version hg19, and Ensembl gene annotation v62, which can be integrated with the xmapcore packagses. In some instances, the method for annotating a PSR comprises (a) annotating a PSR as Non_Coding (intronic), wherein the PSR is returned by the intronic( ) function; and/or (b) further analyzing a PSR, wherein the PSR is returned by the exonic( ) function. Further analysis of the PSR can comprise (a) annotating the PSR as Coding, wherein the PSR is returned by the coding.probesets( ) function; (b) annotating the PSR as Non_Coding (UTR), wherein the PSR is returned by the utr.probestes( ) function; and/or (c) annotating the PSR as Non_Coding (ncTRANSCRIPT), wherein the PSR is not annotated as Coding or NON_Coding (UTR). PSRs that are not annotated as Non_Coding (intronic), Non_Coding (UTR), Non_Coding (ncTRANSCRIPT), or Coding can be referred to as the remaining PSRs.

The methods disclosed herein can further comprise detailed annotation of the remaining PSRs. Detailed annotation of the remaining PSRs can comprise determining the chromosome, start position, end position, and strand for each remaining PSR. Detailed annotation of the remaining PSRs can comprise utilization of the probeset.to.hit( ) function. In some instances, the remaining PSRs can be further annotated. Further annotation of the remaining PSRs can comprise inspection of a genomic span of each remaining PSR for the presence of genes, exons and protein-coding sequences. Often, the opposite strand of the PSR is used in the inspection of the genomic span. In some instances, inspection of the genomic span can comprise the use of one or more computer functions. In some instances, the computer functions are a genes.in.range( ) function, exons.in.range( ) function, and/or proteins.in.range( ) function (respectively). The remaining PSRs can be annotated as (a) Non_Coding (CDS_Antisense), wherein a protein is returned for the proteins.in.range( ) function; (b) Non_Coding (UTR_Antisense), wherein (i) a protein is not returned for the proteins.in.range( ) function, and (ii) the overlapping feature of the gene in the opposite strand is a UTR; (c) Non_Coding (ncTRANSCRIPT_Antisense), wherein (i) a protein is not returned for the proteins.in.range( ) function, and (ii) the overlapping feature of the gene in the opposite strand is not a UTR; (d) Non_Coding (Intronic_Antisense), wherein (i) a gene is returned for the genes.in.range( ) function, (ii) an exon is not returned for the exons.in.range( ), and (iii) a protein is not returned for the proteins.in.range( ) function; and (e) Non_Coding (Intergenic), wherein the remaining PSR does not overlap with any coding or non-coding gene feature in the sense or antisense strand.

In some instances, the methods disclosed herein further comprise additional annotation of a PSR with respect to transcripts and genes. Additional annotation of the PSR can comprise the use of the probeset.to.transcript( ) and/or probeset.to.gene( ) functions. In some instances, PSRs are annotated as Non_Coding (Non_Unique), wherein the PSR is obtained using the unreliable( ) function from xmapcore. In some instances, a PSR is annotated as Non_Coding (Intergenic) when the PSR maps to more than one region.

Data Analysis

In some embodiments, one or more pattern recognition methods can be used in analyzing the expression level of target sequences. The pattern recognition method can comprise a linear combination of expression levels, or a nonlinear combination of expression levels. In some embodiments, expression measurements for RNA transcripts or combinations of RNA transcript levels are formulated into linear or non-linear models or algorithms (e.g., an ‘expression signature’) and converted into a likelihood score. This likelihood score can indicate the probability that a biological sample is from a patient who may exhibit no evidence of disease, who may exhibit local disease, who may exhibit systemic cancer, or who may exhibit biochemical recurrence. The likelihood score can be used to distinguish these disease states. The models and/or algorithms can be provided in machine readable format, and may be used to correlate expression levels or an expression profile with a disease state, and/or to designate a treatment modality for a patient or class of patients.

Assaying the expression level for a plurality of targets may comprise the use of an algorithm or classifier. Array data can be managed, classified, and analyzed using techniques known in the art. Assaying the expression level for a plurality of targets may comprise probe set modeling and data pre-processing. Probe set modeling and data pre-processing can be derived using the Robust Multi-Array (RMA) algorithm or variants GC-RMA, fRMA, Probe Logarithmic Intensity Error (PLIER) algorithm or variant iterPLIER. Variance or intensity filters can be applied to pre-process data using the RMA algorithm, for example by removing target sequences with a standard deviation of <10 or a mean intensity of <100 intensity units of a normalized data range, respectively.

Alternatively, assaying the expression level for a plurality of targets may comprise the use of a machine learning algorithm. The machine learning algorithm may comprise a supervised learning algorithm. Examples of supervised learning algorithms may include Average One-Dependence Estimators (AODE), Artificial neural network (e.g., Backpropagation), Bayesian statistics (e.g., Naive Bayes classifier, Bayesian network, Bayesian knowledge base), Case-based reasoning, Decision trees, Inductive logic programming, Gaussian process regression, Group method of data handling (GMDH), Learning Automata, Learning Vector Quantization, Minimum message length (decision trees, decision graphs, etc.), Lazy learning, Instance-based learning Nearest Neighbor Algorithm, Analogical modeling, Probably approximately correct learning (PAC) learning, Ripple down rules, a knowledge acquisition methodology, Symbolic machine learning algorithms, Subsymbolic machine learning algorithms, Support vector machines, Random Forests, Ensembles of classifiers, Bootstrap aggregating (bagging), and Boosting. Supervised learning may comprise ordinal classification such as regression analysis and Information fuzzy networks (IFN). Alternatively, supervised learning methods may comprise statistical classification, such as AODE, Linear classifiers (e.g., Fisher's linear discriminant, Logistic regression, Naive Bayes classifier, Perceptron, and Support vector machine), quadratic classifiers, k-nearest neighbor, Boosting, Decision trees (e.g., C4.5, Random forests), Bayesian networks, and Hidden Markov models.

The machine learning algorithms may also comprise an unsupervised learning algorithm. Examples of unsupervised learning algorithms may include Artificial neural network, Data clustering, Expectation-maximization algorithm, Self-organizing map, Radial basis function network, Vector Quantization, Generative topographic map, Information bottleneck method, and IBSEAD. Unsupervised learning may also comprise association rule learning algorithms such as Apriori algorithm, Eclat algorithm and FP-growth algorithm. Hierarchical clustering, such as Single-linkage clustering and Conceptual clustering, may also be used. Alternatively, unsupervised learning may comprise partitional clustering such as K-means algorithm and Fuzzy clustering.

In some instances, the machine learning algorithms comprise a reinforcement learning algorithm. Examples of reinforcement learning algorithms include, but are not limited to, Temporal difference learning, Q-learning and Learning Automata. Alternatively, the machine learning algorithm may comprise Data Pre-processing.

Preferably, the machine learning algorithms may include, but are not limited to, Average One-Dependence Estimators (AODE), Fisher's linear discriminant, Logistic regression, Perceptron, Multilayer Perceptron, Artificial Neural Networks, Support vector machines, Quadratic classifiers, Boosting, Decision trees, C4.5, Bayesian networks, Hidden Markov models, High-Dimensional Discriminant Analysis, and Gaussian Mixture Models. The machine learning algorithm may comprise support vector machines, Naïve Bayes classifier, k-nearest neighbor, high-dimensional discriminant analysis, or Gaussian mixture models. In some instances, the machine learning algorithm comprises Random Forests.

The methods, systems, devices, and kits disclosed herein can further comprise a computer, an electronic device, computer software, a memory device, or any combination thereof. In some instances, the methods, systems, devices, and kits disclosed herein further comprise one or more computer software programs for (a) analysis of the target (e.g., expression profile, detection, quantification); (b) diagnosis, prognosis and/or monitoring the outcome or status of a cancer in a subject; (c) determination of a treatment regimen; (d) analysis of a classifier, probe set, probe selection region, ICE block, or digital Gleason score predictor as disclosed herein. Analysis of a classifier, probe set, probe selection region, ICE block or digital Gleason score predictor can comprise determining the AUC value, MDF value, percent accuracy, P-value, clinical significance, or any combination thereof. The software program can comprise (a) bigmemory, which can be used to load large expression matrices; (b) matrixStats, which can be used in statistics on matrices like row medians, column medians, row ranges; (c) genefilter, which can be used as a fast calculation of t-tests, ROC, and AUC; (d) pROC, which can be used to plot ROC curves and calculate AUC's and their 95% confidence intervals; (e) ROCR, which can be used to plot ROC curves and to calculate AUCs; (f) pROCR, which can be used to plot ROC curves and to calculate AUCs; (g) snow or doSMP, which can be used for parallel processing; (h) caret, which can be used for K-Nearest-Neighbour (KNN), Null Model, and classifier analysis; (i) e1071, which can be used for Support Vector Machines (SVM), K-Nearest-Neighbour (KNN), Naive Bayes, classifier tuning, and sample partitioning; (j) randomForest, which can be used for Random forest model; (k) HDClassif, which can be used for HDDA model; (l) rpart, which can be used for recursive partitioning model; (m) rms, which can be used for logistic regression model; (n) survival, which can be used for coxph model, km plots, and other survival analysis; (o) iterator, intertools, foreach, which can be used for iteration of large matrices; (p) frma, which can be used to package for frozen robust microarray analysis; (q) epitools, which can be used for odds ratios; (r) Proxy, which can be used for distance calculations; (s) boot, which can be used for Bootstrapping; (t) glmnet, which can be used to regularize general linear model; (u) gplots, which can be used to generate plots and figures; (v) scatterplot3d, which can be used to generate 3d scatter plots, (w) heatmap.plus, which can be used to generate heatmaps; (x) vegan, which can be used to determine MDS p-values; (y) xlsx, which can be used to work with excel spread sheets; (z) xtable, which can be used to work with R tables to latex; (aa) ffpe, which can be used for Cat plots; and (ab) xmapcore, which can be used for annotation of PSRs with respect to Ensembl annotation. In some instances, the software program is xmapcore. In other instances, the software program is caret. In other instances, the software program is e1071. The software program can be Proxy. Alternatively, the software program is gplots. In some instances, the software program is scatterplot3 d.

Additional Techniques and Tests

Factors known in the art for diagnosing and/or suggesting, selecting, designating, recommending or otherwise determining a course of treatment for a patient or class of patients suspected of having cancer can be employed in combination with measurements of the target sequence expression. The methods disclosed herein may include additional techniques such as cytology, histology, ultrasound analysis, MRI results, CT scan results, and measurements of PSA levels.

Certified tests for classifying disease status and/or designating treatment modalities may also be used in diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject. A certified test may comprise a means for characterizing the expression levels of one or more of the target sequences of interest, and a certification from a government regulatory agency endorsing use of the test for classifying the disease status of a biological sample.

In some embodiments, the certified test may comprise reagents for amplification reactions used to detect and/or quantitate expression of the target sequences to be characterized in the test. An array of probe nucleic acids can be used, with or without prior target amplification, for use in measuring target sequence expression.

The test is submitted to an agency having authority to certify the test for use in distinguishing disease status and/or outcome. Results of detection of expression levels of the target sequences used in the test and correlation with disease status and/or outcome are submitted to the agency. A certification authorizing the diagnostic and/or prognostic use of the test is obtained.

Also provided are portfolios of expression levels comprising a plurality of normalized expression levels of the target sequences described Table 6. Such portfolios may be provided by performing the methods described herein to obtain expression levels from an individual patient or from a group of patients. The expression levels can be normalized by any method known in the art; exemplary normalization methods that can be used in various embodiments include Robust Multichip Average (RMA), probe logarithmic intensity error estimation (PLIER), non-linear fit (NLFIT) quantile-based and nonlinear normalization, and combinations thereof. Background correction can also be performed on the expression data; exemplary techniques useful for background correction include mode of intensities, normalized using median polish probe modeling and sketch-normalization.

In some embodiments, portfolios are established such that the combination of genes in the portfolio exhibit improved sensitivity and specificity relative to known methods. In considering a group of genes for inclusion in a portfolio, a small standard deviation in expression measurements correlates with greater specificity. Other measurements of variation such as correlation coefficients can also be used in this capacity. The invention also encompasses the above methods where the expression level determines the status or outcome of a cancer in the subject with at least about 45% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 50% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 55% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 60% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 65% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 70% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 75% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 80% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 85% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 90% specificity. In some embodiments, the expression level determines the status or outcome of a cancer in the subject with at least about 95% specificity.

The invention also encompasses any of the methods disclosed herein where the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 45%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 50%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 55%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 60%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 65%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 70%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 75%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 80%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 85%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 90%. In some embodiments, the accuracy of diagnosing, monitoring, and/or predicting a status or outcome of a cancer is at least about 95%.

The invention also encompasses the any of the methods disclosed herein where the sensitivity is at least about 45%. In some embodiments, the sensitivity is at least about 50%. In some embodiments, the sensitivity is at least about 55%. In some embodiments, the sensitivity is at least about 60%. In some embodiments, the sensitivity is at least about 65%. In some embodiments, the sensitivity is at least about 70%. In some embodiments, the sensitivity is at least about 75%. In some embodiments, the sensitivity is at least about 80%. In some embodiments, the sensitivity is at least about 85%. In some embodiments, the sensitivity is at least about 90%. In some embodiments, the sensitivity is at least about 95%.

In some instances, the methods disclosed herein may comprise the use of a genomic-clinical classifier (GCC) model. A general method for developing a GCC model may comprise (a) providing a sample from a subject suffering from a cancer; (b) assaying the expression level for a plurality of targets; (c) generating a model by using a machine learning algorithm. In some instances, the machine learning algorithm comprises Random Forests.

Cancer

The systems, compositions and methods disclosed herein may be used to diagnosis, monitor and/or predict the status or outcome of a cancer. Generally, a cancer is characterized by the uncontrolled growth of abnormal cells anywhere in a body. The abnormal cells may be termed cancer cells, malignant cells, or tumor cells. Many cancers and the abnormal cells that compose the cancer tissue are further identified by the name of the tissue that the abnormal cells originated from (for example, breast cancer, lung cancer, colon cancer, prostate cancer, pancreatic cancer, thyroid cancer). Cancer is not confined to humans; animals and other living organisms can get cancer.

In some instances, the cancer may be malignant. Alternatively, the cancer may be benign. The cancer may be a recurrent and/or refractory cancer. Most cancers can be classified as a carcinoma, sarcoma, leukemia, lymphoma, myeloma, or a central nervous system cancer.

The cancer may be a sarcoma. Sarcomas are cancers of the bone, cartilage, fat, muscle, blood vessels, or other connective or supportive tissue. Sarcomas include, but are not limited to, bone cancer, fibrosarcoma, chondrosarcoma, Ewing's sarcoma, malignant hemangioendothelioma, malignant schwannoma, bilateral vestibular schwannoma, osteosarcoma, soft tissue sarcomas (e.g. alveolar soft part sarcoma, angiosarcoma, cystosarcoma phylloides, dermatofibrosarcoma, desmoid tumor, epithelioid sarcoma, extraskeletal osteosarcoma, fibrosarcoma, hemangiopericytoma, hemangiosarcoma, Kaposi's sarcoma, leiomyosarcoma, liposarcoma, lymphangiosarcoma, lymphosarcoma, malignant fibrous histiocytoma, neurofibrosarcoma, rhabdomyosarcoma, and synovial sarcoma).

Alternatively, the cancer may be a carcinoma. Carcinomas are cancers that begin in the epithelial cells, which are cells that cover the surface of the body, produce hormones, and make up glands. By way of non-limiting example, carcinomas include breast cancer, pancreatic cancer, lung cancer, colon cancer, colorectal cancer, rectal cancer, kidney cancer, bladder cancer, stomach cancer, prostate cancer, liver cancer, ovarian cancer, brain cancer, vaginal cancer, vulvar cancer, uterine cancer, oral cancer, penic cancer, testicular cancer, esophageal cancer, skin cancer, cancer of the fallopian tubes, head and neck cancer, gastrointestinal stromal cancer, adenocarcinoma, cutaneous or intraocular melanoma, cancer of the anal region, cancer of the small intestine, cancer of the endocrine system, cancer of the thyroid gland, cancer of the parathyroid gland, cancer of the adrenal gland, cancer of the urethra, cancer of the renal pelvis, cancer of the ureter, cancer of the endometrium, cancer of the cervix, cancer of the pituitary gland, neoplasms of the central nervous system (CNS), primary CNS lymphoma, brain stem glioma, and spinal axis tumors. In some instances, the cancer is a skin cancer, such as a basal cell carcinoma, squamous, melanoma, nonmelanoma, or actinic (solar) keratosis. Preferably, the cancer is a prostate cancer. Alternatively, the cancer may be a thyroid cancer. The cancer can be a pancreatic cancer. In some instances, the cancer is a bladder cancer.

In some instances, the cancer is a lung cancer. Lung cancer can start in the airways that branch off the trachea to supply the lungs (bronchi) or the small air sacs of the lung (the alveoli). Lung cancers include non-small cell lung carcinoma (NSCLC), small cell lung carcinoma, and mesotheliomia. Examples of NSCLC include squamous cell carcinoma, adenocarcinoma, and large cell carcinoma. The mesothelioma may be a cancerous tumor of the lining of the lung and chest cavity (pleura) or lining of the abdomen (peritoneum). The mesothelioma may be due to asbestos exposure. The cancer may be a brain cancer, such as a glioblastoma.

Alternatively, the cancer may be a central nervous system (CNS) tumor. CNS tumors may be classified as gliomas or nongliomas. The glioma may be malignant glioma, high grade glioma, diffuse intrinsic pontine glioma. Examples of gliomas include astrocytomas, oligodendrogliomas (or mixtures of oligodendroglioma and astocytoma elements), and ependymomas. Astrocytomas include, but are not limited to, low-grade astrocytomas, anaplastic astrocytomas, glioblastoma multiforme, pilocytic astrocytoma, pleomorphic xanthoastrocytoma, and subependymal giant cell astrocytoma. Oligodendrogliomas include low-grade oligodendrogliomas (or oligoastrocytomas) and anaplastic oligodendriogliomas. Nongliomas include meningiomas, pituitary adenomas, primary CNS lymphomas, and medulloblastomas. In some instances, the cancer is a meningioma.

The cancer may be leukemia. The leukemia may be an acute lymphocytic leukemia, acute myelocytic leukemia, chronic lymphocytic leukemia, or chronic myelocytic leukemia. Additional types of leukemias include hairy cell leukemia, chronic myelomonocytic leukemia, and juvenile myelomonocytic-leukemia.

In some instances, the cancer is a lymphoma. Lymphomas are cancers of the lymphocytes and may develop from either B or T lymphocytes. The two major types of lymphoma are Hodgkin's lymphoma, previously known as Hodgkin's disease, and non-Hodgkin's lymphoma. Hodgkin's lymphoma is marked by the presence of the Reed-Sternberg cell. Non-Hodgkin's lymphomas are all lymphomas which are not Hodgkin's lymphoma. Non-Hodgkin lymphomas may be indolent lymphomas and aggressive lymphomas. Non-Hodgkin's lymphomas include, but are not limited to, diffuse large B cell lymphoma, follicular lymphoma, mucosa-associated lymphatic tissue lymphoma (MALT), small cell lymphocytic lymphoma, mantle cell lymphoma, Burkitt's lymphoma, mediastinal large B cell lymphoma, Waldenström macroglobulinemia, nodal marginal zone B cell lymphoma (NMZL), splenic marginal zone lymphoma (SMZL), extranodal marginal zone B cell lymphoma, intravascular large B cell lymphoma, primary effusion lymphoma, and lymphomatoid granulomatosis.

Cancer Staging

Diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise determining the stage of the cancer. Generally, the stage of a cancer is a description (usually numbers I to IV with IV having more progression) of the extent the cancer has spread. The stage often takes into account the size of a tumor, how deeply it has penetrated, whether it has invaded adjacent organs, how many lymph nodes it has metastasized to (if any), and whether it has spread to distant organs. Staging of cancer can be used as a predictor of survival, and cancer treatment may be determined by staging. Determining the stage of the cancer may occur before, during, or after treatment. The stage of the cancer may also be determined at the time of diagnosis.

Cancer staging can be divided into a clinical stage and a pathologic stage. Cancer staging may comprise the TNM classification. Generally, the TNM Classification of Malignant Tumours (TNM) is a cancer staging system that describes the extent of cancer in a patient's body. T may describe the size of the tumor and whether it has invaded nearby tissue, N may describe regional lymph nodes that are involved, and M may describe distant metastasis (spread of cancer from one body part to another). In the TNM (Tumor, Node, Metastasis) system, clinical stage and pathologic stage are denoted by a small “c” or “p” before the stage (e.g., cT3N1M0 or pT2N0).

Often, clinical stage and pathologic stage may differ. Clinical stage may be based on all of the available information obtained before a surgery to remove the tumor. Thus, it may include information about the tumor obtained by physical examination, radiologic examination, and endoscopy. Pathologic stage can add additional information gained by examination of the tumor microscopically by a pathologist. Pathologic staging can allow direct examination of the tumor and its spread, contrasted with clinical staging which may be limited by the fact that the information is obtained by making indirect observations at a tumor which is still in the body. The TNM staging system can be used for most forms of cancer.

Alternatively, staging may comprise Ann Arbor staging. Generally, Ann Arbor staging is the staging system for lymphomas, both in Hodgkin's lymphoma (previously called Hodgkin's disease) and Non-Hodgkin lymphoma (abbreviated NHL). The stage may depend on both the place where the malignant tissue is located (as located with biopsy, CT scanning and increasingly positron emission tomography) and on systemic symptoms due to the lymphoma (“B symptoms”: night sweats, weight loss of >10% or fevers). The principal stage may be determined by location of the tumor. Stage I may indicate that the cancer is located in a single region, usually one lymph node and the surrounding area. Stage I often may not have outward symptoms. Stage II can indicate that the cancer is located in two separate regions, an affected lymph node or organ and a second affected area, and that both affected areas are confined to one side of the diaphragm—that is, both are above the diaphragm, or both are below the diaphragm. Stage III often indicates that the cancer has spread to both sides of the diaphragm, including one organ or area near the lymph nodes or the spleen. Stage IV may indicate diffuse or disseminated involvement of one or more extralymphatic organs, including any involvement of the liver, bone marrow, or nodular involvement of the lungs.

Modifiers may also be appended to some stages. For example, the letters A, B, E, X, or S can be appended to some stages. Generally, A or B may indicate the absence of constitutional (B-type) symptoms is denoted by adding an “A” to the stage; the presence is denoted by adding a “B” to the stage. E can be used if the disease is “extranodal” (not in the lymph nodes) or has spread from lymph nodes to adjacent tissue. X is often used if the largest deposit is >10 cm large (“bulky disease”), or whether the mediastinum is wider than ⅓ of the chest on a chest X-ray. S may be used if the disease has spread to the spleen.

The nature of the staging may be expressed with CS or PS. CS may denote that the clinical stage as obtained by doctor's examinations and tests. PS may denote that the pathological stage as obtained by exploratory laparotomy (surgery performed through an abdominal incision) with splenectomy (surgical removal of the spleen).

Therapeutic Regimens

Diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise treating a cancer or preventing a cancer progression. In addition, diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise identifying or predicting responders to an anti-cancer therapy. In some instances, diagnosing, predicting, or monitoring may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapy. Alternatively, determining a therapeutic regimen may comprise modifying, recommending, continuing or discontinuing an anti-cancer regimen. In some instances, if the sample expression patterns are consistent with the expression pattern for a known disease or disease outcome, the expression patterns can be used to designate one or more treatment modalities (e.g., therapeutic regimens, anti-cancer regimen). An anti-cancer regimen may comprise one or more anti-cancer therapies. Examples of anti-cancer therapies include surgery, chemotherapy, radiation therapy, immunotherapy/biological therapy, photodynamic therapy.

Surgical oncology uses surgical methods to diagnose, stage, and treat cancer, and to relieve certain cancer-related symptoms. Surgery may be used to remove the tumor (e.g., excisions, resections, debulking surgery), reconstruct a part of the body (e.g., restorative surgery), and/or to relieve symptoms such as pain (e.g., palliative surgery). Surgery may also include cryosurgery. Cryosurgery (also called cryotherapy) may use extreme cold produced by liquid nitrogen (or argon gas) to destroy abnormal tissue. Cryosurgery can be used to treat external tumors, such as those on the skin. For external tumors, liquid nitrogen can be applied directly to the cancer cells with a cotton swab or spraying device. Cryosurgery may also be used to treat tumors inside the body (internal tumors and tumors in the bone). For internal tumors, liquid nitrogen or argon gas may be circulated through a hollow instrument called a cryoprobe, which is placed in contact with the tumor. An ultrasound or MRI may be used to guide the cryoprobe and monitor the freezing of the cells, thus limiting damage to nearby healthy tissue. A ball of ice crystals may form around the probe, freezing nearby cells. Sometimes more than one probe is used to deliver the liquid nitrogen to various parts of the tumor. The probes may be put into the tumor during surgery or through the skin (percutaneously). After cryosurgery, the frozen tissue thaws and may be naturally absorbed by the body (for internal tumors), or may dissolve and form a scab (for external tumors).

Chemotherapeutic agents may also be used for the treatment of cancer. Examples of chemotherapeutic agents include alkylating agents, anti-metabolites, plant alkaloids and terpenoids, vinca alkaloids, podophyllotoxin, taxanes, topoisomerase inhibitors, and cytotoxic antibiotics. Cisplatin, carboplatin, and oxaliplatin are examples of alkylating agents. Other alkylating agents include mechlorethamine, cyclophosphamide, chlorambucil, ifosfamide. Alkylating agens may impair cell function by forming covalent bonds with the amino, carboxyl, sulfhydryl, and phosphate groups in biologically important molecules. Alternatively, alkylating agents may chemically modify a cell's DNA.

Anti-metabolites are another example of chemotherapeutic agents. Anti-metabolites may masquerade as purines or pyrimidines and may prevent purines and pyrimidines from becoming incorporated in to DNA during the “S” phase (of the cell cycle), thereby stopping normal development and division. Antimetabolites may also affect RNA synthesis. Examples of metabolites include azathioprine and mercaptopurine.

Alkaloids may be derived from plants and block cell division may also be used for the treatment of cancer. Alkyloids may prevent microtubule function. Examples of alkaloids are vinca alkaloids and taxanes. Vinca alkaloids may bind to specific sites on tubulin and inhibit the assembly of tubulin into microtubules (M phase of the cell cycle). The vinca alkaloids may be derived from the Madagascar periwinkle, Catharanthus roseus (formerly known as Vinca rosea). Examples of vinca alkaloids include, but are not limited to, vincristine, vinblastine, vinorelbine, or vindesine. Taxanes are diterpenes produced by the plants of the genus Taxus (yews). Taxanes may be derived from natural sources or synthesized artificially. Taxanes include paclitaxel (Taxol) and docetaxel (Taxotere). Taxanes may disrupt microtubule function. Microtubules are essential to cell division, and taxanes may stabilize GDP-bound tubulin in the microtubule, thereby inhibiting the process of cell division. Thus, in essence, taxanes may be mitotic inhibitors. Taxanes may also be radiosensitizing and often contain numerous chiral centers.

Alternative chemotherapeutic agents include podophyllotoxin. Podophyllotoxin is a plant-derived compound that may help with digestion and may be used to produce cytostatic drugs such as etoposide and teniposide. They may prevent the cell from entering the G1 phase (the start of DNA replication) and the replication of DNA (the S phase).

Topoisomerases are essential enzymes that maintain the topology of DNA Inhibition of type I or type II topoisomerases may interfere with both transcription and replication of DNA by upsetting proper DNA supercoiling. Some chemotherapeutic agents may inhibit topoisomerases. For example, some type I topoisomerase inhibitors include camptothecins: irinotecan and topotecan. Examples of type II inhibitors include amsacrine, etoposide, etoposide phosphate, and teniposide.

Another example of chemotherapeutic agents is cytotoxic antibiotics. Cytotoxic antibiotics are a group of antibiotics that are used for the treatment of cancer because they may interfere with DNA replication and/or protein synthesis. Cytotoxic antibiotics include, but are not limited to, actinomycin, anthracyclines, doxorubicin, daunorubicin, valrubicin, idarubicin, epirubicin, bleomycin, plicamycin, and mitomycin.

In some instances, the anti-cancer treatment may comprise radiation therapy. Radiation can come from a machine outside the body (external-beam radiation therapy) or from radioactive material placed in the body near cancer cells (internal radiation therapy, more commonly called brachytherapy). Systemic radiation therapy uses a radioactive substance, given by mouth or into a vein that travels in the blood to tissues throughout the body.

External-beam radiation therapy may be delivered in the form of photon beams (either x-rays or gamma rays). A photon is the basic unit of light and other forms of electromagnetic radiation. An example of external-beam radiation therapy is called 3-dimensional conformal radiation therapy (3D-CRT). 3D-CRT may use computer software and advanced treatment machines to deliver radiation to very precisely shaped target areas. Many other methods of external-beam radiation therapy are currently being tested and used in cancer treatment. These methods include, but are not limited to, intensity-modulated radiation therapy (IMRT), image-guided radiation therapy (IGRT), Stereotactic radiosurgery (SRS), Stereotactic body radiation therapy (SBRT), and proton therapy.

Intensity-modulated radiation therapy (IMRT) is an example of external-beam radiation and may use hundreds of tiny radiation beam-shaping devices, called collimators, to deliver a single dose of radiation. The collimators can be stationary or can move during treatment, allowing the intensity of the radiation beams to change during treatment sessions. This kind of dose modulation allows different areas of a tumor or nearby tissues to receive different doses of radiation. IMRT is planned in reverse (called inverse treatment planning). In inverse treatment planning, the radiation doses to different areas of the tumor and surrounding tissue are planned in advance, and then a high-powered computer program calculates the required number of beams and angles of the radiation treatment. In contrast, during traditional (forward) treatment planning, the number and angles of the radiation beams are chosen in advance and computers calculate how much dose may be delivered from each of the planned beams. The goal of IMRT is to increase the radiation dose to the areas that need it and reduce radiation exposure to specific sensitive areas of surrounding normal tissue.

Another example of external-beam radiation is image-guided radiation therapy (IGRT). In IGRT, repeated imaging scans (CT, MRI, or PET) may be performed during treatment. These imaging scans may be processed by computers to identify changes in a tumor's size and location due to treatment and to allow the position of the patient or the planned radiation dose to be adjusted during treatment as needed. Repeated imaging can increase the accuracy of radiation treatment and may allow reductions in the planned volume of tissue to be treated, thereby decreasing the total radiation dose to normal tissue.

Tomotherapy is a type of image-guided IMRT. A tomotherapy machine is a hybrid between a CT imaging scanner and an external-beam radiation therapy machine. The part of the tomotherapy machine that delivers radiation for both imaging and treatment can rotate completely around the patient in the same manner as a normal CT scanner. Tomotherapy machines can capture CT images of the patient's tumor immediately before treatment sessions, to allow for very precise tumor targeting and sparing of normal tissue.

Stereotactic radiosurgery (SRS) can deliver one or more high doses of radiation to a small tumor. SRS uses extremely accurate image-guided tumor targeting and patient positioning. Therefore, a high dose of radiation can be given without excess damage to normal tissue. SRS can be used to treat small tumors with well-defined edges. It is most commonly used in the treatment of brain or spinal tumors and brain metastases from other cancer types. For the treatment of some brain metastases, patients may receive radiation therapy to the entire brain (called whole-brain radiation therapy) in addition to SRS. SRS requires the use of a head frame or other device to immobilize the patient during treatment to ensure that the high dose of radiation is delivered accurately.

Stereotactic body radiation therapy (SBRT) delivers radiation therapy in fewer sessions, using smaller radiation fields and higher doses than 3D-CRT in most cases. SBRT may treat tumors that lie outside the brain and spinal cord. Because these tumors are more likely to move with the normal motion of the body, and therefore cannot be targeted as accurately as tumors within the brain or spine, SBRT is usually given in more than one dose. SBRT can be used to treat small, isolated tumors, including cancers in the lung and liver. SBRT systems may be known by their brand names, such as the CyberKnife®.

In proton therapy, external-beam radiation therapy may be delivered by proton. Protons are a type of charged particle. Proton beams differ from photon beams mainly in the way they deposit energy in living tissue. Whereas photons deposit energy in small packets all along their path through tissue, protons deposit much of their energy at the end of their path (called the Bragg peak) and deposit less energy along the way. Use of protons may reduce the exposure of normal tissue to radiation, possibly allowing the delivery of higher doses of radiation to a tumor.

Other charged particle beams such as electron beams may be used to irradiate superficial tumors, such as skin cancer or tumors near the surface of the body, but they cannot travel very far through tissue.

Internal radiation therapy (brachytherapy) is radiation delivered from radiation sources (radioactive materials) placed inside or on the body. Several brachytherapy techniques are used in cancer treatment. Interstitial brachytherapy may use a radiation source placed within tumor tissue, such as within a prostate tumor. Intracavitary brachytherapy may use a source placed within a surgical cavity or a body cavity, such as the chest cavity, near a tumor. Episcleral brachytherapy, which may be used to treat melanoma inside the eye, may use a source that is attached to the eye. In brachytherapy, radioactive isotopes can be sealed in tiny pellets or “seeds.” These seeds may be placed in patients using delivery devices, such as needles, catheters, or some other type of carrier. As the isotopes decay naturally, they give off radiation that may damage nearby cancer cells. Brachytherapy may be able to deliver higher doses of radiation to some cancers than external-beam radiation therapy while causing less damage to normal tissue.

Brachytherapy can be given as a low-dose-rate or a high-dose-rate treatment. In low-dose-rate treatment, cancer cells receive continuous low-dose radiation from the source over a period of several days. In high-dose-rate treatment, a robotic machine attached to delivery tubes placed inside the body may guide one or more radioactive sources into or near a tumor, and then removes the sources at the end of each treatment session. High-dose-rate treatment can be given in one or more treatment sessions. An example of a high-dose-rate treatment is the MammoSite® system. Bracytherapy may be used to treat patients with breast cancer who have undergone breast-conserving surgery.

The placement of brachytherapy sources can be temporary or permanent. For permanent brachytherapy, the sources may be surgically sealed within the body and left there, even after all of the radiation has been given off. In some instances, the remaining material (in which the radioactive isotopes were sealed) does not cause any discomfort or harm to the patient. Permanent brachytherapy is a type of low-dose-rate brachytherapy. For temporary brachytherapy, tubes (catheters) or other carriers are used to deliver the radiation sources, and both the carriers and the radiation sources are removed after treatment. Temporary brachytherapy can be either low-dose-rate or high-dose-rate treatment. Brachytherapy may be used alone or in addition to external-beam radiation therapy to provide a “boost” of radiation to a tumor while sparing surrounding normal tissue.

In systemic radiation therapy, a patient may swallow or receive an injection of a radioactive substance, such as radioactive iodine or a radioactive substance bound to a monoclonal antibody. Radioactive iodine (131I) is a type of systemic radiation therapy commonly used to help treat cancer, such as thyroid cancer. Thyroid cells naturally take up radioactive iodine. For systemic radiation therapy for some other types of cancer, a monoclonal antibody may help target the radioactive substance to the right place. The antibody joined to the radioactive substance travels through the blood, locating and killing tumor cells. For example, the drug ibritumomab tiuxetan (Zevalin®) may be used for the treatment of certain types of B-cell non-Hodgkin lymphoma (NHL). The antibody part of this drug recognizes and binds to a protein found on the surface of B lymphocytes. The combination drug regimen of tositumomab and iodine I 131 tositumomab (Bexxar®) may be used for the treatment of certain types of cancer, such as NHL. In this regimen, nonradioactive tositumomab antibodies may be given to patients first, followed by treatment with tositumomab antibodies that have 131I attached. Tositumomab may recognize and bind to the same protein on B lymphocytes as ibritumomab. The nonradioactive form of the antibody may help protect normal B lymphocytes from being damaged by radiation from 131I.

Some systemic radiation therapy drugs relieve pain from cancer that has spread to the bone (bone metastases). This is a type of palliative radiation therapy. The radioactive drugs samarium-153-lexidronam (Quadramet®) and strontium-89 chloride (Metastron®) are examples of radiopharmaceuticals may be used to treat pain from bone metastases.

Biological therapy (sometimes called immunotherapy, biotherapy, or biological response modifier (BRM) therapy) uses the body's immune system, either directly or indirectly, to fight cancer or to lessen the side effects that may be caused by some cancer treatments. Biological therapies include interferons, interleukins, colony-stimulating factors, monoclonal antibodies, vaccines, gene therapy, and nonspecific immunomodulating agents.

Interferons (IFNs) are types of cytokines that occur naturally in the body. Interferon alpha, interferon beta, and interferon gamma are examples of interferons that may be used in cancer treatment.

Like interferons, interleukins (ILs) are cytokines that occur naturally in the body and can be made in the laboratory. Many interleukins have been identified for the treatment of cancer. For example, interleukin-2 (IL-2 or aldesleukin), interleukin 7, and interleukin 12 have may be used as an anti-cancer treatment. IL-2 may stimulate the growth and activity of many immune cells, such as lymphocytes, that can destroy cancer cells. Interleukins may be used to treat a number of cancers, including leukemia, lymphoma, and brain, colorectal, ovarian, breast, kidney and prostate cancers.

Colony-stimulating factors (CSFs) (sometimes called hematopoietic growth factors) may also be used for the treatment of cancer. Some examples of CSFs include, but are not limited to, G-CSF (filgrastim) and GM-CSF (sargramostim). CSFs may promote the division of bone marrow stem cells and their development into white blood cells, platelets, and red blood cells. Bone marrow is critical to the body's immune system because it is the source of all blood cells. Because anticancer drugs can damage the body's ability to make white blood cells, red blood cells, and platelets, stimulation of the immune system by CSFs may benefit patients undergoing other anti-cancer treatment, thus CSFs may be combined with other anti-cancer therapies, such as chemotherapy. CSFs may be used to treat a large variety of cancers, including lymphoma, leukemia, multiple myeloma, melanoma, and cancers of the brain, lung, esophagus, breast, uterus, ovary, prostate, kidney, colon, and rectum.

Another type of biological therapy includes monoclonal antibodies (MOABs or MoABs). These antibodies may be produced by a single type of cell and may be specific for a particular antigen. To create MOABs, human cancer cells may be injected into mice. In response, the mouse immune system can make antibodies against these cancer cells. The mouse plasma cells that produce antibodies may be isolated and fused with laboratory-grown cells to create “hybrid” cells called hybridomas. Hybridomas can indefinitely produce large quantities of these pure antibodies, or MOABs. MOABs may be used in cancer treatment in a number of ways. For instance, MOABs that react with specific types of cancer may enhance a patient's immune response to the cancer. MOABs can be programmed to act against cell growth factors, thus interfering with the growth of cancer cells.

MOABs may be linked to other anti-cancer therapies such as chemotherapeutics, radioisotopes (radioactive substances), other biological therapies, or other toxins. When the antibodies latch onto cancer cells, they deliver these anti-cancer therapies directly to the tumor, helping to destroy it. MOABs carrying radioisotopes may also prove useful in diagnosing certain cancers, such as colorectal, ovarian, and prostate.

Rituxan® (rituximab) and Herceptin® (trastuzumab) are examples of MOABs that may be used as a biological therapy. Rituxan may be used for the treatment of non-Hodgkin lymphoma. Herceptin can be used to treat metastatic breast cancer in patients with tumors that produce excess amounts of a protein called HER2. Alternatively, MOABs may be used to treat lymphoma, leukemia, melanoma, and cancers of the brain, breast, lung, kidney, colon, rectum, ovary, prostate, and other areas.

Cancer vaccines are another form of biological therapy. Cancer vaccines may be designed to encourage the patient's immune system to recognize cancer cells. Cancer vaccines may be designed to treat existing cancers (therapeutic vaccines) or to prevent the development of cancer (prophylactic vaccines). Therapeutic vaccines may be injected in a person after cancer is diagnosed. These vaccines may stop the growth of existing tumors, prevent cancer from recurring, or eliminate cancer cells not killed by prior treatments. Cancer vaccines given when the tumor is small may be able to eradicate the cancer. On the other hand, prophylactic vaccines are given to healthy individuals before cancer develops. These vaccines are designed to stimulate the immune system to attack viruses that can cause cancer. By targeting these cancer-causing viruses, development of certain cancers may be prevented. For example, cervarix and gardasil are vaccines to treat human papilloma virus and may prevent cervical cancer. Therapeutic vaccines may be used to treat melanoma, lymphoma, leukemia, and cancers of the brain, breast, lung, kidney, ovary, prostate, pancreas, colon, and rectum. Cancer vaccines can be used in combination with other anti-cancer therapies.

Gene therapy is another example of a biological therapy. Gene therapy may involve introducing genetic material into a person's cells to fight disease. Gene therapy methods may improve a patient's immune response to cancer. For example, a gene may be inserted into an immune cell to enhance its ability to recognize and attack cancer cells. In another approach, cancer cells may be injected with genes that cause the cancer cells to produce cytokines and stimulate the immune system.

In some instances, biological therapy includes nonspecific immunomodulating agents. Nonspecific immunomodulating agents are substances that stimulate or indirectly augment the immune system. Often, these agents target key immune system cells and may cause secondary responses such as increased production of cytokines and immunoglobulins. Two nonspecific immunomodulating agents used in cancer treatment are bacillus Calmette-Guerin (BCG) and levamisole. BCG may be used in the treatment of superficial bladder cancer following surgery. BCG may work by stimulating an inflammatory, and possibly an immune, response. A solution of BCG may be instilled in the bladder. Levamisole is sometimes used along with fluorouracil (5-FU) chemotherapy in the treatment of stage III (Dukes' C) colon cancer following surgery. Levamisole may act to restore depressed immune function.

Photodynamic therapy (PDT) is an anti-cancer treatment that may use a drug, called a photosensitizer or photosensitizing agent, and a particular type of light. When photosensitizers are exposed to a specific wavelength of light, they may produce a form of oxygen that kills nearby cells. A photosensitizer may be activated by light of a specific wavelength. This wavelength determines how far the light can travel into the body. Thus, photosensitizers and wavelengths of light may be used to treat different areas of the body with PDT.

In the first step of PDT for cancer treatment, a photosensitizing agent may be injected into the bloodstream. The agent may be absorbed by cells all over the body but may stay in cancer cells longer than it does in normal cells. Approximately 24 to 72 hours after injection, when most of the agent has left normal cells but remains in cancer cells, the tumor can be exposed to light. The photosensitizer in the tumor can absorb the light and produces an active form of oxygen that destroys nearby cancer cells. In addition to directly killing cancer cells, PDT may shrink or destroy tumors in two other ways. The photosensitizer can damage blood vessels in the tumor, thereby preventing the cancer from receiving necessary nutrients. PDT may also activate the immune system to attack the tumor cells.

The light used for PDT can come from a laser or other sources. Laser light can be directed through fiber optic cables (thin fibers that transmit light) to deliver light to areas inside the body. For example, a fiber optic cable can be inserted through an endoscope (a thin, lighted tube used to look at tissues inside the body) into the lungs or esophagus to treat cancer in these organs. Other light sources include light-emitting diodes (LEDs), which may be used for surface tumors, such as skin cancer. PDT is usually performed as an outpatient procedure. PDT may also be repeated and may be used with other therapies, such as surgery, radiation, or chemotherapy.

Extracorporeal photopheresis (ECP) is a type of PDT in which a machine may be used to collect the patient's blood cells. The patient's blood cells may be treated outside the body with a photosensitizing agent, exposed to light, and then returned to the patient. ECP may be used to help lessen the severity of skin symptoms of cutaneous T-cell lymphoma that has not responded to other therapies. ECP may be used to treat other blood cancers, and may also help reduce rejection after transplants.

Additionally, photosensitizing agent, such as porfimer sodium or Photofrin®, may be used in PDT to treat or relieve the symptoms of esophageal cancer and non-small cell lung cancer. Porfimer sodium may relieve symptoms of esophageal cancer when the cancer obstructs the esophagus or when the cancer cannot be satisfactorily treated with laser therapy alone. Porfimer sodium may be used to treat non-small cell lung cancer in patients for whom the usual treatments are not appropriate, and to relieve symptoms in patients with non-small cell lung cancer that obstructs the airways. Porfimer sodium may also be used for the treatment of precancerous lesions in patients with Barrett esophagus, a condition that can lead to esophageal cancer.

Laser therapy may use high-intensity light to treat cancer and other illnesses. Lasers can be used to shrink or destroy tumors or precancerous growths. Lasers are most commonly used to treat superficial cancers (cancers on the surface of the body or the lining of internal organs) such as basal cell skin cancer and the very early stages of some cancers, such as cervical, penile, vaginal, vulvar, and non-small cell lung cancer.

Lasers may also be used to relieve certain symptoms of cancer, such as bleeding or obstruction. For example, lasers can be used to shrink or destroy a tumor that is blocking a patient's trachea (windpipe) or esophagus. Lasers also can be used to remove colon polyps or tumors that are blocking the colon or stomach.

Laser therapy is often given through a flexible endoscope (a thin, lighted tube used to look at tissues inside the body). The endoscope is fitted with optical fibers (thin fibers that transmit light). It is inserted through an opening in the body, such as the mouth, nose, anus, or vagina. Laser light is then precisely aimed to cut or destroy a tumor.

Laser-induced interstitial thermotherapy (LITT), or interstitial laser photocoagulation, also uses lasers to treat some cancers. LITT is similar to a cancer treatment called hyperthermia, which uses heat to shrink tumors by damaging or killing cancer cells. During LITT, an optical fiber is inserted into a tumor. Laser light at the tip of the fiber raises the temperature of the tumor cells and damages or destroys them. LITT is sometimes used to shrink tumors in the liver.

Laser therapy can be used alone, but most often it is combined with other treatments, such as surgery, chemotherapy, or radiation therapy. In addition, lasers can seal nerve endings to reduce pain after surgery and seal lymph vessels to reduce swelling and limit the spread of tumor cells.

Lasers used to treat cancer may include carbon dioxide (CO2) lasers, argon lasers, and neodymium:yttrium-aluminum-garnet (Nd:YAG) lasers. Each of these can shrink or destroy tumors and can be used with endoscopes. CO2 and argon lasers can cut the skin's surface without going into deeper layers. Thus, they can be used to remove superficial cancers, such as skin cancer. In contrast, the Nd:YAG laser is more commonly applied through an endoscope to treat internal organs, such as the uterus, esophagus, and colon. Nd:YAG laser light can also travel through optical fibers into specific areas of the body during LITT. Argon lasers are often used to activate the drugs used in PDT.

For patients with high test scores consistent with systemic disease outcome after prostatectomy, additional treatment modalities such as adjuvant chemotherapy (e.g., docetaxel, mitoxantrone and prednisone), systemic radiation therapy (e.g., samarium or strontium) and/or anti-androgen therapy (e.g., surgical castration, finasteride, dutasteride) can be designated. Such patients would likely be treated immediately with anti-androgen therapy alone or in combination with radiation therapy in order to eliminate presumed micro-metastatic disease, which cannot be detected clinically but can be revealed by the target sequence expression signature.

Such patients can also be more closely monitored for signs of disease progression. For patients with intermediate test scores consistent with biochemical recurrence only (BCR-only or elevated PSA that does not rapidly become manifested as systemic disease only localized adjuvant therapy (e.g., radiation therapy of the prostate bed) or short course of anti-androgen therapy would likely be administered. Patients with scores consistent with metastasis or disease progression would likely be administered increased dosage of an anti-cancer therapy and/or administered an adjuvant therapy. For patients with low scores or scores consistent with no evidence of disease (NED) or no disease progression, adjuvant therapy would not likely be recommended by their physicians in order to avoid treatment-related side effects such as metabolic syndrome (e.g., hypertension, diabetes and/or weight gain), osteoporosis, proctitis, incontinence or impotence. Patients with samples consistent with NED or no disease progression could be designated for watchful waiting, or for no treatment. Patients with test scores that do not correlate with systemic disease but who have successive PSA increases could be designated for watchful waiting, increased monitoring, or lower dose or shorter duration anti-androgen therapy.

Target sequences can be grouped so that information obtained about the set of target sequences in the group can be used to make or assist in making a clinically relevant judgment such as a diagnosis, prognosis, or treatment choice.

A patient report is also provided comprising a representation of measured expression levels of a plurality of target sequences in a biological sample from the patient, wherein the representation comprises expression levels of target sequences corresponding to any one, two, three, four, five, six, eight, ten, twenty, thirty, fifty or more of the target sequences depicted in Table 6, or of the subsets described herein, or of a combination thereof. In some instances, the target sequences correspond to any one, two, three, four, five, six, eight, ten, twenty, thirty, fifty or more of the target sequences selected from SEQ ID NOs.: 1-903. In other instances, the target sequences correspond to any one, two, three, four, five, six, eight, ten, twenty, thirty, fifty or more of the target sequences selected from SEQ ID NOs.: 1-352. Alternatively, the target sequences correspond to any one, two, three, four, five, six, eight, ten, twenty, thirty, fifty or more of the target sequences selected from SEQ ID NOs.: 353-441. In some embodiments, the representation of the measured expression level(s) may take the form of a linear or nonlinear combination of expression levels of the target sequences of interest. The patient report may be provided in a machine (e.g., a computer) readable format and/or in a hard (paper) copy. The report can also include standard measurements of expression levels of said plurality of target sequences from one or more sets of patients with known disease status and/or outcome. The report can be used to inform the patient and/or treating physician of the expression levels of the expressed target sequences, the likely medical diagnosis and/or implications, and optionally may recommend a treatment modality for the patient.

Also provided are representations of the gene expression profiles useful for treating, diagnosing, prognosticating, and otherwise assessing disease. In some embodiments, these profile representations are reduced to a medium that can be automatically read by a machine such as computer readable media (magnetic, optical, and the like). The articles can also include instructions for assessing the gene expression profiles in such media. For example, the articles may comprise a readable storage form having computer instructions for comparing gene expression profiles of the portfolios of genes described above. The articles may also have gene expression profiles digitally recorded therein so that they may be compared with gene expression data from patient samples. Alternatively, the profiles can be recorded in different representational format. A graphical recordation is one such format. Clustering algorithms can assist in the visualization of such data.

Exemplary Embodiments

Disclosed herein, in some embodiments, is a method for diagnosing, predicting, and/or monitoring a status or outcome of a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a coding target and a non-coding target, wherein the non-coding target is a non-coding RNA transcript selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) for diagnosing, predicting, and/or monitoring a status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-352. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises further comprising assaying an expression level of a siRNA. In some embodiments, the method further comprises assaying an expression level of a snoRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some instances, the plurality of targets comprises at least about 25% non-coding targets. In some instances, the plurality of targets comprises at least about 5 coding targets and/or non-coding targets. The plurality of targets can comprise at least about 10 coding targets and/or non-coding targets. The plurality of targets can comprise at least about 15 coding targets and/or non-coding targets. The plurality of targets can comprise at least about 20 coding targets and/or non-coding targets. The plurality of targets can comprise at least about 30 coding targets and/or non-coding targets. The plurality of targets can comprise at least about 40 coding targets and/or non-coding targets. In some instances, the plurality of targets comprise at least about 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425 coding targets and/or non-coding targets. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the malignancy of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes determining the stage of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes assessing the risk of cancer recurrence. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining the efficacy of treatment. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen.

Further disclosed herein, is some embodiments, is a method for diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein (i) the plurality of targets comprises a coding target and a non-coding target; and (ii) the non-coding target is not selected from the group consisting of a miRNA, an intronic sequence, and a UTR sequence; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. Alternatively, the plurality of targets comprises a coding and/or non-coding target selected from SEQ ID NOs.: 1-352. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the non-coding target is a non-coding RNA transcript. In some embodiments, the non-coding RNA transcript is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the non-coding RNA is not a siRNA. In some embodiments, the non-coding RNA is not a snoRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the malignancy of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes determining the stage of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes assessing the risk of cancer recurrence. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining the efficacy of treatment. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen.

Further disclosed herein, in some embodiments, is a method for diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets consist essentially of a non-coding target or a non-exonic transcript; wherein the non-coding target is selected from the group consisting of a UTR sequence, an intronic sequence, or a non-coding RNA transcript, and wherein the non-coding RNA transcript is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. In some embodiments, the non-coding target is an intronic sequence or partially overlaps with an intronic sequence. In some embodiments, the non-coding target is a UTR sequence or partially overlaps with a UTR sequence. In some embodiments, the non-coding target is a non-coding RNA transcript. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the non-coding target is a nucleic acid sequence. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a miRNA. In some embodiments, the method further comprises further comprising assaying an expression level of a siRNA. In some embodiments, the method further comprises assaying an expression level of a snoRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the malignancy of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes determining the stage of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes assessing the risk of cancer recurrence. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining the efficacy of treatment.

Further disclosed herein, in some embodiments, is a method for diagnosing, predicting, and/or monitoring the status or outcome of a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a non-coding target, wherein the non-coding target is a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated; and (b) diagnosing, predicting, and/or monitoring the status or outcome of a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. In some embodiments, the non-coding RNA transcript is selected from the group consisting of PASR, TASR, aTASR, TSSa-RNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the method further comprises assaying an expression level of a coding target. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer comprises determining the malignancy of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes determining the stage of the cancer. In some embodiments, the diagnosing, predicting, and/or monitoring the status or outcome of a cancer includes assessing the risk of cancer recurrence. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining the efficacy of treatment. In some embodiments, diagnosing, predicting, and/or monitoring the status or outcome of a cancer may comprise determining a therapeutic regimen. Determining a therapeutic regimen may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen.

Further disclosed, in some embodiments, is a method for determining a treatment for a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein (i) the plurality of targets comprises a coding target and a non-coding target; and (ii) the non-coding target is a non-coding RNA transcript selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) determining the treatment for a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises further comprising assaying an expression level of a siRNA. In some embodiments, the method further comprises assaying an expression level of a snoRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some embodiments, determining the treatment for the cancer includes determining the efficacy of treatment. Determining the treatment for the cancer may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen.

Further disclosed herein, in some embodiments, is a method of determining a treatment for a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein (i) the plurality of targets comprises a coding target and a non-coding target; (ii) the non-coding target is not selected from the group consisting of a miRNA, an intronic sequence, and a UTR sequence; and (b) determining the treatment for a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. In some embodiments, the non-coding target is a non-coding RNA transcript. In some embodiments, the non-coding RNA transcript is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some embodiments, the non-coding RNA is not a siRNA. In some embodiments, the non-coding RNA is not a snoRNA. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, determining the treatment for the cancer includes determining the efficacy of treatment. Determining the treatment for the cancer may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen

Further disclosed herein, in some embodiments, is a method of determining a treatment for a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets consist essentially of a non-coding target; wherein the non-coding target is selected from the group consisting of a UTR sequence, an intronic sequence, or a non-coding RNA transcript, and wherein the non-coding RNA transcript is selected from the group consisting of piRNA, tiRNA, PASR, TASR, aTASR, TSSa-RNA, snRNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs; and (b) determining the treatment for a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding target is an intronic sequence or partially overlaps with an intronic sequence. In some embodiments, the non-coding target is a UTR sequence or partially overlaps with a UTR sequence. In some embodiments, the non-coding target is a non-coding RNA transcript. In some embodiments, the non-coding RNA transcript is snRNA. In some embodiments, the non-coding target is a nucleic acid sequence. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a miRNA. In some embodiments, the method further comprises further comprising assaying an expression level of a siRNA. In some embodiments, the method further comprises assaying an expression level of a snoRNA. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some embodiments, determining the treatment for the cancer includes determining the efficacy of treatment. Determining the treatment for the cancer may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen

Further disclosed herein, in some embodiments, is a method of determining a treatment for a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a plurality of targets, wherein the plurality of targets comprises a non-coding target, wherein the non-coding target is a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated; and (b) determining a treatment for a cancer based on the expression levels of the plurality of targets. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. In some embodiments, the non-coding RNA transcript is selected from the group consisting of PASR, TASR, aTASR, TSSa-RNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the method further comprises assaying an expression level of a coding target. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, determining the treatment for the cancer includes determining the efficacy of treatment. Determining the treatment for the cancer may comprise administering an anti-cancer therapeutic. Alternatively, determining the treatment for the cancer may comprise modifying a therapeutic regimen. Modifying a therapeutic regimen may comprise increasing, decreasing, or terminating a therapeutic regimen

The methods disclosed herein can use any of the probe sets, probes, ICE blocks, classifiers, PSRs, and primers described herein to provide expression signatures or profiles from a test sample derived from a subject having or suspected of having cancer. In some embodiments, such methods involve contacting a test sample with the probe sets, probes, ICE blocks, classifiers, PSRs, and primers (either in solution or immobilized) under conditions that permit hybridization of the probe(s) or primer(s) to any target nucleic acid(s) present in the test sample and then detecting any probe:target duplexes or primer:target duplexes formed as an indication of the presence of the target nucleic acid in the sample. Expression patterns thus determined can then be compared to one or more reference profiles or signatures. Optionally, the expression pattern can be normalized.

The methods disclosed herein can use any of the probe sets, probes, ICE blocks, classifiers, PSRs, and primers described herein to provide expression signatures or profiles from a test sample derived from a subject to determine the status or outcome of a cancer. The methods disclosed herein can use any of the probe sets, probes, ICE blocks, classifiers, PSRs, and primers described herein to provide expression signatures or profiles from a test sample derived from a subject to classify the cancer as recurrent or non-recurrent. The methods disclosed herein can use any of the probe sets, probes, ICE blocks, classifiers, PSRs, and primers described herein to provide expression signatures or profiles from a test sample derived from a subject to classify the cancer as metastatic or non-metastatic. In some embodiments, such methods involve the specific amplification of target sequences nucleic acid(s) present in the test sample using methods known in the art to generate an expression profile or signature which is then compared to a reference profile or signature.

In some embodiments, the invention further provides for prognosing patient outcome, predicting likelihood of recurrence after prostatectomy and/or for designating treatment modalities.

In one embodiment, the methods generate expression profiles or signatures detailing the expression of the target sequences having altered relative expression with different cancer outcomes. In some embodiments, the methods detect combinations of expression levels of sequences exhibiting positive and negative correlation with a disease status. In one embodiment, the methods detect a minimal expression signature.

The gene expression profiles of each of the target sequences comprising the portfolio can be fixed in a medium such as a computer readable medium. This can take a number of forms. For example, a table can be established into which the range of signals (e.g., intensity measurements) indicative of disease or outcome is input. Actual patient data can then be compared to the values in the table to determine the patient samples diagnosis or prognosis. In a more sophisticated embodiment, patterns of the expression signals (e.g., fluorescent intensity) are recorded digitally or graphically.

The expression profiles of the samples can be compared to a control portfolio. The expression profiles can be used to diagnose, predict, or monitor a status or outcome of a cancer. For example, diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise diagnosing or detecting a cancer, cancer metastasis, or stage of a cancer. In other instances, diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise predicting the risk of cancer recurrence. Alternatively, diagnosing, predicting, or monitoring a status or outcome of a cancer may comprise predicting mortality or morbidity.

Further disclosed herein are methods for characterizing a patient population. Generally, the method comprises: (a) providing a sample from a subject; (b) assaying the expression level for a plurality of targets in the sample; and (c) characterizing the subject based on the expression level of the plurality of targets. In some instances, the plurality of targets comprises one or more coding targets and one or more non-coding targets. In some instances, the coding target comprises an exonic region or a fragment thereof. The non-coding targets can comprise a non-exonic region or a fragment thereof. Alternatively, the non-coding target may comprise the UTR of an exonic region or a fragment thereof. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding RNA transcript is selected from the group consisting of PASR, TASR, aTASR, TSSa-RNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the method further comprises assaying an expression level of a coding target. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some instances, the method may further comprise diagnosing a cancer in the subject. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some instances, characterizing the subject comprises determining whether the subject would respond to an anti-cancer therapy. Alternatively, characterizing the subject comprises identifying the subject as a non-responder to an anti-cancer therapy. Optionally, characterizing the subject comprises identifying the subject as a responder to an anti-cancer therapy.

Further disclosed herein are methods for selecting a subject suffering from a cancer for enrollment into a clinical trial. Generally, the method comprises: (a) providing a sample from a subject; (b) assaying the expression level for a plurality of targets in the sample; and (c) characterizing the subject based on the expression level of the plurality of targets. In some instances, the plurality of targets comprises one or more coding targets and one or more non-coding targets. In some instances, the coding target comprises an exonic region or a fragment thereof. The non-coding targets can comprise a non-exonic region or a fragment thereof. Alternatively, the non-coding target may comprise the UTR of an exonic region or a fragment thereof. In some embodiments, the non-coding target is selected from a sequence listed in Table 6. The plurality of targets can comprise a coding target and/or a non-coding target selected from SEQ ID NOs.: 1-903. In some instances, the plurality of targets comprises a coding target and/or a non-coding target selected SEQ ID NOs.: 1-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 353-441. In other instances, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 322-352. Alternatively, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 292-321. Optionally, the plurality of targets comprises a coding target and/or a non-coding target selected from SEQ ID NOs.: 231-261. In some instances, the plurality of targets comprises a coding target and/or a non-coding target located on chr2q31.3. In some instances, the coding target and/or non-coding target located on chr2q31.3 is selected from SEQ ID NOs.: 262-291. In some embodiments, the non-coding RNA transcript is selected from the group consisting of PASR, TASR, aTASR, TSSa-RNA, RE-RNA, uaRNA, x-ncRNA, hY RNA, usRNA, snaR, vtRNA, T-UCRs, pseudogenes, GRC-RNAs, aRNAs, PALRs, PROMPTs, and LSINCTs. In some embodiments, the method further comprises assaying an expression level of a coding target. In some embodiments, the coding target is selected from a sequence listed in Table 6. In some embodiments, the coding target is an exon-coding transcript. In some embodiments, the exon-coding transcript is an exonic sequence. In some embodiments, the non-coding target and the coding target are nucleic acid sequences. In some embodiments, the nucleic acid sequence is a DNA sequence. In some embodiments, the nucleic acid sequence is an RNA sequence. In some embodiments, the method further comprises assaying an expression level of a lincRNA. In some embodiments, the method further comprises assaying an expression level of a non-exonic sequence listed in Table 6. In some instances, the method may further comprise diagnosing a cancer in the subject. In some embodiments, the cancer is selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some embodiments, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some embodiments, the cancer is a prostate cancer. In some embodiments, the cancer is a pancreatic cancer. In some embodiments, the cancer is a bladder cancer. In some embodiments, the cancer is a thyroid cancer. In some embodiments, the cancer is a lung cancer. In some instances, characterizing the subject comprises determining whether the subject would respond to an anti-cancer therapy. Alternatively, characterizing the subject comprises identifying the subject as a non-responder to an anti-cancer therapy. Optionally, characterizing the subject comprises identifying the subject as a responder to an anti-cancer therapy.

Further disclosed herein are probe sets comprising one or more probes, wherein the one or more probes hybridize to one or more targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the probe sets comprise one or more probes, wherein the one or more probes hybridize to at least about 2 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. Alternatively, or additionally, the probe sets comprise one or more probes, wherein the one or more probes hybridize to at least about 3 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The probe sets can comprise one or more probes, wherein the one or more probes hybridize to at least about 5 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The probe sets can comprise one or more probes, wherein the one or more probes hybridize to at least about 10 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The probe sets can comprise one or more probes, wherein the one or more probes hybridize to at least about 15 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The probe sets can comprise one or more probes, wherein the one or more probes hybridize to at least about 20 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The probe sets can comprise one or more probes, wherein the one or more probes hybridize to at least about 25 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the probe sets comprise one or more probes, wherein the one or more probes hybridize to at least about 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, or 425 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In other instances, the probe sets comprise one or more probes, wherein the one or more probes hybridize to at least about 450, 475, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, or 900 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof.

In some instances, the probe sets disclosed herein comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is identical to at least a portion of a sequence selected from SEQ ID NOs.: 879-903. In some instances, the probe sets comprise one or more probes, wherein the one or more probes hybridize to one or more targets located on chr2q31.3. In some instances, the one or more targets located on chr2q31.3 selected from SEQ ID NOs.: 262-291.

In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The probe sets can comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the probe sets comprise one or more probes, wherein the sequence of the one or more probes is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 879-903.

Further disclosed herein are classifiers comprising one or more targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the classifiers comprise at least about 2 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. Alternatively, or additionally, the classifiers comprise at least about 3 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The classifiers can comprise at least about 5 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The classifiers can comprise at least about 10 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The classifiers can comprise at least about 15 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The classifiers can comprise at least about 20 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. The classifiers can comprise at least about 25 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the classifiers comprise at least about 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, or 425 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In other instances, the classifiers comprise at least about 450, 475, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, or 900 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, 26-30, or any combination thereof. In some instances, the classifiers comprise a classifier selected from Table 17. Alternatively, or additionally, the classifiers comprise a classifier selected from Table 19.

In some instances, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-903. In some instances, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-441. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 292-321. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 460-480. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 231-261. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 442-457. In some instances, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 436, 643-721. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 722-801. The classifiers can comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the classifiers comprise one or more targets comprising a sequence that at least partially overlaps with a sequence selected from SEQ ID NOs.: 879-903. In some instances, the classifiers comprise one or more targets located on chr2q31.3. In some instances, the one or more targets located on chr2q31.3 selected from SEQ ID NOs.: 262-291.

In some instances, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-903. In some instances, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 1-352. Alternatively, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-441. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 353-361, 366, 369, 383-385, 387, 390, 391, 397-399, 410, 411, 421, 422, 434, 436, 458, and 459. In other instances, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 322-352. Alternatively, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 292-321. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 460-480. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 293, 297, 300, 303, 309, 311, 312, 316, and 481-642. Optionally, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 231-261. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 442-457. In some instances, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 436, 643-721. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 722-801. The classifiers can comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 653, 663, 685 and 802-878. In some instances, the classifiers comprise one or more targets comprising a sequence that is complementary to at least a portion of a sequence selected from SEQ ID NOs.: 879-903.

In some instances, the classifiers disclosed herein have an AUC value of at least about 0.50. In other instances, the classifiers disclosed herein have an AUC value of at least about 0.55. The classifiers disclosed herein can have an AUC value of at least about 0.60. Alternatively, the classifiers disclosed herein have an AUC value of at least about 0.65. In some instances, the classifiers disclosed herein have an AUC value of at least about 0.70. In other instances, the classifiers disclosed herein have an AUC value of at least about 0.75. The classifiers disclosed herein can have an AUC value of at least about 0.80. Alternatively, the classifiers disclosed herein have an AUC value of at least about 0.85. The classifiers disclosed herein can have an AUC value of at least about 0.90. In some instances, the classifiers disclosed herein have an AUC value of at least about 0.95.

The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 50%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 55%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 60%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 65%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 68%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 69%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 70%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 71%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 72%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 73%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 74%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 75%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 76%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 77%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 78%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 79%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 80%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 81%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 82%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 83%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 84%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 85%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 86%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 87%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 88%. In some instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 90%. In other instances, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 93%. Alternatively, the probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 95%. The probe sets, probes, PSRs, primers, ICE blocks, and classifiers disclosed herein can diagnose, predict, and/or monitor the status or outcome of a cancer in a subject with an accuracy of at least about 97%.

Disclosed herein, in some embodiments, are methods for diagnosing, predicting, and/or monitoring a status or outcome of a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for one or more targets, wherein the one or more targets are based on a genomic classifier; and (b) for diagnosing, predicting, and/or monitoring a status or outcome of a cancer based on the expression levels of the one or more targets. The genomic classifier can be any of the genomic classifiers disclosed herein. In some instances, the methods further comprise analysis of one or more clinical variables. The clinical variables can be age, lymphovascular invasion, lymph node involvement and intravesical therapy, or any combination thereof. In some instances, the clinical variable is age. Alternatively, the clinical variable is lymphovascular invasion. The clinical variable can be lymph node involvement. In other instances, the clinical variable is intravesical therapy. In some instances, the methods disclosed herein can predict tumor stage.

Further disclosed herein, in some embodiments, are methods of determining a treatment for a cancer in a subject, comprising: (a) assaying an expression level in a sample from the subject for a one or more targets, wherein the one or more targets are based on a genomic classifier; and (b) determining the treatment for a cancer based on the expression levels of the one or more targets. The genomic classifier can be any of the genomic classifiers disclosed herein. In some instances, the methods further comprise analysis of one or more clinical variables. The clinical variables can be age, lymphovascular invasion, lymph node involvement and intravesical therapy, or any combination thereof. In some instances, the clinical variable is age. Alternatively, the clinical variable is lymphovascular invasion. The clinical variable can be lymph node involvement. In other instances, the clinical variable is intravesical therapy. In some instances, the methods disclosed herein can predict tumor stage.

Further disclosed herein are methods for characterizing a patient population. Generally, the method comprises: (a) providing a sample from a subject; (b) assaying an expression level in a sample from the subject for a one or more targets, wherein the one or more targets are based on a genomic classifier; and (c) characterizing the subject based on the expression level of the one or more targets. The genomic classifier can be any of the genomic classifiers disclosed herein. In some instances, the methods further comprise analysis of one or more clinical variables. The clinical variables can be age, lymphovascular invasion, lymph node involvement and intravesical therapy, or any combination thereof. In some instances, the clinical variable is age. Alternatively, the clinical variable is lymphovascular invasion. The clinical variable can be lymph node involvement. In other instances, the clinical variable is intravesical therapy. In some instances, the methods disclosed herein can predict tumor stage.

Further disclosed herein are methods for selecting a subject suffering from a cancer for enrollment into a clinical trial. Generally, the method comprises: (a) providing a sample from a subject; (b) assaying an expression level in a sample from the subject for a one or more targets, wherein the one or more targets are based on a genomic classifier; and (c) characterizing the subject based on the expression level of the one or more targets. The genomic classifier can be any of the genomic classifiers disclosed herein. In some instances, the methods further comprise analysis of one or more clinical variables. The clinical variables can be age, lymphovascular invasion, lymph node involvement and intravesical therapy, or any combination thereof. In some instances, the clinical variable is age. Alternatively, the clinical variable is lymphovascular invasion. The clinical variable can be lymph node involvement. In other instances, the clinical variable is intravesical therapy. In some instances, the methods disclosed herein can predict tumor stage.

Disclosed herein, in some embodiments, is a system for analyzing a cancer, comprising (a) a probe set comprising a plurality of probes, wherein the plurality of probes comprises (i) a sequence that hybridizes to at least a portion of a non-coding target; or (ii) a sequence that is identical to at least a portion of a non-coding target; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the target hybridized to the probe in a sample from a subject suffering from a cancer. In some instances, the plurality of probes further comprises a sequence that hybridizes to at least a portion of a coding target. In some instances, the plurality of probes further comprises a sequence that is identical to at least a portion of a coding target. The coding target and/or non-coding target can be selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. The coding target and/or non-coding target can comprise a sequence selected from SEQ ID NOs.: 1-903. The coding target and/or non-coding target can comprise any of the coding targets and/or non-coding targets disclosed herein.

In some instances, the system further comprises an electronic memory for capturing and storing an expression profile. The system can further comprise a computer-processing device, optionally connected to a computer network. The system can further comprise a software module executed by the computer-processing device to analyze an expression profile. The system can further comprise a software module executed by the computer-processing device to compare the expression profile to a standard or control. The system can further comprise a software module executed by the computer-processing device to determine the expression level of the target. In some instances, the system further comprises a machine to isolate the target or the probe from the sample. The system can further comprise a machine to sequence the target or the probe. The system can further comprise a machine to amplify the target or the probe. Alternatively, or additionally, the system comprises a label that specifically binds to the target, the probe, or a combination thereof. The system can further comprise a software module executed by the computer-processing device to transmit an analysis of the expression profile to the individual or a medical professional treating the individual. In some instances, the system further comprises a software module executed by the computer-processing device to transmit a diagnosis or prognosis to the individual or a medical professional treating the individual.

The plurality of probes can hybridize to at least a portion of a plurality or targets. Alternatively, or additionally, the plurality of probes can comprise a sequence that is identical to at least a portion of a sequence of a plurality of targets. The plurality of targets can be selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. In some instances, the plurality of targets comprise at least about 5 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. In other instances, the plurality of targets comprise at least about 10 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. The plurality of targets can comprise at least about 15 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. Alternatively, the plurality of targets comprise at least about 20 targets selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. The sequences of the plurality of targets can comprise at least about 5 sequences selected from SEQ ID NOs: 1-903. The sequences of the plurality of targets can comprise at least about 10 sequences selected from SEQ ID NOs: 1-903. The sequences of the plurality of targets can comprise at least about 15 sequences selected from SEQ ID NOs: 1-903. The sequences of the plurality of targets can comprise at least about 20 sequences selected from SEQ ID NOs: 1-903.

The cancer can be selected from the group consisting of a carcinoma, sarcoma, leukemia, lymphoma, myeloma, and a CNS tumor. In some instances, the cancer is selected from the group consisting of skin cancer, lung cancer, colon cancer, pancreatic cancer, prostate cancer, liver cancer, thyroid cancer, ovarian cancer, uterine cancer, breast cancer, cervical cancer, kidney cancer, epithelial carcinoma, squamous carcinoma, basal cell carcinoma, melanoma, papilloma, and adenomas. In some instances, the cancer is a prostate cancer. In other instances, the cancer is a bladder cancer. Alternatively, the cancer is a thyroid cancer. The cancer can be a colorectal cancer. In some instances, the cancer is a lung cancer.

In some instances, disclosed herein, is a probe set for assessing a cancer status or outcome of a subject comprising a plurality of probes, wherein the probes in the set are capable of detecting an expression level of one or more targets. In some instances, the one or more targets are selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. In some instances, the one or more targets comprise a non-coding target. The non-coding target can be an intronic sequence or partially overlaps with an intronic sequence. The non-coding target can comprise a UTR sequence or partially overlaps with a UTR sequence. The non-coding target can be a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated. Alternatively, or additionally, the one or more targets comprise a coding target. In some instances, the coding target is an exonic sequence. The non-coding target and/or coding target can be any of the non-coding targets and/or coding targets disclosed herein. The one or more targets can comprise a nucleic acid sequence. The nucleic acid sequence can be a DNA sequence. In other instances, the nucleic acid sequence is an RNA sequence.

Further disclosed herein is a kit for analyzing a cancer, comprising (a) a probe set comprising a plurality of plurality of probes, wherein the plurality of probes can detect one or more targets; and (b) a computer model or algorithm for analyzing an expression level and/or expression profile of the target sequences in a sample. In some instances, the kit further comprises a computer model or algorithm for correlating the expression level or expression profile with disease state or outcome. The kit can further comprise a computer model or algorithm for designating a treatment modality for the individual. Alternatively, the kit further comprises a computer model or algorithm for normalizing expression level or expression profile of the target sequences. The kit can further comprise a computer model or algorithm comprising a robust multichip average (RMA), probe logarithmic intensity error estimation (PLIER), non-linear fit (NLFIT) quantile-based, nonlinear normalization, or a combination thereof.

Assessing the cancer status can comprise assessing cancer recurrence risk. Alternatively, or additionally, assessing the cancer status comprises determining a treatment modality. In some instances, assessing the cancer status comprises determining the efficacy of treatment.

The probes can be between about 15 nucleotides and about 500 nucleotides in length. Alternatively, the probes are between about 15 nucleotides and about 450 nucleotides in length. In some instances, the probes are between about 15 nucleotides and about 400 nucleotides in length. In other instances, the probes are between about 15 nucleotides and about 350 nucleotides in length. The probes can be between about 15 nucleotides and about 300 nucleotides in length. Alternatively, the probes are between about 15 nucleotides and about 250 nucleotides in length. In some instances, the probes are between about 15 nucleotides and about 200 nucleotides in length. In other instances, the probes are at least 15 nucleotides in length. Alternatively, the probes are at least 25 nucleotides in length.

In some instances, the expression level determines the cancer status or outcome of the subject with at least 40% accuracy. The expression level can determine the cancer status or outcome of the subject with at least 50% accuracy. The expression level can determine the cancer status or outcome of the subject with at least 60% accuracy. In some instances, the expression level determines the cancer status or outcome of the subject with at least 65% accuracy. In other instances, the expression level determines the cancer status or outcome of the subject with at least 70% accuracy. Alternatively, the expression level determines the cancer status or outcome of the subject with at least 75% accuracy. The expression level can determine the cancer status or outcome of the subject with at least 80% accuracy. In some instances, the expression level determines the cancer status or outcome of the subject with at least 64% accuracy.

Further disclosed herein is a method of analyzing a cancer in an individual in need thereof, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets; and (b) comparing the expression profile from the sample to an expression profile of a control or standard.

Disclosed herein, in some embodiments, is a method of diagnosing cancer in an individual in need thereof, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) diagnosing a cancer in the individual if the expression profile of the sample (i) deviates from the control or standard from a healthy individual or population of healthy individuals, or (ii) matches the control or standard from an individual or population of individuals who have or have had the cancer.

Further disclosed herein is a method of predicting whether an individual is susceptible to developing a cancer, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) predicting the susceptibility of the individual for developing a cancer based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

Also disclosed herein is a method of predicting an individual's response to a treatment regimen for a cancer, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) predicting the individual's response to a treatment regimen based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

Disclosed herein is a method of prescribing a treatment regimen for a cancer to an individual in need thereof, comprising (a) obtaining an expression profile from a sample obtained from the individual, wherein the expression profile comprises one or more targets; (b) comparing the expression profile from the sample to an expression profile of a control or standard; and (c) prescribing a treatment regimen based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

In some instances, the one or more targets are selected from Tables 4, 6-8, 14, 15, 17, 19, 22, 23, and 26-30. In some instances, the one or more targets comprise a non-coding target. The non-coding target can be an intronic sequence or partially overlaps with an intronic sequence. The non-coding target can comprise a UTR sequence or partially overlaps with a UTR sequence. The non-coding target can be a non-coding RNA transcript and the non-coding RNA transcript is non-polyadenylated. Alternatively, or additionally, the one or more targets comprise a coding target. In some instances, the coding target is an exonic sequence. The non-coding target and/or coding target can be any of the non-coding targets and/or coding targets disclosed herein. The one or more targets can comprise a nucleic acid sequence. The nucleic acid sequence can be a DNA sequence. In other instances, the nucleic acid sequence is an RNA sequence. The targets can be differentially expressed in the cancer.

The methods disclosed herein can further comprise a software module executed by a computer-processing device to compare the expression profiles. In some instances, the methods further comprise providing diagnostic or prognostic information to the individual about the cardiovascular disorder based on the comparison. In other instances, the method further comprises diagnosing the individual with a cancer if the expression profile of the sample (i) deviates from the control or standard from a healthy individual or population of healthy individuals, or (ii) matches the control or standard from an individual or population of individuals who have or have had the cancer. Alternatively, or additionally, the methods further comprise predicting the susceptibility of the individual for developing a cancer based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. The methods disclosed herein can further comprise prescribing a treatment regimen based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer.

In some instances, the methods disclosed herein further comprise altering a treatment regimen prescribed or administered to the individual based on (i) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (ii) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. In other instances, the methods disclosed herein further comprise predicting the individual's response to a treatment regimen based on (a) the deviation of the expression profile of the sample from a control or standard derived from a healthy individual or population of healthy individuals, or (b) the similarity of the expression profiles of the sample and a control or standard derived from an individual or population of individuals who have or have had the cancer. The deviation can be the expression level of one or more targets from the sample is greater than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. Alternatively, the deviation is the expression level of one or more targets from the sample is at least about 30% greater than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. In other instances, the deviation is the expression level of one or more targets from the sample is less than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals. The deviation can be the expression level of one or more targets from the sample is at least about 30% less than the expression level of one or more targets from a control or standard derived from a healthy individual or population of healthy individuals.

The methods disclosed herein can further comprise using a machine to isolate the target or the probe from the sample. In some instances, the method further comprises contacting the sample with a label that specifically binds to the target, the probe, or a combination thereof. The method can further comprise contacting the sample with a label that specifically binds to a target selected from Table 6.

In some instances, the method further comprises amplifying the target, the probe, or any combination thereof. Alternatively, or additionally, the method further comprises sequencing the target, the probe, or any combination thereof. Sequencing can comprise any of the sequencing techniques disclosed herein. In some instances, sequencing comprises RNA-Seq.

The methods disclosed herein can further comprise converting the expression levels of the target sequences into a likelihood score that indicates the probability that a biological sample is from a patient who will exhibit no evidence of disease, who will exhibit systemic cancer, or who will exhibit biochemical recurrence.

EXAMPLES
Example 1
Non-Coding RNAs Discriminate Clinical Outcomes in Prostate Cancer

In this study, we performed whole-transcriptome analysis of a publicly available dataset from different types of normal and cancerous prostate tissue and found numerous previously unreported ncRNAs that can discriminate between clinical disease states. We found, by analysis of the entire transcriptome, differentially expressed ncRNAs that accurately discriminated clinical outcomes such as BCR and metastatic disease.

Materials and Methods

Microarray and Clinical Data

The publically available genomic and clinical data was generated by the Memorial Sloan-Kettering Cancer Center (MSKCC) Prostate Oncogenome Project, previously reported by (Taylor et al., 2010). The Human Exon arrays for 131 primary prostate cancer, 29 normal adjacent and 19 metastatic tissue specimens were downloaded from GEO Omnibus at http://www.ncbi.nlm.nih.gov/geo/ series GSE21034. The patient and specimen details for the primary and metastases tissues used in this study were summarized in Table 2. For the analysis of the clinical data, the following ECE statuses were summarized to be concordant with the pathological stage: inv-capsule: ECE−, focal: ECE+, established: ECE+.

Microarray Pre-Processing

Normalization and Summarization

After removal of the cell line samples, the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors (McCall M N, et al., 2010, Biostatistics, 11:254-53) was used to normalize and summarize the 179 microarray samples. These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369).

Sample Subsets

The normalized and summarized data were partitioned into three groups. The first group contained the matched samples from primary localized prostate cancer tumor and normal adjacent samples (n=58) (used for the normal versus primary comparison). The second group contained all of the samples from metastatic tumors (n=19) and all of the localized prostate cancer specimens which were not matched with normal adjacent samples (n=102) (used for the primary versus metastasis comparison). The third group contained all of the samples from metastatic tumors (n=19) and all of the normal adjacent samples (n=29) (used for the normal versus metastasis comparison).

Feature Selection

Probe sets comprising one or more probes that did not align uniquely to the genome were annotated as ‘unreliable’ and were excluded from further analysis. After cross hybridization, the PSRs corresponding to the remaining probe sets were subjected to univariate analysis and used in the discovery of differentially expressed PSRs between the labeled groups (primary vs. metastatic, normal adjacent vs. primary and normal versus metastatic). For this analysis, the PSRs were selected as differentially expressed if their Holm adjusted t-test P-value was significant (<0.05).

Feature Evaluation and Model Building

Multidimensional-scaling (Pearson's distance) was used to evaluate the ability of the selected features to segregate samples into clinically relevant clusters based on metastatic events and Gleason scores on the primary samples.

A k-nearest-neighbour (KNN) model (k=1, Pearson's correlation distance metric) was trained on the normal and metastatic samples (n=48) using only the features which were found to be differentially expressed between these two groups.

Re-Annotation of the Human Exon Microarray Probe Sets

In order to properly assess the nature of the PSRs found to be differentially expressed in this study, we re-annotated the PSRs using the xmapcore R package (Yates, 2010) as follows: (i) a PSR was re-annotated as coding, if the PSR overlaps with the coding portion of a protein-coding exon, (ii) a PSR was re-annotated as non-coding, if the PSR overlaps with an untranslated region (UTR), an intron, an intergenic region or a non protein-coding transcript, and (iii) a PSR was re-annotated as non-exonic, if the PSR overlaps with an intron, an intergenic region or a non protein-coding transcript. Further annotation of non-coding transcripts was pursued using Ensembl Biomart.

Statistical Analysis

Survival analysis for biochemical recurrence (BCR) and logistic regression for clinical recurrence were performed using the ‘survival’ and ‘lrm’ packages in with default values.

Results

Re-Annotation and Categorization of Coding and Non-Coding Differentially Expressed Features

Previous transcriptome-wide assessments of differential expression on prostate tissues in the post-prostatectomy setting have been focused on protein-coding features (see Nakagawa et al., 2008 for a comparison of protein-coding gene-based panels). Human Exon Arrays provided a unique opportunity to explore the differential expression of non-coding parts of the genome, with 75% of their probe sets falling in regions other than protein coding sequences. In this study, we used the publicly available Human Exon Array data set from normal, localized primary and metastatic tissues generated by the MSKCC Prostate Oncogenome Project to explore the potential of non-coding regions in prostate cancer prognosis. Previous attempts on this dataset focused only on mRNA and gene-level analysis and concluded that expression analysis was inadequate for discrimination of outcome groups in primary tumors (Taylor et al., 2010). In order to assess the contribution of ncRNA probe sets in differential expression analysis between sample types, we re-assessed the annotation of all PSRs found to be differentially expressed according to their genomic location and categorized them into coding, non-coding and non-exonic. Briefly, a PSR was classified as coding if it fell in a region that encoded for a protein-coding transcript. Otherwise, the PSR was annotated as non-coding. The ‘non-exonic’ group referred to a subset of the non-coding that excluded all PSRs that fell in UTRs.

Based on the above categorization, we assessed each set for the presence of differentially expressed features for each possible pairwise comparison (e.g. primary versus normal, normal versus metastatic and primary versus metastatic). The majority of the differentially expressed PSRs were labeled as ‘coding’ for a given pairwise comparison (60%, 59% and 53% for normal-primary, primary-metastatic and normal-metastatic comparisons, respectively). For each category, the number of differentially expressed features was highest in normal versus metastatic tissues, which was expected since the metastatic samples have likely undergone major genomic alterations through disease progression as well as possible different expression patterns from interactions with tissues they have metastasized to (FIG. 1). Additionally, for each category there were a significant number of features that were specific to each pairwise comparison. For example, 22% of the coding features were specific to the differentiation between normal and primary and 9% were specific to the primary versus metastatic comparison. The same proportions were observed for the non-coding and non-exonic categories, suggesting that different genomic regions may play a role in the progression from normal to primary and from primary to metastatic.

Within the non-coding and non-exonic categories, the majority of the PSRs were ‘intronic’ for all pairwise comparisons (see FIGS. 2a, 2b and 2c for non-exonic). Also, a large proportion of the PSRs fell in intergenic regions. Still, hundreds of PSRs were found to lie within non-coding transcripts, as reflected by the ‘NC Transcript’ segment in FIG. 2. The non-coding transcripts found to be differentially expressed in each pairwise comparison were categorized using the ‘Transcript Biotype’ annotation of Ensembl. For all pairwise comparisons the ‘processed transcript’, ‘lincRNA’, ‘retained intron’, and ‘antisense’ were the most prevalent (FIG. 2d, FIG. 2e and FIG. 2f; see Table 3 for a definition of each transcript type). Even though ‘processed transcript’ and ‘retained intron’ categories were among the most frequent ones, they have a very broad definition.

Previous studies have reported several long non-coding RNAs to be differentially expressed in prostate cancer (Srikantan et al., 2000; Berteaux et al., 2004; Petrovics et al., 2004; Lin et al., 2007; Poliseno et al., 2010; Yap et al., 2010; Chung et al., 2011; Day et al., 2011). Close inspection of our data reveals that four of them (PCGEM1, PCA3, MALAT1 and H19) were differentially expressed (1.5 Median Fold Difference (MFD) threshold) in at least one pairwise comparison (Table 4). After adjusting the P-value for multiple testing however, only seven PSRs from these ncRNA transcripts remain significant (Table 4). In addition, we found two microRNA-encoding transcripts to be differentially expressed in primary tumour versus metastatic (MIR143, MIR145 and MIR221), two in normal versus primary tumour comparison (MIR205 and MIR7) and three in normal versus metastatic (MIR145, MIR205 and MIR221). All these miRNA have been previously reported as differentially expressed in prostate cancer (Clape et al., 2009; Barker et al., 2010; Qin et al., 2010; Szczyrba et al., 2010; Zaman et al., 2010).

Therefore, in addition to the handful of known ncRNAs, our analysis detected many other ncRNAs in regions (e.g., non-coding, non-exonic) that have yet to be explored in prostate cancer and may play a role in the progression of the disease from normal glandular epithelium through distant metastases of prostate cancer.

Assessment of Clinically Significant Prostate Cancer Risk Groups

Using multidimensional scaling (MDS) we observed that the non-exonic and non-coding subsets of features better segregated primary tumors from patients that progressed to metastatic disease than the coding subset (FIG. 3). Similarly, we found the non-exonic and non-coding subset better discriminated high and low Gleason score samples than the coding subset (FIG. 5). In order to assess the prognostic significance of differentially expressed coding, non-coding and non-exonic features, we developed a k-nearest neighbour (KNN) classifier for each group, trained using features from the comparison of normal and metastatic tissue types (see methods). Next, we used unmatched primary tumors (e.g. removing those tumors that had a matched normal in the training subset) as an independent validation set for the KNN classifier. The higher the KNN score (ranging from 0 to 1), the more likely the patient will be associated to worse outcome. Each primary tumor in the validation set was classified by KNN as either more similar to normal or metastatic tissue. Kaplan-Meier analysis of the two groups of primary tumor samples classified by KNN using the biochemical recurrence (BCR) end point (FIG. 4, ‘normal-like’=dark grey line, ‘metastatic-like’=light gray line) was done for KNN classifiers derived for each subset of features (e.g., coding, non-coding and non-exonic). As expected, primary tumors classified by KNN as belonging to the metastasis group had a higher rate of BCR. However, we found that for the KNN classifier derived using only the coding subset of features, no statistically significant differences in BCR-free survival were found using log-rank tests for significance (p<0.08) whereas they were highly significant for the non-coding (p<0.00005) and non-exonic (p<0.00003) KNN classifiers. Furthermore, multivariable logistic regression analysis to predict for patients that experienced metastatic disease (e.g., castrate or non-castrate resistant clinical metastatic patients) for each of the three KNN classifiers (e.g., coding, non-coding and non-exonic) was evaluated (Table 5). Adjusting the KNN classifiers for known prognostic clinical variables (e.g. SVI, SMS, Lymph Node Involvement (LNI), pre-treatment PSA values, ECE and Gleason score) revealed that the KNN based on coding feature set had an odds ratio of 2.5 for predicting metastatic disease, but this was not significant (χ², p<0.6). The KNN obtained based on the non-coding feature set had a much higher odds ratio of 16 though again being not statistically significant (χ², p<0.14). In multivariable analysis, only the KNN based solely on the non-exonic feature set had a statistically significant odds ratio of 30 (χ², p<0.05). These results suggest that significantly more predictive information can be obtained from analysis of non-exonic RNAs and that these may have the potential to be used as biomarkers for the prediction of a clinically relevant outcome in primary tumours after prostatectomy.

Discussion

One of the key challenges in prostate cancer was clinical and molecular heterogeneity (Rubin et al., 2011); therefore this common disease provides an appealing opportunity for genomic-based personalized medicine to identify diagnostic, prognostic or predictive biomarkers to assist in clinical decision making. There have been extensive efforts to identify biomarkers based on high-throughput molecular profiling such as protein-coding mRNA expression microarrays (reviewed in Sorenson and Orntoft, 2012), but while many different biomarkers signatures have been identified, none of them were actively being used in clinical practice. The major reason that no new biomarker signatures have widespread use in the clinic was because they fail to show meaningful improvement for prognostication over PSA testing or established pathological variables (e.g., Gleason).

In this study, we assessed the utility of ncRNAs, and particularly non-exonic ncRNAs as potential biomarkers to be used for patients who have undergone prostatectomy but were at risk for recurrent disease and hence further treatment would be considered. We identified many thousands of coding, non-coding and non-exonic RNAs differentially expressed between the different tissue specimens in the MSKCC Oncogenome Project. In a more focused analysis of these feature subset groups (derived from comparison of normal adjacent to primary tumor and metastatic prostate cancer), we found that the coding feature subsets contained substantially less prognostic information than their non-coding counterparts as measured by their ability to discriminate two clinically relevant end-points. First, we observed clustering of those primary tumors from patients that progressed to metastatic disease with true metastatic disease tissue when using the non-exonic features; this was not observed with the coding features. Next, Kaplan-Meier analysis between KNN classifier groups (e.g., more ‘normal-like’ vs. more ‘metastatic-like’) among primary tumors showed that only the non-coding and non-exonic feature sets had statistically significant BCR-free survival. Finally, multivariable analysis showed only the non-exonic feature subset KNN classifier was significant after adjusting for established prognostic factors including pre-operative PSA and Gleason scores with an odds ratio of 30 for predicting metastatic disease.

Based on these three main results, we concluded that non-exonic RNAs contain previously unrecognized prognostic information that may be relevant in the clinic for the prediction of cancer progression post-prostatectomy. Perhaps, the reason that previous efforts to develop new biomarker based predictors of outcome in prostate cancer have not translated into the clinic have been because the focus was on mRNA and proteins, largely ignoring the non-coding transcriptome.

These results add to the growing body of literature showing that the ‘dark matter’ of the genome has potential to shed light on tumor biology, characterize aggressive cancer and improve in the prognosis and prediction of disease progression.

Example 2
Method of Diagnosing a Leukemia in a Subject

A subject arrives at a doctor's office and complains of symptoms including bone and joint pain, easy bruising, and fatigue. The doctor examines the subject and also notices that the subject's lymph nodes were also swollen. Bone marrow and blood samples were obtained from the subject. Microarray analysis of the samples obtained from the subject reveal aberrant expression of a classifier disclosed herein comprising non-coding targets and coding targets and the subject was diagnosed with acute lymphoblastic leukemia.

Example 3
Method of Determining a Treatment for Breast Cancer in a Subject

A subject was diagnosed with breast cancer. A tissue sample was obtained from the subject. Nucleic acids were isolated from the tissue sample and the nucleic acids were applied to a probe set comprising at least ten probes capable of detecting the expression of at least one non-coding target and at least one coding target. Analysis of the expression level of the non-coding targets and coding targets reveals the subject has a tamoxifen-resistant breast cancer and gefitinib was recommended as an alternative therapy.

Example 4
Method of Determining the Prognosis for Pancreatic Cancer in a Subject

A subject was diagnosed with pancreatic cancer. A tissue sample was obtained from the subject. The tissue sample was assayed for the expression level of biomarkers comprising at least one non-coding target and at least one coding target. Based on the expression level of the non-coding target, it was determined that the pancreatic cancer has a high risk of recurrence.

Example 5
Method of Diagnosing a Prostate Cancer in a Subject

A subject arrives at a doctor's office and complains of symptoms including inability to urinate standing up, blood in urine, and dull, incessant pain in the pelvis and lower back. The doctor conducts a digital prostate exam and recommends that blood samples were obtained from the subject. The PSA was abnormal, a biopsy was ordered and microarray analysis of the blood and tissue samples obtained from the subject reveal aberrant expression of non-coding targets and the subject was diagnosed with prostate cancer.

Example 6
Method of Determining a Treatment for Lung Cancer in a Subject

A subject was diagnosed with non-small cell lung cancer (NSCLC). A tissue sample was obtained from the subject. Nucleic acids were isolated from the tissue sample and the nucleic acids were applied to a probe set comprising at least five probes capable of detecting the expression of at least one non-coding target. Analysis of the expression level of the non-coding targets reveals the subject has a cisplatin-resistant NSCLC and gemcitabine was recommended as an alternative therapy.

Example 7
Genome-Wide Detection of Differentially Expressed Coding and Non-Coding Transcripts and Clinical Significance in Prostate Cancer Using Transcript-Specific Probe Selection Regions

In this study, we performed whole-transcriptome analysis of a publicly available dataset from different types of normal and cancerous prostate tissue and found numerous differentially expressed coding and non-coding transcripts that discriminate between clinical disease states.

Materials and Methods

Microarray and Clinical Data

The publically available genomic and clinical data was generated by the Memorial Sloan-Kettering Cancer Center (MSKCC) Prostate Oncogenome Project, previously reported by Taylor et al., 2010. The Human Exon arrays for 131 primary prostate cancers, 29 normal adjacent and 19 metastatic tissue specimens were downloaded from GEO Omnibus at http://www.ncbi.nlm.nih.gov/geo/ series GSE21034. The patient and specimen details for the primary and metastases tissues used in this study were reported in Vergara I A, et al., 2012, Frontiers in Genetics, 3:23. For the analysis of the clinical data, the following ECE statuses were summarized to be concordant with the pathological stage: inv-capsule: ECE−, focal: ECE+, established: ECE+.

Microarray Pre-Processing

Normalization and Summarization

The normalization and summarization of the 179 microarray samples (cell lines samples were removed) was conducted with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:254-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369) including all MSKCC samples. Normalization was done by the quantile normalization method and summarization by the robust weighted average method, as implemented in fRMA. Gene-level expression values were obtained by summarizing the probe selection regions (or PSRs) using fRMA and the corresponding Affymetrix Cluster Annotation (www.affymetrix.com/).

Sample Subsets

The normalized and summarized data was partitioned into three groups. The first group contains the samples from primary localized prostate cancer tumor and normal adjacent samples (used for the normal versus primary comparison). The second group contained all of the samples from metastatic tumors and all of the localized prostate cancer specimens (used for the primary versus metastasis comparison). The third group contained all of the samples from metastatic tumors and all of the normal adjacent samples (used for the normal versus metastasis comparison).

Detection of Transcript-Specific PSRs in Human Exon Microarray Probe Sets

Using the xmapcore R package (Yates, 2010), all exonic PSRs that were specific to only one transcript were retrieved, generating a total of 123,521 PSRs. This set of PSRs was further filtered in order to remove all those that correspond to a gene but such that (i) the gene has only one transcript, or (ii) the gene has multiple transcripts, but only one can be tested in a transcript-specific manner. Applying these filters reduced the total number of transcript-specific PSRs to 39,003 which were the main focus of our analysis.

Feature Selection

Based on the set of transcript specific PSRs, those annotated as ‘unreliable’ by the xmapcore package (Yates, 2010) (one or more probes do not align uniquely to the genome) as well as those not defined as class 1 cross-hybridizing by Affymetrix were excluded from further analysis (http://www.affymetrix.com/analysis/index.affx). Additionally, those PSRs that present median expression values below background level for all of the three tissue types (normal adjacent, primary tumor and metastasis) were excluded from the analysis. The remaining PSRs were subjected to univariate analysis to discover those differentially expressed between the labeled groups (primary vs. metastatic, normal adjacent vs. primary and normal vs. metastatic). For this analysis, PSRs were selected as differentially expressed if their FDR adjusted t-test P-value was significant (<0.05) and the Median Fold Difference (MFD) was greater or equal than 1.2. The t-test was applied as implemented in the row t-tests function of the genefilter package (http://www.bioconductor.org/packages/2.3/bioc/html/genefilter.html). The multiple testing corrections were applied using the p-adjust function of the stats package in R.

For a given transcript with two or more transcript-specific PSRs significantly differentially expressed, the one with the best P-value was chosen as representative of the differential expression of the transcript. In order to avoid complex regions, cases for which a transcript specific PSR would overlap with more than one gene (for example within the intron of another gene) were filtered out from the analysis.

Feature Evaluation and Model Building

A k-nearest-neighbour (KNN) model (k=1, Euclidean distance) was trained on the normal and metastatic samples (n=48) using only the top 100 features found to be differentially expressed between these two groups.

Statistical Analysis

Biochemical recurrence and metastatic disease progression end points were used as defined by the “BCR Event” and “Mets Event” columns of the supplementary material provided by (Taylor et al., 2010), respectively. Survival analysis for BCR was performed using the survfit function of the survival package.

Results

Detection of Transcript-Specific PSRs in Human Exon Arrays

Detection of transcript-specific differential expression was of high interest as different spliced forms of the same gene might play distinct roles during progression of a given disease. For example, in the case of prostate cancer, it has been recently reported that not only does the main transcript associated with the Androgen Receptor (AR) gene play a role in prostate cancer, but other variants, such as v567, function in a distinct manner to that of the main spliced form (Chan et al, J. Biol. Chem, 2012; Li et al, Oncogene, 2012; Hu et al, Prostate, 2011). Affymetrix HuEx arrays provided a unique platform to test the differential expression of the vast majority of exonic regions in the genome. Based on Ensembl v62 and xmapcore (Yates et al 2010), there were 411,681 PSRs that fell within exons of protein-coding and non-coding transcripts. Within this set, a subset of 123,521 PSRs (˜10% of the PSRs in the array) allowed for the unequivocal testing of the differential expression of transcripts, as they overlap with the exon of only one transcript. These PSRs, which we called transcript-specific PSRs (TS-PSRs), cover 49,302 transcripts corresponding to 34,599 genes. In this study, we used the publicly available Human Exon Array data set generated by the MSKCC Prostate Oncogenome Project to explore the transcript-specific differential expression through progression of prostate cancer from normal, primary tumor and metastatic tissues. In particular, we focus on the assessment of two or more different transcripts within a gene in a comparative manner. Hence, the set of 123,521 TS-PSRs was further filtered in order to remove all those that correspond to a gene, such that (i) the gene has only one transcript (69,591 TS-PSRs; FIG. 15A), or (ii) the gene has multiple transcripts, but only one can be tested in a transcript-specific manner (14,927 TS-PSRs; FIG. 15B). This generated a final set of 39,003 TS-PSRs corresponding to 22,517 transcripts and 7,867 genes that were used as the basis of this analysis (FIG. 15C).

Differential Expression of Coding and Non-Coding Transcripts Through Prostate Cancer Progression

Assessment of the defined set of TS-PSRs yielded 881 transcripts that were differentially expressed between any pairwise comparison on the normal adjacent, primary tumor and metastatic samples (see methods; FIG. 11). These 881 transcripts corresponded to 680 genes, due to genes with two or more transcripts differentially expressed at the same or different stages of cancer progression. Interestingly, 371 (42%) of the differentially expressed transcripts were non-coding. Inspection of their annotation reveals that they fell into several non-coding categories, the most frequent being “retained intron” (n=151) and “processed transcript” (n=186). Additionally, most of the genes associated with these non-coding transcripts were coding, (i.e. they encode at least one functional protein). Examples of non-coding genes with differentially expressed transcripts found in this dataset include the lincRNAs PART1 (Prostate Androgen-Regulated Transcript 1, Lin et al 2000, Cancer Res), MEG3 (Ribarska et al 2012), the PVT1 oncogene, located in the 8q24 susceptibility region (Meyer et al 2011, PLoS Genetics), and the testis-specific lincRNA TTTY10. Other ncRNAs include the small nucleolar RNA host gene 1 (SNHG1) which has been suggested as a useful biomarker for disease progression (Berretta and Moscato, 2011, PLoS ONE), as well as GAS5, located in the 1q25 risk loci (Nam et al 2008; Prstate Cancer Prostatic Dis). Additionally, three pseudogenes were found differentially expressed in this dataset: EEF1DP3, located in a region previously found to be a focal deletion in metastatic tumors (Robbins et al 2011, Genome Research), the Y-linked pseudogene PRKY, which has been found expressed in prostate cancer cell lines (Dasari et al, 2000, Journal of Urology) and PABPC4L.

In addition to the non-coding genes, many coding genes presented one or more non-coding transcripts that were differentially expressed. Table 7 provides a list of genes that have been shown to participate in prostate cancer and that contain one or more non-coding transcripts differentially expressed according to our analysis, including the Androgen Receptor (Chan et al, J. Biol. Chem, 2012; Li et al, Oncogene, 2012; Hu et al, Prostate, 2011), ETV6 (Kibel et al, 2000, The Journal of Urology) and the fibroblast growth receptors FGFR1 and FGFR2 (Naimi et al 2002, The Prostate). Focusing on the individual transcripts of genes known to play a role in prostate cancer progression and their coding ability might shed light on the mechanisms in which each transcript was involved. Overall, the set of non-coding transcripts in both coding and non-coding genes reported here add to the current stream of evidence showing that non-coding RNA molecules may play a significant role in cancer progression (Vergara et al 2012, Kapranov et al 2010).

Genes with Multiple Transcripts Differentially Expressed Through Prostate Cancer Progression

The majority of the 881 differentially expressed transcripts came from the comparison between normal adjacent and metastatic samples, in agreement with previous analyses of differential expression of tissue on the MSKCC dataset (Vergara et al., 2012). As shown in FIG. 11, 28 of the differentially expressed transcripts were found throughout the progression from normal adjacent through primary tumor to metastasis, with 22 of them across all three pairwise comparisons (Table 8, top). These 22 transcripts reflected instances of a significant increase or decrease of expression through all stages in the same direction (i.e. always upregulated or downregulated). The remaining 6 transcripts found to be differentially expressed in the normal adjacent vs primary tumor as well as in the primary tumor versus metastatic sample comparison (but not in the normal adjacent versus metastatic samples comparison) were a reflection of differential expression that occurs in different directions in the progression from normal to primary tumor compared to that from primary tumor to metastasis, suggesting that these transcripts play a major role during the primary tumor stage of the disease (Table 8, bottom). In particular, within this set of 28 transcripts there were two AR-sensitive genes, FGFR2 and NAMPT, that presented two transcripts that were differentially expressed throughout progression. In the case of the FGFR2 gene (a fibroblast growth receptor), our observation of significant decrease in expression from normal to metastasis was in agreement with a previous study that shows downregulation of isoforms ‘b’ and ‘c’ to be associated with malignant expression in prostate (Naimi et al, 2002, The Prostate). In the case of NAMPT (a nicotinamide phosphoribosyltransferase), the two transcripts showed a peak of expression in the primary tumor tissues compared to normal and metastasis; the rise in primary tumors compared to normal was in full agreement with previously reported elevation of expression during early prostate neoplasia for this gene (Wang et al, 2011, Oncogene). For both genes, the transcripts were differentially expressed in the same direction as the tumor progresses, suggesting that both transcripts were functioning in a cooperative manner. In order to determine if this was a general pattern of the transcripts analyzed here, all of the genes for which at least two transcripts presented differential expression were inspected (FIG. 12). Among the 140 genes for which we find such cases, there was a clear trend for groups of transcripts of the same gene to express in the same direction as the tumor progresses. Two exceptions that were found were genes CALD1 and AGR2. For both of them, the differential expression of one of their transcripts in the progression from primary tumor to metastasis went in the opposite direction compared to the other transcripts. In the case of AGR2, transcript AGR2-001 was downregulated in metastasis compared to primary tumor, whereas AGR2-007 was upregulated. This observation was in agreement with previous reports on a short and long isoform of the same gene (Bu et al, 2011, The Prostate). Even though the correspondence of the short and long isoforms to those annotated in Ensembl was not straightforward, alignment of the primers used in Bu et al. (2011) showed overlapping of the short isoform with AGR2-001, and of the long isoform with AGR2-007, which agreed with their divergent expression patterns. In the case of CALD1, while transcript CALD1-012 was upregulated, CALD1-005 and CALD1-008 were downregulated in the progression from primary tumor to metastasis. A previous study on 15 prostate cancer samples showed that CALD1-005 was downregulated in metastatic samples compared to primary tumor, in agreement with our results.

Transcripts Level Resolution of Differential Expression on Fully Tested Genes

Of the 7,867 genes for which one or more transcripts were assessed in this analysis, 1,041 genes were such that all of their transcripts have at least one TS-PSR. Of these, 92 genes were such that at least one of their transcripts was found to be differentially expressed in any pairwise comparison among normal adjacent, primary tumor and metastatic samples. As depicted in FIG. 13, the majority of the genes only have one differentially expressed transcript. This included cases like KCNMB1 and ASB2, two genes that have been previously reported to be differentially expressed in prostate cancer, but for which no observation at the transcript level has been made (Zhang et al 2005, Cancer Genomics and Proteomics; Yu et al 2004, JCO). In the case of KCNMB1, only transcript KCNMB1-001 of the two transcripts was found to be differentially expressed, whereas for ASB2, only transcript ASB2-202 was found to be differentially expressed of the three transcripts annotated for this gene. Also, other genes presented differential expression of their non-coding transcripts only. One example of this was PCP4 (also known as PEP-19), a gene known to be expressed in prostate tissue (Kanamori et al 2003, Mol. Hum. Reprod).

In addition to the expression profile of each transcript for these 92 genes, FIG. 13 shows the corresponding summarized gene-level expression profile for each gene. Of these, only 18 genes present differential expression at the gene level, clearly illustrating that summarization of expression can result in significant loss of information.

TS-PSRs Constitute a Clinically Significant Prostate Cancer Risk Group

In order to assess the prognostic significance of the differentially expressed transcripts, the corresponding TS-PSRs were used to train a KNN classifier on normal and metastatic samples and validated on the primary tumors, such that each primary tumor sample was classified as normal or metastatic based on its distance to the normal and metastatic groups. The higher the KNN score (ranging from 0 to 1), the more likely the patient will be associated to worse outcome. As shown in FIG. 14, the difference in the Kaplan-Meier (KM) curves for the two groups was statistically significant using biochemical recurrence as an endpoint and was comparable to that of the Kattan nomogram (Kattan et al 1999). Further assessment of coding and non-coding differentially expressed transcripts showed both sets to yield statistically significant differences in their KM curves. The corresponding set of differentially expressed genes still presented a statistically significant difference of the KM curves, despite the observed loss of information from the summarization when comparing different tissue types. A multivariable logistic regression analysis of the groups of transcripts and genes differentially expressed showed that the transcripts remain highly statistically significant after adjusting for the Kattan nomogram (p<0.005), whereas the genes resulted in borderline significance after adjustment (p=0.05) (Table 9). These results suggest that differential expression of specific transcripts have unique biomarker potential that adds value to that of classifiers based on clinicopathological variables such as nomograms.

Example 8
Differentially Expressed Non-Coding RNAs in Chr2q31.3 has Prognostic Potential and Clinical Significance Based on Fresh Frozen Samples

Methods

The publicly available expression profiles of normal and prostate tumor samples, Memorial Sloan Kettering Cancer Center (MSKCC) (Taylor et al., 2010) were downloaded from http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034. The Human Exon arrays for 131 primary prostate cancer, 29 normal adjacent and 19 metastatic tissue specimens were downloaded from GEO Omnibus at http://www.ncbi.nlm.nih.gov/geo/ series GSE21034. Information on Tissue samples, RNA extraction, RNA amplification and hybridization were disclosed in Taylor et al., 2010. The normalization and summarization of the 179 microarray samples (cell lines samples were removed) was conducted with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:254-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

Feature selection was conducted using a t-test for differential expression on the 857 Probe Selection Regions (or PSRs) within chr2q31.3 region. A PSR was regarded as significantly differentially expressed if the P-value of the t-test was lower than 0.05 in any of the following comparisons: BCR vs non-BCR, CP vs non-CP, PCSM vs non-PCSM. Additionally, a PSR was found significant if the P-values of the differences between the KM curves for BCR vs non-BCR, CP vs non-CP, PCSM vs non-PCSM was lower than 0.05. Table 6, SEQ ID NOs.: 262-291 provides the detail of which comparison(s) yielded the PSR as significant.

Non-Coding Analysis

Using annotation data from the human genome version hg19/GRCh37 (Ensembl annotation release 62) and xmapcore (Yates, 2007), we categorized the PSRs depending on the chromosomal location and orientation with respect to coding and non-coding gene annotation as Coding, Non-coding (UTR), Non-coding (ncTranscript), Non-coding (Intronic), Non-coding (CDS_Antisense), Non-coding (UTR_Antisense), Non-coding (ncTranscript_Antisense), Non-coding (Intronic_Antisense), Non-coding (Intergenic). We additionally used xmapcore to annotate the gene symbol, gene synonym, Ensembl gene ID and biological description for any PSRs that overlapped with a transcript; this excludes alignments to non-coding (non-unique) and non-coding (intergenic) sequences.

Ontology Enrichment Analysis

DAVID Bioinformatics tool was used to assess enrichment of ontology terms (Huang da W, et al., 2009, Nat Protoc, 4:44-57; Huang da W, et al., 2009, Nucleic Acids Res, 37:1-13).

Results

Based on the criteria defined above, 429 PSRs were found to be differentially expressed within chr2q31.3 (Table 6, SEQ ID NOs.: 262-291). Of these 429 PSRs, the vast majority were non-coding, with only 20% mapping to a protein-coding region of a gene (FIG. 16). The most represented groups in the non-coding category were Intronic PSRs (26%) and Intergenic PSRs (27%). The fact that one of the largest groups was the intergenic one demonstrates that chr2q31.3 had significant unexplored prognostic potential. In fact, DAVID assessment of the functional annotation of these PSRs yielded no significant Gene Ontology terms for Biological Processes, in agreement with the idea that DAVID was a tool built mostly upon protein-coding gene information.

Additionally, approximately 8% of the PSRs overlapped with transcripts that did not encode for a functional protein. The distribution of the non-coding transcripts according to Ensembl annotation (http://www.ensembl.org) were as follows: 6 “processed transcript”, 3 “retained intron”, 7 “large intergenic non-coding RNA”, 4 “processed_pseudogene”, 1 “non-sense mediated decay” and 1 snoRNA.

In order to further assess the clinical significance of the selected PSRs, KM curves were built using Biochemical Recurrence (BCR), as endpoint. As depicted in FIG. 17, the PSR corresponding to the probe set ID 2518027 showed a statistically significant difference of the KM curves for BCR endpoint, further demonstrating the prognostic potential of this region.

Example 9
Digital Gleason Score Predictor Based on Differentially Expressed Coding and Non-Coding Features

In this study we evaluated the use of differentially expressed coding and non-coding features.

Methods

The publicly available expression profiles of normal and prostate tumor samples, Memorial Sloan Kettering Cancer Center (MSKCC) (Taylor et al., 2010) were downloaded at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034 and the German Cancer Research Center (DKFZ) (Brase et al., 2011) http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29079 were pooled and used to define a training set and a testing set. The training set consisted of all of the samples with a Gleason Score lower than 7 (hereafter called GS<7) and higher than 7 (hereafter called GS>7), whereas the testing set comprised all of the samples with a Gleason Score of 7 (hereafter called GS7). The group of GS7 patients was further split into 3+4 and 4+3 based on the Primary and Secondary Gleason Grades.

Information on tissue samples, RNA extraction, RNA amplification and hybridization can be found elsewhere (Taylor et al., 2010; Brase et al., 2011). The normalization and summarization of the 179 microarray samples (cell lines samples were removed) was conducted with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:254-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

Feature selection was done using a t-test for differential expression between those GS<7 and GS>7 samples. 102 Probe Selection Regions (PSRs) were kept after a Holm P-value adjustment threshold of 0.05. The top 12 PSRs were used to build a random forest classifier with the following parameters: mtry=1, nodesize=26, ntree=4000. The mtry and nodesize parameters were selected via the random forest tune function. The classifier generated with this methodology is hereafter called RF12.

Results

Of the 102 PSRs found differentially expressed, 43% of them were in coding regions (FIG. 18). The rest of the PSRs were distributed within introns, untranslated regions (or UTRs), non-coding transcripts or were non-unique. Non-unique PSRs composed 13% of the differentially expressed PSRs. Some of these PSRs required thorough manual assessment in order to understand their nature; while some of them could be annotated as non-unique due to the presence of allelic variants in the genome assembly, others likely provided differential expression information through the existence of copy-number variations. A partial list of the 102 PSRs identified can be found in Table 6, SEQ ID NOs.: 292-321.

Using the trained RF12 classifier on the GS<7 and GS>7 samples, each GS7 (3+4 and 4+3) sample was assigned a probability of risk. The RF12 score, which ranges from 0 to 1, is the percentage of decision trees in the random forest which label a given patient as having the Gleason grade of the profiled tissue as greater than 3. A higher RF12 score means a worse prognosis for a patient as correlated with Gleason score. The higher the probability, the higher the risk associated to the sample. As shown in FIG. 19A, the probability distributions of the 3+4 samples versus 4+3 samples were significantly different. Those samples with a primary Gleason grade of 3 tended to have a lower probability than those with a primary Gleason grade of 4, which was in agreement with a higher Gleason grade corresponding to a higher risk of prostate cancer progression. Assessment of RF12 performance yielded an accuracy of 74%, which was significantly different to the 61% accuracy that was achieved with a null model. The high performance of the RF12 classifier was confirmed with the AUC metric, yielding an AUC of 77%.

In order to further illustrate the prognostic potential and to assess the clinical significance of this classifier, KM curves on the groups predicted by RF12 were generated using the probability of BCR-free survival as endpoint. As shown in FIG. 19B, the difference between the low and high risk groups was statistically significant (p<0.01), demonstrating the ability of RF12 to discriminate between those samples from patients that were at high risk of progressing to biochemical recurrence versus those that were at low risk.

Example 10
KNN Models Based on PSR Genomic Subsets

In this study, Probe Selection Regions (PSRs) were annotated using xmapcore into the following categories: Intronic, Intergenic, Antisense, ncTranscript and Promoter Region. Antisense refers to a PSR being located in the opposite strand of a gene. Promoter Region was defined as the 2 kbp upstream region of a transcript, excluding the 5′UTR. Following the feature selection methodology in Example 1 based on MSKCC data, all significant PSRs were grouped into categories (e.g., Intronic, Intergenic, Antisense, ncTranscript and Promoter Region). In order to assess the prognostic significance of the PSRs differentially expressed within the categories, we developed a k-nearest neighbour (KNN) classifier for each group based on the top 156 PSRs (k=1, correlation distance), trained using features from the comparison of normal and metastatic tissue types (see Example 1 methods). Next, we used unmatched primary tumors (e.g. removing those tumors that had a matched normal in the training subset) as an independent validation set for each KNN classifier. Each primary tumor in the validation set was classified by each KNN as either more similar to normal or metastatic tissue (FIG. 9). Kaplan-Meier analysis of the two groups of primary tumor samples classified by KNN using the biochemical recurrence (BCR) end point was done for KNN classifiers derived for each subset of features. As expected, primary tumors classified by KNN as belonging to the metastasis group had a higher rate of BCR.

Example 11
Genomic Signature of Coding and Non-Coding Features to Predict Outcome after Radical Cystectomy for Bladder Cancer

Methods

251 muscle invasive bladder cancer specimens from University of Southern California/Norris Cancer Center were obtained from patients undergoing radical cystectomies with extended pelvic lymph node dissection between years 1998 and 2004. Archived FFPE specimens sampled corresponded to 0.6 mm punch cores and had a median block age of 13 years. For patients, median follow up was 5 years, median age was 68 years old and the event rate corresponds to 109 patients with progression (43%).

Total RNA was extracted and purified using a modified protocol for the commercially available Agencourt Formapure kit (Beckman Coulter, Indianapolis Ind.). RNA concentrations were determined using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Rockland, Del.). Purified total RNA was subjected to whole-transcriptome amplification using the WT-Ovation FFPE system according to the manufacturer's recommendation with minor modifications (NuGen, San Carlos, Calif.) and hybridized to Human Exon 1.0 ST GeneChips (Affymetrix, Santa Clara, Calif.) that profiled coding and non-coding regions of the transcriptome using approximately 1.4 million probe selection regions (or PSRs, also referred to as features).

Samples showing a variation of higher than two standard deviation for their average intensities, average background, Relative Log Expression and Median Absolute Deviation were discarded. In addition, filtering was also performed using GNUSE (Global Normalized Unscaled Standard Error), positive versus negative AUC and Percentage of Detected Calls using [0.6,1.4], >0.6 and 20% as thresholds, respectively.

A multivariate outlier detection algorithm was run using the QC metrics provided by Affymetix Power tools available at http://www.affymetrix.com/partners_programs/programs/developer/tools/powertools.affx. Samples identified as outliers were also discarded.

The normalization and summarization of the microarray samples were performed with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:254-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

Results

Table 14 shows the raw clinical data, QC results and classifier scores for each of the 251 samples. The characteristics of the study population is summarized in Table 10. Assessment of the prognostic potential of the clinical factors was assessed by multivariable Cox proportional hazards modeling. As shown in Table 11, Tumor Stage (p=0.04) and Lymph Nodes (p<0.001) were found to have statistically significant prognostic potential based on hazard ratios. In order to assess the discriminatory potential of the clinical and pathological factors, samples were divided into a training set (trn) and a testing set (tst) (see Table 14, ‘Set’ column) and the performance of each variable was assessed by AUC (Table 12) for the progression-free survival endpoint. Progression was defined as any measurable local, regional or systemic disease on post-cystectomy imaging studies.

In agreement with the multivariable analysis, Tumor Stage and Lymph Nodes status had significant performance with a respective AUC of 0.62 and 0.66 for the training set and AUCs of 0.66 and 0.65 for the testing set. Combination of clinical-pathological variables into a multivariate model by either Cox modeling or Logistic Regression resulted in an improved performance (AUCs of 0.72 and 0.71 in the testing set, respectively) compared to these variables as sole classifiers (Table 12).

A genomic classifier (GC) was built based on the Human Exon arrays as follows. First, a ranking of the features by Median Fold Difference (MFD) was generated. Then, a k-nearest neighbour algorithm was applied to an increasingly larger set of features from 10 to 155 based on the MFD ranking. The classifiers (herein referred to as KNN89) were constructed by setting k=21 and number of features=89, achieving an AUC of 0.70 for the training set (FIG. 21A) and an AUC of 0.77 for the testing set (FIG. 21B) based on survival ROC curves at 4 years. The probability, which ranges from 0 to 1, an individual would be classified as having a progression event was based on the expression values of the closest 21 patients in the training cohort of muscle-invasive bladder cancer samples. Low probabilities represent a lower chance a patient would have progression while higher probabilities represent a higher chance a patient would have progression event. The 89 individual features (a.k.a. PSRs) of the KNN89 classifier correspond to coding and non-coding regions of the genome (Table 6, SEQ ID NOs.: 353-441, Table 15) including introns, untranslated regions (or UTRs), features antisense to a given gene as well as intergenic regions. Assessment of the pathways associated to the overlapping genes using KEGG pathway annotation shows that the most represented correspond to Regulation of actin cytoskeleton, focal adhesion and RNA transport (www.genome.jp/kegg/pathway.html).

When combining the GC with the clinical variables Age, Lymphovascular Invasion, Lymph Node Involvement and Intravesical therapy, a new classifier (hereafter referred to as GCC, for Genomic-Clinical Classifier) with enhanced performance was generated, based on the AUC of 0.82 and 0.81 in the training set and testing set respectively (FIG. 21A, FIG. 21B) based on survival ROC curves at 4 years. Discrimination plots for both GC and GCC demonstrated that the separation between the two groups of progression and non-progression samples was statistically significant for both classifiers (FIG. 22). Whereas both calibration plots for GC and GCC showed a good estimation with respect to the true values (FIG. 23), the enhanced performance of the GCC classifier became evident when inspecting the calibration plots, as GCC corrected overestimation of probabilities above 0.5. Still, multivariable analysis of the GC showed that this classifier has unique prognostic potential for the prediction of disease progression after radical cystectomy when adjusted for clinical pathological variables (Table 13).

Cumulative incidence plots depicting the frequency of progression over time were generated for GC-low and GC-high risk groups, as well as for GCC-low and GCC-high risk groups (FIG. 24). The cumulative incidence probabilities of progression were significantly different between the two risk groups for both classifiers. In the case of GC, a 15% incidence for the GC-low risk group was obtained, compared to a 60% incidence for the GC-high risk group at 3 years after radical cystectomy. For the GCC, a 20% incidence of progression for the GCC-low risk group was obtained, compared to a 70% incidence for the GCC-high risk group at 3 years. The 3-fold to 4-fold difference in incidence observed between the low and high risk groups for GC and GCC illustrates the clinical significance of these classifiers.

Example 12
Genomic Signatures of Varying Number of Coding and Non-Coding Features to Predict Outcome after Radical Cystectomy for Bladder Cancer

Methods

251 muscle invasive bladder cancer specimens from University of Southern California/Norris Cancer Center were obtained from patients undergoing radical cystectomies with extended pelvic lymph node dissection between years 1998 and 2004. Archived FFPE specimens sampled correspond to 0.6 mm punch cores and have a median block age of 13 years. For patients, median follow up was 5 years, median age was 68 years and the event rate corresponds to 109 patients with progression (43%).

Total RNA was extracted and purified using a modified protocol for the commercially available Agencourt Formapure kit (Beckman Coulter, Indianapolis Ind.). RNA concentrations were determined using a Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Rockland, Del.). Purified total RNA was subjected to whole-transcriptome amplification using the WT-Ovation FFPE system according to the manufacturer's recommendation with minor modifications (NuGen, San Carlos, Calif.) and hybridized to Human Exon 1.0 ST GeneChips (Affymetrix, Santa Clara, Calif.) that profiles coding and non-coding regions of the transcriptome using approximately 1.4 million probe selection regions (or PSRs, also referred to as features).

Samples showing a variation higher than two standard deviation for their average intensities, average background, Relative Log Expression and Median Absolute Deviation were discarded. In addition, filtering was also performed using GNUSE (Global Normalized Unscaled Standard Error), positive versus negative AUC and Percentage of Detected Calls using [0.6,1.4], >0.6 and 20% as thresholds, respectively.

Finally, a multivariate outlier detection algorithm was run using the QC metrics provided by Affymetix Power tools available at http://www.affymetrix.com/partners_programs/programs/developer/tools/powertools.affx.

Samples identified as outliers were also discarded.

The normalization and summarization of the microarray samples was conducted with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:254-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

The dataset was separated into a training (trn) and a testing set (tst) as specified in column ‘Set’ of Table 14. Based on this separation, several machine learning algorithms were trained with different number of features (See Table 16 for methods used for feature selection) and their performance assessed on both training and testing sets independently. Performance of the generated classifiers on the training and the testing set based on AUC was also in Table 16.

Results

FIG. 26 shows the performance of a classifier, NB20, based on 20 features that were a combination of coding, intronic, intergenic, UTR and antisense regions (Table 17). The probability, which ranges from 0 to 1, an individual would be classified as having a progression event was based on the combined proportion of the progression samples in the training cohort which have similar expression values. Low probabilities represent a lower chance a patient would have progression while higher probabilities represent a higher chance a patient would have progression. This classifier had an AUC of 0.81 on the training set (trn) and an AUC of 0.73 on the testing set (tst), with both AUCs being statistically significant based on Wilcoxon test (FIG. 26A). In order to assess the clinical significance of the classification, after splitting the NB20 classifier scores into two groups by Partitioning Around Medoids (PAM) clustering, Kaplan-Meier curves showed that the two groups represented significantly different groups of high-risk of recurrence vs low-risk of recurrence (FIG. 26B).

FIG. 27 shows the performance of a classifier, KNN12, based on 12 features that were a combination of coding, intronic, intergenic, UTR and antisense regions (Table 17). The probability, which ranges from 0 to 1, an individual would be classified as having a progression event was based on the expression values of the closest 51 patients in the training cohort of muscle-invasive bladder cancer samples. Low probabilities represent a lower chance a patient would have progression while higher probabilities represent a higher chance a patient would have progression. This classifier had an AUC of 0.72 on the training set and an AUC of 0.73 on the testing set, with both AUCs being statistically significant based on Wilcoxon test (FIG. 27A). In order to assess the clinical significance of the classification, after splitting the KNN12 classifier scores into two groups by PAM clustering, Kaplan-Meier curves showed that the two groups represented significantly different groups of high-risk of recurrence vs low-risk of recurrence (FIG. 27B).

FIG. 28 shows the performance of a classifier, GLM2, based on 2 features that corresponded to a pseudogene (HNRNPA3P1) and the intronic region of a protein-coding gene (MECOM) (Table 17). The probability an individual would be classified as having a progression event was based on the best fit expression profile of the training samples. The probabilities range from 0 to 1, where low probabilities represent a lower chance a patient would have progression while high probabilities represent a higher chance a patient would have progression. This classifier had an AUC of 0.77 on the training set and an AUC of 0.74 on the testing set, with both AUCs being statistically significant based on Wilcoxon test (FIG. 28A). In order to assess the clinical significance of the classification, after splitting the GLM2 classifier scores into two groups by PAM clustering, Kaplan-Meier curves showed that the two groups represented significantly different groups of high-risk of recurrence vs low-risk of recurrence (FIG. 28B).

FIG. 29 shows the performance of a single probe selection region corresponding to probe set ID 2704702 that corresponded to the intronic region of a protein-coding gene (MECOM) (Table 17). This classifier had an AUC of 0.69 on the training set and an AUC of 0.71 on the testing set, with both AUCs being statistically significant based on Wilcoxon test (FIG. 29A). In order to assess the clinical significance of the classification, after splitting this classifier scores into two groups by PAM clustering, Kaplan-Meier curves showed that the two groups represented significantly different groups of high-risk of recurrence vs low-risk of recurrence (FIG. 29B).

Example 13
Genomic Signatures of Varying Number of Coding and Non-Coding Features to Predict Gleason Score of 6 Versus Gleason Score Greater than or Equal to 7

Methods

The publicly available expression profiles of normal and prostate tumor samples from the Memorial Sloan Kettering Cancer Center (MSKCC) (Taylor B S, et al., 2010, Cancer Cell, 18:11-22) was downloaded from http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034. Information on Tissue samples, RNA extraction, RNA amplification and hybridization can be found in Taylor B S et al. (2010, Cancer Cell, 18:11-22). The normalization and summarization of the 179 microarray samples (cell lines samples were removed) was performed with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:242-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

With the goal of generating classifiers that segregated between samples of Gleason Score of 6 (GS6) versus those with GS greater than or equal to 7 (GS7+), the complete dataset was split into a training set (60%, 78 samples) and a testing set (40%, 52 samples). In the training set, 25 samples were GS6 versus 53 samples that were GS7+. In the testing set, 16 samples were GS6 versus 36 samples that were GS7+.

Based on this separation, several machine learning algorithms were trained with different number of features (see Table 18 for methods used for feature selection) and their performance assessed on both training (trn) and testing (tst) sets independently. Performance of the generated classifiers on the training and the testing set based on AUC was also in Table 18.

Results

FIG. 30 shows the performance of a classifier, SVM20, based on 20 features that were a combination of coding, non-coding transcript, intronic, intergenic and UTR (Table 19). The certainty in which an individual would be classified as having a pathological Gleason grade 4 or higher in their profiled tumor sample was based on the expression values of the top 20 features as ranked by AUC. The GC scores range from negative infinity to positive infinity. Larger values indicate the likelihood that the sample has a pathological Gleason grade of 4 or higher in their profiled tumor sample while smaller values indicate the likelihood that the sample has a pathological Gleason grade of 3 in their profiled tumor sample. This classifier had an AUC of 0.96 on the training set (trn) and an AUC of 0.8 on the testing set (tst), with both AUCs being statistically significant based on Wilcoxon test (FIG. 30A). The fact that notches within box-plots representing 95% confidence intervals of the SVM20 scores associated to those GS6 samples and GS7+ samples don't overlap (FIG. 30B) shows that the segregation generated by this classifier was statistically significant.

FIG. 31 shows the performance of a classifier, SVM11, based on 11 features that were a combination of coding, non-coding transcript, intronic, intergenic and UTR (Table 19). The certainty in which an individual would be classified as having a pathological Gleason grade 4 or higher in their profiled tumor sample was based on the expression values of the top 11 features ranked by AUC. The GC scores range from negative infinity to positive infinity. Larger values indicate the likelihood that the sample has a pathological Gleason grade of 4 or higher in their profiled tumor sample while smaller values indicate the likelihood that the sample has a pathological Gleason grade of 3 in their profiled tumor sample. This classifier had an AUC of 0.96 on the training set (trn) and an AUC of 0.8 on the testing set (tst), with both AUCs being statistically significant based on Wilcoxon test (FIG. 31A). The fact that notches within box-plots representing 95% confidence intervals of the SVM11 scores associated to those GS6 samples and GS7+ samples don't overlap (FIG. 31B) shows that the segregation generated by this classifier was statistically significant.

FIG. 32 shows the performance of a classifier, SVM5, based on 5 features that were a combination of coding and intronic (Table 19). The certainty in which an individual would be classified as having a pathological gleason grade 4 or higher in their profiled tumor sample was based on the expression values of the top 5 features ranked by AUC. The GC scores range from negative infinity to positive infinity. Larger values indicate the likelihood the sample has a pathological gleason grade of 4 or higher in their profiled tumor sample while smaller values indicate the likelihood the sample has a pathological gleason grade of 3 in their profiled tumor sample. This classifier had an AUC of 0.98 on the training set (trn) and an AUC of 0.78 on the testing set (tst), with both AUCs being statistically significant based on Wilcoxon test (FIG. 32A). The fact that notches within box-plots representing 95% confidence intervals of the SVM5 scores associated to those GS6 samples and GS7+ samples don't overlap (FIG. 32B) shows that the segregation generated by this classifier was statistically significant.

FIG. 33 shows the performance of a classifier, GLM2, based on 2 features, one of them being intronic to gene STXBP6 and the other corresponding to an intergenic region (Table 19). The probability an individual would be classified as having a pathological gleason grade 4 or higher in their profiled tumor sample was based on the best fit expression profile of the training samples. The probabilities range from 0 to 1 where low probabilities represent a lower chance the pathological gleason grade of the profiled tumor is 4 or higher while high probabilities represent a higher chance the pathological gleason grade of the profiled tumor is 4 or higher. This classifier had an AUC of 0.86 on the training set (trn) and an AUC of 0.79 on the testing set (tst), with both AUCs being statistically significant based on Wilcoxon test (FIG. 33A). The fact that notches within box-plots representing 95% confidence intervals of the GLM2 scores associated to those GS6 samples and GS7+ samples don't overlap (FIG. 33B) shows that the segregation generated by this classifier was statistically significant.

Example 14
Prognostic Potential of Inter-Correlated Expression (ICE) Blocks with Varying Composition of Coding and Non-Coding RNA

Methods

The publicly available expression profiles of normal and prostate tumor samples, Memorial Sloan Kettering Cancer Center (MSKCC) (Taylor B S, et al., 2010, Cancer Cell, 18:11-22) were downloaded from http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034.

The Human Exon arrays for 131 primary prostate cancer, 29 normal adjacent and 19 metastatic tissue specimens were downloaded from GEO Omnibus at http://www.ncbi.nlm.nih.gov/geo/ series GSE21034. Information on Tissue samples, clinical characteristics, RNA extraction, RNA amplification and hybridization can be found as described in Taylor B S, et al., (2010, Cancer Cell, 18:11-22). The normalization and summarization of the 179 microarray samples (cell lines samples were removed) was performed with the frozen Robust Multiarray Average (fRMA) algorithm using custom frozen vectors as described in McCall M N, et al. (2010, Biostatistics, 11:242-53). These custom vectors were created using the vector creation methods described in McCall M N, et al. (2011, Bioinformatics, 12:369). Quantile normalization and robust weighted average methods were used for normalization and summarization, respectively, as implemented in fRMA.

Annotation of PSRs

Definition of Inter-Correlated Expression (ICE) Blocks

Affymetrix Human Exon ST 1.0 Arrays provide ˜5.6 million probes which were grouped into ˜1.4 million probe sets (average of 4 probes per probe set). The expression value captured for each probe was summarized for each probe set. The PSRs corresponding to each probe set fell within coding and non-coding (introns, UTRs) regions of protein-coding and non-protein-coding genes, as well as antisense to genes and intergenic regions.

An additional level of summarization provided by Affymetrix corresponds to probe sets that were grouped into so called transcript clusters. The genomic location of transcript clusters was defined based on the annotation of gene structures from multiple sources. The probe sets that compose these transcript clusters usually correspond to coding segments of protein-coding genes. This summarization was done with the goal of representing into one value the expression of the gene.

The predefined Affymetrix transcript clusters have a number of drawbacks including (i) they were static definitions of the transcribed sequence for a given gene, (ii) they do not account for the expression levels of the samples being assessed, and hence might correspond to sub-optimal representations of the expressed unit. Additionally, novel types of transcribed sequences that challenge the standard exon/intron structure of a gene such as chimeric RNAs (Kannan et al 2011) and very long intergenic non-coding regions (or vlincs, Kapranov et al 2010) have been found to be differentially expressed in cancer, and hence approaches that detect such transcripts were needed.

We proposed a new method that found blocks of neighboring correlated PSRs based on their expression values and show that they have prognostic potential. The correlated expression of these blocks of PSRs should represent one or more molecules that were being transcribed as either a single unit (e.g. chimeric RNAs) or as separate units (e.g. two separate genes) through cancer progression. We call these blocks syntenic blocks or Inter-Correlated Expression (ICE) Blocks.

Given a pooled set of samples from two groups A and B (e.g. primary tumor tissue versus metastatic tumor tissue) a window size W measured in number of PSRs, a correlation threshold T between 0 and 1, a counter C set to 0 and the chromosome, chromosomal location and strand for each PSR, ICE blocks were computed as follows:

- 1) Define the first block L as the single first PSR in the first chromosome.
- 2) Measure its correlation to the immediate adjacent PSR P downstream on the same strand using Pearson's correlation metric.
- 3) If the correlation was greater or equal than T, then merge P to block L. If not, then skip P and add one to counter C.
- 4) Repeat steps 1)-3) using the right-most PSR of block L. If a new PSR was added to the block, reset C=0.
- 5) Return block L when C>W or when reached the last PSR within the chromosome. Set C=0.
- 6) Repeat 1)-4) for each strand of each chromosome.

Once the ICE blocks were defined, the expression values for each of them were summarized based on the median value of the expression associated to the PSRs that compose the ICE Block for each patient. The significance of the differential expression between groups A and B for block L was assessed by computation of a Wilcoxon test P-value.

Results

Given the publicly available MSKCC samples described in Methods, the following comparisons were pursued: (i) Normal Adjacent Tissue versus Primary Tumor, (ii) Primary Tumor versus Metastatic Tissue, (iii) Gleason Score >=7 versus Gleason Score <7 and (iv) Biochemical Recurrence (BCR) vs non-BCR.

The algorithm for ICE block detection was applied to each of the pairwise comparisons. The number of ICE blocks found for each comparison and for a number of different Pearson correlation thresholds is shown in Table 20. As expected, as the correlation threshold gets lower more ICE blocks were found, consistent with the idea that more adjacent PSRs can be merged with lower correlation thresholds. Also shown in Table 20 is the number of ICE blocks found to be significantly differentially expressed (P-value<0.05) between the two conditions for each pairwise comparison. For those comparisons involving different progression states of cancer, the number of ICE blocks found differentially expressed can range from several hundreds (e.g. BCR endpoint with correlation threshold of 0.9) to tens of thousands (e.g. Primary vs Metastasis comparison, correlation threshold of 0.6).

Since ICE Blocks were composed of two or more PSRs, the proportion of coding and non-coding regions that the ICE block consists of can vary depending on where the associated PSRs fell into. Table 21 shows, for different comparisons and correlation thresholds, the frequency of ICE blocks found differentially expressed that correspond to a number of compositions including those that were composed only of coding regions, only intronic regions, only intergenic regions, only antisense regions as well as all other combinations. Additionally, ICE blocks can overlap with two or more adjacent genes (Multigene column in Table 21), suggesting that the two units were being differentially co-expressed either as separate units or as chimeric RNAs. For example, for the BCR endpoint and correlation threshold of 0.8, a previously reported chimeric RNA consisting of genes JAM3 and NCAPD3 was found as an ICE block composed of 65 coding and non-coding PSRs across the genomic span chr11:134018438 . . . 134095174;—with statistically significant differential expression (P-value<0.04).

Table 22 provides a list of all those ICE blocks found differentially expressed for the Gleason Score comparison when using a strict correlation threshold of 0.9. Table 23 provides a list of all those ICE blocks found differentially expressed for the Biochemical Recurrence endpoint when using a strict correlation threshold of 0.9. For each block, the associated P-value that demonstrated the differential expression (p<0.05), the PSRs included within the block, the percentage composition of coding and non-coding as well as the overlapping gene(s) within the same chromosomal location were shown. As seen in Tables 22 and 23, the proportion of coding and non-coding PSRs that an ICE block can be composed of can vary from fully coding to fully non-coding, with multiple proportions in between.

In order to further illustrate the discriminatory ability of these ICE blocks, FIGS. 34-39 show the box-plots (A) and ROC curves (B) for five different ICE blocks (FIG. 34: Block_—7716, FIG. 35: Block_—4271, FIG. 36: Block_—5000, FIG. 37: Block_—2922 and FIG. 38: Block_—5080) of varying composition of coding and non-coding found to be differentially expressed in GS6 vs GS7+ comparison (Table 22, see Table 24 for sequences associated to each PSR composing these ICE Blocks). For each of these ICE Blocks, box-plots depicting the distribution of the ICE Block expression were displayed for both groups. The fact that notches within box-plots representing 95% confidence intervals of the expression associated to those GS6 samples and GS7+ samples didn't overlap (FIGS. 34A, 35A, 36A, 37A, and 38A) shows that the segregation generated by this classifier was statistically significant. The statistical significance of this segregation was further confirmed by the AUC associated to each of the ROC curves for these ICE Blocks, as the 95% confidence intervals associated to each of the AUCs do not cross the 0.5 lower bound FIGS. 34B, 35B, 36B, 37B and 38B).

FIGS. 39-45 show the box-plots (A), ROC curves (B) and Kaplan-Meier curves (C) for seven different ICE blocks (FIG. 39: Block_—6592, FIG. 40: Block_—4627, FIG. 41: Block_—7113, FIG. 42: Block_—5470, FIG. 43: Block_—5155, FIG. 44: Block_—6371 and FIG. 45: Block_—2879) of varying composition of coding and non-coding found to be differentially expressed in BCR versus non-BCR comparison (Table 23, see Table 24 for sequences associated to each PSR composing these ICE Blocks). For each of these ICE Blocks, box-plots depicting the distribution of the ICE block expression were displayed for both groups. The fact that notches within box-plots representing 95% confidence intervals of the expression associated to those GS6 samples and GS7+ samples don't overlap (FIGS. 39A, 40A, 41A, 42A, 43A, 44A, and 45A) shows that the segregation generated by this classifier was statistically significant. The statistical significance of this segregation was further confirmed by the AUC associated to each of the ROC curves for these ICE blocks, as the 95% confidence intervals associated to each of the AUCs do not cross the 0.5 lower bound (FIGS. 39B, 40B, 41B, 42B, 43B, 44B, and 45B). In order to assess the clinical significance of the classification, after splitting the ICE blocks scores into two groups by median split method, Kaplan-Meier curves show that the two groups represent significantly different groups of high-risk of BCR vs low-risk of BCR (FIGS. 39C, 40C, 41C, 42C, 43C, 44C, and 45C).

Example 15
KNN Models for Tumor Upgrading

Methods

Although pure GG3 (i.e. Gleason 3+3) was rarely lethal, some GG3 cancers were associated with clinically metastatic disease. In this example, a signature was developed based on post-RP prostate tumor samples to identify which have transitioned from low risk, as defined by biopsy GS 6, clinical stage either T1 or T2A, and pretreatment PSA≦10 ng/ml, to high risk tumors, as defined by a pathological GS≧7 or a pathological tumor stage >T3A.

The publically available Memorial Sloan Kettering (MSKCC) Prostate Oncogenome project dataset (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21034) was used for this analysis, which consisted of 131 primary tumor microarray samples (Affymetrix Human Exon 1.0 ST array). Information on Tissue samples, RNA extraction, RNA amplification and hybridization can be found as found in, for example, Taylor B S, et al. (2010, Cancer Cell, 18:11-22). These samples were preprocessed using frozen Robust Multiarray Average (IRMA), with quantile normalization and robust weighted average summarization (see McCall M N, et al., 2010, Biostatistics, 11:242-53 McCall M N, et al., 2011, Bioinformatics, 12:369). Of these patients, 56 net the low risk specification defined above. These patient samples were randomly partitioned into a training (n=29) and testing set (n=27) in a manner which ensures the number of cases and controls remained proportional (Table 25).

The 1,411,399 expression features on the array were filtered to remove unreliable probe sets using a cross hybridization and background filter. The cross hybridization filter removes any probe sets which were defined by Affymetrix to have cross hybridization potential (class 1), which ensures that the probe set was measuring only the expression level of only a specific genomic location, Background. filtering removes features with expression levels lower than the median expression level of the background probe sets. These filters reduced the number of features to 891,185. The training set was further processed using median fold difference (MFD>1.4) filter to 157 genomic features then ranked by T-Test P-value. The top 16 features (Table 26) of the training set were used for modeling a KNN classifier (k=3, Euclidean distance).

Results

The KNN model (hereafter called KNN16) was applied to the testing set and analyzed for its ability to distinguish tumors which underwent upgrading from those that remained low risk (FIG. 46). The KNN16 score, which ranges from 0 to 1, is the percentage of the 3 closest training set patients which upgraded as defined by biopsy (Gleason <6, PSA≦10 ng/ml, clinical stage T1 or T2A) transitioning to a higher risk tumor following RP (pathological GS≧7 or a pathological tumor stage >T3A). The higher the KNN16 score, the more likely the patient will experience an upgrading event. As depicted by the non-overlap of the notches for the discrimination plots for both groups (FIG. 46), the low-risk and upgraded groups were significantly different. Additionally, KNN16 (AUC=0.93) had a better ability to discriminate upgraded patients compared to the clinical factors: pretreatment PSA (preTxPSA, AUC=0.52), clinical tumor stage (c1 Stage, AUC=0.63), and patient age (AUC=0.56) (FIG. 47). In terms of accuracy, the model performed with an accuracy of 81% (P-value <0.005) over an accuracy of 56%, achieved by labeling all samples with the majority class (null model).

In order to assess how the expression profiles group, clustering analysis was also performed for the pooled samples from training and testing sets (n 56) (FIG. 48). The 157 genomic features were subjected to a T-Test filter (P-value <0.05) resulting in 98 features. The two distinct clusters observed, one mostly corresponding to samples which had upgrading and the other corresponding mostly to low risk samples, confirm the ability of the selected features to discriminate between low-risk and upgraded samples.

The results based on this signature show that the selected markers have the potential to provide more accurate risk stratification than predictive models based only on clinical parameters, and identify patients who should consider definitive local therapy rather than AS.

Example 16
Non-Coding RNAs Differentially Expressed Through Lung and Colorectal Cancer

Data Sets and Methodology

Lung Samples

The cohort contains 40 samples corresponding to 20 tumor samples and their paired normal tissue. Methodology on the generation and processing of samples was disclosed in Xi L et al (2008, Nucleic Acids Res, 36:6535-47). Files with raw expression values for each sample were publicly available at http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE12236.

Colorectal Samples

The cohort contains 173 samples, 160 of which correspond to tumor and the remaining 13 correspond to normal colonic mucosa biopsy. Methodology on the generation and processing of samples was disclosed in Sveen A, et al. (2011, Genome Med, 3:32). Files with raw expression values for each sample were publicly available at http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE24551.

Normalization and Summarization

Dataset normalization and summarization was performed with fRMA (McCall M N, et al., 2010, Biostatistics, 11:242-53). The fRMA algorithm relates to the RMA (Irizarry R A, et al., 2003, Biostatistics, 4:249-64) with the exception that it specifically attempts to consider batch effect during data summarization and was capable of storing the model parameters in so called frozen vectors. fRMA then uses these frozen vectors to normalize and summarize raw expression probes into so-called probes selection regions (PSRs) in log 2 scale. The frozen vectors negate the need to reprocess the entire data set when new data was received in the future. For both colorectal and lung samples, batches were defined based on the date used to measure the expression on the samples as provided in the raw data. In the case of lung samples, a custom set of frozen vectors was generated by randomly selecting 6 arrays from each of 4 batches in the data set; one batch was discarded from the vector creation due to the small number of samples in that batch (McCall M N, et al., 2011, Bioinformatics, 12:369). For the colorectal samples, a custom set of frozen vectors was generated by randomly selecting 4 arrays from each of 24 batches in the data set. Seventeen batches were discarded from the vector creation due to the small number of samples (McCall M N, et al., 2011, Bioinformatics, 12:369).

Filtering

Cross hybridization and background filtration methods were applied to all PSRs on the array in order to remove poorly behaving PSRs. Two sources of cross-hybridization were used for filtering: (i) probe sets defined as cross-hybridizing by affymetrix (http://www.affymetrix.com) and (ii) probe sets defined as “unreliable” by the xmapcore R package (http://xmap.picr.man.ac.uk). The cross hybridization filters reduce the number of PSRs in the analysis from 1,432,150 to 1,109,740.

PSRs with associated expression levels at or below the chip's background expression level did not contain reliable expression information. The background expression of the chip was calculated by taking the median of the linear scale expression values of the 45 anti-genomic background PSRs (Affymetrix Technical Note, 2011). For any type of comparison (e.g. normal tissue versus tumor), if the median expression of both groups was less than the background expression level, then the PSR was removed from further analysis. It should be made clear that, if the expression level for a PSR tended to be above the background threshold in one group but not the other, the PSR remained in the analysis as this could be a sign of a genuine biological difference between the two groups.

Unsupervised Analysis

A PSR was defined as differentially expressed between two groups if the median fold difference was greater or equal than 1.5. For those PSRs complying to that threshold, assessment of the ability to segregate between two groups was done using multidimensional scaling (MDS). MDS plots were shown to visualize the differences between the marker expression levels of two groups in three dimensions. The Pearson distance metric was used in these MDS plots, and the permanova test was used to assess the significance of the segregation (http://cran.r-project.org/web/packages/vegan/index.html).

Annotation of Probe Sets (PSRs)

Ontology Enrichment Analysis

DAVID Bioinformatics tool was used to assess enrichment of ontology terms (Huang da W, et al., 2009, Nat Protoc, 4:44-57; Huang da W, et al., 2009, Nucleic Acids Res, 37:1-13)

Results

Non-Coding RNAs Differentially Expressed Between Normal Tissue and Lung Cancer

Based on the methodology described above, and after filtering 480,135 PSRs because of low expression values compared to background (17.18 threshold), the differential expression of all remaining PSRs was tested. 3,449 PSRs were found to have a Median Fold Difference (MFD) greater or equal than 1.5 (Table 27 provides the top 80 non-coding PSRs). Of these, 1,718 PSRs (˜50%) were of non-coding nature (i.e. falling in regions of the genome other than protein-coding regions). Furthermore, ˜35% of the PSRs (1,209/3,449) fall within non-coding parts of a protein-coding gene such as UTRs and introns.

Additionally, ˜4% of the PSRs were found to overlap with 202 transcripts that did not encode for a functional protein. The distribution of these non-coding transcripts, according to Ensembl annotation (http://www.ensembl.org), were as follows: 79 “processed transcript”, 43 “retained intron”, 32 “large intergenic non-coding RNA”, 23 “antisense”, 11 “pseudogene”, 10 “non-sense mediated decay”, 2 “non_coding”, 1 “sense intronic” and 1 “miRNA”.

Most of the PSRs were found within the boundaries of a gene, with only ˜6% of PSRs (207/3449) being intergenic. In total, 1,205 genes were found to overlap with the PSRs. Ontology enrichment analysis of the genes corrected for multiple testing shows multiple cellular processes expected to be found significantly enriched in the differentiation between normal adjacent and tumor tissues, including cell division, cell adhesion and regulation for muscle development.

The utility of the differentially expressed non-coding features can be seen from their ability to separate normal versus tumor cancer samples using unsupervised techniques (FIG. 49A). The multidimensional scaling (MDS) plot shows that these non-coding features generate a clear segregation between the normal samples and the matched tumor samples; the segregation was found to be statistically significant (p<0.001).

Non-Coding RNAs Differentially Expressed Between Normal Tissue and Colorectal Cancer

Based on the methodology described above, and after filtering 672,236 PSRs because of low expression values compared to background (33.3 threshold), the differential expression of all remaining PSRs was tested. 4,204 PSRs were found to have a Median Fold Difference (MFD) greater or equal than 1.5 (Table 28 provides the top 80 non-coding PSRs). Of these, 2,949 PSRs (˜70%) were of non-coding nature (i.e. falling in regions of the genome other than protein-coding regions). Furthermore, ˜55% of the PSRs (2,354/4,204) fall within non-coding parts of a protein-coding gene such as UTRs and introns.

Additionally, ˜8% of the PSRs were found to overlap with 368 transcripts that did not encode for a functional protein. The distribution of these non-coding transcripts distribute, according to Ensembl annotation (http://www.ensembl.org), were as follows: 143 “processed transcript”, 141 “retained intron”, 26 “large intergenic non-coding RNA”, 25 “non-sense mediated decay”, 18 “pseudogene”, 9 “antisense”, 2 “sense intronic”, 2 “miscRNA”, 1 “snRNA” and 1 “non_coding”.

Most of the PSRs were found within the boundaries of a gene, with only ˜5% of the PSRs (209/4204) being intergenic. In total, 1,650 genes were found to overlap with the PSRs. Ontology enrichment analysis of the genes corrected for multiple testing shows cell adhesion, collagen metabolism and catabolism to be significantly enriched in the differentiation between normal adjacent and tumor tissues; the differential expression of features associated to collagen processes was in agreement with previous studies in colorectal carcinogenesis (Skovbjerg H, et al., 2009, BMC Cancer, 9:136).

The utility of the differentially expressed non-coding features can be seen from their ability to separate normal versus tumor cancer samples using unsupervised techniques (FIG. 49B). The multidimensional scaling (MDS) plot shows that these non-coding features generate a clear segregation between the normal and tumor samples; the segregation was found to be statistically significant (p<0.001).

Non-Coding RNAs Differentially Expressed Between Different Stages of Lung Cancer

Based on the methodology described above, the ability of non-coding RNAs to discriminate between two groups of lung tumor tissues was explored. In particular, the non-coding RNAs were inspected for their discriminatory ability between early stage lung cancer (12 stage I samples) versus more advanced stages of cancer (3 stage II patients and 5 stage III patients, collectively called the II+III group). After filtering 477,912 PSRs because of low expression values compared to background (17.18 threshold), the differential expression of all remaining PSRs was tested. 618 PSRs were found to have a Median Fold Difference (MFD) greater or equal than 1.5 (Table 29 provides the top 80 non-coding PSRs). Of these, 439 PSRs (71%) were of non-coding nature (i.e. falling in regions of the genome other than protein-coding regions). Furthermore, ˜38% of the PSRs (235/618) fell within non-coding parts of a protein-coding gene such as UTRs and introns.

Additionally, ˜11% of the PSRs were found to overlap with 67 transcripts that did not encode for a functional protein. The distribution of these non-coding transcripts distribute, according to Ensembl annotation (http://www.ensembl.org), were as follows: 19 “processed transcript”, 11 “retained intron”, 9 “large intergenic non-coding RNA”, 15 “pseudogene”, 6 “non-sense mediated decay”, 3 “antisense”, 1 “misc RNA”, 1 “retrotransposed” and 1 “miRNA”.

Most of the PSRs were found within the boundaries of a gene; however, approximately 17% of the PSRs (104/618) fell in intergenic regions. In total, 472 genes were found to overlap with the PSRs. Ontology and pathway enrichment analysis of the genes corrected for multiple testing shows no processes or pathways found to be significantly enriched in the differentiation between tumor stages. Given that most of the differentially expressed features were of non-coding nature, and as enrichment analyses greatly rely on the annotation of protein-coding genes, these results suggest that further functional studies on non-coding RNAs were critical for understanding the biology that was involved in the progression of lung cancer.

The utility of the differentially expressed non-coding features can be seen from their ability to separate tumor stage I versus II+III cancer samples using unsupervised techniques (FIG. 50A). The multidimensional scaling (MDS) plot shows that these non-coding features generate a better segregation between different stages than coding features; the segregation was found to be statistically significant (p<0.001).

XIST Non-Coding RNA was Differentially Expressed Between Stages II and III of Colorectal Cancer.

The ability of non-coding RNAs to discriminate between two groups of colorectal tumor tissues was explored. In particular, the non-coding RNAs were inspected for their discriminatory ability between stage II (90 samples) and stage III (70 samples) colorectal cancer samples. Based on the methodology described above, and after filtering 703,072 PSRs because of low expression values compared to background (33.3 threshold), the differential expression of all remaining PSRs was tested. 35 PSRs were found to have a Median Fold Difference (MFD) greater or equal than 1.5 (Table 30 list the non-coding PSRs found with this threshold). Of these, 25 PSRs (71%) were of non-coding nature (i.e. falling in regions of the genome other than protein-coding regions). In addition to two of these non-coding PSRs falling within the UTRs of protein-coding genes DDX3Y (DEAD (Asp-Glu-Ala-Asp) box polypeptide 3) and KDM5D (lysine (K)-specific demethylase 5D), both Y-linked, the remaining 23 differentially expressed non-coding PSRs correspond to the X-inactive-specific transcript (XIST), a long non-coding RNA gene residing in the X chromosome that plays an essential role in X-chromosome inactivation (Brown C J, 1991, Nature, 349:38-44). FIG. 50B illustrates the density of a PSR representative of XIST. As seen there, stage II samples tend to have low expression values whereas stage III samples tend to have high expression values of XIST, suggesting that this gene gets overexpressed through colorectal cancer progression. Highly variable expression of this lncRNA has been detected within BRCA1 primary tumors in breast cancer (Vincent-Salomon A, et al., 2007, Cancer Res, 67:5134-40); a recent study shows that XIST presents DNA copy-number variations in microsatellite-unstable sporadic colorectal carcinomas, a particular type of tumor generally regarded as diploid (Lassman S, et al., 2007, J Mol Med (Berl), 85:293-304). Interestingly, 38 of the 160 colorectal tumor samples used for this example correspond to microsatellite-unstable colorectal carcinomas. These suggest that the DNA copy-number variation that involves XIST might have an impact on the dosage of the gene at the transcript level that was detected in this analysis due to the inclusion of microsatellite-unstable tumor samples.

Example 17
Comparison of Genomic Signatures with Coding and Non-Coding Features and Genomic Signatures with Coding Features

The performance of several previously published classifiers can be compared to new classifiers based on the publicly available genomic and clinical data generated by the Memorial Sloan-Kettering Cancer Center (MSKCC) Prostate Oncogenome Project (Taylor et al., 2010) available from GEO Omnibus at http://www.ncbi.nlm.nih.gov/geo/ series GSE21034. The previously published classifiers are designed for predicting Biochemical recurrence (BCR) or other endpoint that indicates disease progression based solely on coding features. The newly developed classifiers are designed for predicting BCR and are composed of coding and non-coding features. CEL files for the arrays from the dataset are pre-processed using the fRMA algorithm. The normalized and summarized expression values can be used as input for ranking methods such as Wilcoxon P-test or Median Fold Difference, and a ranking of the features can be generated. This ranking of coding and non-coding features can be used as input to train multiple machine learning algorithms (e.g., Support Vector Machines, K-Nearest Neighbors, Random Forest) that generate classifiers. Classifiers can be selected based on the performance of one or more metrics from Area under the ROC curve (AUC), Accuracy, Sensitivity, Specificity, Negative Predictive Value (NPV) and Positive Predictive Value (PPV). The performance of previously published classifiers and the new classifier can be compared by one or more of the metrics disclosed herein. The newly developed classifiers, containing both coding and non-coding features, that outperform the previously published coding classifiers by a statistically significant difference of the metrics disclosed herein, either measured by a P-value threshold of ≦0.05 or non-overlapping confidence intervals for the metric of performance applied can be used in any of the methods, systems, or kits disclosed herein.

Example 18
Generation of Prognostic Genomic Signatures with Coding and Non-Coding Features for Gastric Cancer

Based on the publicly available genomic and clinical data from GEO Omnibus, which can be downloaded at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27342 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13195, a newly developed classifier can be created for discriminating different stages of gastric cancer and can be composed of coding and non-coding features. CEL files for the arrays from the dataset can be pre-processed using the fRMA algorithm. The normalized and summarized expression values can be used as input for ranking methods such as Wilcoxon test or Median Fold Difference (MFD), and a ranking of the features can be generated. This ranking of coding and non-coding features can be used as input to train multiple machine learning algorithms (e.g., Support Vector Machines, K-Nearest Neighbors, and Random Forest) that generate classifiers. Selection of the classifiers for gastric cancer can be based on the performance of one or more metrics from Area under the ROC curve (AUC), Accuracy, Sensitivity, Specificity, Negative Predictive Value (NPV) and Positive Predictive Value (PPV). The newly developed classifier, containing both coding and non-coding features, can show prognostic ability as supported by the statistical significance of the metrics applied.

Example 19
Generation of Prognostic Genomic Signatures with Coding and Non-Coding Features for Neuroblastoma

Based on the publicly available genomic and clinical data from GEO Omnibus, which can be downloaded at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27608, a newly developed classifier can be created for discriminating different stages of neuroblastoma and can be composed of coding and non-coding features. CEL files for the arrays from the dataset can be pre-processed using the fRMA algorithm. The normalized and summarized expression values can be used as input for ranking methods such as Wilcoxon test or Median Fold Difference, and a ranking of the features can be generated. This ranking of coding and non-coding features can be used as input to train multiple machine learning algorithms (e.g., Support Vector Machines, K-Nearest Neighbors, and Random Forest) that generate classifiers. Selection of the classifier for neuroblastoma can be based on the performance of one or more metrics from Area under the ROC curve (AUC), Accuracy, Sensitivity, Specificity, Negative Predictive Value (NPV) and Positive Predictive Value (PPV). The newly developed classifier for neuroblastoma, containing both coding and non-coding features, can show prognostic ability as supported by the statistical significance of the metrics applied.

Example 20
Generation of Prognostic Genomic Signatures with Coding and Non-Coding Features for Glioma

Based on the publicly available genomic and clinical data from GEO Omnibus, which can be downloaded at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30472, a newly developed classifier is created for discriminating different grades of glioma and can be composed of coding and non-coding features. CEL files for the arrays from the dataset can be pre-processed using the fRMA algorithm. The normalized and summarized expression values can be used as input for ranking methods such as Wilcoxon test or Median Fold Difference, and a ranking of the features can be generated. This ranking of coding and non-coding features can be used as input to train multiple machine learning algorithms (e.g., Support Vector Machines, K-Nearest Neighbors, and Random Forest) that generate classifiers. Selection of the classifiers for glioma can be based on the performance of one or more metrics from Area under the ROC curve (AUC), Accuracy, Sensitivity, Specificity, Negative Predictive Value (NPV) and Positive Predictive Value (PPV). The newly developed classifier, containing both coding and non-coding features, can show prognostic ability as supported by the statistical significance of the metrics applied.

TABLE 1

Abbreviation
Description

AUC
Area Under Curve

BCR
Biochemical Recurrence

CM
Clinical Model

CR
Clinical Recurrence

ECE
Extra Capsular Extensions

FFPE
Formalin Fixed Paraffin Embedded

fRMA
Frozen Robust Multiarray Average

GC
Genomic Classifier

GCC
Genomic Clinical Classifier

IQR
Interquartile Range

LNI
Lymph Node Invasion

MDA
Mean Decrease in Accuracy

MDG
Mean Decrease in Gini

MSE
Mean Squared Error

NED
No Evidence of Disease

OOB
Out of Bag (sampling)

PCSM
Prostate Cancer Specific Mortality

PSA
Prostate Specific Antigen

PSR
Probe Selection Region

RP
Radical Prostatectomy

SVI
Seminal Vesicle Invasion

SMS
Surgical Margin Status

UTR
Untranslated Region

TABLE 2

Primary

tumour
Metastasis

N

131
19

Median age at Dx

58
58

(years)

Pre-op PSA (ng/ml)
<10
108
7

≧10 <20
16
1

≧20
6
9

NA
1
2

Pathological Gleason
≦6
41
0

Score
7
74
2

≧8
15
7

NA
1
10

Pathological Stage
T2
85
1

T3
40
7

T4
6
2

NA
0
9

TABLE 3

Name
Definition

Processed Transcript
Non-coding transcript that does not contain an ORF.

Retained Intron
Non-coding transcript containing intronic sequence.

Non-sense Mediated Decay
The transcript is thought to go non-sense mediated decay, a process

(NMD)
which detects non-sense mutations and prevents the expression of

truncated ande erroneous proteins.

LincRNA
Large Intergenic Non-Coding RNA, or Long non-coding RNA,

usually associated with open chromatin signatures such as histone

modification sites.

Antisense
Non-coding transcript believed to be an antisense product used in

the regulation of the gene to which it belongs.

Processed Pseudogene
Non-coding Pseudogene produced by integration of a reverse

transcribed mRNA into the genome.

Unprocessed Pseudogene
A non-coding pseudogene arising from gene duplication.

Pseudogene
A non-coding sequence similar to an active protein

MiRNA
MicroRNA is single stranded RNA, typically 21-23 by long, that is

thought to be involved in gene regulation (specially inhibition of

protein expression)

Non Coding
Transcript does not result in a protein product

Sense Intronic
Has a long non-coding transcript in introns of a coding gene that

does not overlap any exons (from VEGA definition)

TABLE 4

MFD: Median Fold Difference in this dataset in various comparisons.

Probe set

P-
Adjusted P-

Gene
ID
Type
Comparison
MFD
value
value

H19
3359088
Intron
Metastatic Vs Primary
1.86
<0.3
1

MALAT1
3335167
Exon
Normal Vs Primary
1.56
<0.1
1

MALAT1
3335168
Exon
Normal Vs Primary
1.73
<0.2
1

MALAT1
3335176
Exon
Normal Vs Primary
1.78
<0.05
1

MALAT1
3335179
Exon
Normal Vs Primary
1.59
<0.7
1

MALAT1
3335194
Exon
Metastatic Vs Primary
0.53
0.000
0.029

MALAT1
3335196
Exon
Metastatic Vs Primary
0.63
0.000
0.001

PCA3
3175539
Exon
Metastatic Vs Primary
1.50
<0.02
1

PCA3
3175540
Exon
Normal Vs Primary
1.90
0.000
1.36E−11

PCA3
3175545
Intron
Normal Vs Primary
1.53
0.000
2.33E−09

PCGEM1
2520743
Exon
Metastatic Vs Primary
0.63
<0.002
0.05

PCGEM1
2520744
Exon
Metastatic Vs Normal
1.53
<0.3
1

PCGEM1
2520744
Exon
Normal Vs Primary
0.64
<0.002
0.07

PCGEM1
2520745
Intron
Normal Vs Primary
1.52
0.000
0.04

PCGEM1
2520746
Exon
Metastatic Vs Normal
1.61
<0.5
1

PCGEM1
2520749
Exon
Metastatic Vs Normal
1.55
<0.2
1

PCGEM1
2520749
Exon
Metastatic Vs Primary
0.62
0.000
0.01

TABLE 5

SVI: Seminal Vesicle Invasion ECE: Extracapsular Extension,

SMS: Surgical Margin Status, LNI: Lymph node Involvement,

PreTxPSA: Pre-operative PSA, PGS: Pathological Gleason Score.

Classifier

Coding
Non-Coding
Non-Exonic

Odd
P-
Odd
P-

P-

Predictor
Ratio
value
Ratio
value
Odd Ratio
value

KNN Positive*
2.49
0.63
15.89
0.14
29.74
0.05

SVI
0.26
0.42
0.29
0.44
0.52
0.69

SMS
0.64
0.73
1.06
0.97
0.89
0.94

LNI
32.37
0.05
22.7
0.1
55.74
0.09

log2(Pre-Op PSA)
0.15
0.01
0.09
0.02
0.06
0.02

ECE
41.46
0.04
225.84
0.06
356.81
0.06

Path Gleason Score
8.65
0.03
6.48
0.06
6.65
0.07

*KNN Positive: Metastatic-like

TABLE 6

SEQ ID

NO.
Type
Sequence

1
CODING
CCTGCCATGTACGTCGCCATTCAAGCTGTGCTCTCCCTCTATGCCTC

TGGCCGCACGACA

2
CODING
GGCTCAGAGCAAGCGAGGGATCCTAACTCTCAAATACCCCATTGAA

CACGGC

3
CODING
GGATTCAGGTGATGGCGTCACCCACAATGTCCCCATCTATGAAGGC

TATGCCCTGCCCCATGCCATCATGCGCCTGGACTTGGCTGGCCGTG

ACCTCACGGACTACCTCATGAAGATCCTCACAGAGAGAGGCTATTC

CTTTGTGAC

4
CODING
TGAAGGTGGTATCATCGGTCCTGCAGCTT

5
CODING
CTGCGTGTAGCACCTGAAGAGCACCCCACCCTGCTCACAGAGGCTC

CCCTAAATCCCAAGGCCAACAGGGAAAAGATGACCCAG

6
CODING
CATCCGCATCAACTTCGACGTCACGG

7
CODING
GCATGGAGTCCGCTGGAATTCATGAGACAACCTACAATTCCATCAT

GAAGTGTGACATTGACATCCGTAAGGACTTATATGCCAAC

8
CODING
TGCTCAGAAAGTTTGCCACCTCATGGGAATTAATGTGACAGATTTC

ACCAGATCCATCCTCACTCCTCGTATCAAGGTTGGGCGAGATGT

9
CODING
TTTGGCCAAGGCAACATATGAGCGCCTTTTCCGCTGGATACTCACC

CGCGTGAACAAAGCCCTGGACAAGACCCATCGGCAAGGGGCTTCC

TTCCTGGGGATCCTGGATATAGCTGGATTT

10
CODING
CTATAATGCGAGTGCCTGGCTGACCAAGAATATGGACCCGCTGAAT

GACAACGTGACTTCCCTGCTCAATGCCTCCTCCGACAAGTTT

11
CODING
AGAGAGAAATTGTGCGAGACATCAAG

12
CODING
GGAGGAGTCCCAGCGCATCAACGCCAACCGCAGGAAGCTGCAGCG

GGAGCTGGATGAGGCCACGGAGAGCAACGAGGCCATGGGCCGCGA

GGTGAACGCACTCAAGAGC

13
CODING
ATCGGGAGGACCAGTCCATTCTATGCAC

14
CODING
AAGCAGCTTCTACAAGCAAACCCGATTCTGGAGGCTTTCGGCAACG

CCAAAACAGTGAAGAACGACAACTCCTCA

15
CODING
GGAGGGCTTCAACAACTACACCTTCCTCTCCAATGGCTTTGTGCCC

ATCCCAGCAGCCCAGGATGATGAGATGTTCCAGGAAACCGTGGAG

GCCATGGCAATCATGGGTTTCAGCGAGGAGGA

16
CODING
ACCAGTCAATCAGGGAGTCCCGCCACTTCCAGATAGACTACGATGA

GGACGGGAACTGCTCTTTAATTATTAGTGATGTTTGCGGGGATGAC

GATGCCAAGTACACC

17
CODING
AAGGTCTGGAGGACGTAGAGTTATTGAAAATGCAGATGGTTCTGA

GGAGGAAACGGACACTCGAGACGCAGACTTCAATGGAACCAAGGC

18
CODING
CCTGGACCAGATGGCCAAGATGACGGAGAGCTCGCTGCCCAGCGC

CTCCAAGACCAAGAAGGGCATGTTCCGCACAGTGGGGCAGCTGTA

CAAGGAGCAGCTGGGCAAGCTGATGACCACGCTACGCAACACCAC

GCCCAACTTC

19
CODING
GGAAGATGCCCGTGCCTCCAGAGATGAGATCTTTGCC

20
CODING
CTTCACGAGTATGAGACGGAACTGGAAGACGAGCGAAAGCAACGT

GCCCTGGC

21
CODING
CAAGCTGGATGCGTTCCTGGTGCTGGAGCAGCTGCGGTGCAATGGG

GTGCTGGAAGGCATTCGCATCTGCCGGCAGG

22
CODING
CTGCTAGAAAAATCACGGGCAATTCGCCAAGCCAGAGAC

23
CODING
CACCACGCACACAACTACTACAATTCCGCCTAG

24
CODING
CCTGTTCACGGCCTATCTTGGAGTCGGCATGGCAAACTTTATGGCT

GAG

25
CODING
GCCAAACTGCGGCTGGAAGTCAACATGCAGGCGCTCAAGGGCCAG

TTCGAAAGGGATCTCCAAGCCCGGGACGAGCAGAATG

26
CODING
GCTGAAACGGAAGCTGGAGGGTGATGCCAGCGACTTCCACGAGCA

GATCGCTGACCTCCAGGCGCAGATCGCAGAGCTC

27
CODING
CCAGCTGGATGGAGATTCTTCTCAAATCTGATGGACTCAGGACGTT

GCAATCTGTGTGGGGAAGAGAGC

28
CODING
GCTACTCTAGCTCGCATTGACCTGGAGCGCAGAATTGAATCTCTCA

ACGAGGAGATCGCGTTCCTTAAGAAAGTGCA

29
CODING
AGGTGACGGTGCTGAAGAAGGCCCTGGATGAAGAGACGCGGTCCC

ATGAGGCTCAGGTCCAGGAGATGAGGCAGAAACACGCACAGGCGG

T

30
CODING
CCCAGAGCGGAAGTACTCAGTCTGGATCGGGGGCTCTATCCTGGCC

TCTCTCTCCACCTTCCAGCAGATGTGGATCAGCAAGCCTGAGTATG

ATGAGGCAGGGCCCTCCATTGTCCACAGGAAGTGCT

31
CODING
TTGCCAGCACCGTGGAAGCTCTGGAAGAGGGGAAGAAGAGGTTCC

AGAAGGAGATCGAGAACCTCACCCAGCAGTACGAGGAGAAGGCGG

CCGCTTATGATAAACTGGAAAAGACCAAGAACAGGCTTCAGCAGG

AGCTGGACGACCTGGTTGTTGATTTGGACAACCAGCGGCAACTCGT

G

32
CODING
GCCATCCCGCTTAGCCTGCCTCACCCACACCCGTGTGGTACCTTCA

GCCCTGGC

33
CODING
GAAAAGGCCAAGAATCTTACCAAGCTGAAAA

34
CODING
GCAGCTGACCGCCATGAAGGTGATTCAGAGGAACTGCGCCGCCTA

CCT

35
CODING
CGCAGAAGGGCCAACTCAGTGACGATGAGAAGTTCCTCTTTGTGGA

CAAAAACTTCATCAACAGCCCAGTGGCCCAGGCTGACTGGGCCGCC

AAGAGACTCGTCTGGGTCCCCTCGGAGAAGCAGGGCTTCGAGGCA

GCCAGCATTAAGGAGGAGAAGGGGGATGAGGTGGTTGTGGAGCTG

GTGGAGAATGGCAAGAAGGTCACGGTTGGGAAAGATGACATCCAG

AAGATGAACCCACCCAAGTTCTCCAAGGTGGAGGACATGGCGGAG

CTGACGTGCCTCAACGAAGCCTCCGTGCTACACAACCTGAGGGAGC

GGTACTTCTC

36
CODING
TGAGAGCGTCACAGGGATGCTTAACGAGGCCGAGGGGAAGGCCAT

TAAGCTGGCCAAGGACGTGGCGTCCCTCAGTTC

37
CODING
AAAACGGGCAATGCTGTGAGAGCCATTGGAAGACTGTCCTC

38
CODING
CTACGAGATCCTGGCGGCGAATGCCATCCCCAA

39
CODING
CTGCAACTTGAGAAGGTCACGGCTGAGGCCAAGATCAAG

40
CODING
AGAACCCCACAGACGAATACCTGGAGGGCATGATGAGCGAGGCCC

CGGGGCCCATCAACTTCACCATGTTCCTCACCATGTTTGGGGAGAA

GCTGAACGGCACGGACCCCGAGGATGTGATTCGCAACGCCTTTGCC

TGCTTCGACGAGGAAGCCTCA

41
CODING
CCACATCTCTTTCTTATTGGCTGCATTGGAGTTAGTGGCAAGACGA

AGTGGGATGTGCTCGATGGGGTGGTTAGACGGCTGTTCAAA

42
CODING
GGTCAAGGAACTCAAGGTTTCGCTGCCGTGGAGTGGATGCCAATAG

AAACTGG

43
CODING
TTACCGGCGGGGAGCTGTTTGAAGACAT

44
CODING
GCAGATGATGGCGGCTTGACTGAACAGAGTG

45
CODING
TAGGGCCTGAGCTGCCTATGAATTGGTGGATTGTTAAGGAGAGGGT

GGAAATGCATGACCGATGTGCTGGGAGGTCTGTGGAAATGTGTGA

CAAGAGTGTGAGTGTGGAAGTCAGCGTCTGCGAAACAGGCAGCAA

CACAGAGGAGTCTGTGAACGACCTCACACTCCTCAAGACAAACTTG

AATCTCAAAGAAGTGCGGTCTATCGGTTGTGGAGATTGTTCTGTTG

ACGTGACCGTCTGCTCTCCAAAGGAGTGCGCCTCCCGGGGCGTGAA

CACTGAGGCTGTTAGCCAGGTGGAAGCTGCCGTCATGGCAGTGCCT

CGTACTGCAGACCAGGACACTAGCACAGATTTGGAACAGGTGCAC

CAGTTCACCAACACCGAGACGGCCACCCTCATAGAGTCCTGCACCA

ACACTTGTCTAAGCACTTTGGACAAGCAGACCAGCACCCAGACTGT

GGAGACGCGGACAGTAGCTGTAGGAGAAGGCCGTGTCAAGGACAT

CAACTCCTCCACCAAGACGCGGTCCATTGGTGTTGGAACGTTGCTT

TCTGGCCATTCTGGGTTTGACAGGCCATCAGCTGTGAAGACCAAAG

AGTCAGGTGTGGGGCAGATAAATATTAACGACAACTATCTGGTTGG

TCTCAAAATGAGGACTATAGCTTGTGGGCCACCACAGTTGACTGTG

GGGCTGACAGCCAGCAGAAGGAGCGTGGGGGTTGGGGATGACCCT

GTAGGGGAATCTCTGGAGAACCCCCAGCCTCAAGCTCCACTTGGAA

TGATGACTGGCCTGGATCACTACATTGAGCGTATCCAGAAGCTGCT

GGCAGAACAGCAGACACTGCTGGCTGAGAACTACAGTGAACTGGC

AGAAGCTTTCGGGGAACCTCA

46
CODING
ATTGGCCTGGACCAGATCTGGGACGACCTCAGAGCCGGCATCCAGC

AGGTGTACACACGGCAGAGCATGGCCAAGTCCA

47
CODING
CAGTAGAGCCAAGTTGGGAGGTGGTGAAAA

48
CODING
CTGTGTCCAGTCAGGCTGCGCAGGCG

49
CODING
GTTGGTGGTTCGTCAGCACTGCCGAGGAGCAAGGCTGGGTCCCTGC

AACGTGCCTCGAAGGC

50
CODING
GGGGCAGACACTACCGAAGATGGGGATGAGAAGAGCCTGGAGAA

ACAGAAGCACAGTGCCACCACTGTGTTCGGAGCAAACACCCCCA

51
CODING
TATGCGCTGATGGAGAAAGACGCCCTCCAGGTGGCC

52
CODING
GGTTAGAGTGGACAGCCCCACTATG

53
CODING
TCCTGGGGGACCAGACGGTCTCAGACAATGAG

54
CODING
GGTGCAGACCGTACTCCATCCCTCCCTGTGAGCACCACGTCAACGG

CTCCCGGCC

55
CODING
CAGAGTCCGCCCAGTCATGCACAGACTCCAGTGGAAGTTTTGCCAA

ACTGAATGGTCTCTTTGACAGCCCTGTCAAGGAATACCAACAGAAT

ATTGATTCTCCTAAACTGTATAGTAACCTGCTAACCAGTCGGAAAG

AGCTACCACCCAATGGAGATACTAAATCCATGGTAATGGACCATCG

AGGGCAACCTCCAGAGTTGGCTGCTCTTCCTACTCCTGAGTCTACA

CCCGTGCTTCACCAGAAGACCCTGCAGGCCATGAAGAGCCACTCAG

AAAAGGCCCATGGCCATGGAGCTTCAAGGAAAGAAACCCCTCAGT

TTTTTCCGTCTAGTCCGCCACCTCATTCCCCATTAAGTCATGGGCAT

ATCCCCAGTGCCATTGTTCTTCCAAATGCTACCCATGACTACAACA

CGTCTTTCTCAAACTCCAATGCTCACAAAGCTGAAAAGAAGCTTCA

AAACATTGATCACCCTCTCACAAAGTCATCCAGTAAGAGAGATCAC

CGGCGTTCTGTTGATTCCAGAAATACCCTCAATGATCTCCTGAAGC

ATCTGAATGACCCAAATAGTAACCCCAAAGCCATCATGGGAGACA

TCCAGATGGCACACCAGAACTTAATGCTGGATCCCATGGGATCGAT

GTCTGAGGTCCCACCTAAAGTCCCTAACCGGGAGGCATCGCTATAC

TCCCCTCCTTCAACTCTCCCCAGAAATAGCCCAACCAAGCGAGTGG

ATGTCCCCACCACTCCTGGAGTCCCAATGACTTCTCTGGAAAGACA

AAGAGGTTATCACAAAAATTCCTCCCAGAGGCACTCTATATCTGCT

ATGCCTAAAAACTTAAACTCACCAAATGGTGTTTTGTTATCCAGAC

AGCCTAGTATGAACCGTG

56
CODING
TTAGCCATCCTGGTGATAGTGATTATGGAGGTGTACAAATCGTGGG

CCAAGATGAGACTGATGACCGGCCTGAATGTCCCTATGGACCATCC

TGTTA

57
CODING
CCTCCTTCTCAGTAGCAGAGTCCAGTGCCTTGCAGAGCCTGAAGCC

TGGGGA

58
CODING
GTTGCCAGAGGTGTACTGTGTCATCAGCCGCCTTGGCTG

59
CODING
GTGCATCAAGTACATGCGGCAGATCTCGGAGGGAGTGGAGTACAT

CCACAAGCAGGGCATCGTGCACCTGGACCTCAAGCCGGAGAACAT

CATGTGTGTCAACAAGACGGGCACCAGGATCAAGCTCATCGACTTT

GGTCTGGCCAG

60
CODING
TTGGGTCAGTTCCAACATGCCCTGGATGAGCTCCTGGCATGGCTGA

CACACACCGAGGGCTTGCTAAGTGAGCAGAAACCTGTTGGAGGAG

ACCCTAAAGCCATTGAAA

61
CODING
TTTGAAGATTCTGCAACCGGGGCACAGCCACCTTTATAACAACC

62
CODING
TGCTTGCCATATCCAATTGAACACCCCTACCACACACACATCTGTC

GCGGCGCC

63
CODING
TCTGGAGTCAATACCTGGCGAGATCAACTGAGACCAACACAGCTGC

TTCAAAATGTCGCCAGATTCAAAGGCTTCCCACAACCCATCCTTTC

CGAAGATGGGAGTAGAATCAGATATGGAGGACGAGACTACAGCTT

G

64
CODING
AAAGCTGGACAAGATCTGGCCTAAGCTTCGGGTCCTGGCGCGATCT

TCTCCCACTGACAAG

65
CODING
GTAGGAGAGTTGAGTGCTGCAATGGAT

66
CODING
GTTCACCAACCCATGCAAGACCATGAAGTTCATCGTGTGGCGCCGC

TTTAAGTGGGTCATCATCGGCTTGCTGTTCCTGCT

67
CODING
TTCGGATCTACCCTCTGCCGGATGACCCCAGCGTGCCAGCCCCTCC

CAGACAGTTTCGGGAATTACCTGACAGCGTCCCACAGGAATGCACG

GTTAGGATTTACATTGTTCGAGGCTTAGAGCTCC

68
CODING
TCTGGTCTTTGAGAAGTGCGAGCTGGCGACCTGCACTCCCCGGGAA

CCTGGAGTGGCTGGCGGAGACGTCTGCTCCTCCGACTCCTTCAACG

AGGACATCGCGGTCTTCGCCAAGCAG

69
CODING
GTACAGGACAGCCAGCGTCATCATTGCTTTGACTGATGGAG

70
CODING
CTGAGGTCACCCAGTCAGAGATTGCTCAGAAGCAAA

71
CODING
TTTCCACCGCAAAGCATCAGTGATCATGGTAGACGAGCTGCTGTCA

GCCTACCCACACCAGCTTTCCTTCTCTGAGGCTGGCCTTCGAATCAT

GATAACCAGCCACTTTCCCCCCAAGACCCGGCTCTCCATGGCCAGT

CGCATGTTGATCAATGA

72
CODING
CGGCAGCGGTGGAAGGCCCTTTTGTCACCTTGGACATGGAAG

73
CODING
CGGCGGCCCATGGACTCAAGGCTGGAGCACGTGGACTTTGAGTGCC

TTTTTACCTGCCTCAGTGTGCGCCAGCTCATCCGAATCTTTGCCTCA

CTG

74
CODING
TACGATGAGCTGCCCCATTACGGCGGG

75
CODING
TGCGGGACCACAATAGCGAGCTCCGCTTC

76
CODING
CTGCTCGTTGCTCTGTCTCAGTATTTCCGCGCACCAATTCGACTCCC

AGACCATGTTTCCATCCAAGTGGTTGTGGTCCAG

77
CODING
GGCTGTGGTGTCTCTTCATTGGGATTGGAGA

78
CODING
TGCAGGGAGTTCCAGCGAGGAAACTGTGCCCGGGGAGAGACCGAC

TGCCGCTTTGCACACCCCGCAGACAGCACCATGATCGACACAAGTG

ACAACACCGTAACCGTTTGTATGGATTACATAAAGGGGCGTTGCA

79
CODING
GAGCCCAGTGAAGGCCTCATATTCCCCTGGGTTCTGAATATAACTA

GAGCCCCTTAGCCCCAACGGCTTTCCTAAATTTTCCACATCCAAGC

CTAACAGTCTCCCCATGTGTTTGTGTA

80
CODING
GCCTTTGACACCTTGTTCGACCATGCCCCAGACAAGCTGAATGTGG

TGA

81
CODING
GGAGAAGAACCTGCTACAGGAACAGCTGCAGGCAGAGACAGAGCT

GTATGCAGAGGCTGAGGAGATGCGGGTGCGGCTGGCGGCCAAGAA

GCAGGAGCTGGAGGAGATACTGCATGAGATGGAGGCCCGCCTGGA

GGAGGAGGAAGACAGGGGCCAGCAGCTACAGGCTGAAAGGAAG

82
CODING
CTCCTTGAGGAGAGGATTAGTGACTTAACGACAAATCTTGCAGAAG

83
CODING
AAGGGGTTCTGAGGTCCATACCAAGAAGACGGTGATGATCAAGAC

CATCGAGACACGGGATGG

84
CODING
GAAGAAGATCAATGAGTCAACCCAAAATT

85
CODING
GCCAAGGCGAACCTAGACAAGAATAAGCAGACGCTGGAGAAAGA

GAACGCAGACCTGGCCGGGGAGCTGCGGGTCCTGGGCCAGGCCAA

GCAGGAGGTGGAACATAAGAAGAAGAAGCTGGAGGCGCAGGTGC

AGGAGCTGCAGTCCAAGTGCAGCGATGGGGAGCGGGCCCGGGCGG

AGCTCAATGACAAAGT

86
CODING
TCTCTTCCAAATACGCGGATGAGAGGGACAGAGCTGAGGCAGAAG

CCAGGGAGAAGGAAACCAAGGCCCTGTCCCTGGCTCGGGCCCTTG

AAGAGGCCTTGGAAGCCAAAGAGGAACTCGAGCGGACCAACAAAA

TGCTCAAAGCCGAAATGGAAGACCTGGTCAGCTCCAAGGATGACG

TGGGCA

87
CODING
GCCTCTTCTGCGTGGTGGTCAACCCCTATAAACACCTGCCCATCTAC

TCGGAGAAGATCGTCGACATGTACAAGGGCAAGAAGAGGCACGAG

ATGCCGCCTCACATCTACGCCATCGCAGACACGGCCTACCGGAGCA

TGCTTCAA

88
CODING
TGAAGCCCCACGACATTTTTGAGGCCAACGACCTGTTTGAGAACAC

CAACCATACACAGGTGCAGTC

89
CODING
CTTGAGTCCCTGAGAATGCCTAGCAAAGTCCTCAACTTACTTAATTT

CAGATATGTCACCTCCTAATCTGGGTCCAAGGAGTATAATATTTTT

AATGAGTCAAAAATCCAACTCAGATTGACCTAAAATATATTTATCT

TCTTTGCACACTTAAAAAATCCAGGAGCACCCCAAAATAGACATGT

ACCGTTATATTAAGTAAGCAGGAGACTTAGGATTTGTGCTGTAGCC

ACAAGAAAGACAGTGATCAGTGATATCAAACATCAGGAATCAGCC

TTTATGTAACATAACAGCTGTCCTCCTATGGTGAAAGGTTCAAATG

TAGTGAAGGTATAACCTATATTGACTGAGATTTCCCTTTTAGGTAGT

GCCTTATCTCTATTACTAGTGTTAAAGGAATAAGGAATCTATGAAG

GACAGGGAGCAGCTCTGGTCTGTCAATCTCAGCCACCTGTTTGATA

TCACAGAGAAGATACTCGGAGGATTGTTGGAATGTATATAGTTTAG

TAAGAAGTGGGTAAGAAAGAGGGTCTTAATTACTGAGCACTTATTA

TGTATTAGGTTCTTTGCCAGATGTTTTTACATATATAAACTCATTTC

AGAAAACTTATTTAAAGTAAATGGGGCCGGGTATGGTGGTTCATGC

CTGGAATCCTAGCACTTTGGGAGGCTGAGGTAGGAGGACTGCTTGA

GGCCGGGAGTTGGAGACCAGCCTGAGCAACATAGTGAGACCCTGT

CTCAATAATAATAATAATAATAGTAATAATGAAGTAAATGGGATA

AGGAAAGAAGGATAATTATCTTTAAAGGTTGATTCCCACCCTCCCT

CCCCAGTTACTTAAGGAACTAAGTGAGTACATCTCCAGTTGCCCAT

GAAAGCATAAGTTTGTTTTCCTCAGCTGAGGCAAGTGGTAGAGTAT

ACAGGATAACGAAGTAACATGTAAAAGGCAGGACGCACATAAAGG

TGTACATGGCTATTGTTTCACCTGGAGAAACCACATGATTGGGACC

TGAAGGTTTACTGACTGACTACAGGGGCTGATTGTGAAGCACGAGG

AACCCCATGTGTGTGGAGACTGTAGGGTGAGAGCACACAATTATTA

GCATCATTTCTGAGTGATCTCACAGATTTTTTTTCTTGTGTTTGCTTT

GCTTTTTGACAACTGCTTCTCCCACGTTCCTTGCAATTCTATTCTCTC

ACCTTCACTTTACTATTTGTATTCGATGGACCAGGATAATTCAGGCA

AGGTTACCTTGTAAACTTTAATTGGCCACACACCATGTTGTCACCC

AGCTGGCTATGAAGTGAATAATGGTACTGAAAGTAAACCTGAAGA

CCTTTCTCAGATCTATTTTAAGTCTGAGTCTGACCAACCATGGAAA

ATATTCGACATGAATTAATGTAGAGAACTATAAAGCATTTATGACA

GCTCCAAGAAAAATCATCTACTCTATGCAGGAGATATGTTTAGAGA

CCTCTCAGAAAAACTTGCCTGGTTTGAGGGTACACA

90
CODING
ACGGACAAGTCTTTCGTGGAGAAGCTGTGCACGGAGCAGGGCAGC

CACCCCAAGTTCCAGAAGCCCAAGCAGCTCAAGGACAAGACTGAG

TTCTCCATCATCCATTATGC

91
CODING
GAGAATGAGCTTAAGGAGCTGGAACAG

92
CODING
GGGGCAACCAATGGAAAAGACAAGACA

93
CODING
TGCTTCAAGAAGAAACCCGGCAGAAGCTCAACGTGTCTACGAAGC

TGCGCCAG

94
CODING
ACAAATCCTATCACTATACCGACTCACTACTACAGAGGGAAAATGA

AAGGAATCTATTTTCAAGGCAGAAAGCACCTTTGGCAAGTTTCAAT

CACAGCTCGGCACTGTATTC

95
CODING
AGCAAAATCTTCTTCCGAACTGGCGTCCTGGCCCACCTAGAGGAGG

AGCGAGATTTGAAGATCACCGATGTCATCATGGCCTTCCAGGCGAT

GTGTCGTGGCTACT

96
CODING
GTGTGGAAACCATCTGTTGTGGAAGAGTAA

97
CODING
TCTACAGTTTTGCACCACGGCAAGAAAACCAAAAACCAAAACAAA

CAAACAAAAAAAACCCAACAACAACCCAGAACAAAGCAAAACCC

AGCAGACTGTACTTAGCATTGTCTAAATCCATTCTCAAATTCCAAA

TATCACAGACACCCCTCACACAAGGAATATAAAAACCACCACCCTC

CAGCCTGGGCAACGTAGTAAAACCTCATCTATACAAGAATTTAAAA

ATAAGCTGGGCGTGGTGGTACACACCTGTGGTCCCAGCTACTAGGG

AGGCTGAGCCAGGAAGAACGCTCCAGCCCAGGACTTCGAGGCTGC

AATGAGCTATAATTGCATCATTGCACTCCAGCCTGGGCAACAGAGA

CCCTGTCTCAACCACCACCACCACCACCACCCCTACTACCCCTGTAT

TCAAGGTAAAAATTGAAGTTTGTATGATGTAAGAGATGAGAAAAA

CCCAACAGGAAACACAGACACATCCTCCAGTTCTATCAATGGATTG

TGCAGACACTGAGTTTTTAGAAAAACATATCCACGGTAACCGGTCC

CTGGCAATTCTGTTTACATGAAATGGGGAGAAAGTCACCGAAATGG

GTGCCGCCGGCCCCCACTCCCAATTCATTCCCTAACCTGCAAACCTT

TCCAACTTCTCACGTCAGGCCTTTGAGAATTCTTTCCCCCTCTCCTG

GTTTCCACACCTCAGACACGCACAGTTCACCAAGTGCCTTCTGTAG

TCACATGAATTGAAAAGGAGACGCTGCTCCCACGGAGGGGAGCAG

GAATGCTGCACTGTTTACACCCTGACTG

98
NON_CODING
CAGCAGTTGATACCTAGCAGCGTTATTGATGGGCATTAATCTATGT

(UTR)
TAGTTGGCACCTTAAGATACTAGTGCAGCTAGATTTCATTTAGGGA

AATCACCAGTAACTTGACTGACCAATTGATTTTAGAGAGAAAGTAA

CCAAACCAAATATTTATCTGGGCAAAGTCATAAATTCTCCACTTGA

ATGCGCTCATGAAAAATAAGGCCAAAACAAGAGTTCTGGGCCACA

GCTCAGCCCAGAGGGTTCCTGGGGATGGGAGGCCTCTCTCTCCCCA

CCCCCTGACTCTAGAGAACTGGGTTTTCTCCCAGTACTCCAGCAATT

CATTTCTGAAAGCAGTTGAGCCACTTTATTCCAAAGTACACTGCAG

ATGTTCAAACTCTCCATTTCTCTTTCCCCTTCCACCTGCCAGTTTTGC

TGACTCTCAACTTGTCATGAGTGTAAGCATTAAGGACATTATGCTT

CTTCGATTCTGAAGACAGGTCCCTGCTCATGGATGACTCTGGCTTCC

TTAGGAAAATATTTTTCTTCCAAAATCAGTAGGAAATCTAAACTTA

TCCCCTCTTTGCAGATGTCTAGCAGCTTCAGACATTTGGTTAAGAAC

CCATGGGAAAAAAAAAATCCTTGCTAATGTGGTTTCCTTTGTAAAC

CAGGATTCTTATTTGTGCTGTTATAGAATATCAGCTCTGAACGTGTG

GTAAAGATTTTTGTGTTTGAATATAGGAGAAATCAGTTTGCTGAAA

AGTTAGTCTTAATTATCTATTGGCCACGATGAAACAGATTTC

99
NON_CODING
GGCCGAGGGAGTCTATGAAAATCTCCCCTTTTTTACTTTTTTAAAGA

(UTR)
GTACTCCCGGCATGGTCAATTTCCTTTATAGTTAATCCGTAAAGGTT

TCCAGTTAATTCATGCCTTAAAAGGCACTGCAATTTTATTTTTGAGT

TGGGACTTTTACAAAACACTTTTTTCCCTGGAGTCTTCTCTCCACTT

CTGGAGATGAATTTCTATGTTTTGCACCTGGTCACAGACATGGCTT

GCATCTGTTTGAAACTACAATTAATTATAGATGTCAAAACATTAAC

CAGATTAAAGTAATATATTTAAGAGTAAATTTTGCTTGCATGTGCT

AATATGAAATAACAGACTAACATTTTAGGGGAAAAATAAATACAA

TTTAGACTCTAAAAAGTCTTTTCAAAAAGAAATGGGAAATAGGCAG

ACTGTTTATGTTAAAAAAATTCTTGCTAAATGATTTCATCTTTAGGA

AAAAATTACTTGCCATATAGAGCTAAATTCATCTTAAGACTTGAAT

GAATTGCTTTCTATGTACAGAACTTTAAACAATATAGTATTTATGGC

GAGGACAGCTGTAGTCTGTTGTGATATTTCACATTCTATTTGCACAG

GTTCCCTGGCACTGGTAGGGTAGATGATTATTGGGAATCGCTTACA

GTACCATTTCATTTTTTGGCACTAGGTCATTAAGTAGCACACAGTCT

GAATGCCCTTTTCTGGAGTGGCCAGTTCCTATCAGACTGTGCAGAC

TTGCGCTTCTCTGCACCTTATCCCTTAGCACCCAAACATTTAATTTC

ACTGGTGGGAGGTAGACCTTGAAGACAATGAAGAGAATGCCGATA

CTCAGACTGCAGCTGGACCGGCAAGCTGGCTGTGTACAGGAAAATT

GGAAGCACACAGTGGACTGTGCCTCTTAAAGATGCCTTTCCCAACC

CTCCATTCATGGGATGCAGGTCTTTCTGAGCTCAAGGGTGAAAGAT

GAATACAATAACAACCATGAACCCACCTCACGGAAGCTTTTTTTGC

ACTTTGAACAGAAGTCATTGCAGTTGGGGTGTTTTGTCCAGGGAAA

CAGTTTATTAAATAGAAGGATGTTTTGGGGAAGGAACTGGATATCT

CTCCTGCAGCCCAGCACCGAGATACCCAGGACGGGCCTGGGGGGC

GAGAAAGGCCCCCATGCTCATGGGCCGCGGAGTGTGGACCTGTAG

ATAGGCACCACCGAGTTTAAGATACTGGGATGAGCATGCTTCATTG

GATTCATTTTATTTTACACGTCAGTATTGTTTTAAAGTTTCTGTCTGT

AAAGTGTAGCATCATATATAAAAAGAGTTTCGCTAGCAGCGCATTT

TTTTTAGTTCAGGCTAGCTTCTTTCACATAATGCTGTCTCAGCTGTA

TTTCCAGTAACACAGCATCATCGCACTGACTGTGGCGCACTGGGGA

ATAACAGTCTGAGCTAGCACCACCCTCAGCCAGGCTACAACGACA

GCACTGGAGGGTCTTCCCTCTCAGATTCACCTGGAGGCCCTCAGAC

CCCCAGGGTGCACGTCTCCCCAGGTCCTGGGAGTGGCTACCGCAGG

TAGTTTCTGGAGAGCACGTTTTCTTCATTGATAAGTGGAGGAGAAA

TGCAGCACAGCTTTCAAGATACTATTTTAAAAACACCATGAATCAG

ATAGGGAAAGAAAGTTGATTGGAATAGCAAGTTTAAACCTTTGTTG

TCCATCTGCCAAATGAACTAGTGATTGTCAGACTGGTATGGAGGTG

ACTGCTTTGTAAGGTTTTGTCGTTTCTAATACAGACAGAGATGTGCT

GATTTTGTTTTAGCTGTAACAGGTAATGGTTTTTGGATAGATGATTG

ACTGGTGAGAATTTGGTCAAGGTGACAGCCTCCTGTCTGATGACAG

GACAGACTGGTGGTGAGGAGTCTAAGTGGGCTCAGTTTGATGTCAG

TGTCTGGGCTCATGACTTGTAAATGGAAGCTGATGTGAACAGGTAA

TTAATATTATGACCCACTTCTATTTACTTTGGGAAATATCTTGGATC

TTAATTATCATCTGCAAGTTTCAAGAAGTATTCTGCCAAAAGTATTT

ACAAGTATGGACTCATGAGCTATTGTTGGTTGCTAAATGTGAATCA

CGCGGGAGTGAGTGTGCCCTTCACACTGTGACATTGTGACATTGTG

ACAAGCTCCATGTCCTTTAAAATCAGTCACTCTGCACACAAGAGAA

ATCAACTTCGTGGTTGGATGGGGCCGGAACACAACCAGTCTT

100
NON_CODING
CAGCTTGCAGCCCAACCGAGATACAAACAGAACATCATTGCAAGA

(INTRONIC)
ACTCAGGCCCCATCTGACTACCCCTCCCCTGAAGACTCAAAGAGGG

ACCGTCTTTTTGGCGAGCAGGCCTGTTGAGTGTGGGTGATTTCTTGG

CTCAGCTAGAAGCATCCCTCCAGAAGGGGGCCCGTTTTGTGAAATG

AGAATAAGCCCTTTCCTTCCATAGCGAGATCTTCCTCCACGTCGGG

101
NON_CODING
CTGCCACCAGAGACCGTCCTCACCCC

(UTR)

102
NON_CODING
CCTCTACAGGGTTAGAGTTTGGAGAGAGCAGACTGGCGGGGGGCC

(UTR)
CATTGGGGGGAAGGGGACCCTCCGCTCTGTAGTGCTACAGGGTCCA

ACATAGAGCCGGGTGTCCCCAACAGCGCCCAAAGGACGCACTGAG

CAACGCTA

103
NON_CODING
CAAGGATCCCCTCGAGACTACTCTGTTACCAGTCATGAAACATTAA

(UTR)

104
NON_CODING
CCCAGATGTCATTCGTGCTGAAAGAACCAGAACAACTCTCTGCTCC

(UTR)
CTGCCAAGCATGAAGCGGTTGTGACCCCAGGAAACCACAGTGACTT

TGACTCTGGTTCAGCTGACATGCTCGAGTC

105
NON_CODING
CAGTGGCGTTTGTAATGAGAGCACTTTCTTTTTTTTCTATTTCACTG

(UTR)
GAGCACAATAAATGGCTG

106
NON_CODING
GGAGCAAACTGCATGCCCAGAGACCCAGCGGACACACGCGGTTTG

(UTR)
GTTTGCAGCGACTGGCATACTATGTGGATGTGA

107
NON_CODING
TGGTCCCCAACAGCGACATAGCCCATCCCTGCCTGGTCACAGGGCA

(UTR)
TGCCCCGGCCACCT

108
NON_CODING
CAAGCAACAGAGGACCAATGCAACAAGAACACAAATGTGAAATCA

(UTR)
TGGGCTGACTGAGACAATTCTGTCCATGTA

109
NON_CODING
TGCAGCCATGGTCACGAGTCATTTCTGCCTGACTGCTCCAGCTAAC

(UTR)
TTCCAGGGTCTCAGCAAACTGCTGTTTTTCACGAGTATCAACTTTCA

TACTGACGCGTCTGTAATCTGTTCTTATGCTCATTTTGTATTTTCCTT

TCAACTCCAGGAATATCCTTGAGCATATGAGAGTCACATCCAGGTG

ATGTGCTCTGGTATGGAATTTGAAACCCCAATGGGGCCTTGGCACT

AAGACTGGAATGTA

110
NON_CODING
GGCTCTGTCACTGAGCAATGGTAACTGCACCTGGGCA

(UTR)

111
NON_CODING
GCTGCTGTCACAAATACCCATCTTAGGATCCCATCAGCTTCCCATCC

(UTR)
CCCACCAGACAGCCACAGTACCCTCACTTTCTCCCTATTGTTCTTTC

AAATCCTGTTCTCAGGAAAGAAACTGCCACTAATTCATTCACACTA

AGGTGTAAATGATTGATAATAGGAATGAGTTACCTCTTCCCACAGA

CATTTGTTTTTAAGTATGACAGAGCAGGGCCTTAATCCCAAGGGAA

AAGGTTATGGAACTGGAGGGGGTGAGCTTTCTGGGTAGAAGGAGA

CTTCCTGAATTTCCTTAAAACCCAGTAAGAGTAAGACCTGTTGTTTT

GGAAGGTCTGCTCCACCATCTAAGAGCACTGTTTTTTTTTTTTTGTT

GTTGTTGTTGTTTTACGGTCTCTGAGGGAATATAGTAAAAATGCAT

ATGCACGTGCAATTTGCACGGCAGCATTTCACCGATTGTGGACTGT

ATTGGCTAATGTGTTTCCTGGTCTTTAGATGCAAACCATTAATAACA

CTATCTTATCTCATAGTTTTTTCAGGGGTGCTTCTTGATTAGTAGGG

AATTTTGAACACCTCTTTAAATACAGCTAGAAAATAAAACCAATTT

GTAAAGCCACATTTGCATATGATGCCAGCCTCACGCATTTGTATAT

CTCCAGAAATTCAGGTATGCCTCACCAATTTGCCCGTC

112
NON_CODING
TCTTCTGTTGCAGGACTAACCTTTGAGAAATCCTTTTGTGAAGTCAT

(UTR)
TGCCTGCTCAAGAATGTACAGTGGCTCCCCAATGCCTTGGAGGCCA

TAAGGCCAGCCAGTTCTAGCTCTCTATTACCTGTCCCCACTCAACTG

ACTCATACCTGTTTCCGGCTGCATCACTATGTGCCCCACAGAGAAC

GATGATCGTCACCTCTGTGCCTGA

113
NON_CODING
ATCATTGAATGGATCGGCTATGCCCTGGCCACTTGGTCCCTCCCAG

(ncTRANSCRIPT)
CACTTGCATTTGCATTTTTCTCACTTTGTTTCCTTGGGCTGCGAGCTT

TTCACCACCATAG

114
NON_CODING
TCCAGTGTTCGCCATTCCAGATGTCACTTTGCGTCCTCAGAGGGGA

(INTRONIC)
CTCTGGGGCAGCCACCATGGCCGGCTTGTCTGGAGGCCCTTGGAGA

TCTAGGATGGGCGCTGGTCGTGGCTTTGGAGAACTTTCCTTCTCCA

AACAAATGCAGGAAACTCAAGATTCAGCATCCTAGAATTGTCTCTG

GCAAGTTGGTTTCCAGCCATAGTGAGTGGGAACAATGGCCCCAGA

GGCTGTGTGGCAGTTTAAACACAGTTTCCACTGCCTTCCCTTTCCCT

AAAGAGTAAACACAGGAGATAATACTTTCTAACAACTCATCGTTAT

CAAGGGCCTACTATGTGCTGCTTGTTTTGGCTGCATGCGTAAACAC

ATCTC

115
NON_CODING
GTCAGATCCGAGCTCGCCATCCAGTTTCCTCTCCACTAGTCCCCCCA

(UTR)
GTTGGAGATCT

116
NON_CODING
TATAACCTTTGTGTGCGTGTATGTTGTGTGTGTGCATGTGTGGCGTA

(UTR)
TATGTGTGTTACAGGTTAATGCCTTCTTGGAATTGTGTTAATGTTCT

CTTGGTTTATTATGCCATCA

117
NON_CODING
TCCAAATCATTCCTAGCCAAAGCTCTGACTCGTTACCTATGTGTTTT

(UTR)

118
NON_CODING
TGTGATTCTAAGTCAGGCCCTTGTGACTGAACCACCATGAGGCTGG

(INTRONIC)
ACTGTGGGGACTCGGGTATCCCAGAGGCAGAGCACACCAGGTCTG

GGAGGGGGGCCACTCAGACGGCAACATTGTC

119
NON_CODING
GATCACGCCGTTATGTTGCCTCAAATAGTTTTAGAAGAGAAAAAAA

(UTR)
AATATATCCTTGTTTTCCACACTATGTGTGTTGTTCCCAAAAGAATG

ACTGTTTTGGTTCATCAGTGAATTCACCATCCAGGAGAGACTGTGG

TATATATTTTAAACCTGTTGGGCCAATGAGAAAAGAACCACACTGG

AGATCATGATGAACTTTTGGCTGAACCTCATCACTCGAACTCCAGC

TTCAAGAATGTGTTTTCATGCCCGGCCTTTGTTCCTCCATAAATGTG

TCCTTTAGTTTCAAACAGATCTTTATAGTTCGTGCTTCATAAGCCAA

TTCTTATTATTATTTTTGGGGGACTCTTCTTCAAAGAGCTTGCCAAT

GAAGATTTAAAGACAGAGCAGGAGCTTCTTCCAGGAGTTCTGAGCC

TTGGTTGTGGACAAAACAATCTTAAGTTGGGCAGCTTTCCTCAACA

CAAAAAAAAGTTATTAATGGTCATTGAACCATAACTAGGACTTTAT

CAGAAACTCAAAGCTTGGGGGATAAAAAGGAGCAAGAGAATACTG

TAACAAACTTCGTACAGAGTTCGGTCTATTAATTGTTTCATGTTAGA

TATTCTATGTGTTTACCTCAATTGAAAAAAAAAAGAATGTTTTTGCT

AGTATCAGATCTGCTGTGGAATTGGTATTGTATGTCCATGAATTCTT

CTTTTCTCAGCACGTGTTCCTCACTAGAAGAA

120
NON_CODING
TTGGGTTGTCACTCTAGAGCATGTCAAACTTTGTACTTCAAAATATA

(INTRONIC)
TTTAGTATGATTGTTAGTGGTAACATATATCAAGGCTTTGAATTAAC

TGTTTTATTTAATTTTCACAAGAAGCACTTATTTTAGCCATAGGAAA

ACCAATCTGAGCTACAAATAGTTCTTTAAAATAAGCCCAGGTTATT

TAGCTATTCTAGAAAGTGCCGACTTCTTTCAAGAAGCAGGCATTGT

AGGACAGCTGAGAATTATCACATAGCCTAAATTCTAGCCTGGCAGC

AAGAGTCACATCTGAGATGTCCAAAAAAAAAAAAAAAACACCTGA

TCTACATTGAAAGGGGGTAGACTAACGTATGTGAGACCATTTTCCT

ATTTGCAGTTACAAGGTTAAAGAACTTTGAAGGTCATTCGGCTGCT

AAGAGGCATGTCGAACACTCTGTGTGGCTCTTTCACAGTAAACCCT

CCTAAGAGCAGAAGACACATGGCTGTTAGTGTCTGCGTTTAGATTT

AATTTCTCAAATAAAGGCCCTTGGCTGCGTATCATTTCATCCAGTTA

TAAACTAGGGCTCCTGCAAGCACCCCCATTCTAAGGGTGAATTATT

GAAATCAGTTGCTATTTGATGAGTCACAACTGGCCCAGCAGGCAGG

GCATTTGAAGTCATGGTCATCAAAAAGAAATGATTGTTTTTTGAAA

AGCTAAATGCTTAAAATGCTTCTAGAGGGAAGTCGTGGGGCGTGTG

CTCATTCTCTTTAAAATCAGGGTTGTTGAGTTTGTTTTTAAACATTT

TTATAAGTTCATGAGAAAAAATATATAAATTCTAAGAACCAACACT

GTATTCCCAGAAACATGACCCTCGCTGGTCTTGGGTCCACATATCA

TTGGACTCTGGGGGACACAAAGATGCCTGTGACACTTTGGTGTTGC

CGAGTTAGTCA

121
NON_CODING
TCTCTGGGTATAACAAGTCACAAGCAATTCACTCTCCAGTATTAAC

(INTRONIC)
ACAGAAACTTAATCCAATATTCCTGACAACGAAATCATTTTGCTGC

CTATAATGCATCCATGATGATTTACAAAGATAAAGTTTAAATAGTA

AAAATTGTATTTTCAGAGTATCCACTACATGCCAAGTTTTTGCACAT

GATATGGTAAGGTATGAGATTTCATAGTCACATTACAAAAAAAAAT

TTTCCCAGAGAATAAATACAACATTATGGGTATGAGAAGAGGCAA

GTAAGTCAAGTCTGCAGGGAGTTTTGAAAAAGAGAAATACTGGAA

AGAGCTGCGCTCTCTTGTGTGTTCTCCTGGTGTTCTCCTGTGCTCAC

CTCTTAGCTTGCTAAACGTGACCTTCCC

122
NON_CODING
CTTGGCACCCACAGTAAGCCTTGTAGGAGCTCAAAGTGCCTCAGGC

(INTRONIC)
AATCTGTGAGCAGAATAGCAATTTTATTACTTTGTCATTAAACCAA

TTTCACAGCAGTATTGTTTGTTAATGAGCAGCGGCAAACGAGCGAA

GATGTCACACACTGGAATAGCAGAGAGATTTGTGACCCAAGCTCAC

AGCACTAAGATGGAAAGACCACGGCTATAAAAAAGGAAATACTTT

GGGATGAAATGCAAAGTCTATACAGCAGAGCTTGTGTTTATGAGCT

ACCATTTTGCTAAGAGCTGTGAGAGAAATAAAGGTCTGGAAATATG

CAGTTAAAACAGGGCCTATAAAATTAAAACCAAATTAAAGTATAG

CAGAGGATTACTGCACAGACTGTACTCGACAAAATATATTTTAAGT

GACGAGGTGAAATCTAAATCAGTTTTGTTTGAATTTGGTTGGTATTT

ATGAAATTCAATAAAAAAAAATGAAAAAATATCCAAACAAAGCAG

CCGCCTCACCCTTGTGTGGTCTCTGAGCCATAAACGTGCATCACTTT

GAGGAAATTCAACTTGCCAATCCTTAAATAATTAGCAACTTCTTGA

TTCACAGGGTGCGCCCCTCCATCTTCATGAAAGCCTTCTCTGTTACT

TTATCTCTTCGTAAGGACGTTGCCCATG

123
NON_CODING
GAAAGCCGCACTGCTCTGATGCTGAGATAGTGTTCCTACTTGTTCA

(INTRONIC)
AGAGTGAGTTCAAAAGTGAGCCTAGCCACCTAATTTTCACTAGCAG

CACAGACTGGAAATGCCCAGCAGGATTACAGCTTTGAGACTCACTC

TGGAGTACAACAGACTATCCCGCCCCTCTCAGATCAGACCCTAAAG

TCTGTTCTAAAATTGTCCACTGTGGGTGCTGAGAGAAGGGGGCCCA

AACATAGCGTGTGTTTCATGTCAAACTAATGGGCTACCCTGGAGAG

ATTTCAGAGTTCTCATTTGTTTACTCACTTGGGCCCTCAGTCAAGGT

CTGATCTTTGGAAGAGCAAATTTTTCCAAATTTTGAATAATCTCTTT

CTAGCAAGAGGCTATGAATTCCTTTGTCCATCACTTTTTGGCTACTC

GGAGCCACCTTCAACATACCACTCAAAGCTTTTCCTCATTTAACAA

TAGGCTGTAATATACTAGTTCTGAACCTTTGCTGGGTCATGGACTTC

TC

124
NON_CODING
TGCCCACTTGCAAAAGAGGCTGTTGGCAGCAACACTTCACCACTAG

(ncTRANSCRIPT)
AAACCTTTACTCCAATTCGAAACATGCCTTAACGCACAGTGTGAAT

TACCCACTCTCGTGGCCCACAGAGGTTGACTCATTCAGGCCCCCTTT

TGTTCAGATGAGGAAACTGAGGCTGACTCCGAAGCCTGGGGGCTTT

CAGATGTGGAGTGGGTCCCTGTGCCCAGGTGATGAGGGGACCAGG

CGGGTCTGGAGCAGGGCTGGAGTGGGGCTCAGATGTAGTAGGCTG

GCAGTTAAAGGTGCCAGATGTGAGCCAGGCTGCTGGGTTTGAATCC

TGGAGCTGCCTCATAGCAGCAGTAGGACTTTGGGTAACTTACATAG

GTGCTGTATGCCTCAGTGACCTCATCTGTAATATAGAGATGATAAG

AGTACCTGTCTCATTGGTCTACTGAGTTGTCCGGATTAACTCATTAA

ATGAGTTAAAACTCATGAAGCCCTTGGAACTGTGACTGACACATAG

TAAGTACTCAATAAAAAATAACTGCTAAGACCAGCCACAGTGGCTC

ACACCTGTAATCTGAGCATTCTGGGAGGCCAAGGCGGAAGAATCC

CTTGAGCCCAGTATTTCAAGACCAGCCTAAAGGTCAACATAGGCAG

ACTCTGTCTCTACTATACATTTTTAGATTAAATTTTTATAATAATAA

TAACCACTAAAATGTGATTACTAAAGACAGCTTCTTCACAGTACAA

AGAGATGCTCTTCTGAGTACCAACTCTTTGGAGGATAAACTGCCCT

TATACCTTCAAAAATAACACTTGCCATATATCAAGTCCTTTCAAGT

ACCTGGAGATTTACCCAGCACTCTGAGATAAATACCATTATCCCTC

TGGGCACACAGAGGCTCAGAGAGGTTTAGTCATTTGCCCAAAGTCA

CACAGCCTGTACGAGGCCAGGCTGGGACTCAAACTCAGTTCTGACT

GATTCTAAAATCATGTGTTTAACTGCTGCACTCTAGGACCACCCGC

AATGGATCTGTG

125
NON_CODING
CCATCCCGTGTCTCGATGGTCTTGATCATCACCGTCTTCTTGGTATG

(INTRONIC)
GACCTC

126
NON_CODING
CTAGTGCTTGGGATCGTACATGTTAATTTTCTGAAAGATAATTCTAA

(UTR)
GTGAAATTTAAAATAAATAAATTTTTAATGACCTGGGTCTTAAGGA

TTTAGGAAAAATATGCATGCTTTAATTGCATTTCCAAAGTAGCATC

TTGCTAGACCTAGTTGAGTCAGGATAACAGAGAGATACCACATGGC

AAGAAAAACAAAGTGACAATTGTAGAGTCCTCAATTGTGTTTACAT

TAATAGTGGTGTTTTTACCTATGAAATTATTCTGGATCTAATAGGAC

ATTTTACAAAATGGCAAGTATGGAAAACCATGGATTCTGAAAGTTA

AAAATTTAGTTGTTCTCCCCAATGTGTATTTTAATTTGGATGGCAGT

CTCATGCAGATTTTTTAAAAGATTCTTTAATAACATGATTTGTTTGC

CTTTCTAGATTTCTTTATCTTTCTGACCAGCAACTTAGGGAGCAGAA

TTTAAATTAGGAAGACAAAGGGAAAGATTCATTTAAACCATATTTT

TACAAAGTTTGTCATTTGCCCCAAGGTCAAATTTTAAATTCTTAATT

TTCATTTTATTTCCCATTTTAGGTAAAAGTTTGCATTTAATCTTAGA

ATTATGTTATTTTTGTTAGTAGTGTGGAAACTTAGAGAACTTATTGT

ATGGTGCCTTGCA

127
NON_CODING
CTCCTATGTCTTTCACCGGGCAATCCAAGTACATGTGGCTTCATACC

(ncTRANSCRIPT)
CACTCCCTGTCAATGCAGGACAACTCTGTAATCAAGAATTTTTTGA

CTTGAAGGCAGTACTTATAGACCTTATTAAAGGTATGCATTTTATA

CATGTAACAGAGTAGCAGAAATTTAAACTCTGAAGCCACAAAGAC

CCAGAGCAAACCCACTCCCAAATGAAAACCCCAGTCATGGCTTCCT

TTTTCTTGGTTAATTAGGAAAGATGAGAAATTATTAGGTAGACCTT

GAATACAGGAGCCCTCTCCTCATAGTGCTGAAAAGATACTGATGCA

TTGACCTCATTTCAAATTTGTGCAGTGTCTTAGTTGATGAGTGCCTC

TGTTTTCCAGAAGATTTCACAATCCCCGGAAAACTGGTATGGCTAT

TCTTGAAGGCCAGGTTTTAATAACCACAAACAAAAAGGCATGAAC

CTGGGTGGCTTATGAGAGAGTAGAGAACAACATGACCCTGGATGG

CTACTAAGAGGATAGAGAACAGTTTTACAATAGACATTGCAAACTC

TCATGTTTTTGGAAACTAGTGGCAATATCCAAATAATGAGTAGTGT

AAAACAAAGAGAATTAATGATGAGGTTACATGCTGCTTGCCTCCAC

CAGATGTCCACAACAATATGAAGTACAGCAGAAGCCCCAAGCAAC

TTTCCTTTCCTGGAGCTTCTTCCTTGTAGTTCTCAGGACCTGTTCAA

GAAGGTGTCTCCTAGGGGCAGCCTGAATGCCTCCCTCAAAGGACCT

GCAGGCAGAGACTGAAAATTGCAGACAGAGGGGCACGTCTGGGCA

GAAAACCTGTTTTGTTTGGCTCAGACATATAGTTTTTTTTTTTTTTAC

AAAGTTTCAAAAACTTAAAAATCAGGAGATTCCTTCATAAAACTCT

AGCATTCTAGTTTCATTTAAAAAGTTGGAGGATCTGAACATACAGA

GCCCACATTTCCACACCAGAACTGGAACTACGTAGCTAGTAAGCAT

TTGAGTTTGCAAACTCTTGTGAAGGGGTCACCCCAGCATGAGTGCT

GAGATATGGACTCTCTAAGGAAGGGGCCGAACGCTTGTAATTGGA

ATACATGGAAATATTTGTCTTCTCAGGCCTATGTTTGCGGAATGCA

128
NON_CODING
GCAGTGTGTTGCTCAGTAACTTCCAGGACCATCCTCACTATCCAAG

(INTRONIC)
GAGATGATGGGATGAAGTTTTGCAAATGGCAAGGCCTGGCTCTAAT

GCACAGAGCAAAGCACATCTTTCTTTGCTGTGTGAAGTTGCAAAAT

GATTACACTATTTCCTTGAGGAGAACAGTTATAGACACCCAGTGTT

ATGCATTAGTCAGTGTTGTATAATTGATCTTTTTTTAATCCCCTCCA

TTAGCAAATAGAAGAAGATTGTGCAGAGACTGAAGATGGCATGGT

GTGGTGATTGGCAGGAGACATTGTGATAGGACTCGAGTCCCAACTC

TGCTACTCAGTAGCTCTGTGAGCTTGGACAAGTTAACCAACCATAG

TCTCTTTATTTGTAAAATGGGGATAATAATAGACCCTATATCACAT

GATTGTTATCAGTATTAAATGGAAGAACGCATGTGGAATACTTGAC

ATAGAGTAAGCATTCAATAATTGTTAGCTATTAACAGTGATACTTA

TTAATAGCTAACACAGTGACATATGTGTATTCAGATTCTAAGCCGG

TGCACCCAGTCCTCCCTTCACAAGAGGAAAGTGTCAGCATTGCCAG

AAACATTGTATGTCCTCAGTGCTGGTGGCTCCAGCTACCTGTCCTCC

CCTTAGCAATTTGGTATTGTCCAAACATTTAGGTTTCTGAACATGCC

TGAGGCTTA

129
NON_CODING
GTGTGTGTGACATTCTCTCATGGGACAATGTTGGGGTTTTTCAGACT

(UTR)
GACAGGACTGCAAGAGGGAGAAAGGAATTTTGTCAATCAAAATTA

TTCTGTATTGCAACTTTTCTCAGAGATTGCAAAGGATTTTTTAGGTA

GAGATTATTTTTCCTTATGAAAAATGATCTGTTTTAAATGAGATAA

AATAGGAGAAGTTCCTGGCTTAACCTGTTCTTACATATTAAAGAAA

AGTTACTTACTGTATTTATGAAATACTCAGCTTAGGCATTTTTACTT

TAACCCCTAAATTGATTTTGTAAATGCCACAAATGCATAGAATTGT

TACCAACCTCCAAAGGGCTCTTTAAAATCATATTTTTTATTCATTTG

AGGATGTCTTATAAAGACTGAAGGCAAAGGTCAGATTGCTTACGG

GTGTTATTTTTATAAGTTGTTGAATTCCTTAATTTAAAAAAGCTCAT

TATTTTTTGCACACTCACAATATTCTCTCTCAGAAATCAATGGCATT

TGAACCACCAAAAAGAAATAAAGGGCTGAGTGCGGTGGCTCACGC

CTGTAATCCCAGCACTTTGGGGAGCCCAGGCGGGCAGATTGCTTGA

ACCCAGGAGTTCAAGACCAGCCTGGGCAGCATGGTGAAACCCTGT

ATCTACAAAAAATACAAAAATTAGCCAGGCATGGTGGTGGGTGCC

TGTAGTTCCAGCTACTTGGGAGGCTGAGGTGGGAAAATGACTTGAG

CCCAGGAGGAGGAGGCTGCAGTGAGCTAAGATTGCACCACTGCAC

TCCAACCTGGGCGACAAGAGTGAAACTGTGTCTCTCAAAAAAAAA

AAAAAACAAACAAAAACAAAAACAAAACAAAACAAAACAAAACA

AAACAGGTAAGGATTCCCCTGTTTTCCTCTCTTTAATTTTAAAGTTA

TCAGTTCCGTAAAGTCTCTGTAACCAAACATACTGAAGACAGCAAC

AGAAGTCACGTTCAGGGACTGGCTCACACCTGTAATCCCAGCACTT

TGGGAGATGGAGGTAAAAGGATCTCTTGAGCCCAGGAGTTCAAGA

CCAGCTTGGGCAACATAGCAAGACTCCATCTCTTAAAAAATAAAAA

TAGTAACATTAGCCAGGTGTAGCAGCACACATCTGCAGCAGCTACT

CAGGAGGCTGAGGTGGAAAGATCGCTTGTGCACAGAAGTTCGAGG

CTGCAGTGAGCTATATGATCATGTCACTGCACTCCAGCCTGTGTGA

CCGAGCAAGACCCTATCTCAAAAAAATTAATTAATTAATTAATTAA

TTAATTTAAAAAGGAAGTCATGTTCATTTACTTTCCACTTCAGTGTG

TATCGTGTAGTATTTTGGAGGTTGGAAAGTGAAACGTAGGAATCCT

GAAGATTTTTTCCACTTCTAGTTTGCAGTGCTCAGTGCACAATATAC

ATTTTGCTGAATGAATAAACAGAAATAGGGAAGTAAACCTACAAA

TATTTTAGGGAGAAGCTCACTTCTTCCTTTTCTCAGGAAACCAAGC

AAGCAAACATATCGTTCCAATTTTAAAACCCAGTGACCAAAGCCTT

TGGAACTATGAATTTGCA

130
NON_CODING
CCTGGCTGATTTCTTGGTCTCTTGCCCTCATTCACCGAATTAATTCT

(INTRONIC)
CTACACTGCTGCAAAACTGATCTTTCTAAACACAGGTCAGCTCATG

TCACTCACCTCCTCAGAAATCTTCAGTAGCTCTTCATTAACCAACAG

GGGGTTCCTAACTCCCCGTCTTGGCATTGGAGGACCTTTCCCTGCCT

GATCCCCGCGATCATCTTTTCCTGCAATATTTACTCAGGCCAGTGCT

CACCCCTTCTTTAAAATGCTGGTGCTGGCTCAAGAGAGGCAAACAG

CCATCTCTCTCATTCTTATCTTCCCTGTCAAGACTTCACATAGGTGG

ACTGATGCTAGACTATGATGATGAGTCTCCAGTGAAAGTTTCTAAG

TAGAACTCTCTCAGGGTTTCTAGAAGCATTTTTGTTTAAGAAAATAT

TGTGGGGGGAGCGGGATTTTTAAATGGTGGAGCTCATGGTAAACA

AAATTATGTGTGCAAAATGTTAATAGAGCCTTTCTAATATTCTTGTG

ATTAACTCTGGTGACAGTTGGCTGAGTGTTCTTGTTTCTGCAACGCC

TGTCTTTG

131
NON_CODING
CTGATTTTATCAAAGGTTTGCCAGCCAATAAAGTGCATCCCAAGTA

(INTRONIC)
TACAGGGGAGAAAGCTAGACTCCTACAGGGTC

132
NON_CODING
TCTCAGGCATTGTTGGGGCATAAGCTCACACTGTAAGCTTTTCTCAT

(UTR)
GAATTCACTAGACATAACGTGGAAGGAAAACGTAGTCTTTTGGGA

GTACAGGGAAGCCAGCCCCTCAAAGCTTATGGAAGACATACCTGC

AATGGAAGCTGTTGCCCAATGTCTCCATTACTATCTTTCAAAAGAG

AAGCCAGACCCAGCTTCAGATCAAAAGTTCTTGAGACAGAGGAAC

AAAACCAATCGATTTCCAGGGAAGCTAATCAACTCTCTTTTCCCTCT

ACCACAAAACTGCCCTGCTGGAGTGGTTCTGAACCTGTACCCAGGA

CTCGATGTGGTCACTAATAACAATTAACCTGAACTGAGTCCACAGA

ACTCCACTCGGAACTTTCTTCTTTTTTAACTAGTGGCCCAATCATTC

CCACCATCTCTGTGCTGATAAGTACGTGTCCTAGATGAGAACCCTG

AAGAATGCAGACCTTCTTCCCCCGAAGGAGATGCCACAAGCTCTCC

AACACAGCCCCCTTTAGTTCCAAAGACTAGAGATGACCACATTGGT

AGAAGTATATCTCGAGGCACAGGAAGGGAGCCCCACCAGGGATAA

TTCAGACAGGACTAGAGAATAACATCATTTCACATACCCTGGGATA

AACACCCTGGGTTCCTATAGAAGGACTATTACTTATGGGAGTCCAA

CTTCTCCTTTTGTTTTGTTATTATCAGTTTATCTTTCTCCCACTCCAC

TTTTCCTTCAAGGTACCAATCCTTTCCTGTTCCTCGTTTGGCCATCTT

TCTTTTTCTGCCTCCACATTGGGAGGGGAGGACTTCTCAGTTCTAAC

AAGCTGCCATACTCCTAAGAAAGCCATTTTTGAAAAATTTAACAAT

CCAGGTTCTTCTGGAGAACTCATTCTCCACACGCACAGTTTGCTGC

AAAAGGAAGTTGCAAGAATTTCTTGAGGAAGAAACTGGTGACTTG

GTCCATCAGTCACGAAGTTCTTTCTATTCTCGTTTAGTTTTCAAGAA

ATTATTGGTTTGTGTTGCTCTGGGGAAATTGGAAATCATTACATTGT

AAAGACAAATATGGATGATATTTACAAGAGAGAATTTCAGATCTG

GGTTTTTGAAAGAAAACAGAATTGCGCATTGAAAACGATGGAAGG

AAAAAGACAATGGTCTAATGTGCATTCCTCATTACCTCTCGTGGCT

TTGGCTGGGAGTTGGAAAAAGCTAAAATTTCAGAACAGTCTCTGTA

AGGCTCTCTGTGGCTCCAGTTCACCATTTTATATTGTTGCATGCTGT

AGAAAGGAGCTATTGCTGTTGTTTTGTTTTTTTATTTAAATCACTAA

GGCACTGTTTTTATCTTTTGTAAAAAAAAAAAAAAAGTTGTTCACT

GTGCACTTATAGAAAAAATAATCAAAAATGTTGGGATTTTAGAAGC

TCTCTTTTTGATAAACCAAAGATTTAGAAGTCATTCCATTGTTAACT

TGTAAAAATGTGTGAACACAGAGAGTTTTTGGTGATTGCTACTCTG

AAAGCTGCCAGATCTTATTCTGGGGGTGGGATGTGGAGGAATACAC

ATACACACACAAACATACATGTATGTATAATAGATATATACATATG

TGTATATTATATCTGTGTGTGCATGTATCTCCAAAAGCGGCGTTACA

GAGTTCTACACCAAAAGCCTTTAACCCTTAATCTGCTGTGAATGAT

ACCTGGCCTTTCTCACTATGAATTTCTGATTAACCAACCAGACTACA

CGTTGCCTCTCTGTGTATGACTAACGGCTCCAACCCGATGACTCAC

AGCTACTTGCTTATCGTGAACAAGCTCATCTTGGCAATGAATATGG

ATGTGAAAAGACAGAACAGCTTCACCATTAGTAGCTGGAAATGGT

ATCACAGTCTCTTATAGAGGAATATGAAAGGAACAAGAAAATCAT

TTTACATTCCTTTTATCTGTATTGTGCTTTAAAAGATCCACATGGTA

AATTTTTTATTTTGCTTTTATGTCAGTCATCAGAACCAAAAAAATCC

AGAAGAAAAAATTGCCAGTGTTTCCTTTGAAGATGAAGCTACTGGG

GAAGAAAACCTTATTAATACACTCCACACATTTGTTCATTCCTCAG

CTGTTGGTGTTTTCTTGGGGTCTTGACAAAGCTTGCTGGTCAGTGCA

CTTTTCAGGTGTCACGTTTTGCTGTTTGTATGTTTTTTCTTCCCCTTA

CTTCCTTTGGAAAACAAACTCACACAGTGCCCCTACTCTGAGACCT

GGGACTGAGTGTTAATTATTTTTTCCTTGGGTATTTCTATCTGAGAG

ACTAGACCTAGTTAGGAGGCCTCTGTACTTCTCCAGATTGTACCTTT

TTATGGGGATCTTTGAGGCTATGACCCAGGACTGATAGATATGCCT

TACGGAAGACAAAAGATAAAATGGTTCCTATATCCTAATGCAAACC

AACACAGTTAAAAGAGCAGATCTCTGGATAACTGCTCTCAACCTGC

TTCTACAGTCTCCACAAACCGCATTCACCCTCTCTCTTCATAGCTCA

GACATGAAATTTGAGGGAGAAAACTGGAGATAATTGGGAGAAAAT

TGATGAAGTTGGCTGCTTCCAGTAGATCAGATAATCCATGAATTTG

TCTCCCATTGAGAATTTTATTTTAAATTCTTTTAAACTCTTCGTTGTG

TCTTTTGTGATGACAAATCAGGCATGACTAAAAGATGTACAGAGAC

TTACGAAGATGGTCACATTCAAGTTCCCTAATGCTCTTAGAACCTG

AAGATGACCATGTGTAGTTTTCTTAAGACCTCTGAACCCCCATGGT

GATGAAGACTTGAAGACATTTGCAGCTATCTGCTGCAGTCTGGTAG

ATTCATACTTATCTAAAGAAGTCAAAAAATTTATTCGTGCAAGTGC

TTGCAGGAAGCCAGTGCTTATTAGTAGTGACCCTGCTTCTATCAAC

GTTATTG

133
NON_CODING
GATCGCTGTGCTAGGTCTGACCAAAACCAGAGGGCAGTCTAGTCCT

(UTR)
GGGGGTAAAGCCCTCAGATCCCAGGGTACACTCTTCTCCATTCCCT

CCACCCACTTGCCTGTCACCCCAGTCACCTAAGCAATCACTGGGCC

CAGAGGAGAGGAGACAGACACACACTGGCTCCTGGACCTAAAGGG

TATGAGCTGGAGCTAAGGCCAGCTAGAGCTTCCACTGTCAGCCCTC

ACTGTCAGTCCCACTGCACCCCCCTGTGCCTGCTGGGCACTGGGCA

CTAGCTAGATGCTTTAGGTTGCTTCAGCTGATCCTTCAACTCTGTGA

GGTGGATACCAATATTCTA

134
NON_CODING
CCCTGGAGGGATCCTAGAAAGCATTGTCATATTGCCATCTCCATTA

(UTR)
GCTCACTTTTAAACAACTAGGGTGCTGGAAGAACCTTTGTCTGAGG

GTAGTTCA

135
NON_CODING
GTACACCCTGGCAAGGCTTCTCTTCAGACTGAAGCAGCAATTCTGC

(UTR)
CACTACCAGCAGCAACCAGGACGTCTGTTCTTTGTGGGGGCCAGAT

CAGAAGAGAGAGGCCCCTGTGACGCCCGGGCTGCTTGGTCACAAC

TCTGTCCAATTCAAGGATGTTTATCGGCCTCTCTTA

136
NON_CODING
GGCTGCATGGTTATCCCTCTCAGTGCAATATAGCTAAAGGGGCTTG

(INTRONIC)
AAATGCTGGGAGTAGTCTTAAACAGCCCATTCTTGAAAGGTTTTCA

TTAACTCACTCTAAACATCTAAATTAAAAATGTTTTTGTTTTCACTA

TAGTAAACAGGAGTGTAACATTGCAGGTTTGGTACATTTCTGAATG

CCTCTCCACACACTGAAGCACAAGAGCCACTGAAAAAAGCTATAT

GATAAATATTTTAAAAATTATTTATCTGTGTTGCATTACATGAGGCC

TTATCTCCCAGACACTTAATAAAAGAGCTAATGAGAAGAAGAGCT

AAATTCTAAGATTTTGATGTTTGGTCATTAAACATTACAGACACCA

GTGATCAGAGAAAAAAACAGAAGAAATAATGAGAAAGTGACATA

AAAAATTTTAAATGCAGCAAGATATATCAGAATCACGATATCTGGC

CTTTTATTTATCTATCGGCTCACTACTACTACTACGCACACAATTTA

TCACTTAAAAGAAAAATACATAATGTTGTTAGAATTTATCAGCAGT

AATGCTCCAAGCTCTATCTTTCTACAAAAATTTCATATCAGTAGGTT

TGCTTGAGGATTCTAGATTTGGTAAGATTGCAGTTTGCACAGAGAA

AAAGATATCAATATCAATAGGAAAATATTCTTTTAGAATTTCTCCA

TGGAGCTGACAACATCTTAGAATGTATCGTCCTAGACAGAGACTAT

TGGAAGAAAAAACTTTCCTTATTTCTAAAATTTAAATTCAAAGTAT

CTTCTGGTGGGGACGAAGAGAGAGAGAGGAGAAAGGTTGCTTGCT

GTGACTGGCAGGATTTTTTGAGCAGTCTGCTGCTTTCACTCCACTAA

AGAAACAAAACTTTCAGAAGTTTCATTTCCCTTCTATAAACCACAA

ATCCAAAACAAAAGAAAGTGGAATAAGATAGTCTTTAAAGCTAAT

CTTGGTTTTGCTAATTTGTAAGCTTTCACCAGCAGTTCTTGTTTTGCT

CTGTTTTGATTTTGAGTGAATCTCATATTCCTGGCTCTGGTGGAGAA

TTTTCGTGCTTTTAAAGATTAATTAATTTAGTCCTTTTTGCAATGGTT

TGTTCTTTTCGGCATCTAGGAATTAAAGAAAGTGCTCAACCATAAA

TAAATGTAGTTATGTCCAAAGTACCTTCACATAGACACACTATACA

CAGGCGTGGGCCTTTTGGAAACACCTGAAGGCCAAATGTCTGACTG

TGAGTGGAAGATCCAGAGTGTGCTGATAGAGGAAGCTTTTCTCATC

CCTCGAGAGCAAAGAGGGTGATGGAGGCAAGAGTCAGAGAGCCCT

GTTCTCTTCTTCATGTACACTGCAAAGGGCAACTTCTCTAGAAGCAT

TAAAAGTGTCAATTAGGTTTTCAAGTAAGCGTCATTTATTCATATAT

ACATTCATTTGTCTTTTTATTTACAAAATTAAATCATTTTCCCATGA

ACATTAAAATGGGAAGAGAGAACAAAGAAAATAGAGTTGAATAAT

AATAACATTGATTCTGGACCAGACACTGGGCTGGACAATAACTCGA

GGGTTACCTTATTTATTTACACAAAGACCCGATGAGGTACACACTA

ATTATTTTCATCTCCCTATTACCAATCATGAGACTGAAGCTGAGAA

GGGTTAAAAACTTGCCTAAGCTCACACAACTAAGAAGTGTCCGAGC

TGGGCTTTGAACCCAAGGTTTGATCAAGGGTTGTGCCCTTAACTGC

CATACCATCCTGCCTCACAGATCTGGGTTA

137
NON_CODING
CTCACAAATAGGAGTAGCAATTCTAGGTGGTAGGGTTGTGTACGGA

(UTR)
ACCCCTGGCTGTCTGCATATATCTCAGAATTACCCCAGGACCATTG

TCCCAAAGTCTAG

138
NON_CODING
TTCCCGACAATAAGCTCCAACGTGGGCATAGTTGAACAAGCTATGC

(UTR)
CTCAAAATGCCAACGCCATATGCTTATTAGCCTGTGTGCATCATTCC

AGACGGGCCTAATCATTCCAGGACTGAAACCAGAATCGCTGAAAG

CCCTTGAAATACATTCAATAATTCATATGTTAAAACTTGGATATCTG

TTCAGCCCAAATGAAATCTTCCTTTTAAAAAACGTCTACATTATTGA

AAATTGTTCAATGTGCTTTTCAGAGTGACGGTGAGAATTTTATGCA

TGTATCTTGCCTGCATATTTGATATGTTACAAACTTCCAAAATTCAA

GGTGCAGCGATCCACAGAACGTTGTACATTTAAGAAGTGATTCCTT

CAAGCTAATTTAAAATTTCATTGAACACATGGTGACCAGGAAAACT

TTTTTTCAAGCACTGTTGGAAAGCACCACAAAGCCCTTTAGAATTA

ATCTGGATTTGTTTCTCAAGTTCTGCTGAAGTTTAAAAAAAAACTTT

ATTATACAAATAACTCAAAATTTTCCTGTGTAAAACTAAACCTGTA

GTTTTAAAACATAATCCTGTTTGCATTAGAGCTCACTGTCTTTTTGT

GATGGAAACTGTGTTCGTATGGAATGACTAAAAATCTTTTATTTGG

TTTGTTTCAAATTACAATTGCTGATGGACAATTTGTATTGCAGCGAG

AACAACAGAATGAAAGAAATGTATCTCTGTGCGGCTATACATATAC

ATACATAAAATTGATTTTTAAATTTAAAACATATGGAAAACAAAAC

ATTGAACAGTTTGAATTTTGCCAAGTTGGACATTAAAGTAAAAATG

AAGTGAAATCATGCATTGAAAGAAAACATTTTGTTTCTAAATTAGT

CTACCATTGAGTGAGAATAATCAATATCAAGAAAGAAGACTATCTT

TCTCAACTAAACAATAATATTCCAATCAGCTTGGGAAGACCTGAAA

CTTGAATAAGCAGTGGAAATGCCAAATATAACAGAGGGTATGTGC

TACAGAGAAGTAAAAAGGGTTTGACTTTTTATGATGGGATTTTTTTT

TTCTGGGTATGTAATCTATTTTTTTTTTAAACTGGAAAGCATTTTTG

TCAGTGTGAATGAGGGTCAATAGTGCAGCCAGTGGTGACATTTTTC

TTTATTTTGCAAAATGCTTTTAAAACCAAAGGCTGCTCTAGTTGATG

GACAGTATCAGTCTTGATCTAAATTGTAGGACACTTTTTCATGTAAC

ATAACATTTGGGGATTGGGTTTATTTAGTGTAATGAAGATAATTTG

ATATAAAAATATTTTGTGTATATATATATATTTTTACTTTGTTTTCTA

AATTGCTGTTTGCAGTAACAGTAAGCGCAAAGCAAAATATATAAGT

TATGACTGTATGATCAGATGAAGTATGAGTTCTTTTGGTTTGCATCC

TTAAATAGTTAGAGATCTCTGATAAAAACTTTGGAATCTTTGCAAA

ACAATACAAAAATGCCAAAATGTGAGCATGTCAATGAAAACTAAA

GACAAATACTTCACTCTTTTTCATACTATTATAAGTTATTCTGGTAT

TAAATATGTTAATAAAAGTGTTTTTGTTTTGACATATTTCAGTTAAA

TGAATGAATGCTGGTTGTATTTTATTTGAATGAGTCATGATTCATGT

TTGCCATCTTTTTAAAAAAATCAGCAAATTTCTTCTATGTTATAAAT

TATAGATGACAAGGCAATATAGGACAACTATTCACATGATTTTTTT

TAATACCAAAGGTTGGAAGATTTTATAATTAACATGTCAAGAAGAC

TTTATAGTAAGCACATCCTTGGTAATATCTCCAATTGCAATGACTTT

TTAATTTATTTTTTCTTTTGCTGCTTTAACATTTTCTGGATATTAAAA

TCCCCCCAGTCCTTTAAAAGAATCTTGAACAATGCTGAGCCGGCAG

CTGAAAATCTAACTCATAATTTATGTTGTAGAGAAATAGAATTACC

TCTATTCTTTGTTTTGCCATATGTAATCATTTTAATAAAATTAATAA

CTGCCAGGAGTTCTTGACAGATTTAAA

139
NON_CODING
GTCGCCTTCCTATGTATGACGAAACAAGAAACAGAGATTTCCAATT

(UTR)
GCTCTTTTGTCTTCAGACATTTAGTAATATAAAGTACCTATTTTTAT

GCTGAAATGTTTATACAGGTTTATTAATAGCAAGTGCAACTAACTG

GCGGCATGCCTTGCAACACATTTTGATATATTAGCCATGCTTCCGG

GTAAAGGCAAGCCCCAAACTCCTTATCTTTTGCAGTCTCTCTGGGA

TCAGTAAAAGAAAAAAAAAATAATGTGCTTAAGAAGTGGGACTGT

AAATATGTATATTTAACTTTGTATAGCCCATGTACCTACCTTGTATA

GAAAAATAATTTTAAAAATTTGAATGGAAGGGGGTAAAGGAAGTC

ATGAAGTTTTTTTGCATTTTTATTTAAATGAAGGAATTCCAAATAAC

TCACCTACAGATTTTTAGCACAAAAATAGCCATTGTAAAGTGTTAA

AATTTACGATAAGTATTCTATTGGGGAGGAAAGGTAACTCTGATCT

CAGTTACAGTTTTTTTTTCCTTTTTAATTTCATTATTTTGGGTTTTTG

GTTTTTGCAGTCCTATTTATCTGCAGTCGTATTAAGTCCTATTG

140
NON_CODING
TCTCAGCATATGTTGCAGGACACCAAAAGGAAGAAAACAATCAAG

(UTR)
CAAATAAAATAAACAGTCAAACAAACCAGGAGTTTAAAACAACAA

CCCCAACAACAGAAGCCTTGGCAAAGAGGAATAAGTGATCAGCAA

GTGAACACACTCTATGTCAACTCTCCTTTTATCCAGCTGAGATTTAT

GGTAACTTATTTAATTAATGGTCCTGTCTGATGCATCCTTGATGGCA

AGCTTCAAATCTGATTTGGTATCACCGAGGAAACCTTGCCCCCATC

ACTCAGCATTGCACTTAGATACAGAATGAGTTAGATAAACTTGGCT

TGTCTAGAGACCCATGTCATCTTAACCTAAAGGGAAATCTTATTGC

GTTATCATAAAATTGATGATATCTTAGGGTCAGAATTGCCCTTTTTT

TTTATTTTGAATGGGAAGTTCTCACTAAAACAATCCTGAGATTTCTT

AATTTCATGGTTCTTTAAATATTATAAACACAGAGTCAACATAGAA

TGAAATTGTATTTGTTAAAATACACACATTGGAGGACAAGAGCAGA

TGACTACTTTTCGAAGTAATGCTGCTCCTTCCTAAAAGTCTGTTTTC

AATCCTGGTAATATTAGGGGCACTGCGGCACCTAAGAAGCCTTAAA

TGAGAGCTAATCCAATCTAGAGAGCGATGGTGTCAGCATTTCGGTC

TGCATA

141
NON_CODING
CAGGGCATGAGACATTCAGCGTAGAGGTTAAAACGAGGGCCCTGG

(ncTRANSCRIPT)
GTTAGGAACCCCAGCTCAGTTCTCAGCTCTGTACCCTTGGAAAATT

CCCTTCCCATGGAGCTTTGTGGATGCACAAGGACTTGCACA

142
NON_CODING
GTGGCTTGTTTACGTATGTTTCTGGAGCCAATT

(ncTRANSCRIPT)

143
NON_CODING
CCCAAGCCTGTCTAAGGTTACTGTGTATTAGACAGGGCCGAACTAG

(UTR)
TGTGCTGAGCAAAAAGAATTGAAGCAAATTGTATTTACTTAGCCGC

TTCTGGGAGCCACTTCAGCCTTTCCCCTCCCCTCCACTTCTTGGGTA

ATCTGACCTGAAGCATAGTCCAGGAGCAGAGTTAGCCAGAAATGC

CTCCTGCTGCCCCAGCCTTAGAGAGCTCCCATCTCAATCATTGAGC

CTGAAGGCTTCAAGCCCAAGAATGCAACAAGACCCCCAGCCTACA

TTTCTCAGCTCCCCTGGAGCCAGCTGATCCTGTAACGCTGCTGGAG

GTCAGTCTGAGCTACCAAGACTGTCCCTAGACAAAGGTGGAGTCCC

CCACACTGCCCAAGACCAAATCCCTCACTCAACCTGCTGAGGTGTG

GATGGGGAAACAGAGGCAAAACTGAGGCACCTGATGCATTCAGCC

TGCTGTGCAGCAGTGCCATTGACTGCCCTGATGTTCAGAGAGAAAC

GCACACAAGGTTTGCCCATGAGAATTGGGGAGCAGATGGCCAAGC

AGATAGGTTATGTCTGTTTTCTGAGTGATGAAGTCAGGAAGCCCTG

TGGCTCTGGAGGCCACTTGTGGTTCATTCTTTTCCCATATCCTTGGC

TTTTAGAAATGGTTACCTTCAGGACAGTGCAGCTGCATTTATCAGA

GCACTATTGCTAAGTTTTCTTTTCTGGCTTGTGTTTTTCTGGGACAG

TTTAGAATTGGGAGGCCTATTCTCATAGAACA

144
NON_CODING
CCTTCAGAAGCATGGGACTACCTCCCATCTAGTTCTCGTTTCTAAAC

(ncTRANSCRIPT)
CTAGGGGAGATGCTATCTTTGCTGCAATAATCTTAGCCTACATCTTG

GAATGGAAATGGCCTTGGTGGAAATGGTCTTCAACTCCTCTGGTCC

AAGCTCAGGCCCTGTGACCCTGGAACAATCCCCTTCCTGGTCCTCC

ATGTAGGAGCAATAACATTCCCTTGCCAGCAGCACCAGCCATTCTG

ATGATTAAATGGTATCGGACTCTGTTTTCCAAACTCAGTCATTCAG

ATGCCCCCTATTTTATTTCTTCCATGTCTGCAAATGATTATAATATT

TTTAAATGTAGGATGAGTCCTTTTTATTACACATAGAAATAGCTACT

GTAAATAGCAAACTCTAACACTGTGCCTAATTAGGAAATAAAGGTA

ACCATAAATACAGTAAAAATGAAACAATGTTATTATGGTTTAACCT

GATAGTGTGGCTTGCAAGGCCCTGGGCCTGAAGCCTGGGCAATAA

GTGAGAGTTAGAAAGGTGTCAAAGACATGATAGCAGCAAACTGAG

GCTTTGTACCCCACGGTAAATAGGACTGAAAGCAAATTCACAGGG

AGCAACTGATCCATTC

145
NON_CODING
GAGTGGCCACTTGATTAGAGACCTAGCACAGGAGGAAGAGATGGG

(INTERGENIC)
CAGGGAGAGTGACGGGGAGCAGCACAGTCCCTGGGAGCCCGAAGT

GGGTGGGCACAGGGCTCCCTAGGAGAATGGAAGGACATCTATGAG

CTGTAGCCCAAGAGGAAGAGGTCACTGGGGCTAGATGCGGCAGAC

CCTCGCAGGCTTTGGGAAGGGCTTCAGAATTCAGCCTGAGGGCAAT

GGGGAGCCCTTTTGGGATATTAAACTTGAGTAAGATATGAGCATAT

TTGCATCTTGAAAAATCATTATGGGAAGATGGCTGGGAAGAGAGG

AGGAGTGGCAGAAGAAAGATAGGTTGGAGACAATTGATTGCTCGA

TGATATAAAATGTTAAGTACCATGAATGATGCTGTTAGGCTGGAAT

GCGCCAAGCATAAAGGTGGGGCATGGCATCAAAAGGTAGGTCAAC

ATATTAAATAATTCCATGTATTGAAATATCCAGAAAATATATAGAC

AGATCTATAGAGATAGAAACTGGTCTGCCCAGGACTAGGGGTTGTC

TA

146
NON_CODING
CACTGGTCTGCCCTTCCTAAATTAAGTATGCACTTCAATTTGATGAG

(ncTRANSCRIPT)
TGGAAACAGTCTATCTGGGCAGTAACCAGGGAGCTTTGTGCCTAGT

AGATTGCTTCTGTTCTGCACTTCTTTGGTTTCCCACCTCAATGTAAA

AAATAGCTAGCAATGAAGTCCAGAAGTTGTCAATGGTTCATCCCCA

GAAGAATGCATAATGTCCAAAGTTGTATGTGTATGATGTCTTCAAT

GGTATTAAGTTATTTCAAATTCTTAGTTCACCTACATAAATCATTTC

TAACAAGCATCTTCTTAACCAACTTTATGCACAGTGTATGTTTGTAA

GTGCTTCTGCACGAATGTTTATACATGACTGTTTCCATAGTACTTAT

GTTTTTAAAAATATTCAGTCATTTCCTACTATAATCCTCATGTATCC

ATGTAACTGACTCAAAAATACTTCAGCCACAGAAAGCTAAAACTG

AGCAAATCTCATTCTTCTTTTCCATCCCCTTTGCATGTGGCTGGCAT

TTAGTAATGATTAATAATATGGCCAGCTGAATAACAGAGGTTTGAG

ACACAATTCTTTCTCAAAGGAGTCAGCTAAGCTGGGTCTACTTATG

GACAAACATCTAAATGTGTGGAAGTATCTGATATTTGACAATGGTA

AATTTCCACTTAGCTAGCTAGCATTGTCAGACTTCAATCTCCTCATG

GCTCTGGCCGTCCTGTTTTAAGCATGATAATTGTTGGCCACATCTCA

CATAGTTCTC

147
NON_CODING
AGTTTCTAGTTGACTTCCATCTGCAATAAATCATGTACAGGATGAG

(INTRONIC)
GTAATATACTACAACTTATGTCTATTGACTTAGGATTTTATCTTTAA

GAGGATAGATCCTAGATGTGAATAGCTAAGGAAGTTTGAGTGTTTT

CTCCTCCCTTGCTTTCAAATAGCTTTGAAAGATCACTTTTATAGTGC

ATGATAAATAGCTACATATGAATAATCTGATGGCATTCTGTAAGAG

TAACAGTGCTTCAAAATCGTAACCTGCTGGGATGTTTTGTTACATG

CCATCAAGTGTGATTGTATTCATGGAATAGTGTTTACTGTTGCTCAA

TATTGTAAAGGAAATAAAAGATAATTCCCTATCTGAGGGGAAATTT

CTCAAATATTTTAATTAAAAGGTCCCTACAGTTACCCATATAAACC

TTAGTCAAATAAGATAACAAATTTTCTTGATCTCCTTTAAAAATTCT

TTTATGTATAAAAATAATTATATTTATTAAAAACTCCAACAGTACA

GAATTATTTGGAAAAAAAGATAGAAATCTACCATTCTCCTATCCAT

GCCTGAGAGATA

148
NON_CODING
CGGAGAGCCCTCTTGCATGAGTTTCGGCTTTGCCAAGATTCCAGGG

(INTRONIC)
ACTTGAGGACAGCTATTGAGTTATGGTTACGTGACTGCCACATTGG

GGCTTGGAGGCATCTGGCAGATGGTTGGGAATGGGCTGGCACCAC

ACTAATTAGGCCACGATGATCCAGTTTGACTCAGGGAAACCCAGAA

GTCATAGTGCTCTTTGCAGAATGACACAAGATGTCAACATGCTTTG

TTGTGTACTTTGAACAGGGATTGGTTTCACAAGCTGAAAAGTTGAA

TCTGTCACATGTATGCAGCATAAAATCACAGCCGTGAGAACATGTA

TACAGCAGGAAGACAAGCGACTGAGCTAGGCACGGCTGACTAGCT

CTGAGCTTTC

149
NON_CODING
AAAAGCCCTCTCTGCAATCTCGCTTCTCGTGTCCGCCCCGCTTCTCT

(UTR)
TATTCGTGTTA

150
NON_CODING
AGGCTATCGGGAAACTCTGGTCCAGCCACAGTGGTCTGGCCACACA

(INTERGENIC)
GGGAGCCATGTAGAGACCTCCATCTCCAGCCAGGATGACACCGGTC

TGCGGTTCCCAGCTCGTCGTCAAGATGGGATCATCCA

151
NON_CODING
CTGGGATCTGCCAACGAAGATGAGCTCTTGCAG

(INTRONIC)

152
NON_CODING
CTCGGGAAAGGATCATCGCCGTTGAAATGAAAAGAGAGACAGAGA

(UTR)
GAAAAAAAAAAAGAGAACCCACATGAAGCTCTGAAACCAAACAGC

ATCCTGCCATGAGCTTCCCAGAGACAGAAGAGACTGGAGCAAAGT

CGGAAACACAGAGAAGCACGGCTTCCCCTCAGCACAGACCCTCCA

GACTGGGTCTCAGAGCCGTGCCACCCACCCTCCCACACAGCCGGCC

ACAGGGAGAACTGGTGCTAACCAGGGTGCTTGCTTTGGTCACGTTC

AACGCACTACAGAGCTACGACACAGGGAAACC

153
NON_CODING
TGTGGTACCCAATTGCCGCCTTGTGTCTTGCTCGAATCTCAGGACA

(UTR)
ATTCTGGTTTCAGGCGTAAATGGATGTGCTTGTAGTTCAGGGGTTT

GGCCAAGAATCATCAC

154
NON_CODING
TGATGGGCTAAACAGGCAACTTTTCAAAAACACAGCTATCATAGAA

(UTR)
AAGAAACTTGCCTCATGTAAACTGGATTGAGAAATTCTCAGTGATT

CTGCAATGGATTTTTTTTTAATGCAGAAGTAATGTATACTCTAGTAT

TCTGGTGTTTTTATATTTATGTAATAATTTCTTAAAACCATTCAGAC

AGATAACTATTTAATTTTTTTTAAGAAAGTTGGAAAGGTCTCTCCTC

CCAAGGACAGTGGCTGGAAGAGTTGGGGCACAGCCAGTTCTGAAT

GTTGGTGGAGGGTGTAGTGGCTTTTTGGCTCAGCATCCAGAAACAC

CAAACCAGGCTGGCTAAACAAGTGGCCGCGTGTAAAAACAGACAG

CTCTGAGTCAAATCTGGGCCCTTCCACAAGGGTCCTCTGAACCAAG

CCCCACTCCCTTGCTAGGGGTGAAAGCATTACAGAGAGATGGAGCC

ATCTATCCAAGAAGCCTTCACTCACCTTCACTGCTGCTGTTGCAACT

CGGCTGTTCTGGACTCTGATG

155
NON_CODING
TGGGCCTGTCGTGCCAGTCCTGGGGGCGAG

(UTR)

156
NON_CODING
CCCGCCAGGCATTGCAGGCTTAGTCGTGGCTACTGTTCTCCTGTGCC

(UTR)
GCTGCATCGCTCTCTCCCGGGAAA

157
NON_CODING
GGCGGCTATTCTAAAAGTGTCTTTCTATCACTGTTAAGGGGGGGGG

(UTR)
AAAGTGAGGTTCGAGGATGACGTAGGTAACTCTCCCCTCCCAAGTC

CATGTTCCAAGTGGCTATGTAAAGCAAGATGATACAGAAAGCTGCT

CTAAAATCTCACTGAGTGATTTCACCTTCGCCTACTATGAAATGTCT

CATCAGACCTGACATGTCTGAGATAACCAAGGTGATTCAGGATTTG

ATCAAAAGAAGTCTAGTAAGAATTAATTACACAGAAGCCTCCTTTC

ATTTCTATGGGCCAAACAAAGGCCATGGATAACCCTACCCGCTTTA

TGTCATTACCCATTGGGAAACACAATGGCTACTTCTGTTAGGGTAC

ATTGACCTTGGTCAAGCATCTTAAAGAAGGCAACCCTAATTGAGAG

CTGTCTTGGCTAATACTCTGCACCACAATTGTGATGTCCTAGTCCTA

CCACTAGAGGGCATGGTACAGCCTGGCAAAAGTTAAAAGGGGTGT

GGCAGCTCCCATCAGGTCTGGAGGTGGTCTATAAGCACAGTTGACA

GTTGTGCATTGGGATGGGTGGAGAAAGACGACAAGAGAGCAGAGA

ATCTGCTGATGTGGCTGCGCTTACTTTTAGTGACTTTATGTACTTAT

ATTAACAGCTGGAAATAGGTTGTTGGGTTTTGAGCAGGCTGTTATA

GTGAGGAATGTTCATTTTTAAATGTTCCTAACAGATTTTGCTTTTGA

AAAATGCTTGTTACATGAATAATTTGTGGACCAGGGATTGCTTTTCT

GAAGGCAGTATAGGGAACATGAATATTCAAGATGAAATACAAAAA

TTATGTTTAAGGGTCATAGTGTATAAGTAGCTTCCTAGGAAACCCT

TTGTGTATCTTTTCAGACTGGGGTGGGGGCTGAGCATGCTTGTGCA

GAAAGAAGCCATAGCCAGAAAGGACAGAATCTCTCCCCCACTCCC

TTGCCCCATAACCAAACATAAGCTAGCTAGTCTTGTCTAATAGATG

GGATTTACTATAGGTGAAGATAGCCCTCATATTCAAGGACAGAAGC

TCTGGCAGGAGTAAATTAGCAAAGCAGAAATAGTACCCTTTCATTC

TTGGAGGTGCTTTGAAATTTTAGGTAGAATATAATCGAAATTATGG

AGGTTCCTTAGTGCTCAATAATATAAGACCTGGTGTTATTAGAACG

AGTCTTTCTTATAAACTAACAGAGCAGGTATATGCCTGTTAGACCT

TAGCTGTGGGGTTCCTTTACTATTGGGTGAATCATTAGGTATAAAA

AATAATCATCAACCAGGCAAATTACTTTGCTTCCTAGCTGATGTCA

TCCCACATTGGTACAGGTGTTATTCAGTACTGGGTGGTTCAGCAGG

GAAGCCGGGTGGGACCAGTGTGTCTGTCATGAAACCACTAACTGCA

TTCCTGACTGAAGAGCCATCTG

158
NON_CODING
GTGAGGGTGACGTTAGCATTACCCCCAACCTCATTTTAGTTGCCTA

(UTR)
AGCATTGCCTGGCCTTCCTGTCTAGTCTCTCC

159
NON_CODING
TGTCCATGTGCGCAACCCTTAACGAGCAATAGAATGTATGGTCACC

(UTR)
TGGGTGTGGCCAGTGCCCGCTGTGCCCTGCATGATTCTGTGTTGCC

GCTGCTGCATAGTTCCCAGCCCCATCCTGTCCTGCTCACTCATGGGG

GCTTCCAGACCCCGGCCCCACCAGGGCTTGTGTCATAGGGAGCCCT

TTGCACTCCTCGTGTGTTGGCAAACGCA

160
NON_CODING
CCCTGGCAGGCTCCTTCTAAACATGCCTGTTGACCTGGAGCTGGCG

(INTERGENIC)
CCACCAACTCCAGGGCCTTTCCAGGGCCAGACAGGTAACACGCATG

AACCCGAGTGACAGCTCTGACGGGCTGTTTCGGTGTCAGGAGACAA

AGCTGGCAGGGGCAGGGGTGAACTGGAGGCAAGTCAAGTCACCTG

TGGCCTGTGGGGCTGAATGTGGGCCCGGTGTTGCCAGATCCTTTGT

CATAAGAAGCTAGAAATCCAGATTTTATGTGTGTGTAATTTGTAAA

TGCTGAAAGCTAGCCTGAATTTTTTTTTTTTTTTTTTGAGACAGAGT

CTCGCTCTGTCGCCCAGGCTGGAGTGCAGTGGCGCGATCTCAGCTC

ACTGCAAGCTCCGCCTCCTGGGTTCACGCCATCCTCCTGCCTCGGCC

TCCTGAGCAGCTGGGACTACAGGCGCATGCTACGACGCCTGGCTAA

TTTTTTGTATTTTTAGTAGAGACGGGGTTTCACCGTGTTAACCAGGA

TGGTCTCGATCTCCTGACCTTGTGATCCACCCACCTTGGCCTCCCAA

AGTGCTGGGATTACAGGCGTGAGCCACCACGCCCGGCCACTAGCCT

GAATTTCAATCAAGGGTTGGCTGATACTGTGTGTCCAGGGTGGACT

GGATTTGTCCTGGGGGGTTCTCTGGTTTGCTGCCTCCTGACCACATG

ATGGGGCCTTCGAGGTCGAGGACAACTGTTCCCATTAGATTGCACC

CTCTGCCCTCAGGTTCTTGAGGGTGTGTGGACACAGAGGCTTTCCA

TGGGATGTCCCTGAGCCGGCCCTTGATTGGGGCCTCACCATTTACA

GGGCCGTTTTATTCTGCAAACCGAAACTTGGGTCATGTGACCTGAT

GGGATTATGGGACTCCCTCCAGGTGCCCGAGACAAGGTTGATATTT

CCAAAATATTTTGGTGATTTAGTGGGACAAGCAAATGACAGAATAC

CGGAGAAGGCAGGGATCGTGGGTGTCAGGAGCCAGAGGGGAGGG

GGACAGATGTGCTGTGTACAGGACAAGGTGTCAGGTGACTCCTTCC

CAGCAGGGCCTCGCAGATGCACAAGCACGGAGCTGGTGGGTTTTG

CCCAAGAAAGGTCACGCGGCACATG

161
NON_CODING
CTGTCGCGATGGAGAAGTACTAAAATCTATGAAAGAGTTCTAATGT

(INTERGENIC)
AGATTTAAGGTCATGAGAAGTCTCCGGCAAAGTGGCATTTTAAAGT

AATCCCTCAGTCGTGGAGCTACTCCAATGAGAAGCCTGCCACTCCA

GGGCGCACCACGGAGGAGGATCCCCAGACAAGAAGACCTGGCTCC

CCAGAGGAGTGCGGAAAGCCAGCATGGCTAGAGGACACAGAATGA

GGGAGAAGACGGATCCGATCGCAGGCATCGGGAGTGCTGATTTTTC

TCCTTTGAAAAACAGGTTGCCATCTACCTTTTTAAATGTCCCACTGT

GTAGGAAAACTCTGGGGAAAGCTACGTCAGCAATA

162
NON_CODING
CAAGCCGAGATGCTGACGTTGCTGAGCAACGAGATGGTGAGCATC

(UTR)
AGTGCAAATGCACCATTCAGCACATCAGTCATATGCCCAGTGCAGT

TACAAGATGTTG

163
NON_CODING
TGTGGCCCACACGTCATCCGATGCTGCGTGCTCACACTTCACGGCA

(INTRONIC)
TCTCCAGCACCTGCTAGGCCATGCGTGTCCCTTGGTGACGCCGTGG

GGTAGATCCCTGATTTCAGTGGCCCTCATTTAAAGTACACGTGCAA

GTCAGACTGGGAGAGCCCCGACGGGACAGTCTCGGTCTGTACCTGC

ACCTGCCGTGCTGTGCTAGGCGGGTTTCCTTCCTGTGAGAGCTTTTC

TCACTGTTCACCAGGGACAGCAGTCACCTTCCTAGGAGTTCACAGG

CAGTGCGCATGTGGGAGCGGATCTGGGGAGACCTTCATTGGCCGCC

TCTGATGTCCGCAGTGTGTCAGGTCACCAACA

164
NON_CODING
TACCAAGAATGCTGTCAGGGTCATTGCCTACAAACTGATGATGCTG

(INTRONIC)
TGCAGAATTGCGCCTCTACTGTAAGGCTTTCCCGGTCCTACTTGGCG

AGTCTTAAT

165
NON_CODING
CCCAGAAGGCAGCCGTATCAGGAGGTTAG

(INTRONIC)

166
NON_CODING
AACTGAGGACGCGTGGATTCTACTCAAGCCTCCAAGTAGTGGCATA

(UTR)
TCAGTCTTGGAGCTCCTAGCTGGTGATACGGAGAGGGCTTTGGAGG

ACTTGGGACAGCAGGGCCAATTTTTTTGCCCAAGTGCCTAGGCTGC

TAACTCA

167
NON_CODING
GATGGCCACGCAGATCAGCACTCGGGGCAGCCAGTGTACCATTGG

(INTRONIC)
GCAG

168
NON_CODING
CACAGCGGAGTCTGTCCTGTGACGCGCAAGTCTGAGGGTCTGGGCG

(UTR)
GCGGGCGGCTGGGTCTGTGCATTTCTGGTTGCACCGCGGCGCTTCC

CAGCACCAACATGTAACCGGCATG

169
NON_CODING
TGCTAGTCATGCACCTCAGACAGTGCAAGGTGCTTCCTTTGATCTAT

(INTRONIC)
CATGTCAGCAGTGGGAGAGGTCCTTAGCCTAACAGAGGTCTGACTA

AAAGAACAGCCTTCAAAGTGAGTGTCATTTTCAGAAATAACCATGC

TCTGCCAGATCTGTATGGGGTTTTTTAATCGCATGCTGCTGACAGA

ACGTTTC

170
NON_CODING
CGTGCTCATCGTCCATAGTCCCATATTTTCTTATAATAAACAGTAGT

(UTR)
ACTGGCAGGCACAGTAGGGGCACAAGGCATCTGTCTTATTCAAGAC

AAGTTTGAGACACTGGAAAAAAAGATACTTGTTGTGTGTGTTGGAC

AGAGTGGCGAGGCTGAGCACTGTCACAGGGGCCTCCCATGTTAAG

AGGGACTGTGGGGATGATGTCAGAACAAGACGTGGTGGATTTGAG

GTTGATCGAGTATTAATACTACTGCCTCTCCTTGTCTTAGTGGGTAT

TTAAAATAGTAAATAAGAGAGAGGAAGGAGGTGACGTTCAGGTGC

TGTGGGAAGCAGGCTTGGCGGAGGGGTATGATGATGAGACCCTCA

TTGTTCACTGGCTCCATCGCACTCCTCCCTGGGGCCGTGTGCCTGTT

CCATTCTTCCCACCATTCGAACTGAGCGAATCTGGCAAAGGAGACA

CGTCTGTGGGAATGCGTAGATTCCGCCTCGGAAGAGAGCTAGCGCA

ACACTAAGAAAAGCAGGCTTCTTGTTTATTCTCAGGACCTTTTTGTA

ACAGGGCTACATTCTGCAAACTGCTTACAAAGGAAGACTATACGTC

TTAACAAATTATTTAGCCACTGAGTCCTCCCGATTCGGACCTGTTTT

AGTAATGGCAGAAGAATCCCTGAGCAGGTTCAGGTGCCCTAGATG

ACTAGGGTGCTGAGCTCTGGCGCCTTCTGTCCCCACTCTTTGCCTCC

CCGCCCCTTCCCTGAGCCACCCCAGCAAGTGGGTGTCTTTTCTCC

171
NON_CODING
AGAGGGCTGCTCAACTGCAAGGACGCT

(UTR)

172
NON_CODING
TCTGGGGTCACCGAGAAAGTCTAAAAACAGGAGGCTGAAGGTACT

(UTR)
GTGATGGCTTTAAAAATGGCCACCTTATTAAATAGGGATTGTATCA

ATATTGAAATGAAGACAATCTTTCCAACTTTGGGTGTTTCACTTGCT

GTTTTAATTGTTTGTTTTTAACACTTTGTAGGTTTGTGTTTTCATAAT

CTTTAATTTGAAACTCATGTGTCCTCATGGATCGTGGATGCCTTCAT

TTCTTGAGCTCTCAATGCAGACATTTAAATGGCTGCAATCAGTAGA

GTGACCCGCGGATGGCATAAATGCACCTCCTTTTCTTGGCCTTGGA

TCTATGGGTCTGGGATTGTGGTCATCTCCTCAATCCTCAAAAAGAG

GCTGAATCAATGTGGCCGTGGGTGGGAACTTACATACAGAACCCA

ATGAAGAACTTGACTGTCTAAACAAGGGGGCCTCGCATGGAGCTGT

AAAGCATC

173
NON_CODING
CCTGGCTGAGTCTAGACGTCTGATAACCACGTAGGTGGGTAAGGTA

(INTRONIC)
ACCACTGGGATGGCTGGAAGGTGTTACCCAGGGAAACTGAAGGCC

AGGATGAAAATAAAAGCAAACGGTTTCCCCTTGGGCAATGACTGC

CATCAGGATTCTGCTGCTGATAAAATGCTGCTCCTTTGTTCTGCTTC

CTGCGTGTTCATCCATATGATAGCTGTTAGACATTTCATTCAGCTTT

CACCCACCTGGCACTGCTTCAGTGCCAACCAACGGCAAGGTGCTCC

CCAGCTGCCATGGGGAGCCGGGTACAAATAGACCTCAGCGAAGCC

CTGCGTGCATGCAAACTGCGTTTGCCTTTTGCATTCTGCTTTTCTCT

CGGGGCCATGCTTGGGACACTTACACGC

174
NON_CODING
ACAATGGTGTCTTCAGCGGCCGAAAGGAGGGGCAGGGGAAGCCCC

(INTRONIC)
AGCAGCAGGAGCAGGTGTGTGGCAGCCCTTCACAAGGGGCTTTCAT

GTCTCAGTTGTATGTTGCCAGTGTCACTT

175
NON_CODING
TCCCTGTGTAGGATGGCTTCCCGTTATTTTTTTTTTAAGCAAAGTAA

(UTR)
ATGAACATCAAATTTCCATAGTCAGCTGCTGTCTTTCTGCCCACTGA

GAGCTCTTTGGTGAAGGCAAAGTCCTCCTTCTTCATTAGCGGTCTCC

CATGTGGGGCCACATCTTCCCTCACCAGGAACCCAGTGGGCGCGCT

CCAGCCCCCCTCAGCTTGCCTTTTGCGTGGTCATTAGAGCTAGGGC

ACACGTCATGCTGATTC

176
NON_CODING
TGGGGCCAAGACATCAAGAGTAGAGCAG

(ncTRANSCRIPT)

177
NON_CODING
TTTCTCACCTTGCTGCGGCCTGCTGTTTGGCAGGACGACTTGACTGG

(INTRONIC)
CTGCGCTGTGGTTTCTGCGCCTGTGATGGCTCCTTCTGAATGCCCTC

TGAGC

178
NON_CODING
TAGGCCCGTTTTCACGTGGAGCATGGGAGCCACGACCCTTCTTAAG

(UTR)
ACATGTATCACTGTAGAGGGAAGGAACAGAGGCCCTGGGCCCTTC

CTATCAGAAGGACATGGTGAAGGCTGGGAACGTGAGGAGAGGCAA

TGGCCACGGCCCATTTTGGCTGTAGCACATGGCACGTTGGCTGTGT

GGCCTTGGCCCACCTGTGAGTTTAAAGCAAGGCTTTAAATGACTTT

GGAGAGGGTCACAAATCCTAAAAGAAGCATTGAAGTGAGGTGTCA

TGGATTAATTGACCCCTGTCTATGGAATTACATGTAAAACATTATCT

TGTCACTGTAGTTTGGTTTTATTTGAAAACCTGACAAAAAAAAAGT

TCCAGGTGTGGAATATGGGGGTTATCTGTACATCCTGGGGCATT

179
NON_CODING
AATAAGAAAGGCTGCTGACTTTACCATCTGAGGCCACACATCTGCT

(ncTRANSCRIP)T
GAAATGGAGATAATTAACATCACTAGAAACAGCAAGATGACAATA

TAATGTCTAAGTAGTGACATGTTTTTGCACATTTCCAGCCCCTTTAA

ATATCCACACACACAGGAAGC

180
NON_CODING
GCTGAGCCCTAACTGATACGCTGTGTTTCCAGTGTCCCTCATCCACT

(INTERGENIC)
AGACTCAGTGGTGTCAGGAATGGTGTGGTATTTTGTTATAAATTTA

ACTCCTTAGATGGACACACAGAGAGCCTCGATAAATATTTTTAATC

CATCAATGCAAGGAGTGTGGTTGTCAGAAGTCAGCTAAAAGTCCA

AGTTTAAATCTAAGCTCCGCCGTTCACAGCTTGGGTGACCTCAGCT

TCTTTTTTGGAAATGAAGTTCATATTTTCCGAGCACTTTTTCTGTGC

CAGGTGCTTCCAAATGTATCTCGTTTAATCCTCACAACATACCTCAG

AGGAAGACATCATTTTTACAAGTAAGGAAATAGAGGCTCAGAGAG

ATGAAGTGGTTGACCCGGGCTGTCTATCTTGTAAATGGTGGGCTGT

GATTCCCACACGACTGGAGTTT

181
NON_CODING
TTGGCTTATCAGTTGGCATGACCTCTGAAGATCTTTTTGCTCTGAAT

(INTRONIC)
GTTTTAATCATCAAGTTCTGGTGGTTATCCAAGGTGATCCTAATCTA

CTTTGGGGTGGAGGGAGGAAGTGGTGTCAGGAGAGATCAAACCAG

GCCACCTTGAGCTGAAAGCTCTGAAGGAGAAGGATTCCTTGAAATG

GAGGTAATTTTTGAATTATAATAAGTGAGAAGACTGCAAGGGAGA

CAAGCTGAGGGACAAATGCTCTGTGCTTTTCTCCTCACTTTCACAA

ACAGGAGGAGAACTTCCACTGACCTAGCAGTAGTTTGCTCCTCCAG

GCTGTCATGTCTTCTGATCATGTCTTTTATGAGGTGAATTTCTCCTC

ATGAAAGACTAGACTTTAAGGAGAGATTCTGTGCAGGTCCCTACAG

TGTGGAGATGGATTGATTGGGCCTACAGATTGCAGCTAATC

182
NON_CODING
GCGTGCATGTGCGTTTTTAGCAACACATCTACCAACCCTGTGCATG

(UTR)
ACTGATGTTGGGGAAAAAGAAAAGTAAAAAACTTCCCAACTCACT

TTGTGTTATGTGGAGGAAATGTGTATTACCAATGGGGTTGTTAGCT

TTTAAATCAAAATACTGATTACAGATGTACAATTTAGCTTAATCAG

AAAGCCTCTCCAGAGAAGTTTGGTTTCTTTGCTGCAAGAGGAATGA

GGCTCTGTAACCTTATCTAAGAACTTGGAAGCCGTCAGCCAAGTCG

CCACATTTCTCTGCAAAATGTCATAGCTTATATAAATGTACAGTATT

CAATTGTAATGCATGCCTTCGGTTGTAAGTAGCCAGATCCCTCTCC

AGTGACATTGGAACATGCTACTTTTTAATTGGCCCTGTACAGTTTGC

TTATTTA

183
NON_CODING
CCTGCCATGCCGCTGCCACCGCGGAGCCTGCAGGTGCTCCTG

(INTERGENIC)

184
NON_CODING
GCTCACTGTCTTAGGCCTCGTCTTGGTTCCTGCATGCTCCACCTGCC

(INTRONIC)
TGTTCTGGTCTCTAAACTCAATTGAATGACTTGATGTTACAGCTTTC

AAGCAGAGAAGTGTGGGGTGATGGTGGCAAGACAGAGGGGCGCCA

TTACTCTCATCGCTCCTTTTGTGGTGGCAGTCGTATTCTCCTCCTGG

GGTTTCTCTTGTGTTGGCGAGTGTATCAAAGTGAAGTGTGTTTCCAT

TGATTCAGTAACTGTTGAGTGTGCCCTCAGTGTGGATGGCACCAGC

CCAGTGGGGTGCACTCCTCAGCATTCGGGATTCTTCCTTTTGTCCCT

CTGGGGCTTGCACACAGGCAGGCACACTCACGTGGAATC

185
NON_CODING
TTTGTGTGCACCCAGTGAGAAGGTTTATTTTGACTTTATAGATGGG

(INTRONIC)
ATATCTAGAGCTGGAGTCCTATATTCAG

186
NON_CODING
AGCCCTGTGCCTGATTCTTATAATAAGTACATATATAAAGTAACTA

(INTRONIC)
TAATTTTTATTTTAATCCAGTTAAATGGCTAGCAGAAGGCTTTGACC

AATGGACCTGGGCATCCAAAGTTACCACATTTGTTCCTGGGATTGT

AGAGATGTAGAGACCAGGTTTTGCCAAACAAATCCCAAATATGGC

CGGTGCAGTGGCTTATGCCTTTAACCCCAACACTTTGGGAGGCTGA

GGTGGGAGGAATGCCTGAAGCTCAGGAGTTTGAGACCAGCCTGGG

CAACACAGCAAGACCCCATCTCTATAATTTTTTTTTTAATTGGCTGGG

CATGGTGGTGCATGCCTGTGGTCCTGGCTGCTTGGCAGTATGAGGT

GGAGCCCAGGAGTCAAAGGCTGCATGGAGCCATGATCACGGCACT

GTACTCCAGGCTGGGTGACAAAGTGAGACCCTGTCTCAAAGAAAA

AATAATAATAATAATAATAATATCCAGGCTGGGGGCGATGACTCAC

GCCTGTAATCCTAGCACTTTGGGAGGCCAAGGGGGGTGGATTGCTT

GAGGCCAGGAGTTCAAGACCAGCCTGGGCAACATGGTGAAACCTC

GTCTCTACTAAAAATACAAAAATTAGCCAGGTGTGTGGGCACACAT

CTATAGTCCCAGCTACTGGGGAGGCTGAGGCACAAGAATTGCTTGA

GCCCGGGAGGTAGAGGTTGCAGTGAGTGGAGACTGTGCCACTGCA

CTCCAGCCTAAAAAAAAGAAAAAAAAATGGAAATACCCCTCAGTA

GGAGAGAACATGGTCTACATTCTGCCTTCCGAAATCCATATTAACA

TTTGGTGGCTGCTTGTTGAAGCTAGGTGATAGCATTAGAGAGTCCT

GGTGTCATGAAAGCCAGAGCATCCTAGTGAACTTTCAGGGATGGG

GTGGAAGGTGGAGAAGAAATGGGCTATGGAGTAGTTCAGAATGTC

TCCAATGGGGCTACTTTTGAGAGAGAATGCTCTCTTTCACCATTTGT

CTTCCAGGATATGAACAGAATATAGAGTTGCTATCTTCCTTAGAGT

GTGAAAGTCTAGGCTGTCTGCAAGACAGCATGTTATGGTTTTTATT

ATTTTTTATTGATTGATTGATTGTAGAGACGGCATCTCGCTGTGTTG

CCCAGGCTGGTCTCAAACTCGTGGCCTCAACTGATCTTCCCACCTC

AGCCTCCCAGAGTGCTGGGATTATGGGTGTGAACCACAGCACTTGG

CCATGGTAATGGTTTTTAAAAAAGGGATCACCAGCTGTGAACTTGG

AAGCCTTAGGTGTGAACTCTGTGATATTATTCAACCTCTCTGAACCT

ATTTCTTACCATCAAAATGAAAGTTATCTGCCCTATTTAGCTGATTG

GGTTGCTGTGTGGCTCAAATGATGCAGTCAATTTGTAAACTGTAAC

GTGCTGCACAGATGTTAGGTATTCTGGTCTTCTGATTGTGTGCTTGG

CTTTCTAGCTGCTTGAAGCCGCTCAGAGCTTATGTATCACCAAGGG

TTAGAGATGTAGTGCTACCCACCTCTTTCATCCTGCACCCCCAATTT

CTCCACTTGTCCATTTCCACAAATGTATCCCTGGAGACACTGTGATA

ATTTC

187
NON_CODING
GAAACTCAAGGCATTTATCTCTTTGGGCTGCTTGTCCTTGCCTGAGC

(INTRONIC)
TGAAGCCTGATGCCTCCCATAAGTTG

188
NON_CODING
TCCATTTCTTCGTTCCACATGACCACAGTTTGCAAGTGTATTCCATG

(INTRONIC)
GAGAAGTGGAGTGATTGGGAATTAC

189
NON_CODING
GGTCCAGGAGTAAATGCCAATTTCACATATAATGTAGACAGATTAT

(INTRONIC)
CTGATGGGCATCTATCAGATACAAAGTCTGCCCCTTTTTCATGTCCT

TTTTGTCTAAATATAGTCATTATCATCATCATCATCATCATCAAATC

ATTTCATCACCATCAGAAATGCTTATACATTATCCTGATGTATACCA

AAGCTACTGTTTGGAAAGAAACTAAAATAAAAGTCCAGGTCACTTA

ACCATACAGGGCTGATGTTAGATGAAAGCAAGCATCGATACCAAA

TGCAATTTTACATAATATTACCTGTCAACAAAATATATTTGGACAG

CCGCATGGTAATTTTACACATTATGTGTAAACAAAGTATTGGTGGC

ATCACATGGTAAAAACTCAGTAATTTCACCTCAGAAATTCTTCTTC

ACATCAGAAATGTAGTTTGTGCATTGAGGCTATCTGATTGATGTTT

ATGCCTCTCTGCTTGGGATATATTCATGAGAATAAATAATAGAAAC

CTCTCCCAATGAATGCAGTCTGTCTGAATTCATTGATCTTTATGCAG

TGGAGATATTCTGCACAAGCCGCTA

190
NON_CODING
CGTACTCTTGCTAGGGCTTTTCATGGAGATGTAGAAATGGTAGTAA

(INTERGENIC)
GTGCCAAGGCCCCAGAACCCTCATGTTTGGGTCCGACTCCCACATT

GCCAGAGACTAGGCAGCTCACACAGGTGTCCCAAGCTGTCTTTCTC

ACAGGCCGCATTGAAGGCATTTATGAAATGAGACCCCCTCTTCCTC

ATCCGTAGTGACAGGGCTG

191
NON_CODING
TGGATAAAACTTCAGCCGGCCTTCTCTTTATGTGCCTGGCGCCTCTC

(INTRONIC)
TTTTCTCTGGGTTTTTGGAAGTCTGCCTGCCCAGCCCCTCAGCTGGG

GCCTTCCCCACTTCTGCCCCGCCCCACTGGGTCCTCCCAGGGTAGG

AGGCAATCTCTGACTGTCTTCCGAGGCTCTGTTGCTTCTCCTTCATC

ACCAAATGCCAGGAATTTGTCAGATGCTGTTTGTAACTCAAAAGAA

AGAAAGAAAAAGAAAAAGATACAGGAAGGAAGGAAGGCAGAAAA

AGAGAAAGAAAGAATGCGTGCAGCAGATGTTGGGAAAGTTAATTT

CTTCATTATTTTGCATCCATCCCAGTTCGGATCTCAGCATGGGGTAG

GGAATCCTCTGTTGTCCCCATCTGTCGAGGCAACAGTGAGTCCCAT

CATG

192
NON_CODING
CAACCAATTGAGACACTGAGGCCTAAAGAAATTATTGGCTATAATA

(INTRONIC)
ATGAGGTGATTGCCTTAGCTATCACGCCAGATTTGCTCTTTTGTTTT

CTCCTGATATTTTAAACTCTTCCTTGCTGGAATATTAATAACTCAAA

GATAAAAAGGGTACAACTTGTTTCCATGTGGGAGGTAGGAAGAAC

ATTGCTTTTGGAGTCAGTTCTAGGCCTGGTGACTCTTTGACTTGCCA

GTTGTGTGCCATGATCACTCCAAGCATCCATTTTCTCATGTGTAAAA

AGCATGTTAAAAATTTTAAATGAGGAGTTTAAAAATTACACTCCCA

GTAGGCTTACTATGAGGACTAAAATAAATAAAAGTGTGAAATGCA

GTGCCAAGCACATAATAGCTGCTCAATAAATGGAAGCTAAATTATT

TTCCACAGTTATCTTTCAAATTTCACTTTGATCAGTTTTCACAGACT

ATCTTCTAAGCAAATTCTGTAGGTGTTTGCCTTCGGAAAAGTGCGTT

TGTTGTCAGTGAATGGTTACAGGGAAAAGGAGATACTTGTCATGCA

GCTGGAAACATGAAAACTTGGCCCTGTGTTCTTAAAAATGAAAACT

CCCTGCAGGATGGGTCAAGTTGCTACCATAGGCTGGAGCCTATGAT

TCTCAGAGCAGCATCACTCTTAATGGCACTGTTCTGCATGCCCTTAC

CTTGCTCATTTTGCTGGGCTCAGTACTAATTTTCATCCCCTAGGCAG

GCAAACTAAGTGTCATTGTGGCAGTTCCTTCCATACTAAGAGGAAG

CATTGATCACTAAGAGTCAGCATGGTTTACTATGAGTAAATTAAAC

CAGACCTATCTTGACCTCTGACAAGGTTGTCGTGATGACCATGTCA

GTTTGGTTCCTTGCTGTATGCCCAGTGTCTGA

193
NON_CODING
CGCCATGGGGTGGTTCGAAGAACCATGATGAAGGCTGGTTCGAATT

(ncTRANSCRIPT)
GTGATGACCATTTTTGTCCACATCTCCTAGGACCCATAAGCCAGAG

TTTCTCTGGAGCTTATAGCTAGAAGGGGTTCTGGGTCCTGGAGTGC

AGGCCTGTCAACTTTACAGGAGAGCACTAGATTGCTTTCTGAAGTG

GCTGAACCAGGTTATGCTTCCATCAGCTGTGTATGAGCATCCCCAT

CTTCTTGACCACACTTGAAGCCATCAGTTTCCTTGAAGCA

194
NON_CODING
TATGTGCAGCACAAAATGTCGTTTCTTATGTTTGTTCCTATAATGCG

(INTRONIC)
TTCTGGCACTTATGTGATGCTTCACTTAAAAATACTTAGCTCTTTCT

TTTTCCCCCCAAATCAATAACTTTAATGCCTGCTCCAAATAAGCTAA

AATAGTTTTGATAATTTTCTAGCAAATGGCAAACTTTTACCTTTTAG

CAGTTAAAAACTTTCTGAAATATTTAAAAATCACTTTGACAGTATA

TTAAAGTGAGTGAAAGTCTTTATCTAAAGATCCCACTCAACTTTTC

GTGTACTTAAAATATTATAGGAAAATTGAGGAGGTGACTTATTATA

GAAATAAGAAGACTTAAATGAATAAATTTTCTGAAAGGAAAGTGA

CTCTTGTGAAAGATCTCAAATGGCAGACTTCATTTTGTGTTTTATCT

TTGCTGGCTTTTACTCACCTACACTCATTTACAAATCCATGAAAATG

GTTCAAAGGTCATTGGTGAAACTTGAGAACAAATGCAAAACTTCCA

ACTATGGGAAATAGGTAGAAATACATTTTAAAAACATTGGGTTTAT

TAAATTGGGTTGATTTTATTACTAATTTATAAATCAGTCAAAAATGT

AACGCCAAGTTCATTGTCCTAGAGCGAA

195
NON_CODING
GCACTGCCGTACTCTTGGGAAATTTGTCCAAGGCCACCCGGCTGAG

(INTERGENIC)
CAGCGGTTGAACCAGGACACCATCAGGCATGCGTTTCTTGTCTCCA

CCACACCCTCAACCCACTTCCCAACGCGCCTTGCGACAGGGGCTGC

GGTATTGCATCCACATGACTGATAAACTAGTAAACACACATGAATT

CATTTTAAAAGTGTATTCAATCAGTTAGGTAAACTAAAAACCTTAA

GTCTTCGTTCGATTTGGAATGCAGCCAGAGAACAAATGGAAAATTT

TTCAAGGTAGAGAAGATGAAAACTCAGAACGCCCTCTTGTGGCATC

TCTACCCACCCTAGGAACACTATGGCTCTTCCCCTACACATGGTGA

TTGCTAACCTTGCTACAAGACGTTGGACACACACACACACACACAC

ACACACACACACACACTGAGGTTCCTTTTGCCCCCTCACTTTTGAGC

CAGTGACTACTGAAACCCTCTCCATTGTTGCACCACCAGCAATGCC

CCCATCACTTCCTCTCATTTACTTCCACAGGCTGGTTCATCCTCAAA

GCCCTCCTTACGTAGATCTGTG

196
NON_CODING
TCTGGCAGCTCTTAGTCATGTCTTGGAGGGAGGACGGGCATCCAGG

(INTRONIC)
GCTGACCGGTCAACGTCCAGCACCTCCCAGGGACTATGGGAAGACT

GAGTGGTGGGTCTCGTCCTCTCGGGATACTTGCGCTT

197
NON_CODING
CCATCCAGCTGATCGGCTCTAGTTCTATGGTCCTGTTGGCTTCTAGG

(ncTRANSCRIPT)
ATTCCTTGTTGTTGTAGTCAATTGGGGGAAGAAGGTGCAGAGGGAG

TGCACAGAGTTAACATCCTATCAGCCCAAGCTTCACCTCGGCACCC

GAGTCTCAGGCAGTCTCCCTGGCTTCTACATAGGCAGTGCTTCTTCC

TCATTGTGTGGGGCTTTGATTTTGTAATTCCAAGAGCCTGGGGCTCC

TGGCAAGGAAAATGGTTTTCAAATAATGGTTTCGAGAAACAAAGCT

GGGGAAGAGGCAATGTAAGCTCAGGCTCTGGCAGGCAGGCAGAGA

TCCTGGGAAGGCTGGGTGCTGACTGCACATGGAGCAATGGGAAGG

GATGCTGGTGAGAGGAGACGGGGGCACTTAAGCTCCGGCCCCAGC

TCTGCTCTCAGTGCCCGGCTCTGTGGTCTTGGGCTGGCCCCCTCCCT

TCTCTGGGCCATAGTTTTCCCATCTGTATAGCAAGGCCATTGGACA

AAATGGTCCCTCTGCAGATGTGGCTTCTGAGTTGTTTGTGCCTGAG

GGACAGCCAGTGTTGGGAAGTTCCCCCAGGAGGTCCCTGAGCCGA

GTCTGAACTTTG

198
NON_CODING
TGTTCTGAGTCAGGCATGGAGGTATCTTCTCATAATCAAAAGATAA

(INTRONIC)
GCAAGAAACAGTTAACTGCCCGCAAGGATTCCACAATTTTGAATCC

TAACTTCAGATGCTATCTCCTTACCTCATTTGGCACGTGCATTTGTG

CTGGTATACATACCTTTTTCAGCACATAAACTCATTTGGCACATGTG

CCAAGGATTGCCAACTATCTTA

199
NON_CODING
GTCACCATGGAACGTGTGCATAGATGATGTTCCCGTGTCTTTCA

(INTRONIC)

200
NON_CODING
CAGTTCTCAGACATTTACGGGAAAGCTCTGGTGGCGTGTTAGATGC

(INTRONIC)
AGTTCATCTCTCTCTGTTTGCAGCGCTCTCAATAGAGACC

201
NON_CODING
CTTGACTGTCACGATAGAAAGAGGAAGCAGAAGAATGAAGACAAA

(INTRONIC)
GCCATTTAAAATTTTCTTGTTCTTTACCTTTTGCATAAAAGGTATTC

AGTTCACAAATGATGTAAAATTTAATTAAGGCAAGTGACTGTCCTG

AGAAAGTCATTAAAACCCTCATGTCATTTCTCTAATCAAAAGGCTG

CCACGCTTCTATTATTTCTTTATTACAACCCTTTATTTTTATTTCTTC

AAGTTAAACTGGAGCCTGAGCCATCATAAGCCTCTTGCTAGTGATT

TTTTAAATCAGTGATTTACACTTTGAAAAACCAATTTTTTTTATTTTT

CCAATTTATATTGGTTAGATCCATAGGGTCACTTTGA

202
NON_CODING
GGCTGATGACTTCTCACAGTGTATCTCAAAGCATTATTGCATGTCCC

(INTRONIC)
ACTTGGTTGATAGGGCATCTCTAGCCTGACAGATTTATCTGTTGAG

AACAGGATTATGCATTTGAAACCAGTTTAATTCTTAGCAAGACAAT

GCACATGTCTTATGTAGATTTTGTTGTTGGTTTTTTTCTCCTTCGTAA

GTTACTCGGGGAAAGTCATGTCAATATAAATCAGTGGTAATGAAAT

CAACATTATAGCATCTTTGATAATGCATTTGCTAAAGCCTTTCTGGA

CGTTTACCCAGCTCTCAATGA

203
NON_CODING
CAATTTCCACCGCGGCCATTTGTTAAACGCATAGCTGCCATCTTCA

(INTRONIC)
GTGATTATTTCCAAGTAACATCTATGTTTCTGAATAAAAATCCATTT

GAATCTCAAGTCAGATTTGCCAG

204
NON_CODING
ACTCGGTGAGCTTAACCGTACACTGAGCTGGTGCAGCCGGGGATCC

(INTRONIC)
ATCTCAGCCCCTGCTTCCCACTCAGCCAGACCCAGACCCTGCATTC

CAGCTTTGGTTGTGTGGATTCTCTAGAGAAGGACCCTTGGCTGTTTG

TCCCCATGCATTTCTTGATGTCAGGCAGCAGCATCTGCCAGTTGTG

ACTGTCCTGCCTGGACTACAGGTTTGGTTGGGTGTGCCCTACAAAC

CTTGCTCCTCTCAAACGTGCTCTGCCGTGGTGTAGCTTCTGGCGCTT

CACTCTTCTGTCCGCTGGGATCCCTAGGGGGGCTGGATGCTCGTAC

CAGACTGTGGA

205
NON_CODING
GTTTGGCGTAATACGGAAGCCCTCAGAGCAGTACGCTTCAAGCAGT

(INTRONIC)
TTATGAAGTCCTTAGCGTCTTTCTTATGGCCGAAAATAGTTTGGAAT

GGGTTGAAACAATGGGCCAACCTAACCAGATGAAACTG

206
NON_CODING
ATAAATAAGTGAAGAGCTAGTCCGCTGTGAGTCTCCTCAGTGACAC

(ncTRANSCRIPT)
AGGGCTGGATCACCATCGACGGCACTTTCTGAGTACTCAGTGCAGC

AAAGAA

207
NON_CODING
TCTATGCGGCCACCCAGATTTCTTGGGATCTGATGCTAGACCTTGG

(INTRONIC)
AGG

208
NON_CODING
CCATATGAAGTAAGGACTGATTATCCTTTTTTTATAAATGAGGAAA

(INTRONIC)
TTGAGTCACAGGGGGGTTGGTAGCTAGTCTAGGATCACACAGTTTG

TTGGAGGGGGTAGTGTATGCACGTGCCCACTTTTTCA

209
NON_CODING
GGCCCTGCTGCCTAAACTGTGCGTTCATAACCAAATCATTTCATATT

(ncTRANSCRIPT)
TCTAACCCTCAAAACAAAGCTGTTGTAATATCTGATCTCTACGGTTC

CTTCTGGGCCCAACATTCTCCATATATCCAGCCACACTCATTTTTAA

TATTTAGTTCCCAGATCTGTACTGTGACCTTTCTACACTGTAGAATA

ACATTACTCATTTTGTTCAAAGACCCTTCGTGTTGCTGCCTAATATG

TAGCTGACTGTTTTTCCTAAGGAGTGTTCTGGCCCAGGGGATCTGT

GAACAGGCTGGGAAGCATCTCAAGATCTTTCCAGGGTTATACTTAC

TAGCACACAGCATGATCATTACGGAGTGAATTATCTAATCAACATC

ATCCTCAGTGTCTTTGCCCATACTGAAATTCATTTCCCACTTTTGTG

CCCATTCTCAAGACCTCAAAATGTCATTCCATTAATATCACAGGAT

TAACTTTTTTTTTTAACCTGGAAGAATTCAATGTTACATGCAGCTAT

GGGAATTTAATTACATATTTTGTTTTCCAGTGCAAAGATGACTAAG

TCCTTTATCCCTCCCCTTTGTTTGATTTTTTTTCCAGTATAAAGTTAA

AATGCTTAGCCTTGTACTGAGGCTGTATACAGCCACAGCCTCTCCC

CATCCCTCCAGCCTTATCTGTCATCACCATCAACCCCTCCCATGCAC

CTAAACAAAATCTAACTTGTAATTCCTTGAACATGTCAGGCATACA

TTATTCCTTCTGCCTGAGAAGCTCTTCCTTGTCTCTTAAATCTAGAA

TGATGTAAAGTTTTGAATAAGTTGACTATCTTACTTCATGCAAAGA

AGGGACACATATGAGATTCATCATCACATGAGACAGCAAATACTA

AAAGTGTAATTTGATTATAAGAGTTTAGATAAATATATGAAATGCA

AGAGCCACAGAGGGAATGTTTATGGGGCACGTTTGTAAGCCTGGG

ATGTGAAGCAAAGGCAGGGAACCTCATAGTATCTTATATAATATAC

TTCATTTCTCTATCTCTATCACAATATCCAACAAGCTTTTCACAGAA

TTCATGCAGTGCAAATCCCCAAAGGTAACCTTTATCCATTTCATGGT

GAGTGCGCTTTAGAATTTTGGCAAATCATACTGGTCACTTATCTCA

ACTTTGAGATGTGTTTGTCCTTGTAGTTAATTGAAAGAAATAGGGC

ACTCTTGTGAGCCACTTTAGGGTTCACTCCTGGCAATAAAGAATTT

ACAAAGAGCTACTCAGGACCAGTTGTTAAGAGCTCTGTGTGTGTGT

GTGTGTGTGTGAGTGTACATGCCAAAGTGTGCCTCTCTCTCTTTGAC

CCATTATTTCAGACTTAAAAACAAGCATGTTTTCAAATGGCACTAT

GAGCTGCCAATGATGTATCACCACCATATCTCATTATTCTCCAGTA

AATGTGATAATAATGTCATCTGTTAACATAAAAAAAGTTTGACTTC

ACAAAAGCAGCTGGAAATGGACAACCACAATATGCATAAATCTAA

CTCCTACCATCAGCTACACACTGCTTGACATATATTGTTAGAAGCA

CCTCGCATTTGTGGGTTCTCTTAAGCAAAATACTTGCATTAGGTCTC

AGCTGGGGCTGTGCATCAGGCGGTTTGAGAAATATTCAATTCTCAG

CAGAAGCCAGAATTTGAATTCCCTCATCTTTTAGGAATCATTTACC

AGGTTTGGAGAGGATTCAGACAGCTCAGGTGCTTTCACTAATGTCT

CTGAACTTCTGTCCCTCTTTGTGTTCATGGATAGTCCAATAAATAAT

GTTATCTTTGAACTGATGCTCATAGGAGAGAATATAAGAACTCTGA

GTGATATCAACATTAGGGATTCAAAGAAATATTAGATTTAAGCTCA

CACTGGTCAAAAGGAACCAAGATACAAAGAACTCTGAGCTGTCAT

CGTCCCCATCTCTGTGAGCCACAACCAACAGCAGGACCCAACGCAT

GTCTGAGATCCTTAAATCAAGGAAACCAGTGTCATGAGTTGAATTC

TCCTATTATGGATGCTAGCTTCTGGCCATCTCTGGCTCTCCTCTTGA

CACATATTA

210
NON_CODING
GTGTCCCTGTTGTGGTACTTCTGCAAGTCCTCCTTCTGGATGGCCAC

(CDS_ANTISENSE)
CTTCCCTGCAACACAAGCAGAGAAGACTTCACCACGGGCACAG

211
NON_CODING
GACCCTCGTAGTGTGCCGGTCAATGCTTGCCTTT

(INTRONIC)

212
NON_CODING
TGCAGGGCGGTTTGCCGCTGCCACCCTCGGCACCATCTCTGAACTG

(INTRONIC)
CCCGCTTTTCCGGAGGAGCGGAA

213
NON_CODING
GGGTGACGTTGCTGATAGCTCAATACTTAACGTACAGCAGGAAGG

(INTRONIC)
AGCACTGAGGCAGTGGCTTGAGCTCAGTCTGTGGGAGGAGACCTGT

TTTGATCCAG

214
NON_CODING
CAGGGTCTGATGATTTTGGCGTTTCCCTGCTTCCCAATTGACCTGGC

(INTRONIC)
TGTGCTGTTGGCTGTTCTTGCACACTCAAGGTGGTTTTGCCATTGGC

TTCCTCCCTCAGCCTGCCTCTGGGATTATGCCACTGCTATTCTTTTTT

ATCTACCATCAGCACAATGAAATCATCATTTTTGTCTTCAAGGTACC

AAATTCTGGTGATATTGGTGCTTTCTTGCAGCTACTTATCATGAGAA

GTGAATGGTCTCATAGTGAACACAGTCATGGTTATAGTGTTCATAC

GTTCCAGAGACATGTTTCCTATAATTATGCCCTGCACATTTTTCTAT

CATACAATCCTTAGATTACAGCTCTTTGGTTTTCAACAGCTTTGTCC

AATTCCATCTTTCCCAGTTTCTCTACCTTGATGAAATATCCTTCTTG

CCTGGTTTTACATATTTAAATAACAAATTCCAAAAGTAAAGAGTAT

CTGAGGCAGTCACATGACATAAGGACAAATTCAAGCCATCTTGGAC

TTGCAGAGGGTGGGGAGACCGTGTCAACACACACAATTTTAAAAA

TTTCTTCCCTTTCAATCTTTTAAAAACAAAACTTTTTATAAAATAAA

AATGTAATTTAAAAAGGCTACCTGTCTTGGCAAGTAGCTGATCAGC

CTGCATTGGTGAGCAGGCCATTCCATAACCTGGTTTCTTGCTCCTTA

ATTGACAGCATGGAGCTAACGTACTTAATTTCAGCTCTTTCTACGTG

ATTTGACTCATTCTGTTAACATTAACTGTTTTTCAGTCTTCTCAACT

AGACTGAACTCCTTAAGTGCAAGAAATACACGCTTAGTAAATGTTT

GTTGGACCAGACACTGCACCTTATGAAATTAAAGACCAGAACATTC

TCATGGTAGCATTACAGACACTGATGGCAAAGGTACTGTGGGATTT

GGGTTTGGCTAATAAGCTCTGTGGTGGTGTTTCAGAAGGAAAATGG

TGCTCTCTTAGTTCTATGGAACATAGTGGTCCAGATCTTCTACTGTA

ACCAGGCCCAAAGCTGGCTAATCTGGAGGGCTCTGCCTTAGGGATA

CTTATA

215
NON_CODING
ATTCTGAGTTACCAACACGTTGTGCGTGCATTGATGACCCGGCTTC

(INTRONIC)
CTGGCCTGCCCTTGGTGCCTGAGCCCCAGTAATGATTGCCCTCTATG

TTGGGAGAAGAAGGGAGAAAGTAGTACAAGTAGTGAAGAAAAAA

ATGTAGGTGGTGTTGGTGGTTGAGAGTACATGGCACA

216
NON_CODING
GTAAGTGAGTGGGCCTGAGTTGAGAAGATCCTGGCCTTGGA

(ncTRANSCRIPT)

217
NON_CODING
ACCTGCCACCGGCTGGCACACACCACCC

(INTRONIC)

218
NON_CODING
CTGCAGCCGAGGGAGACCAGGAAGAT

(ncTRANSCRIPT)

219
NON_CODING
CATCCCGAAGTGTGGCTAAGCCGCCCGGAGGAACACAAAGGGCAT

(INTRONIC)
ACGCGCACGCACACTTAAAGTTTTAAAACACGATTTATTTATTTTTG

TCTGCTGCAACGCTGGGAGAAATGTGGTCTTTGGAAGGAAGCTCTC

CAGTGTGTAACCTTCCTATTATTTTGGCCCCCACACTGTGGCTTTAG

TAGAACAGGAGCAAACAAGTTTATAAGGCAAGGAGGTGGAGAGAT

TAAAAGAGCATTCTCTTGCATTTATGAAGTGTCACTCCGGTGTGTAT

GTAGGTGAAGCCTTTGGCCTCGTCTGAAATGCCCATTAA

220
NON_CODING
TCTGAAGAGCAAGCGCCCACTGATGCTGAGGTCAACAAAATCAGA

(INTRONIC)
GAAGCTGACATTTCCATTTTTTGCCAATACTTCAGGTGACCTCATAA

TGAAACCCTTGCTGCTCTACAGAAAATTGTGCCCAAACCCTCTCAG

GGGAAATAAATGAGCCAAGTTTCCAGTGTACTAGCAAGCAAACAG

AAAAGCCCAGATGAATCTTCCTCTCCTTAAGGGATGGTTTGAACAG

TACTTTCTTGTGGATGTTCAAGACTACTTAAAAGAAAAAAAAATAC

CTTGAATTCAAAGTCCTGCTGATTCTTCAGTCTATTTGGTGCTTCAG

GTACATTTGCCAATATGCATCCTCATGGTAAGGTTGTCTTTATAACT

AGCCACATGTCTGAGATTCTTGAGCCTTTCAGTCAGTGTTTGATCTG

GCCATTCAGGAAGGCTTATTATAAACTAATGTATAACTTTGTTCAC

AATCTCGCAAAGTTTCCACTGTCTGAAAATCCTAGTGCATGAGACT

CCTACATCGTTATTAATGGCATATCCTTAATAAAAGTTTGGCTTTTG

ATTTTTAATGGGTTTTCAGGAGATAACTTCCCAAAGAGGCATTAGA

TAGTTTAACAGAGCCTGTCATTAATGTGACCTGTGAGAAGACTTGG

CTAGAGGTGGTGAAATATCTTTCCTCTATCCCTCCCAAAGACAAGA

AAAACCTATGGATGAGGATGAAAATTTGGCACAAGAGCAATCATT

GGCGGAAGTTGAATCTGAAACTGTTGACACCAATTCAAGTTAATGC

TGCTAGAGGCTGATCCTCAGGAAGCTTTCTTGTCTCCAGAGGTTATT

ATCATAAGTGATGATGAAGACAATTAGGAGGCTGTGGGACTGGAA

ACAAATACAGCAATAAGAAACAGGAGCAAAATTTTTAGAACAAGA

TTAAAACCTCCCTAAGAAGGTAATTAAAATTGGCATCTTTACATGT

GTCAGATATTACCTGTTCAAAATTTGAGTGACTTAGAGTTCTATAA

AGAGGTGCTATGATGCCATCAAACATAATCATATTGGACAGAAAC

AATCTTCAATAGAACTTAAATCATGTGCCATTTAATACTGTTGCTGG

ACAGCTGATAAAACTACCTTCTGACAAAGTTTGATTTAATTAGACT

CTAATAAAAGGTCCTATGAGACTTTCTAAAAGACTATATTGGGAAG

AAAGAAACCTCAGAAAAGTCTAAATTATCAAGTAGTACCATTTAAA

TACTCTTACTGGACAGCTAATAAGCTACCTTCAGACAAAGATTGAA

TGATTAAATTGAACTCCATACAGAACTGCTAAGGTGTCTTCAAAAA

GGACTTGAGAAGATGAAAGCATCTTTAGAAGGGCCACTTAAATTCA

CTTGCTTGATAGAAATAAAGCCTCAAGCAAGTTGTTATAACTTCAG

GATTCGACTTCACTGACTCTAAGAGTATAGACATCCATAATTTGAA

CTAATGAATAGTCCACTTCTGTTCATTGCTTCTCTGTCACCCCCATT

TGCCACTACCATAATGAGTGATAGATACATCTTCATCACCTCTGGA

AATCATCTCAGGATCTAAATGGAAACTGTATAAAGCCTATCATTTT

TACTGATTTAAACTATGTAAACTCATTATTCTTTTTATGTAATGTGC

TGTTGTTATTGTTTACCTGCATAAAAATATTTATGAGGGTTTTCAAC

AGTTTACTTGAGACCTCATTTTTGCCCATTTTTTTCCTTCCCGATATC

ATGATCTCCTCAGCTGAACTTTCTTACCTTGGGGGTTGTTCAGGAAC

TGACTCTCATGGGGAAAGAGGGATTACTATTTCTGTGTTCCTATCTC

TTGGTAACTGCTTAACCACAGTCAGTCTTGAACTAATGGAAGGAGC

ACTGGACTTGGGTTCTTGAGACCTGGGTTCATGTTCAGTTCTGCCAC

TGATTATTGTGACATTGGGCCAGTCACTTGATTTCTCTGAGCCTCAG

TTTCATCACCTGTTAAGTGAGGATAGTAATACCTGGCACAAATATC

ACAATATTAGTGATAATTGAATATAATTATAAGTACCCAATGGCTA

TTAAAAGTAAAACTAGGAAGTGCTGAATAACCATAATATCATTATA

TTTGTAGCATTTTGGACCTTATCAATGAACAACTGAGAAAACTAGG

TTTTTGAATTCTTTTACTTTTTAAAGTAACTTCCTCCCATTTTATGT

CAATTATAGAAAATTTTAAAAAGAAAATTAAATGTGCCTATAATTT

TATAAGCCGGAGGTAACTAAGTTGGTATTTTTCTTCTTAGTACCTCT

TTGTCTCATCATAAATTGTTCATCAATGTCAAAAACTTGGAAAATA

AAGATAAGCATATAGAAAAAAATAAAAACCACCCATAATCACAAA

TCCCAGAAGCAATGTTAATATTTTGGTGGATTTATTTCCAGTCTTTT

TCTATGGCTATATGTGCACATATATAATTTTTACATAGAAAAAGTC

ATAATGCATACAGCTTTGTTGCTTTTAGCATTTTTATCATGAATATT

TTCCTACATTTATGCAAAGTATTTGTAAATATCATTTTCAATGGTGT

ATAATATTTCATCATAGGATGACATCATGGTTTAGTTAACCATTTTC

TTTTGTTGGATATTTGAGGGTCTTTCCAAATTTGGCCATTGTAATTT

CACAATGTCTTTTTCATTACCTAACTGAAAATATTTGCTTTGGTGAA

AGCAGAGGATTTTTTGTTGTTTGTTTGTTTGTTTTTGAAGAAGTCCT

TTTAATAGCTACATTTCATTGACTAAGTGGAACTTCAAGAGACAGG

TAGAAGAAAAAAAAAAAGAAACAGTAGATGTAATTTCAAGATTGA

GGATTTATTTTGTTAGTGACTGTTCCAGAAGCTGAATTTTGGTGTTA

GAGCAATTCAGGAGGGACAGTTTGCCACCATTTTATGATACTTTAC

TGTAGAAAAGTTTTCAGGATTTAGACCAGGAAAGAGACATCCTAAC

CATATGGGTTGATTTTATTTTATGGACCCTGTGAAGTCTGGGACTGA

TCAGGTTTCTCTTTTGTTGGCTACTAGAAAGCTTGGAGTCAAATGTG

TGGTCAATGCATAGCACTTGTAATGGGACTCTACGGTATGTATGCA

CTTTGTATTAGCTTTCTGCCAGGCTCCATTTCGTGTTCCTATCTTTAT

TGTTTTTGTTTTTTCCTTTTACTTTCTTATCTACTTTGAATTTATGCTA

TCATGTTGTATTTTGTGTATTCTTGTAAGCCACCTGACATCCATCTT

GGAACATGGTGGGGAATAAACACACTAATAAATAAATACATTAAT

AAATACATGAATAAATAAACCAATAAGGAAAAAACAATGAGGCAA

ATGAATGCAGCCAGGACTCTGAAAATTGCATAGTGCCTCCAAGAAT

AATCAATGTTAAGGACTTGAAGCTTGGAAGAACATATTGGAAAGA

AGCAGGTGAGGCTGCGAGGCTGCATTTAGAGGTGACGTGTTCTGTG

TGACGTCTGTGTCTACTGAAGCATGC

221
NON_CODING
GAGCTGGAAGTAGACACCATGTATCTTTTCATTAGAGAAGCAAACC

(INTRONIC)
CCCAAAGGAGAAGCATTGTCAGGCTTCTCTCTTTGCCATGGCCTTT

GCCTATACCCTTGAGCAGTGATCTGAGTCGGCTGAGATGCAGATGT

TAAGCCTGGGCAGAAAAGCGCTGCTCTCTGCATGGTCCGGGAGAG

ACCCCTCTCCAGCCGGTGGCATGCTCGTTACGCAACACTG

222
NON_CODING
GCACCATATGTGAGTATTCCAGATATCCAAGGTCCTCTGGACACCC

(INTRONIC)
CAGTCTCTTCCACAAAGCTGCCTCCTCAGAGCCTGCTGTCCCGTCTT

CTAGGAATGTACCCATTTGAAAACCCACACTCACACTACCACAACA

CATACACTGTTTCTTGCTGGTCGTTCCTTTAATCTCAGTGGAAGATA

TCTCATAGAGAACTGTTGGTGATTGCTTAACTTGGTTGGGAGGAAA

ATAGATCAAGCAGGTGACAACCTGCATATTGGGGATTTTCCTATGC

TGAAAATTGTTATTCTGTTGCAGCACTCCACCCTCCCTTCACAGCCC

CAAAAAAGAGAAGTACGAGTGCTGCTGATGTTCAGGGTTTGAATAT

GTTTTGGTTTAAGATGTTCAGTGGAATTAGAGAGAATTTCATCCTG

GGCAGTGCAGTCAGGCTGGAGGAGTATTTTGGTTTCATATTACTAA

ACCTTGTTTTCCCATCCCAGCTGCTTGTGTGCTATCTTGGGGCCACT

GAGAACCTGGCTGGGCTCTGCGGGGTGGGAGTGTTGTCCCGGGGCT

GAGTCCAGCCAGGGGTGAGGTCGTCTTGGTGCACATCTTGCACGTT

GCATGAAGCTCAGAGCC

223
NON_CODING
CCCAGACCCATGTGCGGCTGTGCAAATTCTTTCTGGGTTGA

(INTRONIC_

ANTISENSE)

224
NON_CODING
GCAGCGCTGGATGCCGGAGCAGGTGCTTCTGCAAGAAGCTGTTCTG

(UTR_ANTISENSE)
CATCCTCTCCTTGCTGCATCTTGGTCCACTGCCTC

225
NON_CODING
TCCAGGCCAGCCAGGTATTGATTGAAGAAATCTAGAAAGGCAAAT

(INTRONIC_
GGACCACTGTTATACTGACAGTGTTTGTCTAACCAGCTGAGTGTGG

ANTISENSE)
GCATTTTGAGGAATGGGGCCAGAGAGCCAAGCCCAGGGCTACTGC

AAGTTGGGAAGTCTAATAGATTCTACTTCTACCAGAATTCTGGGAT

TCCAAAGAATGATACCTTCAGTGTAAGGGTAAATTAGAAATAAGCC

TCCATAGTACTCATAATGGGCCACAAGAAAAACTGACCATTTCAAA

TTTTGGCAAGAGTGGAGAAGAGAGAAATTGCCACTGAGAATTTGG

AACCATGAGGCAGCCTCACACAAGTTTGTGG

226
NON_CODING
CAACCTAGCCCTCCATGAGGACTGAGCGCATGAGAGATCCTGAGCC

(ncTRANSCRIPT)
ACAGCCGCCCAGCCCTGCTCCTCTCGAATTTCTGACCTACAGGAAC

TGCAAGAAGTAATGAAAGACTGCTGTTTAAAGCCACTGCATTTTGG

CATGATTTGTTATGCAGTCGTAGATAACCAGAAAACA

227
NON_CODING
GGTTTCAGCACCCAAGACTTAGACCCACAAGAACTTAAAATGAGG

(CDS_ANTISENSE)
AAAAAGAAAAAGTTCAGGTTTAAAGGCCTGTCAGCACTCAGAAAG

ATACCTGTTTCAGCTAAACATTTTCTAACTTATTAAGAGAATCTACT

AATGTCTACTCTACCTGACTAACCTACAAACACTTCTCACAACTTCT

TTTAGGATTGTGACACCAACTGCCC

228
NON_CODING
CTTTCTGGATGCACCATTTACCCTTT

(INTRONIC)

229
NON_CODING
AACATGGGTTTTGTCGTGCTTCTCCTTTTGGCCTCCTGCAATATTCC

(CDS_ANTISENSE)
TGTTCTTTTTGCTGGCACTGAGATCCTCTCATCTCGGGAAGCTATTC

GCTCAGACGAATCGTAAAAGGCTGGCTGGGACCACGGGGCAGGCT

GGGGCCATGGAGGGGGCTGTGCTGGGCCAGCAATCGGACTTGAAA

CCCCTCTGGAGAAGGCGTCAGGGGGAGGAGTGACTGCAGAGTAAG

GTGGAGGTGCAGGAAAGTCAGCAATGGGACTCGTCATGTTTCGGGT

TGGCGAGAAGGGGGTAGCTGGCTGATTCACAGACCCTGGGAAGGG

TTTGGCCGTTCTATTCATGGGGACCATCCTCTGGATGTTTGCTGTCT

CAGATGTCCCACTGAAGCCATTCTGTTGGGGAACATGGCCAAGACC

ATGACTCACCTCGATGTAGCTTTTGCTCA

230
NON_CODING
CCACCATCACCTGGACGCTGAATGGAAAGACCCTCAAGACCACCA

(ncTRANSCRIPT)
AGTTCATCGTCCTCTCCCAGGAA

231
NON_CODING
TCAAGAAGTCGGAATTTTTAGGACAGTTACAGTCTGCATTTAAGGA

(INTRONIC)
TCCTGATGGACAGGCTG

232
NON_CODING
GAGAGCGCAGTCTTTCTGTCTCATGATACTGATTACCACACAAAAG

(INTRONIC)
CATTGGTGAAGAAACAACTGACTGAGTTGAGTTAGGGAGTTTTTTC

AGAGTAATTTTGACTAGTTGCAATTTTCGATTTG

233
NON_CODING
CCGGGACTTGGCAGTACTTGAAACAGGAGGAATACACCAGCCTAA

(INTRONIC)
ATGTACAGACTTTGTAGCCGAGCCCACTCGATCGGTCTGTGCCTTC

ACGTGACCACCATCTGTGCCTCCCTCGCTCCATCCAAATTTGTGTAG

GCTGCTCCTTGGAGCTATGCCTAAAATATAGCTACACCAGAGCCCT

GGAAACTGTAGTCAAGTAACAGGCCTCACTGTTTTTTTTCTTTGGAT

TAAAAGTGTATATCTCTCTACTGAGGGGTTTCCAGCTTTA

234
NON_CODING
ACCCTAATGTTTGCCACAATGTTTGTAT

(INTRONIC)

235
NON_CODING
TTCCTTCTACTCAATCTGACCGAGGTCCTCCAGGTCAAGGACAGCG

(ncTRANSCRIPT)
AGGCTCTCAGTCCCACTTCCCCTTGGCACATAGAAGAGGCAGTGCG

C

236
NON_CODING
TTGGAGCCCGTAGGAATATTGAAGAAGTTAGTGAAGAAATGCTAT

(INTRONIC)
ACAGTCATTTGTTGATTAATGAAGGGGGATAAGGTCTGAGACATGT

GTCGTTAGGTGATTTATTCATTGTGCAAACACCATAGAGTGTATGG

TACTTACACAAACCTAGAGGGTATAGCCTACTAAACACCTAGGCTA

CAAACTTGTACAGTGTGTTACTGTACTGAATACTGTCAACAATTGT

AACACAAATCACCAGGCGATAGGAATTTTTTAGTTCTATTGTAATC

TTATGAGGCTACTCTCATATATGCAGCCCCTCATTGACCAAAACAT

CATTATGCAGTGCATGACCATATTGAGAGTATTCGTTTTTTATTTAC

TAAAAAATAGTCAAAACTTGAGGAGGAAGAGACAGATGTCACTAG

AAAAAGGGAGAAGTCCGGTAAGGGAGAAGTCAGCTTCCTGAGGTG

GAATCGTATTACCTTTGGGATTAGGACATTTCATTG

237
NON_CODING
CCCACAGGCAGCTTTGGTGTTCTCATGTTATAGTTCTTAATCTAAAT

(INTRONIC)
TGTAGGTGCTAAACAAAACTACCTGCCTTAATGGTAGGCAGAGGTA

TTTGAAAAATTAATGATCTACTTGTTTGCTGAATGTCCACAATACA

AGCTTTGATTTAAAAAAATCATGTTAGGATAGCATGTTTATTACAT

ACTATTTATTATCATACTTAATATTTCTTGCCTATCAAAAGTAAAAA

CCTGATGCTTTATGTTAAATGTTTCTTGCCCATTGGAGCCTGTTCAT

GGCAATTCTTTGTCCAAGAAGAGTAATGGTATTGTCTCTTTCTATGT

GTCTCGGTAATTCAGGC

238
NON_CODING
TCTAACCTTGGCTCCGGGGTATTGCCGAAACCAGTCCAGGCACGTC

(INTERGENIC)
ACAAATGTCTGACTTCTCCCAGAGGCTTCAGAAGCACAATGAGCAG

CAGAGGAGAGCCATGGAGCCAAGCACAGTCTCATTTAACCTCCCCA

AAAGCTTGGGAAGTGGGTGGTGTTATAGCCCCATTTTACAGATGAG

AAAAACTGAGGCTTATTTAAGCAGCTCACCTAAAGTCACATATTGA

TTGTGCTGAGCTGAGATTGTACCCTAATCTGCCTTCAAATCCATGTT

TTTACCCATTGCATGTGATTATGGAACCTGGGACCGAGGAGCAGGA

GGAGAACATTCTAAATTCTGCTCCCATCTTGTCTTTACATCTCAGGT

CACTTTTAGCAAAGACAGACCCGGACACTTGCCATTAATACTACAG

GCTTCCTTCCTCCTACCCCCTTCCCCCAATCTTATTCATCTCACCTCT

CCAGTAGGTCGTGGACTCATGCATT

239
NON_CODING
GGCAGGGGTTGGGACAAGTGCTAAGTATGCAAGACTCAAGGGAAG

(ncTRANSCRIPT)
AGCT

240
NON_CODING
CCTGGGATGACCACAATTCCTTCCAATTTCTGCGGCTCCATCCTAAG

(INTERGENIC)
CCAAATAAATTATACTTTAACAAACTATTCAACTGATTTACAACAC

ACATGATGACTGAGGCATTCGGGAACCCCTTCATCCAAAAGAATAA

ACTTTTAAATGGATATAAATGATTTTTAACTCGTTCCAATATGCCTT

ATAAACCACTTAACCTGATTCTGTGACAGTTGCATGATTTAACCCA

ATGGGACAAGTTACAGTGTTCAATTCAATACTATAGGCTGTAGAGT

GAAAGTCAAATCACCATATACAGGTGCTTTAAATTTAATAACAAGT

TGTGAAATATAATAGAGATTGAAATGTTGGTTGTATGTGGTAAATG

TAAGAGTAATACAGTCTCTTGTACTTTCCTCACTGTTTTGGGTACTG

CATATTATTGAATGGCCCCTATCATTCATGACATCTTGAGTTTTCTT

GAAAAGACAATAGAGTGTAACAAATATTTTGTCAGAAATCCCATTA

TCAAATCATGAGTTGAAAGATTTTGACTATTGAAAACCAAATTCTA

GAACTTACTATCAGTATTCTTATTTTCAAAGGAAATAATTTTCTAAA

TATTTGATTTTCAGAATCAGTTTTTTAATAGTAAAGTTAACATACCA

TATAGATTTTTTTTTACTTTTATATTCTACTCTGAAGTTATTTTATGC

TTTTCTTATCAATTTCAAATCTCAAAAATCACAGCTCTTATCTAGAG

TATCATAATATTGCTATATTTGTTCATATGTGGAGTGACAAATTTTG

AAAAGTAGAGTGCTTCCTTTTTTATTGAGATGTGACAGTCTTTACAT

GGTTAGGAATAAGTGACAGTTAAGTGAATATCACAATTACTAGTAT

GTTGGTTTTTCTGCTTCATTCCTAAGTATTACGTTTCTTTATTGCAGA

TGTCAGATCAAAAAGTCACCTGTAGGTTGAAAAAGCTACCGTATTC

CATTTTGTAAAAATAACAATAATAATAATAATAATAATTAGTTTTA

AGCTCATTTCCCACTTCAATGCAATACTGAAAACTGGCTAAAAATA

CCAAATCAATATACTGCTAATGGTACTTTGAAGAGTATGCAAAACT

GGAAGGCCAGGAGGAGGCAAATAATATGTCTTTCCGATGGTGTCTC

241
CODING
GGCGGCCACCAAGTCGCTGAAGCAGAAAGACAAGAAGCTGAAGGA

AATCTTGCTGCAGGTGGAGGACGAGCGCAAGATGGCCGAGCAGTA

CAAG

242
CODING
TCCATTATTGCTGCCCGGAAGCAGAGTGTGGAGGAAATTGTCCGAG

ATCACTGGGCCAAATTTGGCCGCCACTACTATTGCAG

243
CODING
TGGTGAACAGCCTGTACCCTGATGGCTCCAAGCCGGTGAAGGTGCC

CGAGAACCCA

244
CODING
AGGAGACCACCGCGCTCGTGTGTGACAATGGCTCTGGCCTGTGCAA

GGCAGGCTTCGCAGGAGATGATGCCCCCCGGGCTGTCTTCCCCTCC

ATTGTGGGCCGCCCTCGCCA

245
CODING
GCGAAGACGAAAGGAAACAAGGTGAACGTGGGAGTGAAGTACGC

AGAGAAGCAGGAGCGGAAATTCGAGCCGGGGAAGCTAAGAGAAG

GGCGGAACATCATTGGGCTGCA

246
CODING
GACCCTGATGGCTTTGGGCAGCTTGGCAGTGACCAAGAATGATGGG

CACTACCGTGGAGATCCCAACTGGTTTA

247
CODING
ACCCTTCTTCTTGGCGAGACCACGATGATGCAACCTCAACCCACTC

AGCAGGCACCCCAGGGCCCTCCAGTGGGGGCCATGCTTCCCAGAG

CGGAGACA

248
CODING
CACGAACTGTGCGATAACTTCTGCCACCGATACATTAGCTGTTTGA

AGGGGAAAATGCCCATCGACCTCGTCATTGATGAAAGAGACGGCA

GCTC

249
CODING
TCAGACGGGCACATCTATTGGAGGTGATGCCAGAAGAGGCTTCTTG

GGCTCGGGATATTCTTCCTCGGCCACTACCCAGCAGGAAAACTCAT

ACGGAAAAGCCGTCAGCAGTCAAACCAACGTCAGAACTTTCTCTCC

AACCTATGGCCTTTTAAGAAATACTGAGGCTCAAGTGAAAACATTC

CCTGACAGACCAAAAGCCGGAGATA

250
CODING
CTCTTTCTACAATGAGCTTCGTGTTGCCCCTGAAGAGCATCCCACCC

TGCTCACGGAGGCACCCCTGA

251
CODING
TGGGAATGTGCTTTGCAGCCGAGTCAGATGTCCAAATGTTCATTGC

CTTTCTCCTGTGCATATTCCTCATCTGTGCTG

252
CODING
AGCGCAGGAGCATAAGAGGGAATTCACAGAGAGCCAGCTGCAGGA

GGGAAAGCATGTCATTGGCCTTCAGATGGGCAGCAACAGAGGGGC

CTCCCAGGCCGGCATGACAGGCTACGGACGACCTCGGCAGATCATC

AGTTA

253
CODING
GGCCTAAGGATCATTTTCTCGGATGCATCACGGCTCATCTTCCGGCT

CAGTTCCTCCAGTGGTGTGCGGGCCACCCTCAGACTGTACGCAGAG

AGCTACGAGAGGGATC

254
CODING
GGGGTGATGGTGGGAATGGGACAAAAAG

255
CODING
GTTGGATTGCCAGCTTGTACCTGGCCCTTCTGTTTGGCCACGCTATT

GTTCCTCATCATGACCACAAAAAATTCCAACATCTACAAGATGCCC

CTCAGTAAAGTTACTTATCCTGAAGAAAACCGCATCTTCTACCTGC

AAGCCAAGAAAAGAATGGTGGAAAGCCCTTTGTGA

256
CODING
GGCAATGAGCGCTTCCGCTGCCCTGAGACCCTCTTCCAGCCTT

257
CODING
TCATCCTCCCTTGAGAAGAGTTACGAGTTGCCTGATGGGCAAGTGA

TCACCATCGGAAATGAACGTTTCCGCTGCCCAGAGACC

258
CODING
GGTTGGATCCCAAGACGACATATTATATCATGAGGGACCTGGAGGC

CCTGGTCACAGACAAATCCTTCATTGGCCAGCAGTTTGCTGTGGGG

AGCCATGTCTACAGCGTGGCGAAGACGGATAGTTTTGAATACGTGG

ACCCTGTG

259
CODING
AAAGCAGAAGCGAGACCTCGGCGAGGAGCTGGAGGCCCTAAAGAC

AGAGCTGGAA

260
CODING
AGGCCTCCTCACCAGTCAGTGCATCCCCAGTGCCTGTGGGCATTCC

CACCTCGCCAAAGCAAGAATCAGCCTCA

261
CODING
TTGAGGACATCTACTTTGGACTCTGGGGTTTCAACAGCTCTCTGGCC

TGCATTGCAATGGGAGGAATGTTCATGGCGCTCACCTGGCAAACC

262
NON_CODING
GTGACTTGGTCCAAAAGACCTGGGCACTTGGTCTAACTTTTCAAAC

(INTERGENIC)
ATTATCTAACCTCTGAATCTGGAATAACCAAACTGTAAGTTGACTT

AATTCACAGAAGTGCAGTGATGGTAAAATGAAATAGCATGAGTAG

AGTGATAAGTGTGATGCAAATGAAAGTCATATCTTCATTACTAGGC

TTTATTTATTAAATATAGCTAAAGTACTCTAAACGTATATGTCTACA

CTTTTTTGAACATGGATAGTTTTTACATAACTGTACTGAAAGAAAG

GGCACTAATTACTATGCGCTCTAA

263
NON_CODING
AGCTCTCAGGTTCGTGGGAAAGCTAACATACAA

(INTERGENIC)

264
NON_CODING
ATGAATATGTCAATGCTGAATGCAAATCAGGGAAAG

(INTERGENIC)

265
NON_CODING
TGAGTGTAGTATTGGTAGGATCCTTCAGCACCCTGCTTCTGTTATGG

(INTERGENIC)
AAGCTCAATGGGAAAATTCCTCTCTCCCCAGCCCTTGGCAGACAGA

GCTCATGATGGTAGAGTTTT

266
NON_CODING
AGAATTTTCATGGTGTTATGCATGCTGAAAAATGCATTGCATTTTG

(INTERGENIC)
AAAATTTTAGCAAAGGATACGTCAATGACTGCAGCATGATTCAGGC

ACCTTCCCTGGCAGTCCACAACTCTGTTATC

267
NON_CODING
ATGTTCTTGTCATTCGTTAAGTTGCAAAATTCAGCAACTTACAATGA

(INTERGENIC)
GTATTACTACTATTGTACTG

268
NON_CODING
ACTTGAAATTGTGTCCAGAACTGGTGGGTT

(ncTRANSCRIPT)

269
NON_CODING
AATGGTTGTTCAAGCCAGGCCTGCCTCATTGAAAGGGTGAAATCTT

(INTERGENIC)
CCTTCACTGGAAGGAAGTGAGAGAATTAGTCAAGCAGCTATCTGA

GGAAAGAACATTCCAAGTAAAGAATATACAGCCCATACATTGTTG

GATGTGTGTACATTGAAATTTTTGTGCAGTAAAATGAATATTTCATT

TACCTATATAATTTTACATAAAATAAAATATATTTTGAATGTGAGTT

TGTTCCAAACAAATCATTTTCTTGCCTTCAAAACCACTGAGCTTAAA

GAACTCTTTCAAGTGTCATTAGAGATAGATTCCAACTACAATCAAC

ATTGTGGAATCCAGAGGAGGCAAAATGAAGGAAGCAGCACTCATT

ACAAAATGCTGCTTTGTAAAGAATTAATTCTGTCCTGGTATGTTTCA

CATTAGGTAATATGAAGGAAATGAATATGTCATGAACCCTCCTTGA

GGATGTGGGGGAATTAAAAGTAATTTCGCTTAATATCCAACTCTCA

CTTTTGGCTTTGTAGTCAGAGGGAAACAATGCTTTCCCAGGTTCTA

AGGTAAACGTTAAAAGGTTACAAGGAGACTTGGAAGAGTCAAGGA

ACGCTTCCACCAACTATTCCTGCCATTCCAGTTGGGAGGGTT

270
NON_CODING
AATTTACTGCCTGCTCGTTTGGAGATCTATAACCTTTATACTTAGAC

(INTERGENIC)
AGTTTTTTAAAAAGTATAACAGCAATTATTTCTCCCAATTTATTTAA

TGCCGTTTTTTCATTGCATCCATTAAAATATTTTACTTTTATAAGCA

ATGATACCAGGAAGTTATCGTTTGAATAGTCTGCTGGAGGAGTAGG

GCAAAGTAGTTAAGATCAATTGTTCTTTCAGAAGGCTGCTGCTTTCT

AGCTGCATGACTTTGGGTACGTTATTT

271
NON_CODING
CAAACTTTGAGTTTGACCTCTATAAAGACACTAAAA

(INTERGENIC)

272
NON_CODING
GAACAATATGAAAATACTCTACTGAAAATTGATGAAATTGAAGAG

(INTERGENIC)
AAAGGCCATTATGAAA

273
NON_CODING
ACAGCATTGATAAACCTGTAGCTAGACTAACCAAGAGAGAAGACC

(INTERGENIC)
CAAATAAAGAAAAACAGAAATAAAAAAGGAGACATTACAGCTGAT

AACCACAGAAATACAAAAGATTATCAGGCATTATTATAAACTACA

ATACACTAACCAACTGGAA

274
NON_CODING
TAATTCAGTATGCTGTCCAGGGGCCTGGAAATCACTCAGCACAGTC

(INTERGENIC)
TACCACCATTGGCACATGAACACTTCTCCCAGGGTCTAAGGACAGG

CTGACATAACATGCTAATACCACCAGAGCTGGCACTCACCCAGATG

TACCACATCAGGCCAGGAAGCAGAAACTACCAACATCCCAGCAAA

CCATGTGGAGGCCCCCAAATCAGACTGCTTGGGCCTAACA

275
NON_CODING
AGGATATCACTGCAGGTCATAAAGACATTAGAAAGATAGTAAGGG

(INTERGENIC)
ACTACTATAAATAATTTTATGCCAATAAATTTGGAAATTTAGATGA

AATTGACAAGTTCTTGAAAAAATAGCACTAAAACAGATATAAGAA

CAAGTAGCAAATATGAATAGTTTGAAATCTACTAAAGAAATTGTAT

CTGGGGCTCAAGATGCCTGACTAGATGCAACTAGAATGTGCCTCCT

CCATGGATAGGAACCAAAATAGC

276
NON_CODING
TGGCATGACATAGCTAAAGCACTGAAGGAAAAAGTATTTTATCCTA

(INTERGENIC)
GAATAGTATATCCAGTGAAAATATCCTTTAAAAATGTGGGAGAAAT

AAAGACTTCTCCAGACAAACTAAAATAAGGGATTTCATCAATACCA

GATCTGTCCTATAAGAAATGCTGAAAGAAGTTCTTCAGTCTGAAAT

AAAAGGATGTTAATGAATTAGAAATCATTTGAAGGTGAAAAACTC

ACTAATAATAGGAAGTACACAGAAAGAGAACAAAAAAACACTGCA

ATTTTGGTGTGTTAACTACTCATATCTTGAGTAGAAAGATAAAAAA

GATGAACCAATCAGAAATAACCACAACTTCTTAAGACATAGACAG

TACAATAAAATTTAAATGCAAACAACAAAAAGTTTAAAAGCTGGG

GGATGAAGTCAAAGTGTACAGTTTTTATTAGTTTTCTTTCTGAGTGT

TTGTTTATGCAGTTAGTGATAAGTTATCATC

277
NON_CODING
GTAAACTTAGGAGGCGTAGTGCTCCAGGTTGATCTGGCGGTTGA

(UTR_ANTISENSE)

278
NON_CODING
GTCAAAGAGATATTCTCCCACGCCAGATTCGGGCGC

(UTR_ANTISENSE)

279
NON_CODING
TGGAGCGCTCGAGAAGCCTGGGCTCCACTATG

(INTERGENIC)

280
NON_CODING
GGAATTTCGTAATTAAATGATATGTAAAATTTGAATATTATTTGTTC

(INTERGENIC)
AGTCTTATTCTTCCAGAACCTCAGTTACTTTCTTTTATTAATTCAGA

CAGTTACCACAGTACTAGTCAGCTATTACTCAGTTCTGATC

281
NON_CODING
TGGTGTACTAACAGCACTGATTCTGTTAGCAACAAGTAGTGGTAGA

(INTERGENIC)
CAACTAGAAATATGTCAGTTTAAAACTTGTGAAGTTGGTTGTTACA

AATCTCCATTCTGTGTATCTCCATTCTGAATACTAGATACACATCTC

CATGTGTATCTCCATTCTGAATACTAGGTACAACGATTTTGTCTCTT

GGAAAATTTCCTTGTCCACTGAGTA

282
NON_CODING
TCTCACCTGTGGAACTCATTACCTGCATTAAGTTTTCTCTGCTTTCA

(INTERGENIC)
ATATTCAGTTTAGCCGGGCGCGAT

283
NON_CODING
AATATGGCCATGACACCAGAAATCACAAACATGATGAGAATGGAA

(INTERGENIC)
TGACTGGGGAAGAAGTGCCAGATGCTTCACTTGTAAATGAAGACCC

AGCCTCTGGGGATGCAGATACCACCTCCCTGAAGAAGCTGAATATC

TGCAGATA

284
NON_CODING
CATAGCTAGGCAGTGTTGGAGATCAGCAGGAACTAGACACAATGA

(INTERGENIC)
ATGGATATGGCATCAATACTCATGAACATGCCATTCTTCCAGCAGT

GCTTGGCAACTCAGGTTGAGGAACAGAGAAGGTGGATGGCTTAGG

TAATGGAATTGGATGCTTTTTAAATGTCAGTGGCTGTCAAAACTGT

ATA

285
NON_CODING
ATGTCTCAGACCTCTCCATACTTCATCTGTACTTCTTGATCGCTTTT

(INTERGENIC)
ATTCTTGAAATTAATACAAGAAGGTCTCTCATTTA

286
NON_CODING
CTTAGTGGGGTTTGGAACTGCCTGAGAATATTCCTATAGAAACTGG

(INTERGENIC)
GTCATCTTGCCTTCTGTGCCACTAGAACCTCCTGTCTCTCCAATAGC

TGCTTCTCTCTAATTCTTCACCATAGTTTTCTTTCTGTGGTCTTTTGA

GGTTCTCTCCT

287
NON_CODING
CTTTCACTGTTATGCCGGTGATTTGAATGTAAAGCAGTTTTATTTAA

(INTERGENIC)
ATCAATATAATTTAATAAAAACATATTTAAATTTTGGGTTAGATTA

AAAATTTTCTCTATTGCCAATACTTGGTTTGAACTCAATTAGGCTCT

CTTTACATAAGAGACTACATTAAACACAGACATATATGAGGTATTT

TTGAGACATTTGAATGTAATATATTGTAATTTTACCATTTATTTTGT

CTCCTAAATTGACATTTAAATAATCAGAATCTCTAGCTCAATATTCA

AATTAACATTTTCTTCCCTTAAAATGGTGGGTTACCTCCTTCCTGGA

AGGAGCGGAATGTGAGTAACATTTCTTCCTTTCCATGTTTTTCTCAA

TCAAATGGCACAAAGGATTTTCTTGACTGCTTGAAAACTAAAAACA

GTTTCCCAGAGTTTATTAAGTTCATATTAATTTTTAATGCAAATACC

TGTTATTAAAACTCTAAGTAGGGCAGGCGC

288
NON_CODING
CATTGGGCTCCAGAGTATCGACGGCGCTCTCCTGTGATGTAGGCCG

(INTERGENIC)
TGAATTTCACGTGATGTGCACCTTG

289
NON_CODING
TGCACCTGTTTAGTTTGTGACAATCTGAGCCCAGTACATGGTTCTCT

(INTERGENIC)
GATTCCTAAGCCAGGAGTCTCTCTGTAACCAAACTGCTATTATGTG

AGCATAGAACAGCTCTCAAAGTAAATGTCCCACTTCTATTTCTGGC

AGGTTATGTTTAGCTACCTTTCCAAAAGAGTCCCAATCCTAGTATG

CCTTTCAACAGTGTC

290
NON_CODING
TGAATAAACTCATTCGTCCCTCAAACCAGAAATTATTTGAGGTTAT

(INTERGENIC)
CAATAACTTCTCCATGGAAGAGTTTGTTAGAGTTTTGGTCAGGAAA

ACA

291
NON_CODING
AAGTTCCTGAAGTGTGTCATCCCTCTGCTAGACATCTAAGGGATGA

(INTERGENIC)
CTTTTTTCACAAATCATATTAACTCACCAGTACAATAGTAGTAATAC

TCATTGTAAGTTGCTGAATTTTGCAACTTAACGAATGACAAGAACA

TGGCATAGGTCAGTGATGCATGTTATGCTTAATTTTGAGTGAGTGA

CTTGCATGTTATATCTCTGCCTG

292
CODING
GGTCGCCAGTCATCCCGCACAAAAAACCTGTCCCTGGTGTCCTCGT

CCTCCAGAGGCAACACGTCTACCCTCCGTAGGGGCCCAGGGTCCAG

GAGGAAGGTGCCTGGGCAGTTTTCCATCACAACAGCCTTGAACACT

CTCAACCGGATGGTCCATTCTCCTTCAGGGCGCCATATGGTAGAGA

293
NON_CODING
CAGAGAGGTGGTAACTCCCGAGTAAGCAATGCCAATCCTTCAGGC

(INTRONIC)
AAAGATAAGGAAGAACCGCACAGCTGCTCCAACATAAAGTGG

294
CODING
GTATCCTGGCATCCATCTGTGGTGGCCTTGTGATGCTTTTGCCTGAA

ACCAAGGGTATTGCCTTGCCAGAGACAGTG

295
NON_CODING
ACTAACCTCTGCAGTTTAACCTTGAGCGATACCTTTTCCCATGAATA

(INTRONIC)
G

296
CODING
TGGAGGCTGCCTGATCGAGCTGGCACAGGAGCTCCTGGTCATCATG

GTGGGCAAGCAGGTCATCAACAACATGCAGGAGGTCCTCATCC

297
CODING
GATCGCCATTCTTGATTATCATAATCAAGTTCGGGGCAAAGTGTTC

CCACCGGCAGCAAATATGGAATA

298
NON_CODING
AACGATTTCGAGATTTACTACTGCCTCCATCTAGTCAAGACTCCGA

(NON_UNIQUE)
AATTCTGCCCTTCATTCAATCTAGAAATT

299
NON_CODING
TACTGATAATCTCAAGGAGGCAGAGACCCATGCTGAGTTGGCTGAG

(NON_UNIQUE)
AGATCAGTAGCCAAGCTGGAAAAGACAATTGATGACTTGGAAGAT

AAACTGAAATGCACCAAAGAGGAACACCTCTGTACACAAAGGATG

CTGGACCAGACTTTGCTTGACCTGAATGAGA

300
CODING
AAAATCTTGCAAAATCGGCAGAGGCTTGGGCGGCTACTTGCATTTG

GGACCATGGACCTTCTTACTTACTGAGATTTTTGGGCCAAAATCTAT

CTGTACGCACTGGAAG

301
CODING
GTGGTGAATGTACCTGTCACGATGTTGATCCGACTGGGGACTGGGG

AGATATTCATGGGGACACCTGTGAATGTGATGAGAGGGACTGTAG

AGCTGTCTATGACCGATATTCTGATGACTTC

302
NON_CODING
CAGGAGCTGATCCTCCTTGCAAAGCTGTGCCTTGCAGAGATGCACG

(NON_UNIQUE)
TGTGCATTTCAGCTACATCATGCCGCGCTGTTGTAATACTGTATAAA

GACCTCAATCTATCCAGAGTATTTT

303
NON_CODING
TTGCACACTGTTCCAACTTGCCGTGAACACATTTTTTGCTCTTT

(INTRONIC)

304
NON_CODING
CAAAGAAGCTAAGCACATTGCAGATGAGGCAGATGGGAAGTATGA

(NON_UNIQUE)
AGAG

305
CODING
TGTCTGTGTCAATGCGTGGATGCTGGACCTCACCCAAGCCATCCTG

AACCTCGGCTTCCTGACTGGAGCATTCACCTTAGGCTATGCAGCAG

ACAG

306
NON_CODING
TGGAGTCGTATGATGCCCTTGCCTTGTTTTATATTGGCTGTCAGCGC

(INTRONIC)
TTAACTGGGACTGAAGTATCTGGGTAACAAAAATTGATATAATGAC

TTAATGCGCCTTATTCTCTTTGAGCTACATCAGTTTAGAGCACTTCT

GAGAGAAAAATGTCTGGAAAATATCAGGGAGTCATTTATCAACCT

GTTTTCATTAGCATACTGCCTAGCTCTGGCAAGGATTTGA

307
NON_CODING
CGGAGAAGGTTAGAATGGATTTGAAAGAATGTGGTTGGATTCAAA

(INTRONIC)
GAAGCCCTAGGAGACCCAACAAGTCAGCATTTTTCTCTTGTGAAAA

GAACCACCTGCCAACCCCAGCCTGTTCCATTGCTGACATCAGAGG

308
CODING
CTGAAGCTAGACAGGCAGCAGGACAGTGCCGCCCGGGACAGAACA

GACATGCACAGGACCTGGCGGGAGACTTTTCTGGATAATCTTCGTG

CGGCTGG

309
CODING
ATGATAGCAATCTCTGCCGTCAGCAGTGCACTCCTGTTCTCCCTTCT

CTGTGAAGCAAGTACCGTCGTCCTACTCAATTCCACTGACTCATCC

CCGCCAACCAATAATTTCACTGATATTGAAGCAGCTCTGAAAGCAC

AATTAGATTCAGCGGATATCCCCAAAGCCAGGCGGAAGCGCTACA

TTTCGCAG

310
CODING
AGCAGTCATGCCTGAGGGTTTTATAAAGGCAGGCCAAAGGCCCAG

TCTTTCTGGGACCCCTCTTGTTAGTGCCAACCAGGGGGTAACAGGA

ATGCCTGTGTCTGCTTTTACTGTTATTCTCTCCAAAGCTTACCCAGC

AATAGGAACTCCCATACCATTTGATAAAATTTTGTATAACAGGCAA

CAGCATTATGACCCAAGGACTGGAATCTTTACTTGTCAGATACCAG

GAATATACTATTTTTCATACCACGTGCATGTGAAAGGGACTCATGT

TTGGGTAGGCCTGTATAAGAATGGCACCCCTGTAATGTACACCTAT

GATGAATACACCAAAGGCTACCTGGATCAGGCTTCAGGGAGTGCC

ATCATCGATCTCACAGAAAATGACCAGGTGTGGCTCCAGCTTCCCA

ATGCCGAGTCAAATG

311
CODING
ATATCGCTCTATTCTCCAGTTGGTCAAGCCATGGTATGATGAAGTG

AAAGATTATGCTTTTCCATATCCCCAGGATTGCAACCCCAGATGTC

CTATGAGATGTTTTGGTCCCATGTGCACACATTATACGCA

312
CODING
ATCTGTGTGGCGACGTGCAGTTTACTTGGTATGCAACTATGCCC

313
NON_CODING
GCTGTATATTGATGGTCCTTTTGGAAGTCCATTTGAGGAATCACTG

(NON_UNIQUE)
AA

314
NON_CODING
GTCTTCGTTTGATTACTGCCAGTTATTTCCAGCATGCTAAATCCCTA

(NON_UNIQUE)
CCCACGTTCCAGCCTCTAGGTGAGTCAGTGCGTCACTCTGTCTCCCG

TCCAATTAATTATTTCTCATCACTCCCTCAATCCAAGTAACAAACCT

TGAAACACGAACATAGACACCAGGCTTATTGGGGCGTGCACAGCC

AAGAC

315
CODING
CCCGTTGGCTGATTACTCGGAAGAAAGGAGATAAAGCATTACAGA

TCCTGAGACGCATTGCTAAGTGCAATGGGAAATACCTCTCATCAAA

TTACTC

316
NON_CODING
CATTTGGGGCAAATGGTTCACATTCATTTTAGGGTTAGTGGTCATG

(UTR)
CTGTTTATTTTTCTCTGCTATACAAAGTTCCTCTTAGGGGTCTGCCT

CATGACACTAAAAAATGAATAGAGATTCTACTGTAGGTTATCTCCT

AGGCTTGAGTTCAACATTTGTTTGGATTTTTGAAGAAAGTCAAATC

AAGCAATGCTCCCAAATGATGTCTTTGTAAATTCATACCCTCTGGC

CCTA

317
NON_CODING
AGATGACAGCGCAAGAGTCAGATTAATGAAAGATCAATAGACATT

(INTRONIC)
ATTCAGTCTTGAAAAAATTGTGAACAGGGATGCAGGGATCAGTGG

GACAATATCAGAAGCTCTAATACATGTTGTCATAGGATGGGGTGGG

GGTGAATGAAAAAATAATGGCTGAAAATATCCCAAATTTGATGAA

TGATATAAATGTAGAGTCAAGAAGCTCAATCA

318
CODING
ACGGAACAAAGGATGAGCAGCCCGAGGG

319
NON_CODING
TTGGCACCAATCCTAGACTCACGTGTGCCCCAGAATAACATTCAGA

(NON_UNIQUE)
CTCTCAGCTGGTCTTGTGTTACACATCCATGGACCGGTTCACTCCAT

CATATACAGCTCTCTGCTCCGTGTCCCCTGGGCTCAAGTCAAGCAG

TCGGTGACAGATTTCATTCCCAATAACAGAATCGGTTTGCATGACT

CCCCATACATGTTGCAGCTTTGAAAACATTCATCTCAGAGTTAGGT

ATAAAGACATAAAAATGTGTGTCAAGCCCTCGTTAGCTGATGAGGT

AAATGCATGGACAACTTCCTAGGACTTCTCGGCTCTGC

320
NON_CODING
ATCATTGAAGGAGACATGGGATGCACAGAGGAACGAGC

(ncTRANSCRIPT)

321
CODING
AGGACGGGAACACCACAGTGCACTACGCCCTCCTCAGCGCCTCCTG

GGCTGTGCTCTGCTACTACGCCGAAGACCTGCGCCTGAAGC

322
CODING
TGCGAGAGTCTCTTTGCAAATCGAAGAAGGGAGACATGTTGGGAG

CAAGCCCCCCAGAGTCTGGCCATAAACTGGCCCCAAAACTGGCCAT

AAGCAAAACCTCTGCAGCACTAAAACATGTCCATAATGGCCCTAAC

GCCCAATCTGGAAGGTTGTGGGTTTATGGGAATGAGAGCAAGGAA

CACCTGGCCTGCCCAGGGCGGAAAACCGCTTAAAGGCATTCTTAAG

CCACAAACAAAAGCATGAGCGATCTGTGTCTTACGGGTGTGTTCCT

GCTGCAATTAATTCAGCCCATCCCTTTGTTTCCCATAAGGGATACTT

TTAGTTAATTTAATATCTATAGAAACAATGCTAATGACTGGTTTGCT

GTTAAATGAAGGGGTGGGTTGCCCCTCCACACCTGTGGGTGTTTCT

CGTTAGGTGGAACGAGAGACTTGGAAAAGAGACACAGAGACAAAG

TATAGAGAAAGAAAAGTGGGCCCAGGGGACCAGCATTCAGCATAC

AGAGGATCCACACTGGCACCGGCCTCTGAGTTCCCTTAGTATTTAT

TGATCATTATCGAGCATGGCAGGATAATAGGATAATAGTGGAGAG

AAGGTCAGAAGGTAAACACATGAACAAAGGTCTCTGCATCATAAA

CAAGGTAAAGAATTAAGTGCTGTGCTTTAGATATGTATACACATAA

ACATCTCAATGCCTTAAAGAGCAGTATTGCTGCCCGCATGTCATAC

CTACAGCCCTAAGGCGGTTTTCCCCTATCTCAGTAGATGGAAGTAT

ATTCCATGTAAAGTAAATCGGCTTTACACCCAGACATTCCATTGCC

CAGAGACGAGCAGGAGACAGAAGCCTTCCTCTTATCTCAACTGCAA

AGAGGTGTTCCTTCCTCTTTTACTAATCCTCCTCAGCACAGACCCTT

TATGGGTGTCGGGCTGGGGGATGGTCAGGTCTTTCCCTTCCCACGA

GGCCATATTTCAGACTATCACATGGGGAGAAACCTTGGACAATACC

TGGCTTTCCTAGGCAGAGGTCCCTGCGGCCTTTGCAGTATTTTGCGT

CTCTGGGTACTTGAGATTAGGGAGTGGTTTGAGATTAGGGAGTGGT

GATGACTCTTAAGGAGCATGCTGCCTTCAAGCATTTGTTTAACAAA

GCACATCTTGCACAGCCCTTAATCCATTTAACCCTGAGTTGACACA

GCACATGTTTCAGGGAGCACAGGGTTGGGGGTAAGGTTACAGATT

AACGGCATCTCAAGGCAGAAGAATTTTTCTTAATACAGAACAAAAT

GGAGTCTCCTATGTCTACTTCTTTCTACACAGACACAGTAACAATCT

GATCTCTCTTTTCCCCACAGTTAATAAATATGTGGGTAAATCTCTGT

TGGGGGCTCTCAGCTCTGAAGGCTGTGAGACCCCTGATTTTCTACTT

CACACCTCTATATTTTTGTGTGTGTGTCTTTAATTCCTCTAGCGCTG

CTGAGTTAGTGACCGAGCTGGTCTCGGCAGAGGTGGGCGGGTCTTT

TGAGTTCAGGAGTTCAAGAGCAGCCTGGCCAACATGGTGAAACCC

CTTCTCTACTAAAAATATGAAAATTATCCGGGCATGGTGGTGTGCC

TCTGTACTTTCAGCTACTCAGGAAGCTGAGGCACAAGAATTGCTGG

AACATGGGAGGTGGAGGCTGCAGTGAGCTGAGATCATGCCACTGC

ACTCCAGCCCAGGCAATAGAGTAAGACTCTGTCTCAAAACAAAAA

GAGTTTTAGGCCAGGTGTGGTGGCTCACGCCTGTAATCCCAGCACT

TTGGGAGGCTGAGGTGGGCAGATCACCTGAGGTCAGGAGTTCGAG

ACCAGTCTGGCCAACATGGCGAAACCCCATCTCTCTCTACTAAAAA

TACAAAATTTAGCCAGGTGTGGTGGTGGGTGCCTGTAATCACAGCT

GCTTGGGAGGCTGAGGCAGGAGAATTGGTTGAACCCAGGAGGCAG

AGGTTACAGTGAGCAGAGATCGTGCCACTGCATTCCAGCCGGGGTA

AGAGAGCGAGACTCTGCCTCAAAAAAAGAAGGCTTAGTGTGCAAC

TCATCAGAGTTGCACAGGGCAGAGAAAGAATGGGAAAAAAACAAT

TTCTAGAAAACTTTTCGAATTTTCTGATCAACACCAAATATTCCAAA

TAGGAAAAATACAAAAAAATCCATACCTATATGTGGCATAATATG

ATTGTAGAGCACCAAAGTAAAAGATCTTATTTTTTATTAAAATTAA

AAAAAAATTAAAATAGAGGGTCTCACTATGCTGCCCAGGCTGGTCT

TGAACTCCTGGCTTCAAGCTATCCTCCCACCATGGCATCCTAAAGT

GCTGGGATTGCAGGCATGAGCTGCTGCATCTGGCCCAAAGTAAAA

GATCTTAGAAGCGGCCAGAAAAAATAGATTTGGGCTGGGCATGAA

TAGATTGATCACCAAAAAGGTGGCAGACTAACTTCTCGACAGA

323
CODING
TTTTTGGCATCTAACATGGTGAAGAAAGGA

324
CODING
GCTGTGGAGCCTTAGTTGAGATTTCAGCATTTCC

325
CODING
GTATATGGACGACTTCTTACTCATGTTAGCCCATTCATTTCATCAGA

GCATCTTCACACATCAGTGTTCACTCTCTATAGATTTATTTGCATAT

TGTCTAAATATGTTTTTTTCTGTTATTATTTTACACTTTTTATTTTGCT

TCATTCTCTGTTGAGTTCCTCA

326
NON_CODING
CTTGAGTCCTGGAATCGACCTTTTCTCCAAGGAGCCTTGTTCCTTTT

(ncTRANSCRIPT)
AGTGGGGAAAGGTATTTAGAAGCTAAGATCTTGGTGTTGGCTGTGT

TCACTACAATTGGTGTATCTACTTCTCCATCCTCCAGCGTCCTCTGG

TGATCGAGAATCTGAAGTTCCAGGTTTTCATAGGCC

327
CODING
GGGTTTGCTGTTTGGATCAAGGAATCAATGGATTGCCAGA

328
CODING
GATGGAGAGCATAAGCCATTCACTATTGTGTTAGAAAGAGAAAAT

GACACTTTGGGATTCAATATTATAGGAGGTCGACCAAATCAG

329
NON_CODING
CAGGCATTCTGATTTATTGATTGTGG

(ncTRANSCRIPT)

330
CODING
TCTTCATCTTGTCTTACGCTTTCCGAGCAAGTTCAAACCAGAA

331
CODING
GCACCAACAAATGTGGTTGCTCCATAATGGAGAGAATGTCAAGAA

TGTTGACTATCTTTAGACCTGCTTCATTAATAGATAAGA

332
CODING
AACCGCATGCACGAATCCCTGAAGCTTTTTGACAGCATCTGCAACA

ACAAATGGTTCACAGACACGTCCATCATCCTGTTTCTTAACAAGAA

GGACATATTTGAAGAGAAGAT

333
CODING
CAGGCCCAAGTGCATACTCGGGTTCTTTCCAACTCAGAATCATCTC

TGATTCCACAAAAGTGAGTTTAGTTTCCTATCTGAATTAACAACTTT

AAAGGAGACTATAATAGTTAAAAGTGGAAGAATAGAAATAAATAA

ATTTAAAATGAAATTAATTAAAGTAGAAGAGAAGGGTTCTGTTCCA

TGTACGATTAATGTGCC

334
CODING
CCTGGCATCTATTTCCTCTGTGCAAAGGGAACCATGTATATGAGCT

TATAAATAC

335
NON_CODING
CTCTTTGGCGTTGCTAAGAGACTGCCAT

(ncTRANSCRIPT)

336
CODING
TTCCTACCGCATGCATTTTCTAATGTTTGGGGTGGATGGTGTGTCGG

TTATGGAAGGCATAGACGTCATTACAGGTGCTACGATCTCACACAC

ACACAAGGAAATGTTAGTCTCCTTATTTTATGATTGGAAAATCAAT

GACCTAGAGGCAAAATGGCATGTTTAAGGACCTGGGATGACAAGT

CATTCTGCAGTCAGCCACAGAGCCAAATTTGGACTCCTCAACCAGA

ACTCCATGAAAAGCCTGACTTTGCCAAACACTGTGCTGGAAAAGCT

AAGCCCCTTTCATTTGTGAAGTAAATTTTAAATTCAAGATATTTAGT

TTAGAGAATTGAGTCTTGAGATGTAAACTACATGAGATTTCTTTGG

TTTCAATTGAATAATATTCACTAACAAATGATTTACTAAAATACGT

ATTTCTTGGTCCTTATCATGTAATGACAGATTCACAACAGCAATAA

GGATGGAGATTTCCCCAATAATTAATAACACCGAGAGTAGCAATAT

TTTTTA

337
NON_CODING
GTAGAGCCTACGTCCTTCATGAGAAAAATGACACAAATCTCAGTAT

(ncTRANSCRIPT)
TCTTTGTTTGGAGTCTCTTGACATCCATGTGAG

338
NON_CODING
TTAGGACACGGACATTTCTATTTGGCAGCCAACA

(ncTRANSCRIPT)

339
CODING
CGGAAGACTTGCCACTTTTCATGTCATTTGACATTTTTTGTTTGCTG

AAGTGAAAAAAAAAGATAAAGGTTGTACGGTGGTCTTTGAATTAT

ATGTCTAATTCTATGTGTTTTGTCTTTTTCTTAAATATTATGTGAAAT

CAAAGCGCCATATGTAGAATTATATCTTCAGGACTATT

340
NON_CODING
GCTTCTGTCCCAAGAGGCACTAGCTGGGG

(ncTRANSCRIPT)

341
CODING
AACATTGGAGAAGTATCTCTTTGTAATGCTAAAAAGAAGTGAAAAT

CAACAGACTTATCTAATGAATGCAGATGTGGCAGAAAGAATGAGT

AGCACTACCGTTGACTCTGAAGAGAGA

342
CODING
ACTACTAGACTTGCTAAACTTGGACTGTTGTGAATTAGAACCTAAA

ATTGAAGAGATTAATATTAGGCGCCTATATTTTGCTTCTAAATCAA

GAAATAAAATTATTAGCAGTATGGTTTCTTTTACTGATGAACATGTT

TGTATTGAACAAGGAACACATACTAATATCTATTGAGTGCCTACTA

TGTGCTAATCTCCAACAAATTGATTTGGGGATGCTAAGAAGAATTA

TGTGCCAGTGTTACCCTCAAGGAGCAATACTGTATATA

343
NON_CODING
AATCTCATCTCTATGACATCCCTATCCTG

(ncTRANSCRIPT)

344
NON_CODING
CTCAGTCTATGAAAGCCAGGTTAGCTTGCTTTCTTCCTCCCTAAATC

(ncTRANSCRIPT)
CTCCATCCTCATGACCAACAAAGAAATAGTTGAATCATTTTCCAGG

CACATCTTGGGGAGGATGTGGGGCCATTGGAGGCTGTCCTTCCTAG

ATAAGTCTTTAGGAGTGAGAACAAGGAGTCTTACCCTCCTCTGTCC

ACCCACCCCCATGAATGGGCCTGGCTCCAGCCAGGAGTTGTGGTTT

TTCCTGAGCTCCTCACCTATCTCTTCTGGATTTCACATTGGCAAACG

GGGTTGCAAAGTGCTCTTCGTGCTCTTTGGACAGTGCC

345
NON_CODING
TGGTTGCATTGCACGTAGAAAGTGGAATAATGTAATGAGCTTTGAA

(ncTRANSCRIPT)
ACCATAATAATGAATGTCTGAATAATGACATTATTTCTTGCGTTTGT

AATACTGTTAATTAAATCTATGTCGATCCTGTTGGAATTCATAAAAT

CATCTAAAAATTTTTCTAAATATACAGTGTTGTTTTCCCCATTGTAT

CTTGATCTCAAGCAACAAATGGTAAAAGTATAGCTATTAATGTCAT

TAAATGTGAATTGTTTCAACATTATGAAGGGTTCCTCTTGGTAAGT

GGCAGAAGGAGCCAGGCTTAGGTTTGAAGTGAGACTGACTTTATTC

CCTTCTT

346
NON_CODING
CTCCTGAATGCTGGCCAGACAAATGGAAATCTGCCAGGGTTGGGTA

(ncTRANSCRIPT)
CCCCCATGACAGCAGCCAGCCTGCCCTCTTAGTCCCTGACAGCTGC

AGTGACAGCATCTGTGATTGCAAAGCGTGACAATTTATATCTCTCA

TTTCATCACACCATCTATCAGCAGACAGTCAGGCTTTAAAAATCAA

TCCCACACTGACTCAGTCCCCAGCAGAGATGGCCTCTGACAACAGT

ATCCACACTGCAGGCTGGACAAGGGCCCTATTAATTTTGAGACTCA

GCCAAATTTCCTTCTGACCCTAAGCTGGTGAATCCCTGCTCCTTTGC

TTTGGTTGGGGTTGGTGTGAGCTAAGGCTGTGATCCCATTTGCTCCT

ATGGCCTCCAGGTGGCCTGGGCCTCCATGAATGGGCCACATGGTCA

TACTGAATGCTTGATTACACTCAGACCTAGCAGTCGTCTGGGCGCA

GCTGGTTTATGGATCACTTT

347
NON_CODING
ATGGCCTTTGAATCATACTTAAGTTT

(ncTRANSCRIPT)

348
CODING
ACCGAGGAGGAGATTCTCTTTAATTATCAAAGACACATCTTTTCAG

GGGGCCAACAAAGCATTTATTTCACCCGCCAAACTAAAGGAGAGTT

ATTCCAGTTTAGGAGGAAGATGCAAGCGGTTTGGGACCTTGAACA

349
NON_CODING
TGGAGGCTAATCTTGTTTGTTATACTTTAGTCATTAATTCAAAGTAA

(ncTRANSCRIPT)
AGGAGTTGTTAATGAACTGGAAACTCCTTTTGAATTATGGTAGCAA

TCAGAATATTTTTATATTAGCCAGTTTTACCTTGAAGACCTATTTTT

AAAAACTACCTGTGTCTCTGGACTTAGTTGCAAATGCATATTAAAA

CAAAAATCCCCCAATTTCTGTGCTTTCTTATTTGAAAGGCCATTTCT

AGGGGGAAAACAGTTCCCAAACACATTATACATGTTGGAAAAGTTT

ATCTCTAACCTTTTGAATTAAACAATTTCAGAATTGAAAACAGTAA

GGTGAATTTTAGGCCAATAACTCTTTTCTATAATCTTGACTCTTTTA

AGATTAGGCAGTTCAGATAGTCTTATACTA

350
NON_CODING
ACCTAGTTGGCTTTCATCTAATTCATTGCCATTTTAAGTGTGTATTA

(ncTRANSCRIPT)
TTTTAGAGCAAACTTAGAAAAACAGCACATTTCTAGTAACTTACGA

CATTCGATGAATGATAAATGTTCAAGTTAGACTAAAGGAACTTTAT

TCCAACTTCTAGTAACTACTTTCTTCA

351
CODING
AGCATTATCTAAACTGCAGTCACTGTGAGGTAGACGAATGTCACAT

GGACCCTGAAAGCCACAA

352
NON_CODING
TTCACAGGACTTCGCCACGCTGCTTTGGAATCTTTCACACCCCCCTA

(ncTRANSCRIPT)
CCCCCAGATACCTTTGAAAAATTTGAGGTTCCTGTTCCTTGTTTCTC

AGTGTATTCATTTCTTCCCTGACTATGACATGTTAAAAAA

353
CODING
CTTCAACGATGAGAAGTTTGCAGAT

354
CODING
GGAAAGACGAGAACTATTTATATGACACCAACTATGGTAGCACAG

TAG

355
CODING
TGCCCCTAGATCTGACAGTGAAGAG

356
CODING
GCAGCAGTCCCAAATAGTCAAAATGCTACTATCTCTGTACCTCCAT

TGACTTCTGTTTCTGTAAAGCCTCAGCTTGGCTGTACTGAGGATTAT

TTGCTTTCCAAATTACCATCTGATGGCAAAGAAGTACCATTTGTGG

TGCCCAAGTTTAAGTTATCTTA

357
CODING
GTGGTGTATGCGGATATCCGAAAGAATTAA

358
CODING
GAAGTTCAGAAGCTACAGACTCTTGTTTCTG

359
CODING
GAAGCTTCTGCAGTTCAAGCGTTGGTTCTGGTCAATAGTAGAGAAG

ATGAGCATGACAGAACGACAAGATCTT

360
CODING
CTGTTGCTGAAACTTACTATCAGACAG

361
CODING
GCTCAGAAAAAGAAGTTCGAGCAGCAGCACTTGTATTACAGACAA

TCTGGGGATATAAGGAACTGCGGAAGCCA

362
CODING
CTTACCAGCGTTATAGGCCAGTATCAACTTCAAGTTCAACCACTCC

ATCCTCTTCACTTTCTACTATGAGCAGTTCACTGTATGCTTCAAGTC

AACTAAACAGGCCAAATAGTCTTGTAGGCATAACTTCTGCTTACTC

CA

363
CODING
TGTGCAAGTAGTACTCGATGGACTAAGTAAT

364
CODING
TTGCAAATTCCATATCTACAATGGTACACGTCCATGTGAATCAGTTT

CC

365
CODING
CTGGCCAGTGATTCACGAAAACGCAAATTGCCATGTGATACT

366
CODING
TTGGATGACTGCAATGCCTTGGAAT

367
CODING
CTTCTTCCTGAATCACGATGGAAAAACCTTCTTAACCTTGATGTTAT

TAAG

368
CODING
TCCTCGTTTTATCCTGATGGTGGAG

369
CODING
TTTTTGACAACAGGTCCTATGATTCATTACACAG

370
CODING
GGACCACTGCATGGAATGTTAATCAATACTCCATATGTGACCAAAG

ACCTGCTGCAATCAAAGAGGTTCCAGGCACAATCCTTAGGGACAAC

ATACATATATGATATCCCAGAGATGTTTCGGC

371
CODING
AACCTGTAAGTGTAATGGCTGGAAA

372
CODING
CGCCCTATTAGGAGAATTACACATATCTCAGGTACTTTAGAAGATG

AAGATGAAGATGAAGATAATGATGACATTGTCATGCTAGAGAAAA

AAATACGAACATCTAGTATGCCAGAGCAGGCCCATAAAGTCTGTG

373
CODING
AAACCTAAGACTTGTGAGACTGATGC

374
CODING
CATGAACGGGGACCTGAAGTACTGA

375
CODING
AGTTTTTACAGATTACGAGCATGACAAA

376
CODING
TCCCTCTTATTCTGGAAGTGATATGCCAAGAAATG

377
CODING
AGACCTGGATTTTTTCCGGAAGATGTGGATTGACTGGAA

378
CODING
TAAAGATGATAATCAGGAAATAGCCAGCATGGAAAGACA

379
CODING
AGCAGTGATAATAGCGATACACATCAAAGTGGAGGTAGTGACATT

GAAATGGATGAGCAACTTATTAATAGAACCAAACATGTGCAACAA

CGACTTTCAGACACAGAG

380
CODING
TTCAGAACAAGAGCTAGAGCGATTAAGAAGCGAAAATAAGGA

381
CODING
AAGAACCAGATGACTGCTTCACAGA

382
CODING
GTCGGCAGGTTCTAAAAGATCTAGTTA

383
NON_CODING
ACCTTGCAACGGATGTCCTTGTTGATCAGCACGTTCTTGCCCTTGTA

(CDS_ANTISENSE)
GTTGAAGATGACATGA

384
NON_CODING
ATGATGATGCTGTTAACTACATTCAACAAAAATCCTTTAAAACAGC

(CDS_ANTISENSE)
TGTTTTCAACCAACTTTCGCTGTGAATGTACTTTT

385
NON_CODING
CTGCCAGCTGAATCAACAGGGTAAA

(CDS_ANTISENSE)

386
NON_CODING
CCATCTTCAAGTTTGGACTCATAGACTTGGGTTAAAGATTTTACTTT

(CDS_ANTISENSE)
TTGCTCCATTTCACTATTTTGTTTT

387
NON_CODING
TGGGTCTTCTCTTCAAGCAACAGAC

(CDS_ANTISENSE)

388
NON_CODING
GCATTTTGAGGACTTCGTTTGGATCCCAATTCAAACAAAATAACTG

(CDS_ANTISENSE)
TGAAGAGATTTTTTCGAACAACAGAGGAGATTCAATTACACACTGG

GTTACATGATCTGAAGGAACTGGCATTTTTTTAAATGTGTGATAAC

GGCACTGA

389
NON_CODING
AGGGTGATTAGGAATTAACTGGACAAAGAAGAGGGAAAGTCTTTG

(CDS_ANTISENSE)
CAAGTAGAGGAAAGAATCTGCTTGGAGCTCAGATAACTATTATTTG

AAAACATAATGACATCTAGTTCAAACTTGTGACTGAGTTCCACAGT

AGAATTCACAGAAAAAAAATTATTAAATATAATATTTCCATCAGTC

TGTGTCTAAAAGATTAAAAAAGAGCAAATAACAATCTTAATAAACT

GATGATAGATTATAGCCTCATCTCTTCCAACATCCGATTCTGTG

390
NON_CODING
GAAATGTTCAAGATGGTCAGGAAAG

(INTERGENIC)

391
NON_CODING
CCTGTTTCCTCTCGATATGCTACAG

(INTERGENIC)

392
NON_CODING
CTGTTCATCCTGCTGTAGATCTGTT

(INTERGENIC)

393
NON_CODING
AAATGTTGACAATTGGGACGATGTAAATGTAAAG

(INTERGENIC)

394
NON_CODING
GCAAAGGTGTCCAAATTATGCAGAC

(INTERGENIC)

395
NON_CODING
AGTTATAACATGAAGGGATTTTCATCTTTTGCTGTATGAAGGATAA

(INTERGENIC)
TTGTTATATCACATTTGGGGGGTAATAACA

396
NON_CODING
CAAAACGACTCACTGGGTTTTTCAT

(INTERGENIC)

397
NON_CODING
AGAGAAAGTGAAGATTCGATTTGAG

(INTRONIC)

398
NON_CODING
TCAGAATTAAACCTGTGGCCCAGGT

(INTRONIC)

399
NON_CODING
TGCCAAAGATTAAGGGGAGCCTTTG

(INTRONIC)

400
NON_CODING
CGTCCGATTAGTGCCATGGCTGGCA

(INTRONIC)

401
NON_CODING
CTCATGGGAAGGGAACTCCGTGTCA

(INTRONIC)

402
NON_CODING
AGAGTTATGAAGGAACAGGTTGTCCTTGTCTGGAGTCAAGCTAAAC

(INTRONIC)
ACATGATTTGT

403
NON_CODING
GGATAGGAATAAAGCAAGACAGTTA

(INTRONIC)

404
NON_CODING
TAAGATCTGTAACACTGAGGAAGTACCAATAAAGAGCTGCTAACA

(INTRONIC)
CT

405
NON_CODING
AGGACAAGAGCCCTAGAGTGGCCTG

(INTRONIC)

406
NON_CODING
GCAGATACACGTGGACAAAAGACTT

(ncTRANSCRIPT)

407
NON_CODING
GTAACACAGCAGGAGCTCATGTTTT

(ncTRANSCRIPT)

408
NON_CODING
ATGCCTACAATTCCTGCTACTTGAG

(NON_UNIQUE)

409
NON_CODING
ATTGGCTTTTAGTTTATCAGTGAATAA

(NON_UNIQUE)

410
NON_CODING
TCTCTGGGGGAATTTCATTTGCATCTATGTTTTTAGCTATCTGTGAT

(UTR)
AACTTGTTAAATATTAAAAAGATATTTTGCTTCTATTGGAACATTTG

TATACTCGCAACTATATTTCTGTA

411
NON_CODING
TCAGAAGTCGCTGTCCTTACTACTTTTGCGGAAGTATGGAAGTCAC

(UTR)
AACTACACAGAGATTTCTCAGCCTACAAATTGTGTCTATACATTTCT

AAG

412
NON_CODING
CTTACATACCGTGAGAAGTTACGTAACATTTACTCCTTTGTAAATGT

(UTR)
TTCCCTATCATCAGACAAA

413
NON_CODING
CACTTCATATGGAGTTAAACTTGGTCAG

(UTR)

414
NON_CODING
TGTACTTTTCAGAATATTATCGTGACACTTTCAACATGTAGGGATAT

(UTR)
CAGCGTTTCTCT

415
NON_CODING
CACTGTTGTAGTAAAGAGACATATTTCATGAATGGCATTGATGCTA

(UTR)
ATAAATCCTTTGC

416
NON_CODING
GGAGCACTACCATCTGTTTTCAACATGAAATGCCACACACATAGAA

(UTR)
CTCCAACATCAATTTCATTGCACAGACTGACTGTAGTTAATTTTGTC

ACAGAATCTATGGACTGAATCTAATGCTTCCAAAAA

417
NON_CODING

(UTR)
CTGAAATGAGACTTTATTCTGAAAT

418
NON_CODING
TTTTGTACAACAGTGGAATTTTCTGTCATGGATAATGTGCTTGAGTC

(UTR)
CCTATAATCTATAGAC

419
NON_CODING
TGTTTTTCCGCAATTGAAGGTTGTATGTAA

(UTR)

420
NON_CODING
CCTTGCATATTACTTGAGCTTAAACTGACAACCTGGATGTAAATAG

(UTR)
GAGCCTTTCTACTGG

421
NON_CODING
TTCTCTTCTTTAGGCAATGATTAAGTT

(UTR)

422
NON_CODING
CCACTGGCCTGTAATTGTTTGATATATTTGTTTAAACTCTTTGTATA

(UTR)
ATGTCAGAGACTCATGTTTAATACATAGGTGATTTGTACCTCAGAG

TATTTTTTAAAGGATTCTTTCCAAGCGAGATTTAATTATAAGGTAGT

ACCTAATTTGTTCAATGTATAACATTCTCAGGATTTGTAACACTTAA

ATGATCAGACAGAATAATATTTTCTAGTTATTATGTGCAAGATGAG

TTGCTATTTTTCTGATGCTCATTCTGATACAACTATTTTTCGTGTCAA

ATATCTACTGTG

423
NON_CODING
TACAAGCTTATTCACATTTTGCTTCCTAATCTTTTTGTTGTACAGGG

(UTR)
ATTCAGGTTTCTTATTCTTACAACATGATTGTTTATATGTGAAGCAC

ATCTTGCTGTTGCCTTATTTTTGATGCTTTTATTCATGACAAGAA

424
NON_CODING
ACAGAATCAGGCATGCTGTTAATAAATA

(UTR)

425
NON_CODING
TCTGATTTCATTGTTCGCTTCTGTAATTCTG

(UTR)

426
NON_CODING
CAAGCTGATGATTGTTGCATTTTGGAGTTGCAACAACATTAAAACA

(UTR)

427
NON_CODING
GGCCATGTGCTTTAACGTTACGGTAATACTTTACTTTAGGCATCCCT

(UTR)
CCTGTTGCTAGCAGCCTTTTGACCTATCTGCAATGCAGTGTTCTCAG

TAGGAAATGTTCATCTGTTACATGGAAAAAATGTTGATGGTGCATT

GTAAAATTA

428
NON_CODING
TGCTGGTTTAAGATGATTCAGATTATCCTTGT

(UTR)

429
NON_CODING
TGAATGCGTGACAATAAGATATTCC

(UTR)

430
NON_CODING
TGGCCCAGAAAGTGATTCATTTGTAA

(UTR)

431
NON_CODING
GACAACCCGGGATCGTTTGCAAGTAACTGAATCCATTGCGACATTG

(UTR)
TGAAGGCTTAAATGAGTTTAGATGGGAAATAGCGTTGTTATCGCCT

TGGGTTTAAATTATTTGATGAGTTCCACTTGTATCATGGCCTACCCG

AGGAGAAGAGGAGTTTGTTAACTGGGCCTATGTAGTAGCCTCATTT

ACCATCGTTTGTATTACTGACCACATATGCTTGTCACTGGGAAAGA

AGCCTGTTTCAGCTGCCTGAACGCAGTTTGGATGTCTTTGAGGACA

GACATTGCCCGGAAACTCAGTCTATTTA

432
NON_CODING
GTTAATATTGTCATCGATACAAATAAAGTGAAAT

(UTR)

433
NON_CODING
CAATAACTGTGGTCTATACAGAGTCAATATATTTT

(UTR)

434
NON_CODING
GTCGCCTGCGAGGCCGCTGGCCAGG

(UTR)

435
NON_CODING
CAGGCCTTCTGCAAATCAGTGCTGG

(UTR)

436
NON_CODING
TAAGGATGGAATTCAACTTTACCTA

(UTR_ANTISENSE)

437
NON_CODING
TACACGTAAACCACAAAAGAGTAGCATTCCATTTTCTTGAAGTGCA

(UTR_ANTISENSE)
CATGATATTATGAACAATACAAATGCATTATTTTTATCATTAATAGT

TTAATCATTAATTATCTCATAAGTCAATGCAGAGAGTGAA

438
NON_CODING
CTCACTTATTTAACTGGCAACTATCCATTTAGGTTAGGCAAAGGCA

(UTR_ANTISENSE)
CGGTAACATGTTGCGCAGGATGTTTTACTGA

439
NON_CODING
CAGGGGTATGGAACATGCTGTCATATTTCATTCATAACACACATGT

(UTR_ANTISENSE)
ACTATAGCTCTAGGCAACAGATGGACAATCGCTTGTTTGAACTACA

A

440
NON_CODING
CCACATGGTCATCATTAGCCAGCTG

(UTR_ANTISENSE)

441
NON_CODING
CTTTTGGATGTGATAAGCTTTGTAATTGTCTTTTAATGAGCTCTCAT

(UTR_ANTISENSE)
CTTGGAGAGATACATTCT

442
CODING
GTGATCGCCTACTACGAGACAAAAA

443
CODING
ATTTATCTTCCACTGAATTGGCAGAAA

444
NON_CODING
GTCAGGTAAACATGTATGTTCAGTCCTTCACTA

(INTRONIC)

445
NON_CODING
GGAACTATGAACTTGCCTATCTAAC

(INTRONIC)

446
NON_CODING
ACATGGAATGACTTAGTTACAGACCAGACATATTGTTACTGGGAAT

(INTRONIC_
G

ANTISENSE)

447
NON_CODING
AGAGGAATGTTTGCTACCTTTAGCGGTGAAAAAAGAAAGAGAGTC

(UTR)
AAGAATTTTGTTGGATTGTGTTTGTGTGTGCATATATTTGATATCAT

CATTATATTTGTAATCTTTGGACTTGTAATCATAGCCTGTTTATTCT

ACTGTGCCATTAAATATACTTTACCTTA

448
NON_CODING
AAGTAATGAGCACTTTCTACTCAAGC

(UTR)

449
CODING
CATCCCTAGCACAGATATCTACAAAA

450
CODING
GTCCATCAGGATTCAAACTGTAATGGCATTTGG

451
CODING
AGTTTCTTGTCTTCTACAACAATGATCGGAGTAAGGCCTTTAAA

452
CODING
ACACAAACGTATATCGTATGTTCTCCAAAGAG

453
CODING
TTGACCTCAAATGCAGTGAGTTCTG

454
CODING
GGGCGTGATAGTGCACGCCTACAAA

455
CODING
GTGAGGGAATATGTCCAATTAATTAGTGTGTATGAAAAGAAACTGT

TAAACCTAACTGTCCGAATTGACATCATGGAGAAGGATACCATTTC

TTACACTG

456
CODING
TCTAGGACGAGCTATAGAAAAGCTATTGAGAGTATCTAGTTAATCA

GTGCAGTAGTTGGAAACCTTGCTGGTGTATGTGATGTGCTTCTGTG

CTTTTGAATGACTTTATCATCTAGTCTTTGTCTATTTTTCCTTTGATG

TTCAAGTCCTAGTCTATAGGATTGGCAGTTTAA

457
CODING
TTGCTTTGATCGTTTAAAAGCATCATATGATACACTGTGTGTTT

458
NON_CODING

(INTRONIC)
TATTCAATCTCTGGCACAATGCAGCCTCTGTAGAAAAGATATTAGG

459
NON_CODING
ATGCAGCAATGCGTGCTCGACCATTCAAGGTTGAT

(ncTRANSCRIPT)

460
CODING
TTCAACTGCAGCTCGGGCGACTTCATCTTCTGCTGCGGGACTTGTG

GCTTCCGGTTCTGCTGCACGTTTAAGAAGCGGCGACTGAACCAAAG

CACCTGCACCAACTACGACACGCCGCTCTGGCTCAACACCGGCAAG

CCCCCCGCCCGCAAGGACGACCCCTTGCACGACCCCACCAAGGAC

AAGACCAACCTGATCGTCTACATCATCTGCGGGGTGGTGGCCGTCA

TGGTGCTCGTGGGCATCTTCACCAAGCTGG

461
CODING
GGCCTACTGTGAAGCTCACGTGCGGGAAGATCCTCTCATCATTCCA

GTGCCTGCATCAGAAAACCCCTTTCGCGAGAAGA

462
CODING
TCACTGAATTTTAACCGGACCTGGCAAGACTACAAGAGAGGTTTCG

GCAGCCTGAATGACGAGGGGGAAGGAGAATTCTGGCTAGGCAATG

ACTACCTCCACTTACTAACCCAAAGGGGCTCTGTTCTTAGGGTTGA

ATTAGAGGACTGGGCTGGGAATGAAGCTTATGCAGAATATCACTTC

CGGGTAGGCTCTGAGGCTGAAGGCTATGCCCTCCAAGTCTCCTCCT

ATGAAGGCACTGCGGGTGATGCTCTGATTGAGGGTTCCGTAGAGGA

AGGGGCAGAGTACACCTCTCACAACAACATGCAGTTCAGCACCTTT

GACAGGGATGCAGACCAGTGGGAAGAGAACTGTGCAGAAGTCTAT

GGGGGAGGCTGGTGGTATAATAACTGCCAAGCAGCCAATCTCAAT

GGAATCTACTACCCTGGGGGCTCCTATGACCCAAGGAATAACAGTC

CTTATGAGATTGAGAATGGAGTGGTCTGGGTTTCCTTTAGAGGGGC

AGATTATTCCCTCAGGGCTGTTCGCATGAAAATTA

463
CODING
CCAGTTCCAGGCCTGGGGAGAATGTGACCTGAACACAGCCCTGAA

GACCAGAACTGGAAGTCTGAAGCGAGCCCTGCACAATGCCGAATG

CCAGAAGACTGTCACCATCTCCAAGCCCTGTGGCAAACTGACCAAG

CCCAAACC

464
CODING
ATGAGTGCCAAATCTGCTATCAGCAAGGAAATTTTTGCACCTCTTG

ATGAAAGGATGCTGGGAGCTGTCCAAGTCAAGAGGAGGACAAAGA

AAAAGATTCCTTTCTTGGCAACTGGAGGTCAAGGCGAATATTTAAC

TTATATCTGCC

465
CODING
GGTTCTGCTCCTCGACGGCCTGAACTGCAGGCAGTGTGGCGTGCAG

CATGTGAAAAGGTGGTTCCTGCTGCTGGCGCTGCTCAACTCCGTCG

TGAACCCCATCATCTACTCCTACAAGGACGAGGACATGTATGGCAC

CATGAAGAAGATGATCTGCTGCTTCTCTCAGGAGAACCCAGAGAG

GCGTCCCTCTCGCATCCCCTCCACAGTCCTCAGCAGGAGTGACACA

GGCAGCCAGTACATAGAGGATA

466
CODING
TGGTCATCCGCGTGTTCATCGCCTCTTCCTCGGGCTTCGT

467
CODING
AGGAAGAACAGAGAGCCCGCAAAGACCT

468
CODING
TGCCACCCAGATGAACAACGCAGTGCCCACCTCTCCTCTGCTCCAG

CAGATGGGCCATCCACATTCGTACCCGAACCTGGGCCAGATCTCCA

ACCCCTATGAACAGCAGCCACCAGGAAAAGAGCTCAACAAGTACG

CCTCCTTA

469
CODING
ACTGGGGTGACCTTAACCTGGTGCTGCCCTGTCTGGAGTACCACAA

CAACACATGGACATGGCTAGACTTTGCCATGGCTGTCAAAAGGGAC

AGCCGCAAAGCCCTGGTTG

470
NON_CODING
TGGCACAGTCAGATGTCGAGAAACTTTGCTATGCCTCCGAAGTCAA

(INTERGENIC)
TGCCC

471
NON_CODING
CCTCACAATATGGAAAGACGGGACAACCTATGGAACTATCTGTGAC

(INTERGENIC)
TTCCATGTACCAAGACAAGGACGCTATAGCTAGGGTAGTGAGACC

472
NON_CODING
CAGTGGATGAATGTCGGAACCTTATGAAATGTGACTCATCTGACCT

(INTRONIC)
TTCAGAGATTGGAACTGCCCCACAGTGCTGTTCTGCTAACTCTTCTT

CTCTGCCCTCTAAAGTCCCTGCTTCCCTTTCTTTCCTTTTTAGTACCG

GGGTGTACATAATCGATCCATCATAATCATCAGTTCATGACATGTT

CTCATCATTGATCCATAGCACGGCCTTG

473
NON_CODING
CCTGCAAAGTAAGGTGTATGGGGAAGCAAGTAGATAGT

(INTRONIC)

474
NON_CODING
GCTGATCTCACTGTGATCTTCCTGGTGTT

(INTRONIC)

475
NON_CODING
GACTCGAGAAAAAACAGAGCTCAGACTTGAGACACGGGCTTCCCT

(INTRONIC)
CTATAGGGGTCAAAAACCAGGGCGGAGAGAGATAACCA

476
NON_CODING
TTGTACCTGCAGTTTTCGCAGAGTAGATCAAGGACTGCA

(INTRONIC)

477
NON_CODING
TTGTCTCTCAGTCGGCTAAGTGCTCTCCCACCAGGTCACCTAAAAC

(ncTRANSCRIPT)
GACCAGCAGAGACACCCAAGAGGCTGAGCTGTGAGGATCACCTGA

ACCTGAGCCTGGGAAGTGGAGGTTGCAGTGAGCTGTGATCACACC

ACTGTGCTCCAGCCTGGGCAACGGAGTGAAACCCTGTCTCAAGAAA

GGACCAGCAGTGACATTTGTTAAATATCGA

GGGTGGTTGAACATCCACTATTTATAAGGAAATGTTATTTCCCACA

AATCTCATTCCTCAGAAATCAGTGAAAGACAGACCCTGTCTCGGAT

TCTATAAAGCAGTGTGACTGATGTGGCCAAAC

478
NON_CODING
CAGCGTCCTGGGAATGTCATTTCTGCTCCACTCCTTGGACTCGCTGA

(UTR)
GCTGTCTCCGCCTCCACCTATCTTCCTACAGACCTCCCTTCTAGTTT

TCTGTCAATTCTTTGAGCCAGCAAACTCCATCCAGTACATTCTTTCT

TCTTTCATGAAAGAGCTTGAGTTGGATGTAAATATATATGACCTAA

CAATTCCACCCCTAGGTGTATACCCTACAGAAATGTGTACATGTGT

TCATCCAGAGACATGCTCTAAATCTTCACAAAAACACTCTCCATAA

TAACCCCGAACAGGAAAGCACCCCAATGCCCATGTTGGCTGGATA

AGCACATTAGGGTATATTCACACGATGGAATCCCAGACTGCAATGG

GAATGAGCTGCAACTCCACCCCCAACTTGGAGTGTATTCACCAACC

CTAGTGTTGAACGAGATAAGGCAAAAATGCACCATAGGATTCCATT

TATATAAAGTTTAAAACCCAGCAAAATTCATCCATGCGGTTGCAAG

TAGAGATCAGTCCTAAGAAGACAGTAACCAGAAGCGGGCATGAGG

TGGTGCTTCTGGGGTGTTCTGTTTCTTGATCTGGTTGCCGGTTACCT

GGGTGCTTTCCGTTTGTGAACATTCTTGGAGCTGTACACTTTTGATC

TGGGCA

479
NON_CODING
TCTGAATTCACCTCTCATCTGACGACTGACAGCTGCT

(UTR)

480
NON_CODING
GCAAGCCGCAGAACGGAGCGATTTCCTCCGAGAAAGTTGAGGATG

(UTR)
GAGCCTTTTTTTCCGCACCGTCCCCGCGATGGCATGGGCCCCGAGA

ATGCTGCCCCGAGGCTCCCAGTGTGGGGGAGCTCGGGGTCGCTGCG

CCTCTAGCTTGAGCGCAGAAATCCGCGAATCACTCCGATCTTCGCG

AACTCTGGCATCTTCTAGGAAAATCATTACTGCCAAAACTGAGGCG

AGCTTTTC

481
CODING
ACCTGCACTGGCTCCTGCAAATGCAAAGAGT

482
CODING
CTGCTGCCCCATGAGCTGTGCCAAGTGTGCCCAGGGCTGCATCTGC

AAAGGGGCATCAGAGAAGTGCAGCTGC

483
CODING
TGTGTCTGCAAAGGGACGTTGGAGAACT

484
CODING
CATGGGCTGAGCCAAGTGTGCCCACGGCTGCATCTGCAAAGGGAC

GTCGGAGAAGTGCAGCTG

485
CODING
GAAAAGCGTGCAAGTATCAGTGATGCTGCCCTGTTAGAC

486
CODING
TGCAATTTCATCAGCACCAGAAAGTTTGGGAAGTTTTTCAGATGAG

TAAAGGACCAG

487
CODING
CCAGTACAAACCTACCTACGTGGTGTACTACTCCCAGACTCCGTAC

GCCTTCACGTCCTCCTCCATGCTGAGGCGCAATACACCGCTTCT

488
CODING
TGCTAGCAAACACCATCAGATTGTGAAAATGGACCT

489
CODING
GTATCTGGACTCTCTTAAGGCTATTGTTTTTA

490
CODING
ACCTTTGAAACTCACAACTCTACGACACCT

491
CODING
CCCTCCGATGCCTAATAAAGTTCTCTAGCCCACATCTTCTGGAAGC

ATTGAAATCCTTAGCACCAGCGG

492
CODING
ACTGCTCACTTGCATACCCAACAAGAGAATGAA

493
CODING
GGAAGGACACCACTGGTACCAGCTGCGCCAGGCTCTGAACCAGCG

GTTGCTGAAGCCAGCGGAAGCAGCGCTCTATACGGATGCTTTCAAT

GAGGTGATTGATGACTTTATGACTCGACTGGACCAGCTGCGGGCAG

AGAGTGCTTCGGGGAACCAGGTGTCGGACATGGCTCAACT

494
CODING
TTGCTACATCCTGTTCGAGAAACGCATTGGCTGCCTGCAGCGATCC

ATCCCCGAGGACACCGTGACCTTCGTCAGATCCATCGGGTTAATGT

TCCAGAACTCACTCTATGCCACCTTCCTCCCCAAGTGGACTCGCCCC

GTGCTGCCTTTCTGGAAGCGATACCTGGA

495
CODING
AGCTGATTGATGAGAAGCTCGAAGATATGGAGGCCCAACTGCAGG

CAGCAGGGCCAGATGGCATCCAGGTGTCTGGCTAC

496
CODING
ACACGCTGACATGGGCCCTGTACCACCTCTCAAAGGACCCTGAGAT

CCAGGAGGCCTTGCACGAGGAAGTGGTGGGTGTGGTGCCAGCCGG

GCAAGTGCCCCAGCACAAGGACTTTGCCCACATGCCGTTGCTCAAA

GCTGTGCTTAAGGAGACTCTGCG

497
CODING
ACAAACTCCCGGATCATAGAAAAGGAAATTGAAGTTGATGGCTTCC

TCTTCC

498
CODING
GAGTGTGGCCCGCATTGTCCTGGTTCCCAATAAGAAA

499
CODING
GGTGCTGGGCCTACTAATGACTTCATTAACCGAGTCTTCCATACAG

AATAGTGAGTGTCCACAACTTTGCGTATGTGAAATTCGTCCCTGGT

TTACCCCACAGTCAACTTACAGAGAAGCCACCACTGTTGATTGCAA

TGACCTCCGCTTAACAAGGATTCCCAGTAACCTCTCTAGTGACACA

CAAGTGCTTCTCTTACAGAGCAATAACATCGCAAAGACTGTGGATG

AGCTGCAGCAGCTTTTCAACTTGACTGAACTAGATTTCTCCCAAAA

CAACTTTACTAACATTAAGGAGGTCGGGCTGGCAAACCTAACCCAG

CTCACAACGCTGCATTTGGAGGAAAATCAGATTACCGAGATGACTG

ATTACTGTCTACAAGACCTCAGCAACCTTCAAGAACTCTACATCAA

CCACAACCAAATTAGCACTATTTCTGCTCATGCTTTTGCAGGCTTAA

AAAATCTATTAAGGCTCCACCTGAACTCCAACAAATTGAAAGTTAT

TGATAGTCGCTGGTTTGATTCTACACCCAACCTGGAAATTCTCATG

ATCGGAGAAAACCCTGTGATTGGAATTCTGGATATGAACTTCAAAC

CCCTCGCAAATTTGAGAAGCTTAGTTTTGGCAGGAATGTATCTCAC

TGATATTCCTGGAAATGCTTTGGTGGGTCTGGATAGCCTTGAGAGC

CTGTCTTTTTATGATAACAAACTGGTTAAAGTCCCTCAACTTGCCCT

GCAAAAAGTTCCAAATTTGAAATTCTTAGACCTCAACAAAAACCCC

ATTCACAAAATCCAAGAAGGGGACTTCAAAAATATGCTTCGGTTAA

AAGAACTGGGAATCAACAATATGGGCGAGCTCGTTTCTGTCGACCG

CTATGCCCTGGATAACTTGCCTGAACTCACAAAGCTGGAAGCCACC

AATAACCCTAAACTCTCTTACATCCACCGCTTGGCTTTCCGAAGTGT

CCCTGCTCTGGAAAGCTTGATGCTGAACAACAATGCCTTGAATGCC

ATTTACCAAAAGACAGTCGAATCCCTCCCCAATCTGCGTGAGATCA

GTATCCATAGCAATCCCCTCAGGTGTGACTGTGTGATCCACTGGAT

TAACTCCAACAAAACCAACATCCGCTTCATGGAGCCCCTGTCCATG

TTCTGTGCCATGCCGCCCGAATATAAAGGGCACCAGGTGAAGGAA

GTTTTAATCCAGGATTCGAGTGAACAGTGCCTCCCAATGATATCTC

ACGACAGCTTCCCAAATCGTTTAAACGTGGATATCGGCACGACGGT

TTTCCTAGACTGTCGAGCCATGGCTGAGCCAGAACCTGAAATTTAC

TGGGTCACTCCCATTGGAAATAAGATAACTGTGGAAACCCTTTCAG

ATAAATACAAGCTAAGTAGCGAAGGTACCTTGGAAATATCTAACAT

ACAAATTGAAGACTCAGGAAGATACACATGTGTTGCCCAGAATGTC

CAAGGGGCAGACACTCGGGTGGCAACAATTAAGGTTAATGGGACC

CTTCTGGATGGTACCCAGGTGCTAAAAATATACGTCAAGCAGACAG

AATCCCATTCCATCTTAGTGTCCTGGAAAGTTAATTCCAATGTCATG

ACGTCAAACTTAAAATGGTCGTCTGCCACCATGAAGATTGATAACC

CTCACATAACATATACTGCCAGGGTCCCAGTCGATGTCCATGAATA

500
CODING
AGGACCAACTTCTCAGCCGAATAGCTCCAAGCAAACTGTCCTGTCT

TGGCAAGCTGCAATCGATGCTGCTAGACAGGCCAAGGCTGCC

501
CODING
TCTCCCAAAGAAAACGTCAGCAATACGCCAAGAGCAAA

502
CODING
AACAGCCGACCTGCCCGCGCCCTTTTCTGTTTATCACTCAATAACCC

CATCCGAAGAGCCTGCATTAGTATAGTGGAA

503
CODING
GGCCTTAGCTATTTACATCCCATTC

504
CODING
GCGGGAACCACTCAAGCGGCAAATCTGGAGGCTTTGATGTCAAAG

CCCTCCGTGCCTTTCGAGTGTTGCGACCACTTCGACTAGTGTCAGG

AGTGC

505
CODING
TCAGGGAATGGACGCCAGTGTACTGCCAATGGCACGGAATGTAGG

AGTGGCTGGGTTGGCCCGAACGGAGGCATCACCAACTTTGATAACT

TTGCCTTTGCCATGCTTACTGTGTTTCAGTGCATCACC

506
CODING
TGATGCTATGGGATTTGAATTGCCCTGGGTGTATTTTGTCAGTCTCG

TCATCTTTGGGTCATTTTTCGTACTAAATCTTGTACTTGGTGTATTG

AGCGG

507
CODING
GTGTATTTTGTTAGTCTGATCATCCTTGGCTCATTTTTCGTCCTTAAC

CTG

508
CODING
ACAGTGGCCGACTTGCTTAAAGAGGATAAGAAGAAAAAGAAGTTT

TGCTGCTTTCGGCAACGCAGGGCTAAAGATCA

509
CODING
TGGCGTCGCTGGAACCGATTCAATCGCAGAAGATGTAGGGCCGCC

GTGAAGTCTGTCACGTTTTACTGGCTGGTTATCGTCCTGGTGTTTCT

GA

510
CODING
TTGGCTCTGTTCACCTGCGAGATGCTGGTAAAAATGTACAGCTTGG

GCCTCCAAGCATATTTCGTCTCTCTTTTCAACCGGTTTGATTGCTTC

GTGGTGTGTGGTGGAATCACTGAGACGATCTTGGTGGAACTGGAAA

TCATGTCTCCCCTGGGGATCTCTGTGTTTCGGTGTGTGCGCCTCTTA

AGAATCT

511
CODING
GTGGCATCCTTATTAAACTCCATGAAGTCCATCGCTTCGCTGTTGCT

TCTGCTTTTTCTCTTCATTATCATCTTTTCCTTGCTTGGGATGCAGCT

GTTTGGCGGCAAGTTTAATTTTGATGAAACGCAAACCAAGCGGAGC

ACCTTT

512
CODING
GCGAAGACTGGAATGCTGTGATGTACGATGGCATCATGGCTTACGG

GGGCCCATCCTCTTCAGGAATGATC

513
CODING
ATATTCTACTGAATGTCTTCTTGGCCATCGCTGTA

514
CODING
GGCTGATGCTGAAAGTCTGAACACT

515
CODING
CAGAAGTCAACCAGATAGCCAACAGTGAC

516
CODING
CCCGTCCTCGAAGGATCTCGGAGTTGAACATGAAGGAAAAAATTG

CCCCCATCCCTGAAGGGAGCGCTTTCTTCATTCTTAGCAA

517
CODING
ATCCGCGTAGGCTGCCACAAGCTCATCAACCACCACATCTTCACCA

ACCTCATCCTTGTCTTCATCATGCTGAGCAGCGCTGCCCTGGCCGCA

GAGGACCCCATCCGCAGCCACTCCTTCCGGAACACG

518
CODING
GGGTTACTTTGACTATGCCTTCACAGCCATCTTTACTGTTGAGATCC

TGTTGAAG

519
CODING
TTGGAGCTTTCCTCCACAAAGGGGCCTTCTGCA

520
CODING
AGATTCTGAGGGTCTTAAGGGTCCTGCGTCCCCTCAGGGCCATCA

521
CODING
CACGTGGTCCAGTGCGTCTTCGTGGCCATCCGGACCATCGGCAACA

TCATGATCGTCACCACCCTCCTG

522
CODING
GGGAAGTTCTATCGCTGTACGGATGAAGCCAAAA

523
CODING
TTGACAGTCCTGTGGTCCGTGAACGGATCTGGCAAAACAGTGATTT

CAACTTCGACAACGTCCTCTCTGCTATGATGGCGCTCTTCACAGTCT

C

524
CODING
TGGAGAGAACATCGGCCCAATCTACAACCACCGCGTGGAGATCTCC

ATCTTCTTCATCATCTACATCATCATTGTAGCTTTCTTCATGATGAA

CATCTTTGTGGGCTTTGTCATCGTTACATTTC

525
CODING
CAGTGTGTTGAATACGCCTTGAAAGCACGTCCCTTGCGGAGATACA

TCCCCAAAAACCCCTACCAGTACAAGTTCTGGTACGTGGTGAACTC

TTCGCC

526
CODING
CACTACGAGCAGTCCAAGATGTTCAATGATGCCATGGACATTCTGA

ACATGGTCTTCACCGGGGTGTTCACCGTCGAGATGGTTTTGAAAGT

C

527
CODING
GGAACACGTTTGACTCCCTCATCGTAATCGGCAGCATTATAGACGT

GGCCCTCAG

528
CODING
CTATTTCACTGATGCATGGAACACTTTTGATGCCTTAATTGTTGTTG

GTAGCGTCGTTGATATTGCTATAACTGAA

529
CODING
GTCCCTGTCCCAACTGCTACACCTGGG

530
CODING
AAGAGAGCAATAGAATCTCCATCACCTTTTTCCGTCTTTTCCGAGTG

ATGCGATTGGTGAAGCTTCTCAGCAGGGGGGAAGGCATCCGGACA

TTGCTGTGGA

531
CODING
GCGCTCCCGTATGTGGCCCTCCTCATAGCCATGCTGTTCT

532
CODING
GTTGCCATGAGAGATAACAACCAGATCAATAGGAACAATAACTTC

CAGACGTTTCCCCAGGCGGTGCTGCT

533
CODING
TGAGTCAGATTACAACCCCGGGGAGGAGTATACATGTGGGAGCAA

CTTTGCCATTGTCTATTTCATCAGTTTTTACATGCTCTGTGCATT

534
CODING
ATCATCAATCTGTTTGTGGCTGTCATCATGGATAATTTCGACTATCT

GACCCGGGACTGGTCTATTTTGGGGCCTCACCATTTAGATGAATTC

A

535
CODING
AACACCTTGATGTGGTCACTCTGCTTCGACGCATCCAGCCTCCCCTG

GGGTTTGGGAAGTTATGTCCACACAGGGTAGCGTGCA

536
CODING
CATGTTTAATGCAACCCTGTTTGCTTTGGTTCGAACGGCTCTTAAGA

TCAAGACCGAAG

537
CODING
ATTACTTGACCAAGTTGTCCCTCCAGCT

538
CODING
CGTGGGGAAGTTCTATGCCACTTTCCTGATACAGGACTACTTTAGG

AAATTCAAGAAACGGAAAGAACAAGGACTGGTGGGAAAGTACCCT

GCGAAGAACACCACAATTGCCCTA

539
CODING
TGCTTGAACGGATGCTTTAGAATTTTCTGCCTGAGCTACGGCACCA

AGCTGGTTAGTCGGAAGGCGTTTGTGGCTAAGGCCTTGAAA

540
CODING
GCGGGATTAAGGACACTGCATGACATTGGGCCAGAAATCCGGCGT

GCTATATCGTGTGATTTGCAA

541
CODING
TGGTGCCCTGCTTGGAAACCATGTCAATCATGTTAATAGTGATAGG

AGAGATTCCCTTCAGCAGACCAATACCACCCACCGTCCCCTGCATG

TCCAAAGGCCTTCAATTCCACCTGCAAGTGATACTGAGAAACCGCT

GTTTCCTCCAGCAGGAAATTCGGTGTGTCATAACCATCATAACCAT

AATTCCATAGGAAAGCAAGTTCCCACCTCAACAAATGCCAATCTCA

ATAATGCCAATATGTCCAAAGCTGCCCATG

542
CODING
GCTCCCAACTATTTGCCGGGAAGACCCAGAGATACATGGCTATTTC

AGGGACCCCCACTGCTTGGGGGAGCAGGAGTATTTCAGTAGTGAG

GAATGCTACGAGGATGACAGCTCGCCC

543
CODING
GGCTACTACAGCAGATACCCAGGCAGAAACATCGACTCTGAGAGG

CCCCGAGGCTACCATCATCCCCAAGGATTCTTGGAGGACGATGACT

CGCCCGTTTGCTATGATTCACGGAGATCTC

544
CODING
ATCCGAAGGCTTGGGACGCTATGCAAGGGACCCAAAATTTGTGTCA

GCAACAAAACACGAAATCGCTGATGCCTGTGACCTCACCATCGACG

AGATGGAGAGTGCAGCCAGCACCCTGCTTAATGGGAACGTGCGTC

CCCGAGCCAACGGGGATGTGGGCCCCCTCTCACACCGGCAGGACT

ATGAGCTACA

545
CODING
GATGTGGTCCATGTGATGCTCAATGGATCCCGCAGTAAAATCTTTG

AC

546
CODING
TGGGAGTGTGGAAGTCCATAATTTGCAACCAGAGAAGGTTCAGAC

ACTAGAGGCCTGGGTGATACATGGTGGAAG

547
CODING
CCTGAGGATTCATCTTGCACATCTGAGATC

548
CODING
GGTGCTGGACAAGTGTCAAGAGGTCATC

549
CODING
AGAAGGTTCTGGACAAGTGTCAAGAGGTCATC

550
CODING
TTAGTTGAAAAATGGAGAGATCAGCTTAGTAAAAGA

551
CODING
GTCACAACGGTGGTGGATGTAAAAGAGATCTTCAAGTCCTCATCAC

CCATCCCTCGAACTCAAGTCCCGCTCATTACAAATTCTTCTTGCCAG

TGTCCACACATCCTGCCCCATCAAGATGTTCTCATCATGTGTTACGA

GTGGCGCTCA

552
CODING
CGGTGCAAGTGTAAAAAGGTGAAGCCAACTTTGGCAACGTATCTCA

GCAAAAAC

553
CODING
CAGGAAAGGCCTCTTGATGTTGACTGTAAACGCCTAAGCCC

554
CODING
ATGTTAAGTGGATAGACATCACACCAG

555
CODING
GCGCATCCCTATGTGCCGGCACATGCCCTGGAACATCACGCGGATG

CCCAACCACCTGCACCACAGCACGCAGGAGAACGCCATCCTGGCC

ATCGAGCAGTACGAGGAGCTGGTGGACGTGAACTGCAGCGCCGTG

CTGCGCTTCTTCCTCTGTGCCATGTACGCGCCCATTTGCACCCTGGA

GTTCCTGCACGACCCTATCAAG

556
CODING
ATGGTTTGGGCCACTTCCAATCGGATAG

557
CODING
GGATTGGAGAAGCACCATATAAAGTAGGGGTACCATGTTCATCTTG

TCCTCCAAGTTATGGGGGATCTTGTACTGACAATCTGTGTTTTCCAG

GAGTTACGTCAA

558
CODING
ACTTGGAGGTGGACCATTTCATGCACTGCAACATCTCCAGTCACAG

TGCGGATCTCCCCGTGAACGATGACTGGTCCCACCCGGGGATCCTC

TATGTCATCCCTGCAGTTTATGGGGTTATCATTCTGATAGGCCTCAT

TGGCAACATCACTTTGATCAAGATCTTCTGTACAGTCAAGTCCATG

CGAAACGTTCCAAACCTGTTCATTTCCAGTCTGGCTTTGGGAGACC

TGCTCCTCCTAATAACGTGTG

559
CODING
ATCCCGGAAGCGACTTGCCAAGACAGTGCTGGTGTTTGTGGGCCTG

TTCGCCTTCTGCTGGCTCCCCAATCATGTCATCTACCTGTACCGCTC

CTACCACTACTCTGAGGTGGACACCTCCATGCTCCACTTTGTCACCA

GCATCTGTGCCCGCCTCCTGGCCTTCACCAACTCCTGCGTGAACCCC

TTTGCCCTCTACCTGCTGAGCAAGAGTTTCAGGAAACAGTTCAACA

CTCAGCTGCTCTGTTGCCAGCCTGGCCTGATCATCCGGTCTCACAGC

ACTGGAAGGAGTACAACCTGCATGACCTCCCTCAAGAGTACCAACC

CCTCCGTGGCCACCTTTAGCCTCATCAATGG

560
NON_CODING
TAGTCTTGGCTCGACATGAGGATGGGGGTTTGGGACCAGTTCTGAG

(INTERGENIC)
TGAGAATCAGACTTGCCCCAAGTTGCCATTAGCTCCCCCTGCAGAA

TGTCTTCAGAATCGGGGCCCG

561
NON_CODING
GAGCTTACCTTGAACCTTTGAATTGGGCCAAATTGCGATGACCACT

(INTRONIC)
GCATCCTGGAAAATTTTATTTCACCAGCACTACAACTCCTCAACAG

CACCAACCAATAAACTATGGATTTTTGTACTAAGCCAGTTGCCTCTT

TCAAAACAACTTGTCAACTTGTCTAATCACCCTCAGCTTTTTTTAAA

AACCCCTCCTCTACCCTCTCTCTTCAGAACACAAGTGGCTTCTAGCT

GAATCT

562
NON_CODING
GATGCTTGACATCCCTAACTAGACAGATGAGGGTTGAAGTTAGTTT

(INTRONIC)
TTGGTGGGGTTGGAGGTGAACATCAACTACCTTCCTAGTTCCAGGT

AATATAGAACATGGAGTGAAGTGTAGATAAATGGGTCTGGTGGGT

CCCGAGGTCATCTTATCACATAATGACTAATTTACATTATGGAACC

CAGTACAAAGTGTTCCAGTTAG

563
NON_CODING
TAAAGCCACAAGTCACCCTTTGCTGAAGTCAGTATTAGTAGTTGGA

(INTRONIC)
AGCAGTGTGTTATTCTTGACCCCATGAAGTGGCACTTATTAAGTAG

CTTGCTTTTCCATAATTATGGCCTAGCTTTTTAAAACCTACTATGAA

CACCACAAGCATAGAGTTTTCCAAAAG

564
NON_CODING
TGGAGAACAACATTGGGGCCCTTGACTTTAGATTTCAGTGGGGACC

(INTRONIC)
TACAAAAAGGAAAAATGGAAAGGGAATTCTGAAGTCTTAAGGTGG

GCTATCTGAAAGTTGGATCCCTGGGTGAAAAAGATTTTATAATATT

AGATGAGTTGAGAGAACCAATGTGAATTAAAGCTGACTGGCTTAA

AAAAAATAAACCCATCAAAATTAGTAAGGGAATAATGTTATTCATT

GCCTTTTTTTCGTTGAGTTATGAAAGCTCTTCGAAGATGAAGGTTTT

ATGAAACTCAAGATCTCTCCAGAGGCCGGGCACAGTGGCTCACGCC

TGTAATTCCAGCACTTTGGGAGGCTGAGGTGAGCAGATTGCGAGTC

CAGAAGTGA

565
NON_CODING
TGTGCAGCCGAAGAATGAGTGTAACATGATCCTTGCAACAGAAGA

(INTRONIC)
AAAGGACACGGAGAGGTCATTTGGTAGGAGGCTCCACTGTGAGAT

GACCACCGATGATTACTTCTGCCGAAAACCTAGCAGTCACAGCA

566
NON_CODING
TTTGGGATTGGTTTAGAGGCAGCTGAACGAAACTTATTTTTCATCTG

(INTRONIC)
TAGTAAATACCTTTCATTTAATGTGAATGGTAAAATCAAAGGGCAG

ACGCTG

567

CTTGCCTGTGGCACCAGATGCCTTACAGTGGCCAGGAATGCTGCGG

NON_CODING
GACAGTCTACTTTGATTGCTTTCTTTCCTCCATGGCTGAGATCTGAG

(INTRONIC)
TGTAGTGTTAACTGGGCTTAAAAATCAAGTCCGTTGTATCTGCATG

GTCACGTAGTTCGGCATCTCATGGCTTTTGCACCTAGA

568

TGAATGACCATACAAGGACTCCATGGTATATTCTTGTAGATCATTA

NON_CODING
GTTAATTATCAACAATTGGCTAATGATTAATGTTTGCCTGAGAGGC

(INTRONIC)
TGACTTTTTGTCCATTAGTAATGACATCCCAGGAAACACCTGGCAG

AGTTCGTCTTTAATTTC

569

AGAGAGCCTCAAAATGACCAGAGTAGATGGACTCGTGTAGTAAAA

NON_CODING
CTTTACCCAAAGTTGGTTTCCTAATGATATAATGTGAAACAGTCTAT

(INTRONIC)
GTGCTATACAAATAATTATATCTCTTTTGTTAAGCCTTACGTCATTT

TGACAAAGGCTTTACTTGATTGAGTATTGACGGCTTTTCCA

570
NON_CODING
TTGGGGAAGAAGAATATCCAATCCG

(INTRONIC)

571
NON_CODING
AGTGCAATGTGTCATGGGCTCTGAAGGTCTTACGTTGAGGAATGGC

(INTRONIC)
AATATTATCAGAATTACGTGTCCAGCTTCCCAAGCTTACTACTTTGA

572
NON_CODING
CCCATTTTGAGGGACTGCCAAGCTGCTTGCCAAAGCAGCTGCGCCA

(INTRONIC)
TTTTACATTACCACCAGCAACATGTGGAGGTTCCAATTTCTGTACGT

CTTTGCTAACACTTGTTATTGTCTATCTTTTTAATTATAGCCATCATA

GTGCATATGAAGTGGTATCTCATTGTAGTTTTGATTTGCATTTCTCT

GATGACTAATAATAGTGAGCATCTTTTCATGTGCTTATTAGCCGTTT

GTATCAAATCCTTTGCTCATTTTTAAATTGAATTTTTAAAATTATTG

GTTTGTGGCAGGGCATGGTGGCTCATGCCTGTAATCCCAGCACTTT

GGGAGGCCAAGGCGGGTCGGTCACCTGAGGCCAGGAGTTCGAGAC

CAGCCTGGCCAGCATGGTGAAACCCTGTCTCTACTAAAAATACAAA

AAATAGTCAGACATGGTCACAGGCA

573
NON_CODING
TCTGGACTTTCACCTTGGGACATTCTCAGTTTCCACCCCACTGTTTC

(INTRONIC)
TGAGGGTCGAAAGGTTTGGGTGTATATGTAGGGAAAGATAATTGGT

AGGCTCTGAAGCACACAGTTCATTTGTTTTTCAATAAGGAAGAGTC

ATGTTAGAAATTTTGTCCTTTCTTCCAGAAGGTACACTATATAGCCT

GGAGCCACA

574
NON_CODING
TCCAAAGACAAGCTTAATGACTGCTGTGCCAACACACAAAACTACA

(INTRONIC)
AGATACATTTAAGCA

575
NON_CODING
GTACCTCTCCAGATTAGACAAGATGATATTAAATATTTCCATCTTAC

(INTRONIC)
AGATGAGCAAATTCAGACTTAGAGACGATAAGGTACTAGCCCCCT

GGAAAACAACTGCACTGAACCTAGGTCCTTTATTTCTGAACAAGAC

AGGCATCGTGTTGAACTTCATG

576
NON_CODING
TAGCCATTCTGCACTCTTCAGGAGAGAAGAACAACCTGGGGCCATG

(INTRONIC)
TGTTCAATAAAGAGATGGGGCTGGCACATTGTTGAGGAGGAGAAG

GAGGATTTCAAATGGAGGGCTTTTTGAAGAAGGCATTGAACACCTC

CCCACCCACCCCTGCCCTGCACTTCTCCCTGTAGCTCAGAAACCTTT

TAATAGCCATGGGACCAACATCTAGCAGCTGGCTTGGTTTTGCTGG

TCCTTGCTTTAAAATGGGGATACATATCCCTGCTTTACAGACCTGCT

GTGG

577
NON_CODING
AGTTACGATTAATGTGAGCAGCTTCTCTCATTCCAGAAATGTGACC

(INTRONIC)
TCTGGTTACAGCAAATGTGACAACATGAATTACCTTCAAT

578
NON_CODING
GAAGCAACCCATATATCCCTCAACGGGCGAATGGATAAACTCATTG

(INTRONIC)
TGATGTATTTGTGTAATGGGATATTACAGAACAACAAAAAGAAATG

AACTGCTGATAAAACAACGTGGATGAGTGTCAGAAACATTATG

579
NON_CODING
GTGGGTTTCAGAATCACTGGTGCTTTGAG

(INTRONIC)

580
NON_CODING
AAGTACCCTGGGGAGAGAGTTTATGGAGTGTTCTTTGCTTGGATAA

(INTRONIC)

581
NON_CODING
GGTGGGTCCAATATGTAGAAAGGCACACTTAGAACAGGACTATTTG

(INTRONIC)
GATGTGTGGGAAGTGGGATCATTAAGTTCTGGTGGAAAGAAACCT

ATGGTAGAGTTCTTTGATAAA

582
NON_CODING
GCAGGAGTTTTGTCCTCTACCAAGACCTTTCCTGAAAATCACTTATC

(INTRONIC)
AAGACAGTTTCCTGTAAGAAAAAGCCATATCCCAGCTGATTTTCCT

TCCTGGGGCCAAAATCTGCTATTATTCGGCCTGAAAGCCTTGATGA

CTCTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTG

TGTGTGTGTGTGTGTGTATGGATGCTTGTGTGTGTGTATGGGGAAT

ATGTGATTAATGTGTGTTGGCTGCTGTTGTCTCTGATTTGGCTA

583
NON_CODING
TCCTGAGGACAGTTGCCAAGACCACACAAGCTTTGCTGGATGAGGG

(INTRONIC)
CCGCCAAGAGGGGTTGCCAGACATTTTATGTGTCCTCTGAGATGCT

TTCTTTTCTGCTGAGGCTTCCCAAATCAAGCTGTTTCCTGGAACCTC

ACCAGGCTTCATGAAGGAGAACTATAGAACGATTATTGACCAGAA

ATTAATCAGCATTGTTGCTTGAGATTTAAACAATTTCCATAGCATGC

CCTTTTTTTGTCTGTTCTAAAGTGAGATACATTTATAATTGCTTTATT

TGTCTGGATCCAAATATAATGCAGATTAATTGTTATAAAACGATAG

CAAAATGAGCTGGATTGGGTGGGCTTTTGGTAGTCCCCATTTGTAG

ATTTCAGCCGCTGAGCTTGTCCTTATT

584
NON_CODING
CTCCTAGTAAACCTCAGTGGCCTTAGGCTAGGGTTGGACATGTGAG

(INTRONIC)
GGTGGTGTCTATTCCTGGAGAAATAACATCGCATTTGATTTTGCCA

CAGGAGCTTTCTATACAAGGTTAACAGCAATCCTGTTGTGAATTCC

TTGGCGCCTCATGTCTCCTAAACCCAGCTAAACTGACGGAGGCCAT

G

585
NON_CODING
CTGAGATCCTGTAGAGTGCCCGGCTCTGGTCCAGAGGCGAGGGGTG

(INTRONIC)
CCAGGATGTCTCAGACACAGACAGCGGCCTTGTGCTTAGGCGTTCA

TTATCTCATGGGGTAGCCCATTTTGAAGCAGTGCAGAAGGGCACAT

ATTCAGTAGAGGTGCAGACCCAGAGGCTCTGTGAGCTGCACTAGA

GAGATGAGGAGGCATCTCCCCCGGCGACTGACGATGGGCTGGCAT

GCCTCCACCTCCGCCCCTCCGCCCCCTCGCCCTCCCAACCACCACCT

TCCCTCTCTGCCTGCTACTCCCCTCTTACTTTCCCATTGATATTTTTG

TTGTTGTTTAAGCAAATTATTATTATTTTTTTAAATTTTAGCCTCAA

GAGTCTTCATAATTTTTTAAGGGAACACTAGAGGTACTGC

586
NON_CODING
GGTGCAGGGTACTCTTTGGAAATTCTGGAGTGTAGCATTTTCTGGA

(INTRONIC)
TTTCCCAGCAGGTGGCCACACTTTACACACACATCAACGTTGTACT

CAATGTCACCCAAGAGGTGGCTCTGGAGAATGTGGAAGCACTGTGT

CAGCTGCAAAGTATTACGC

587
NON_CODING
TGTGCTGAGTTGACTTCTCTGTCCGCAGTTCCCCCTCCACCTGTGCT

(INTRONIC)
CTGGGTTGTTGATGTGCAGGTTAGAAGAGGGAGGTTGTTGAGGGTA

TTAGTGTTGCAGGGGAGGCTGTT

588
NON_CODING
GCACCGTGTAGGCACTGCAGTGACAGTGTGGAATGAAATGGTTTCT

(INTRONIC)
TTCTTCGTGAAGCTTATATCTAATGATGGAGGCCAAAATGACAATT

ACAAACTCGTATAAATGCTTTGAAAGAAAGGTTCATGTGCTGTGAG

AGGGTTTAACAGGCACAACTGCTGTCAGTTTATTGGGTAGGAGCAT

CCTGGAAGTGAAGAATGAGTAGTCCACATATCCAGGCAAGGTGGG

ACAAGAAGCTAGGGCAAGGGTATTCTAGTCAAGGGAAAACCCACA

GAAAGGAGGTACAGTAGGAAGGAGCAGAGGATGCTGGAGGAACT

GAATGAAGCTAGGGTGACAGGAACTGGGAGAGCTGGAGATGAAGT

CAGATGAAAGGAAAGAGACTGGCCGGCAGAATCCAGGTCACGTAG

GACCTTTAGACTATGTC

589
NON_CODING
GAAAAGGTAGCAGGTGTTAATTATGGAATCTAGGTGAGGTAGGCA

(INTRONIC)
TATGGGTGTTC

590
NON_CODING
TGGTAGACTGAGAACTTAAGGATGCATATGATAATCTCCAGAGTAA

(INTRONIC)
TGACTTAAAAGGGGTACTAAAAAGCTAAAAGAAGAGATAAAATGG

AATATTAAATAGTACTAAATTATCCAAAATAAGTCAGAAAAGGAA

GAAAAAGGAACAAAGAACATATAGTACCAACAACGAGATGGTAGA

CAAACCCAGTAATATC

591
NON_CODING
CGTGGAACATTCACCGACATAGACCATATCTTGGCCATGAAAGTCT

(INTRONIC)
CATTACCTCTCGATTGAAATTTTACAAAGTATCTTTGTTCTAATGGC

AGTAGATTTAAAACAGAAGCCAATAACAGGCTGTTTATAAACCTTC

CCAAATGTTTGGAAATTAAATAACCTATAACTCAAAAAATAATAAA

AATTAGAAAATACTTTGAAACTGATAAAATCCAACTGGGAAATTGT

ATGATCCGTTGAATGCAGTGCTTGGAGGGACATTTATAGCTATATC

592
NON_CODING
ATGGCAGAGACTCAGGCTGTTTTGCCAAAACCCAGGTCGCTTTCCC

(INTRONIC)
CAGCTGTGCAGGCTCGTATTCTGCTGAAGCTGCTGTTGGTTATTCCT

GGGACCCTGG

593
NON_CODING
CAGATGGGGTGTCACGGGGCCCTGACAAGGAAGGTCCACATGAGG

(INTRONIC)
GGAGATGATTACACTGGTGTGCTAGACCCAGGGGA

594
NON_CODING
TTCCTGCATGCCTATATGAAGTGGCGCCAAGGGGAAATAGAGACAT

(INTRONIC)
GGGAAGAAATACATGAGAAATGGACAGACAACATTGTCCGTTCCT

GCCTGCAAGG

595
NON_CODING
CACGTCCCATATGGTGGATATAGGAACTGCATATGTGTGCAAGTGT

(INTRONIC)
AGTTTTGCATCTGCACGTGAATCTATGAATATCTAGATTTTCTAACC

CACTTAAGGGCTGCATATG

596
NON_CODING
GGCCATGTTTGGAAAGCTACCTAGTGAAGAGTCCTTCCCCAGTCTG

(INTRONIC)
GTGTCCTCTAGGGGTGTCCAGCATAGCGTAGCCCACTTGCGTTCCA

GCTCCACCAGTTCCCTTCATGTTGAAACCTCCTCCATCCCTTGTAGG

GGAGATGGGGATGGAGTCTAATCGCTCTCTCTTCATCCGTGTACTG

TTCCCTCGTCAACCCAGAAAGAACCCACTGTTCAGCCACAGCAGCC

TGAGTGGGCTTTTCTAGTGACCCCACTCTGTATGGCCGCTCGAGAT

CTAAAGGGCATTAGCTGGTATAGGCCACCTGTTAACTACTCGGGCC

AGCTTTA

597
NON_CODING
GTGCTGTGTGGACGCAGTTTTCCGAGCTCTGTGTTGTTAGCATGTAA

(INTRONIC)
CTCT

598
NON_CODING
TGCATGTTCTACTTTCCATTGGGTTTGACCTCTCCATGATAACCC

(INTRONIC)

599
NON_CODING
TAAGAGCCATGCCAAGGACTTCTCTCTTTGTCT

(INTRONIC)

600
NON_CODING
GTAGACGTGTTGGTCACATGTGATGAG

(INTRONIC)

601
NON_CODING
GGCAGACTGCGTGCTAATGGAAAGTGGAGCATGGCCGTCGCAGTG

(INTRONIC)
TGAGCGCAGAAGTGCGGACCTAGGC

602
NON_CODING
GAGTTCCTTTTGTATGCCAGTCCGCCATGACCTCCTGAGCGTCCGGC

(INTRONIC)
CCTGCTCTCTGCAGAGACCCAGTCCAGAATACAGTGAGAAGTGGAC

AGGCCAGGAAGCTCAGATACACCCATTGAAACTAACACATACACC

CGCATGCCAAAACCAATCCAGGCAACACCTCAGGTTCCATCTTAAC

GTGTCCACAGGAAACACCACCACACCCAAACCTCATCTAACATTGT

CCGTCTTTAATTCGTGCTCAGAGCCAGTCTGGGGATGCCTCTTTGGA

AGCAGTGTGGTCTAGTTTCAAGGACACTGGGAGTCAGGGAACCTG

GGTTCTAGTCCCAGTTTCAGCATTCACTTGCTGCGTGACCTTGGGCA

AGACACTTAACCTCTCTGTGCCTCAGTTTCCCCCATCTGTAAAATGG

GGTTAATAATGTCGACCTACCTCACAGGGCTGTTGTGAGGAATAGC

TAAGTGATTGTAAAGCACTTTGAACGTATAATTGCTTATTAAGACT

ACAACAATAATAATATCATATGCCTGTTTACTACCAGAACTTTAAG

AAATTCTTGTTTTCCTTTGATCTCTTTTCTGTTCTGTACCATACTTAC

CCATTGAGAAGGAAAATTCCCCCCTTTTAAAGAAATCTAGGCAATG

CACAAAGATGTCAACAGAGGTAACCCTGCAGGTTGCATTTTCACAT

CTTAAGAATAGCAGATTTTTGCCCAAGATGTTGGTCGATAAGGGTG

TCTGATCTTGAATTCTCAGCTGATTCCAAGTGGTGGTTGGAGTCTGT

ACATCTGATGCTGAGCCCAAGACACCCAAAGTG

603
NON_CODING
TCATAGGCCCTTGAGACCGTGTGGATATAGTGAACCCAACTCTTGG

(INTRONIC)
TAGACTTG

604
NON_CODING
TTCTGGACTTAACACTCCTCAGCTGTAAAATGAGGTAGGAAATCTG

(INTRONIC)
ATGTGATTTCTAGTTGGGGACATTCTAGAAGATTCCATATTGTATCT

CAAATGACTGTTCAGAGACACAGTCTTTAGGTGCTCACTCTAGAGA

GGACTGTGATAAGC

605
NON_CODING
ACAGAAGTGGTGTGCAGATCGTTTCAGATCAATTTATCATAAAATC

(INTRONIC)
TAAGTTGATAGGTGTTCTCTTAATGATGTTCTTATACTGCCTGTTCA

CCTTGACCCTTTAGCTTTGAGTAGATTAGAGAGTGTAGGGGAAAGA

TCTTTTTCCCTTCAAATACTCAAAGGATCATGTGTTCTCTTGAGCAG

TTCTGCAAATCCATATAGGA

606
NON_CODING
TTCATGAACTGTCGGCCTTCCTGTGTAAGTGGGTCAGGCACCATGT

(INTRONIC)
GACCTGCTCACTGCCAGTTTCTTCTTTGAATAGATGTTTATTTCATG

GATCATTTTGAAGATTCTCCGTGGGTGTGCAACATGGTTTTAGAAT

GTTGGGTAATTTCTCATGTGTTCTTTGAGATGGATGGCTTCTCAGTC

GTCTTTGCAGTCAGCCACTGTAGACTTGAGTTTCTCTCTTGCTGTCT

TCATTTTATTGCTCCATATCTGAGGAAAACCATGTGAAAAATCCCT

AGACACATAGGAGCCCTGAGAAGTGGTGGCAGGGAATGCTTGGGG

GACAAAACAGATTTTAGAGTTACGGGTATTTTAATTAAAAAAAGAG

AGACCCAGAATTGTTTTTCACTTAAATGAGCAATTATATCTTTAACT

TGGGGATGGAAATATGTTGTGAAATTTGTTTAGTCAGCTCCCTCTG

AAATAAATAAAATTACAGTGATGATATCATTCTTGTTTAAAATGTT

TGAAAAGGTATCAAGACAAAGTGATTAAGGCCTAACTGTTTGCCAA

ATTTTCTTTAAAGCTCCATTTTTGGGGTATTTCTATGCCAAAAAACA

TCTTAAACTGATGAACATATAGTTCTCCGCACTTGTATTGGCTGGTT

TTTA

607
NON_CODING
TCCACTGGATATAGCCTCGACTGTACTCACCAGGTTCTCCACACCCT

(INTRONIC)
AAGCCACATGCCAGATTTGTTTAGCAGATTCAGTGGAGCAGGTTCA

TTCATGGGGGCACCAAACCAAAAGTCCTTTTAAAAACAGTTACCTA

TGATTTAAAAGTGTGAAGTGATTGTAGTATGATGGGGAAACAGTGG

GCCAACTATCATGAGAATTAGGAGATCTGGACAGCTACATGATCTC

TTTGATCATATAGTTTTCTTACTTGCTCAGTGCAGCAGTAGTGCCAA

CCTGTCCTCAGACGGGGATGTAATA

608
NON_CODING
GGTTGTGGACCACTGAGCTAATGCAGTGCATCTCAGTGATTACTGT

(INTRONIC)
CCATCAGAAGCTTGTTAAAAAATATTCTTGAGCACCACCCCCAAAG

GTTCTGGTTCAGTAGGTCAAGGGTGGGGCCCAAGAATTTGATTTCT

ATAATGCTTTTAAGTGAAGCCAATACAGACCACACTTAGAGTAACA

TGTTCTAATTTTTTTATGAACCAGGAATTAATAAACTGGGCAGATA

GTAAAGCATTGCCCACAGAGGTTGAAAGAGACTTTCAGATTCATCG

AGTCTAATCTCATCAGATGGTTGAGCTTCTTCCACAAAATCCCCAC

CAAGTGGGTCTCTTTGAATGCTCTACCAACAAGGATCC

609
NON_CODING
TCCAGGCCTTTTAATGAACAGTCTTCTGCTTTTTCTCTTAACAATAT

(INTRONIC)
AATTTCTCCTATGGAACAATTTGAAAGCCATGCATGCAAATTTAGA

CTAAAAGCAATGGACACAAAAGAAACCTGTATACATTCTTCGGTAT

TACGCACATGTGATGAGTGGTGCTTTTGGGCACTTGCCTGACAGTA

GCTTGGACAGAAAAGACACTGGAGCCTCAGAGAATAACTATTGAA

GCAATTCTGGAATTAAGAAATAAGGCCTGAAATGAGATGGTAAAA

GATGTTAGAGGAAGAGAAGCAAGGTAAGACAAGGTGACACACAG

AATCAGAAATGATGAACAGGAAGCAACTTTTAAAATAAATGTTTTC

TGAGTAGCTACTAATATGCCAAGCCCTGTGCTGGGCATTGACATTG

CAGCAGTGAACAAAACAGACACGATCCTGGCTCTCGTCAAGTTTAT

ATTT

610
NON_CODING
GATGGAAAGTAAGGGCAACAAAATAAACTTGAGAGCCACAAACCT

(INTRONIC)
GTGGGTTACAGTTAAAATTATAAAACACTGTCAAAATTTAATTAAT

TTTAGGAAGTTCACTTTGTCCTCACAACAGGTTTTTGAAGTATATTT

TTCTAAGTATTTAATACGTACTCTTAACAGTCTGCAAATTTGCAAAA

CCTGAAGTTAATGAGTGGTTAATTGACTTAAGATTTTTTCCAGAATC

AAATTCCTTTCTCCATACATACATGCGTTG

611
NON_CODING
TGAGGGCCAAGACACAAGATGAAGCTTTGGCTTCTTAAAAAGATG

(INTRONIC)
GGACGAATGCATCTGTCAGTGGCTGGTTACAGCAATGGGTTAGAAT

ATTTAATGAGGGAGGTCATCACTCCTGCTTCCCTT

612
NON_CODING
GGGTCACAAGCCAATAGACAAGCCAGTCCTTTTGAATCCTTTACTC

(INTRONIC)
ATGGCCTTGAGAGGAACCA

613
NON_CODING
GGGCTGGGATTATTGTCTTCATATACAAAGGATAGTCTTTTTTTTTT

(INTRONIC)
GTTTCTATTTTGCAAAGTACCCATTTTCAGCACAATACAAAAGGTA

GATATAATGCTGTGTACTTTTTAAAATAATCTTTTGAATATTATACA

TTCATACTGTCCAAAAATTAGAAAATATAAAAAGGAATACAGTGG

AAGCCTCCATGACCCCACAGGTAACCACTAGCATTATTTTCTAGTA

GTCTTTTATGTGTTTATTTTATGCAGTCTTTTATGTATTTTATGTAGT

ATTTTATGCAGTCTTCCAATTTCCTTATGCATATACAAACATAAAAA

TATATTCTGATAGTTTCTTCTTTTGTTACACGAAAATGGTATACTAT

TCATAGGGTTGGGCACCTTGGTTTTGTTTTGTTTTTTTTTTTCCATTT

AAGAAAATATATTGGAAATATTTCTATATCTGTATGTAAAGAGTTT

CCTCCTTTTCTTTCTTTTCCTTTTTTTTAACAAATGTGTAATATTTAT

ATTTATGCCATAATTTATTTAACCAGCCCCTATTGATAGGAATATGG

GTCATTTTTCAATCTTTCATTTTTACAAACAGCATGTATGAATAACT

TGTGCATCTAAATAGTTTCACAAGAATACCTGTGGGATAATA

614
NON_CODING
TCTAATCCCGGCCTTGGCTTTCTGGTGACCAACCCCCATCCTGAAGC

(INTRONIC)
TGGCCAGGGACTGCCAGCCATCAATCAATCATTAGCATGCAAAAA

GACATACTTTGGAGACTCCAAGGATTTTAGGAATTCTATGGCAGAA

AATGGAGATGAACACCAAATAGAAGGCCGGGCACAGTGGCTCACG

CTTGTAATCCCAACACTTTGGGAGACCAAGGTGGGTGATCACCTGA

GGTCAGGAGTTTGAGACCAGCCTGGCCAACTTAGTGAAACCCTGTC

TCTACTAGAAACACAAAAAATTAGCCAGGCGTGGTGGCAGGCGCC

TGTAATCCCAGCTACTCAGGAGGCTGAGGCAAGAGAATCACTTGA

ACCCAGGAGGCGGAGGTTGCAGTGAGCCGAGATGGCGCCACTGCA

TTCCAGCCTGGGCAACAAGAACGAAATTCCGTCTCAAAAAAAAAA

AAAAAAGACCAAATATATATTTCACAATATCATAGATAATGAATGG

CATTTTTAAAAAAAAGTTTGTCTATTAACTGCTTACCGTGTTCTTGC

CATGTAGGTTCTG

615
NON_CODING
ACAGGGGCGCATTTGCCTCACAAGGAACATTTGGCAATGTCGGGA

(INTRONIC)
GATATTCTGGGTTATACAAGTGGGAGATTAGGAATGCTACTGGCAT

CTAGTGGGCAGAGGCCAGGATACTGTGAAACATCCTATAATGCAC

AGGAGAGCTCCCTACAACAAACAATT

616
NON_CODING
TGCTTTGCGATGCATTTGAAATACCGTTTGTGGCCAGATAAATTAC

(INTRONIC)
GATTGCTTTTCAAGGTTACATGGTGTTTC

617
NON_CODING
GGTCCACAGAGAATAGTCCATGATCTGTACAAACATCCAGAGAGCT

(INTRONIC)
GCTTTCTCCCATGGCCTCCCACAGGTCTGACTGCCAGAGAGTAGAA

GCAAGAGGGGTGAAAATAGAGGAGTACCTGCTGTGCTGTCATTTCA

GGTCTGCTCTGGAGAAGAACATGGGCTAAGAATTATCTTTTATGAT

CTGAAAAAGCTGTCTGAAGTTCCTTCCAAGCTTATCAGCCTCCTAA

CCTGAGCTTTAACAAAACCCGGTATGGTAGAGTCCTAGTGTGCCAA

TCCAGCTTTC

618
NON_CODING
TGGAGCTGCGTTGAATGCAAACTTGAGGTGTTTCCCTTGAGGAATT

(INTRONIC_
CTTGTCTTCAAACGTCTGCAGAGTAATGGACCATGTTACAACTTTCC

ANTISENSE)
TGTTC

619
NON_CODING
GATGGCACTGATGCATTAGACCCTCAGCAGCCTGCAATTGCAAATC

(INTRONIC_
TGCGAGGTTTCATTCGGCCCATAAAGCAAACATTTGAACTTACACA

ANTISENSE)
GAATGAGCACTTAAATACGGGTGCAATAA

620
NON_CODING
TGTAGCCCATTTGGTCACAGTAGCCTCACTTCTGCTACGCTTGCAAC

(INTRONIC_
AACAACTCTTTGGAAATCAACCGCTATTCTATATTTGTGTTCACGTT

ANTISENSE)
AGTG

621
NON_CODING
GGGCCTAGGCTTTGTGCACACTGTTCGATGAAACCAAGGCTTACCA

(INTRONIC_
AGCTCTACTTTATTCCGTATCTGGATGGTCATTTCATTTCTCCTAGC

ANTISENSE)
CCACACCCAGACACACACTTCTCAAATACACACGACAATTTCACTA

TCTCACAATCTCTTACTGTAACTTTGGCCTTCAGAAACACCCTTTGT

TATATTGCAGGCGGCCAAGCATTAAGTCCAGCTGA

622
NON_CODING
ACCTGTGCCAGCTCCTGCAAATGCAAAGAGTACAAATGCACCTCCT

(ncTRANSCRIPT)
GC

623
NON_CODING
CCATTGTATACCCTTCCTTGGTGAATGTTCTGATATTTGCTTCCCAT

(ncTRANSCRIPT)
CCCAAGTTGTTTCAGCCCCTATTAG

624
NON_CODING
CGGATCCGTGTTGCACCTTCTCCTGCTGCCACGTGTGAGGCAACTCT

(ncTRANSCRIPT)
GCGTGTCTCCTAGCTGCTCCCTGACAGCTTCTCTGCATGTGTTTGGA

CTCTGATGTCCTCTCAGTGTGTTGCTTTTGGATTGAACTGTGATTCT

TTCTGCCTGTATCTGTCTGTGAGATTCCGTGTTTCCAATGC

625
NON_CODING
GGAGATTTCAGATGGACCTAGAATGAGGAAGGCAGGCTACTCAAC

(ncTRANSCRIPT)
AGTTGTGGATTTGGGAGTCTGGACACTCCTTGAGCTGTGCAGTTTT

AATTCTTTCTTAAATAAAGATACAAAGGACAATTTAGGACATGGAA

AACCCTAGCTA

626
NON_CODING
TGACCTCTGGGGTAGGTTACTATCCTCTTTGTCCTGCCAGTACCCCT

(ncTRANSCRIPT)
AGAAATTTGACTTAATTGCTGCATCTAGGGACTTAGGGATTTTTCCC

AAATGCTGTGTAGAAAGTCACTGGAGTTAAATCTACTCCAACCATT

TTTCTGCTGTTTCTTGAAAAGACAGGATGATTCATTTACATCTCTTT

TCCTTCACAGAATCATGAGGGAAGTATTGTGATTACCAGTGTTAAG

CATTTG

627
NON_CODING
ACAGCTCCTCCTTCTTGATATTGCACATGCACTTCAGTTCATGGCTA

(UTR)
GCTGTATAGCTTCCGTCTGTAAACTTGTATTTTCAAGAATCCTTGGT

ATTGAATTTTTAGAAATGCTCACATAATTGTTGGGACTGATTCATTC

CTCCACGATATGCCTCCTCTCTCTGATATCCTGCTAACTGTAGCCGT

TGTGGCATTTGAGATGACAGGACATATATATATATGGCCCCACACT

TGACCTTGAGTGCCTGAATGCTCTGAAATCAAGCATATGGCACAGC

GCTCAAGACTTTTG

628
NON_CODING
CCAGACTCGAGAGGTGGGAGGAACTCCTTGCACACACCCTGAGCTT

(UTR)
TTGCCACTTCTATCATTTTTGAGCAACTCCCTCTCAGCTAAAAGGCC

ACCCCTTTATCGCATTGCTGTCCTTGG

629
NON_CODING
TGAAATAATTCATGCCACGGACCTGTGCACATGCCTGGAATTGAGA

(UTR)
GACACAGTTAAAAGACTCCAAGTTGCTTTCTGCCTTTTGAAAACTC

CTGAAAACCATCCCTTTGGACTCTGGAATTCTACACAGCTCAACCA

AGACTTTGCTTGAATGTTTACATTTTCTGCTCGCTGTCCTACATATC

ACAATA

630
NON_CODING
CTGTGCTTTTACCAGTAGCATGACCCCTTCTGAAGCCATCCGTAGA

(UTR)
AAGTACTTTGTCCTCCAAAAAGCTAACATACGGTTTTGAAGCAGCA

TTGAAACTTTTGTAGCAATCTGGTCTATAGACTTTTAACTCAAGAA

GCTAAGGCTAGACTTGTTACCTTCGTTGAA

631
NON_CODING
AGAGGAGGGGACAAGCCAGTTCTCCTTTGCAGCAAAAAATTACAT

(UTR)
GTATATATTATTAAGATAATATATACATTGGATTTTATTTTTTTAAA

AAGTTTATTTTGCTCCATTTTTGAAAAAGAGAGAGCTTGGGTGGCG

AGCGGTTTTTTTTTTAAATCAATTATCCTTATTTTCTGTTATTTGTCC

CCGTCCCTCCCCACCCCCCTGCTGAAGCGAGAATAAGGGCAGGGAC

CGCGGCTCCTACCTCTTGGTGATCCCCTTCCCCATTCCGCCCCCGCC

TCAACGCCCAGCACAGTGCCCTGCACACAGTAGTCGCTCAATAAAT

GTTCGTG

632
NON_CODING
AGCCATCGGTCTAGCATATCAGTCACTGGGCCCAACATATCCATTT

(UTR)
TTAAACCCTTTCCCCCAAATACACTGCGTCCTGGTTCCTGTTTAGCT

GTTCTGAAATACGGTGTGTAAGTAAGTCAGAACCCAGCTACCAGTG

ATTATTGCGAGGGCAATGGGACCTCATAAATAAGGTTTTCTGTGAT

GTGACGCCAGTTTACATAAGAGAATATCACTCCGATGGTCGGTTTC

TGACTGTCACGCTAAGGGCAACTGTAAACTGGAATAATAATGCACT

CGCAACCAGGTAAACTTAGATACACTAGTTTGTTTAAAATTATAGA

TTTACTGTACATGACTTGTAATATACTATAATTTGTATTTGTAAAGA

GATGGTCTATATTTTGTAATTACTGTATTGTATTTGAACTGCAGCAA

TATCCATGGGTCCTAATAATTGTAGTTCCCCACTAAAATCTAGAAA

TTATTAGTATTTTTACTCGGGCTATCCAGAAGTAGAAGAAATAGAG

CCAATTCTCATTTATTCAGCGAAAATCCTCTGGGGTTAAAATTTTAA

GTTTGAAAGAACTTGACACTACAGAAATTTTTCTAAAATATTTTGA

GTCACTATAAACCTATCATCTTTCCACAAGATATACCAGATGACTA

TTTGCAGTCTTTTCTTTGGGCAAGAGTTCCATGATTTTGATACTGTA

CCTTTGGATCCACCATGGGTTGCAACTGTCTTTGGTTTTGTTTGTTT

GACTTGAACCACCCTCTGGTAAGTAAGTAAGTGAATTACAGAGCAG

GTCCAGCTGGCTGCTCTGCCCCTTGGGTATCCATAGTTACGGTTTTC

TCTGTGGCCCACCCAGGGTGTTTTTTGCATCGCTGGTGCAGAAATG

CATAGGTGGATGAGATATAGCTGCTCTTGTCCTCTGGGGACTGGTG

GTGCTGCTTAAGAAATAAGGGGTGCTGGGGACAGAGGAGCAACGT

GGTGATCTATAGGATTGGAGTGTCGGGGTCTGTACAAATCGTATTG

TTGCCTTTTACAAAACTGCTGTACTGTATGTTCTCTTTGAGGGCTTT

TATATGCAATTGAATGAGGGCTGAAGTTTTCATTAGAATGCACTCA

CACTCTGACTGTACGTCCTGATGAAAACCCACTTTTGGATAATTAG

AACCGTCAAGGCTTCATTTTCTGTCAACAGAATTAGGCCGACTGTC

AGGTTACCTTGGCAGGGATTCCCTGCAATCAAAAAGATAGATGATA

GGTAGCAATTTTGGTCCAAAATTTTTAATAGTATACAGACAACCTG

TTAATTTTTTTTTTTTTTTTTTTTTTTGTAAATAACAAACACCACTTT

GTTATGAAGACCTTACAAACCTCTTCTTAAGACATTCTTACTCTGAT

CCAGGCAAAAACACTTCAAGGTTTGTAAATGACTCTTTCCTGACAT

AAATCCTTTTTTATTAAAATGCAAAATGTTCTTCAGAATAAAACTGT

GTAATAATTTTTATACTTGGGAGTGCTCCTTGCACAGAGCTGTCATT

TGCCAGTGAGAGCCTCCGACAGGGCAGGTACTGTGCCAGGGCAGC

TCTGAAATTATGGATATTCTTATCCTCCTGGTTCCTTCGGTGCCAAT

GGTAACCTAATACCAGCCGCAGGGAGCGCCATTTCTCCTAAAGGGC

TACACCACTGTCAACATTATCCTGGACTCTGTGTCTCTCTCTGTTGG

GTCTTGTGGCATCACATCAGGCCAAAATTGCCAGACCAGGACCCTA

AGTGTCTGATAGAGGCGATGATCTTTTCCAAAGTCAGTACTTACAA

ACTGGCATTCTTACAGGCTGCACCATTTCCTAGTATGTCTGCTTTAA

GCCTGGTTCAACCTCTCATCGAATA

633
NON_CODING
CCAGTCGCTGTGGTTGTTTTAGCTCCTTGACTCCTTGTGGTTTATGT

(UTR)
CATCATACATGACTCAGCATACCTGCTGGTGCAGAGCTGAAGATTT

TGGAGGGTCCTCCACAATAAGGTCAATGCCAGAGACGGAAGCCTTT

TTCCCCAAAGTCTTAAAATAACTTATATCATCAGCATACCTTTATTG

TGATCTATCAATAGTCAAGAAAAATTATTGTATAAGATTAGAATGA

AAATTGTATGTTAAGTTACTTCACTTTAATTCTCATGTGATCCTTTT

ATGTTATTTATATATTGGTAACATCCTTTCTATTGAAAAATCACCAC

ACCAAACCTCTCTTATTAGAACAGGCAAGTGAAGAAAAGTGAATG

CTCAAGTTTTTCAGAAAGCATTACATTTCCAAATGAATGACCTTGTT

GCATGATGTATTTTTGTACCCTTCCTACAGATAGTCAAACCATAAA

CTTCATGGTCATGGGTCATGTTGGTGAAAATTATTCTGTAGGATAT

AAGCTACCCACGTACTTGGTGCTTTACCCCAACCCTTCCAACAGTG

CTGTGAGGTTGGTATTATTTCATTTTTTAGATGAGAAAATGGGAGC

TCAGAGAGGTTATATATTTAAGTTGGTGCAAAAGTAATTGCAAGTT

TTGCCACCGAAAGGAATGGCAAAACCACAATTATTTTTGAACCAAC

CTAATAATTTACCGTAAGTCCTACATTTAGTATCAAGCTAGAGACT

GAATTTGAACTCAACTCTGTCCAACTCCAAAATTCATGTGCTTTTTC

CTTCTAGGCCTTTCATACCAAACTAATAGTAGTTTATATTCTCTTCC

AACAAATGCATATTGGATTAAATTGACTAGAATGGAATCTGGAATA

TAGTTCTTCTGGATGGCTCCAAAACACATGTTTT

634
NON_CODING
TGTTGTTGCAATGTTAGTGATGTTTTAA

(UTR)

635
NONCODING
AAATAATGCTTGTTACAATTCGACCTAATATGTGCATTGTAAAATA

(UTR)

636
NON_CODING
GTTTGCCCTTTGGTACAGAAGGTGAGTTAAAGCTGGTGGAAAAGGC

(UTR)
TTATTGCATTGCATTCAGAGTAACCTGTGTGCATACTCTAGA

637
NON_CODING
CAAAGTAAACTCGGTGGCCTCTTCT

(UTR)

638
NON_CODING
CGAGGTGATGGGACTTCTTAACACACATTTCTATAATACCCATGAA

(UTR)
ATGATAATTTGTAAAATAACACTTAGTGATATCTGGAAATAATAAT

TCAATTAAGCAACCACGAATTTCACCCTGGAGATATTTTTTCTTATT

TGAGTCCACCAAAGGATAATGCCAACTTATATAAGTTCTCAAATCA

TGCCTTCCGCTTAGTCTCATTTTATTCATTCAGTCGTCATGAGTTGA

GTGCTTACTACATGCAAGGCACTCTGCTAGTTATATTCTAATAATGC

AGAGATAATTAGACATGGTTCCCGCCCTCA

639
NON_CODING
TTCCATACACGTTTGCAGTTTCTTGTACACATTTGGATACTTTGAAA

(UTR)
GATGACAGATTGTTAAATCCATTCAATGGTAAAGAAACTCACCATC

TGGAGATTGAGTCTACTTGTTAATGAATGACTAGCCCAATTATCCTT

ATAAATTGAATATGGTGACCAAATGCTTTGATATCATACTACTCTG

CCTTTGTGGGCACATATGTAGACACTACTAAAAATAAATATTTTTG

GAGATTAAAATGGAGAATAGAAGTAATTACATTATTTAGGTCTTAA

TCCAACTTTTTTCTAATATATCTAAACAATTGAAAGGGAAGCTTATT

CATGGAATATTGGCTTGATTTATCTAGAAAGTTTTTCCTTCTTCAAT

TTTACTATATTCATTCTACAGGAACAGCAATAAGTACTATTAAACA

GAAGATGGCTACACTAAGTTCCAATTTTGTTGCTGAATTGCTTCTGT

GAGTTCACTTTTCAGTTCTAAGGAAGAATAATATTTGCTACATATTT

CACAGGGGTTCTTA

640
NON_CODING
CCCACCTTTCCATGCTTAAGACAAAAATGTCTTAAATATAAAGCTG

(UTR)
TGATTATATCAAAAATCCAGATAAATCATCAAATATATCAGATTAA

GACCAGGGTTTACACACTTAGGCAATAGTC

641
NON_CODING
GTTTTAATTCAACAGTCCAACATTATTTAGGTGTTACAGAGTGTAA

(UTR)
ATATATTTCTTTGGGAGTTATTTTCTTTTTAAAATCTTTTTATAGCTT

GGCAATGTCCAAAGTCAAATATCACCTAAACTGGTTAGATTACTTC

TACAGCTAATAATATTGCAG

642
NON_CODING
TGGCTACTTGACCTACAGCAAAAGCCATTTCTGTACCATAAAAATT

(UTR)
TGTTGTGCAATATTAGAATTATCATATGTTTCCTACATCTGACAGCA

CCTAAAATGTTTGATAATATTAACATGTATCTAAGAGGAAAAAAGA

GTTAATATATTCTGGCACCCACTTTCCTAGTAATGTTTTCCATGATT

TTCCAGTTCTGAGGCACTTATTAAAGTGCTTTTTTTTTTCTGAATTA

ATTAGGTATTGGTAAAATATATTTTTAAATTTAGTTAGCTTTATAAA

CACAATTAGAATTACAATTAATTAACAGAGGTATAATTGTCTCACT

TTCAGAAGTGATCATTTATTTTTATTTAGCACAGGTCATAAGAAAA

ATATATAGAAAAATAATCAATTTCATATATAAAAGGATTATTTCTC

CACCTTTAATTATTGGCCTATCATTTGTTAGTGTTATTTGGTCATATT

ATTGAACTAATGTATTATTCCATTCAAAGTCTTTCTAGATTTAAAAA

TGTATGCAAAAGCTTAGGATTATATCATGTGTAACTATTATAGATA

ACATCCTAAACCTTCAGTTTAGATATATAATTGACTGGGTGTAATCT

CTTTTGTAATCTGTTTTGACAGATTTCTTAAATTATGTTAGCATAAT

CAAGGAAGATTTACCTTGAAGCACTTTCCAAATTGATACTTTCAAA

CTTATTTTAAAGCAGTAGAACCTTTTCTATGAACTAAATCACATGC

AAAACTCCAACCTGTAGTATACATAAAATGGACTTACTTATTCCTC

TCACCTTCTCCAGTGCCTAGGAATATTCTTCTCTGAGCCCTAGGATT

GATTCTATCACACAGAGCAACATTAATCTAAATGGTTTAGCTCCCT

CTTTTTCTCTAAAAACAATCAGCTAATAAAAAAAAAATTTGAGGG

CCTAAATTATTTCAATGGTTGTTTGAAATATTCAGTTCAGTTTGTAC

CTGTTAGCAGTCTTTCAGTTTGGGGGAGAATTAAATACTGTGCTAA

GCTGGTGCTTGGATACATATTACAGCATCTTGTGTTTTATTTGACAA

ACAGAATTTTGGTGCCATAATATTTTGAGAATTAGAGAAGATTGTG

ATGCATATATATAAACACTATTTTTAAAAAATATCTAAATATGTCTC

ACATATTTATATAATCCTCAAATATACTGTACCATTTTAGATATTTT

TTAAACAGATTAATTTGGAGAAGTTTTATTCATTACCTAATTCTGTG

GCAAAAATGGTGCCTCTGATGTTGTGATATAGTATTGTCAGTGTGT

ACATATATAAAACCTGTGTAAACCTCTGTCCTTATGA

643
NON_CODING
TTCATCAACTCAGTCATCAAATTCC

(ncTRANSCRIPT)

644
NON_CODING
TCTTCCCATGCACTATTCTGGAGGTTT

(UTR)

645
NON_CODING
GCACACTCTGATCAACTCTTCTCTGCCGACAGTCATTTTGCTGAATT

(UTR)
TCAGCCAAAAATATTATGCATTTTGATGCTTTATTCAAGGCTATACC

TCAAACTTTTTCTTCTCAGAATCCAGGATTTCACAGGATACTTGTAT

ATATGGAAAACAAGCAAGTTTATATTTTTGGACAGGGAAATGTGTG

TAAGAAAGTATATTAACAAATCAATGCCTCCGTCAAGCAAACAATC

ATATGTATACTTTTTTTCTACGTTATCTCATCTCCTTGTTTTCAGTGT

GCTTCAATAATGCAGGTTA

646
NON_CODING
TTTCCAAAACTTGCACGTGTCCCTGAATTCCATCTGACTCTAATTTT

(UTR)
ATGAGAATTGCAGAACTCTGATGGCAATAAATA

647
NON_CODING
GCTTCAGGTGACCACAATAGCAACACCTCCCTATTCTGTTATTTCTT

(UTR)
AGTGTAGGTAGACAATTCTTTCAGGAGCAGAGCAGCGTCCTATAAT

CCTAGACCTTTTCATGACGTGTAAAAAATGATGTTTCATCCTCTGAT

TGCCCCAATAAAAATCTTTGTTGTCCATCCCTATA

648
NON_CODING

(UTR)
GTTTCGACAGCTGATTACACAGTTGCTGTCATAA

649
NON_CODING
CTGGCAATATAGCAACTATGAAGAGAAAAGCTACTAATAAAATTA

(UTR)
ACCCAACGCATAGAAGACTTT

650
NON_CODING
TCTCTAGCTATAAGTCTTAATTATACAACAAAATACTATTTTTATAT

(UTR)
TTATGTTTGGTAAATTCAATAACTTTCCTCATCATTTGGAAAGTCAA

ATTGTTTATTGCTTCCCTACAGTTTTTTCTGAATC

651
NON_CODING
CTGGGATTCTTACCCTACAAACCAG

(UTR)

652
NON_CODING
TTCAAAGAAATACATCCTTGGTTTACACTCAAAAGTCAAATTAAAT

(UTR)
TCTTTCCCAATGCCCCAACTAATTTTGAGATTCAGTC

653
NON_CODING
AGGGAAAAGTTAAGACGAATCACTG

(INTRONIC)

654
NON_CODING
ATCTTCCAACAACGTTTGTCCTCAAAT

(INTRONIC)

655
NON_CODING
CCTATTACAGCTAATCTCGTTTTAAATCTGCTC

(UTR)

656
NON_CODING
TATGTAACAATCTTGCACAGTGCTGCTAATGTAAATTTCAGTTTTTC

(INTRONIC)
GCCTCTAGGACAAACA

657
NON_CODING
TTTGAAGTCAACTGTATCACGTCGCATAACCTAATCACAAAAGTAA

(INTRONIC)
TATCCACAAAATTAATAGTCCTACAGATGATGTAGGGTGTGTACAG

CAGGAAGCAGGAAATCTTGGGGGTTGTCATAGAATTCTGCTAAATA

TGCCTAGAGACACACATCCTTAACTGGACTTTAGGTTTATCATTTGT

GTTCTCTGGCCTCAGTGTTTTCAATTTGTGGATCATGTACCAATAGC

ATC

658
NON_CODING
GGCCTCATTAATATAGTGGCTGATGGTACCTACTAACCTTCAATGG

(INTRONIC)
GTCGCCTCCTACCTATTCTCATTTCATTAGCTTTTTGAAGGACAGGG

TAGACTAGATCAAGAAAAGAGATAAAAAGAAATAGTACATATTCA

CACTTATGTAATTACATCCCCTTCCATGGAAACTTGGGAATAAAGA

GGTATTTCAAGGTCATGTAGAAAAAGTAAAC

659
NON_CODING
GTTGTGGGGATTAAGACATTAATTC

(INTRONIC)

660
NON_CODING
TCTCACTTTGCATTTAGTCAAAAGAAAAAATGCTTTATAGCAAAAT

(UTR)
GAAAGAGAACATGAAATGCTTCTTTCTCAGTTTATTGGTTGAATGT

GTATCTATTTGAGTCTGGAAATAACTAATGTGTTTGATAATTAGTTT

AGTTTGTGGCTTCATGGAAACTCCCTGTAAACTAAAAGCTTCAGGG

TTATGTCTATGTTCA

661
NON_CODING
AGCCCTCACTCTAAAGTCACTTGTCACACATTCTATCAAATAAGGG

(INTRONIC)
AGAAAAAAACAAACACTATATCCAATTATAGTTTTCCACCTGAAAC

TACCAAAATAGAAAAAAAAAATTTTCCTATTAAAATGGAAAAAGT

CTAAGTGCTCAGGTAGAATCATTGAATTATCATTTTTGCTAGAGTTG

ACCTTATGCATTTCAAGGCTGGCACCATCATGTACAGGAACAATAT

GCTCATTGCTCCTCCCACCCATCCCCACCATGATGAAGAAAAGAGC

TGATTAGTGAACAACTAATAAATATGTGCCATCTGGGTACTAGTAA

CTTTA

662
NON_CODING
CAGGTATAAGGTTAGATGCTACATCTAGGAGCATTCAAGATATACA

(UTR)
TTAATTTAAACTTTTATTAGTCTAACTTTCTGTTAAGTCTCTTAGCTT

TGAAACATAAAAGAGAAATCAAGCCCAAATTTTTAGAGGAAGGCT

AAGGTATACTATTGGCAGTTGTAGTTTTAATTGTAATTGACTGATTA

ACCAAGTAATTTATAAAATGTTACCTATACTGTCAGTG

663
NON_CODING
CCGACTAACATGGTAATAGACCTGAATGCATAATGAGTTCTTACTT

(UTR)
TGCTATCATCAAAAGACTTTTCATCACAGTTACATACTTTCTAATTT

ATGGAAAAACAGCATTTGGAAAACAAATGTTTTGTTTTTATTTTTTT

AAAGATTTAAAAAATAAATCAACTAGGGACTAGGAATCAACAACT

GTGAGTGAGTTAAACTGTGTTGAAATACTAAAGGGTTGT

664
NON_CODING
TTCTTGCCTAAACATTGGACTGTACTTTGCATTTTTTTCTTTAAAAA

(INTERGENIC)
TTTCTATTCTAACACAACTTGGTTGATTTTTCCTGGTCTACTTTATGG

TTATTAGACATACTCATGGGTATTATTAGATTTCATAATGGTCAATG

ATAATAGGAATTACATGGAGCCCAACAGAGAATATTTGCTCAATAC

ATTTTTGTTAATATATTTAGGAACTTAATGGAGTCTCTCAGTG

665
NON_CODING
CTAGAGTTCTCATTTATTCAGGATACCTATTCTTACTGTATTAAAAT

(UTR)
TTGGATATGTGTTTCATTCTGTCTCAAAAATCACATTTTATTCTGAG

AAGGTTGGTTAAAAGATGGCAGAA

666
NON_CODING
GTGCTAGTTGATATCATGATTGATTTGGTCTTCTTGG

(INTRONIC)

667
NON_CODING
TTACGTTAGTACTGCAGAGGAAATAACTTGGAAGTTACAGGGAATA

(INTRONIC)
ACAATAGGTACTAGAAATTGAGTGCTATGGGTACGTATTAGATCGT

TAGCTCATTTAGTATC

668
NON_CODING
CTATAGAAGGTTATTGTAGTTATCTTTAGTACTATGTTATTTTAGGA

(INTRONIC)
GGCCTGTGTTTAAATTTTACAATTCATTAACAGGACTGATGGCATTT

TGTAGGAACTACTTAGGAACAAGTTTGCATTTC

669
NON_CODING
GACACTTAGGTGATAACAATTCTGGTAT

(ncTRANSCRIPT)

670
NON_CODING
GGGCTCTCTAGAAAGGTAATTATTATCTGATATAATAGTTTAGTCT

(INTRONIC)
GTGATGCTTCTTTTAACATATTTGTAAGTTTTAACCAAATGGTTAAA

GAAATTTGCTTTTTAACCCTTAAACCTCACATATCCACAAGTCTCTA

AATTCCATAGGATGCTATGGATTTCTAGTTGCCTAGTTCATGTCTTT

TACTTAGAAAACGTCAGAAAACCCAAACTTCTCGTGACTTCAAAAA

GTGTAATTGTACCTGAAACTTCTTTTCCTTCAGATTTCTTATTTATGT

TTTCTGATAGGTTTTTAAGATTAATCTTTTCAGAAGGATGCTCTAAA

AATCTGGCCAATTTGATTATCCTCTTCCAACTTGGAAAAAATATGT

ATTTAAAATGAGACTAGAATTTGAATGACCTTCTTTCATGGAACTC

TGA

671
NON_CODING
GTTGTTGCCTCTAACATGTATAAAGG

(UTR)

672
NON_CODING
AAGTCATTATCTTGCTTTGGAATCATTATCTGGCATTATCAACTTGC

(CDS_ANTISENSE)
ATTTGGTTCCACAACA

673
NON_CODING
GTGAGAAAAAACAAGTCATATAAAA

(INTRONIC)

674
NON_CODING
AGGAATAATTGATCAAGATGACATAAAATTTACAAATTTATTTGTG

(INTRONIC)
CCTAATAATAGTCTCAAATTACATAAGGCAAAAACTGATAGAATGA

AAGGAAGAAATAGGCAATTATAATTGGAAATTTTAATGTCTCTCAG

AAGTTGATAGAGTAACCAACAAAAAATCAGCAGACAGAAAACCTG

AACAACATTATCAGTCACTTTGA

675
NON_CODING
CTGGGCCCTTTACAGTTGATACCCAAAGCAG

(INTRONIC)

676
NON_CODING
TCTGGGTACTAGGAGTAGACCATCCATTCTTGATTTGAACTGTTTCT

(INTRONIC)
GCAGGTACTCATTTGTTCAAACACTGCCTATTTCGTTTTGCAACAGA

TCTATTTTAGAAAATCTTTATATTGAGCAAACAGCAGTCTCACTATA

GCCTCTACTTGTTGGTCATAATCTGCCAGAGGAAGCTTACCTGATG

ATGATGGTGCTGCTGCTGCTGATAATGATGGTGATGGTAATGACGA

ACATGACACAAGATCACAGGCACTGTGCTAAGCATTAAACACATA

CAATCTTATTTAATCCTCATAATGTTATGGCATAAATATTACCCCTC

TTTTAAAGATGAACAAACAGATGATTAAAGGGGTAAAGTTGCTTTG

ATCTTTAATATTAATTTGTGTCTTTCTCACTTCAAATTCAGCGATGA

ACCCTATTCCTATG

677
NON_CODING
CCTTTGATCTTAAGATTGTTGGCAT

(INTRONIC)

678
NON_CODING
ACTGTGGCTTCAATAGCCTCATAGAAGTGTCCTTCCTTTTTAACAAA

(INTERGENIC)
GGGAATCCAAGATGGCGGAAAGGTCCTAACATTGAGCATATAATC

CATCTCTTTGCTAAACTAGATGTTTCCTTCCAGATTTCTATG

679
NON_CODING
ATGGAAGCAAAAGGGACAGACTTGAAGCTGTACTTCCAGACTCTC

(INTRONIC)
ATGGAAGCTCCAG

680
NON_CODING
GAGCAATGCTTAACCCATCGGAATGTATACCCTAAGCAAAACTGTC

(INTRONIC)
AACCAGGCAAAGGGTGTTCTTTCTCTTCTGGCGCTCTGCTCTTCGTC

CCTGTCCCCAGCAGCCCATCTGCTACTGGAACTTGTTCACAGAGTC

CTTCTGCCAACTTATCATATTCTTGTTCCAGGAACTTTTCTGCTTTA

AGTAAAGGATCTTCTCCCAACGAGTATGCTCCTGCATTTGCAGATA

CAGCACAGCTCCATGCATTTGTAGCCCTGCCATATTAGTGTCCTAG

C

681
NON_CODING
CCCTAGGTAGGAGATAACAAGTATGTACCATTACTGAATATTAAAT

(INTRONIC_
CCTTCTTTACCATAGCTACAGTTAAGTAGGTGTATCTCAGAAACCT

ANTISENSE)
AAGGTAGTTTTAAATGTAGTGAAATTGTCCACAGCAAGCTGGCCCA

AGTGCTCACATTTTATACCCGCTCTGTCTTAGTGCGTTGCAAGAGA

GGAGTATATACAGTAGTTCCCCCTTATCCACAGGGGTACATTCTAA

GACCCCCGGTGGGTGACTGAAACCACAGATAGTACCGAATCTTATA

CATACTATGTTTTTTTTCTAAACATAAATACCTACAATAAAGTTTAA

TTTTTAAATTAGGCACCATAATTAATAATAAAACAGAACAGTTATA

ACAATATACTATAATAAAATTATGTGTATGTGATCTCTCTTTCTCTC

TCCCTCTCAAAATATTTTTAATATCTCTCCAGAATTCAGTGCAAATA

ATTCCATCATACTCACTTCAGAAAAGTGAAGATAGTCTTGTACATG

AGTAGATTCAAATTTTATTGTCGTGGTTTCCAAAGTTTTATTTTTCT

CACCAATGGAACTTTTGATTCAAATAAAATATCCAAGGGATTTCAG

CTTATAAAACACACAAAATTGATAATGAGTTTTCCAAGGTACTGTG

TGTGTGAATGTGTATGTCTGTGTATGTGTGTGTCGTCTGTATGTTTT

TCCCACCTCTTGTAGAAGCTACGAAGCACCTTTCCATATTATTGAG

GTTTCCTGTACGTAGACTGA

682
NON_CODING
ACCTGGACTGAAGTTCGCATTGAACTCTACAACATTCTGTGGGATA

(UTR)
TATTGTTCAAAAAGATATTGTTGTTTTCCATGATTTAGCAAGCAACT

AATTTTCTCCCAAGCTGATTTTATTCAATATGGTTACGTTGGTTAAA

TA

683
NON_CODING
CAGTATATGATATGGCAGAGTTGCACAGAAGAATCAGAACATTGTT

(ncTRANSCRIPT)
TTAGAGAAACGTTGGGCAATTAATTAAGCCAGCTGATTAAGTTTTA

A

684
NON_CODING
TTCACCACTGTAGATCCCATGCATGGATCTATGTAGTATGCTCTGAC

(UTR)
TCTAATAGGACTGTATATACTGTTTTAAGAATGGGCTGAAATCAGA

ATGCCTGTTTGTGGTTTCATATGCAATAATATATTTTTTTAAAAATG

TGGACTTCATAGGAAGGCGTGAGTACAATTAGTATAATGCATAACT

CATTGTTGTCCTAGATA

685
NON_CODING
GCCAAAACCAATATGCTTATAAGAAATAATGAAAAGTTCATCCATT

(UTR)
TCTGATAAAGTTCTCTATGGCAAAGTCTTTCAAATACGAGATAACT

GCAAAATA

686
NON_CODING
TTCCAAATACTCATGGTGCACAAGAAGGTTATGTATGCACAGTATT

(UTR_ANTISENSE)
TCTAATTTATTCAAATTCAATTTGAATTTGGTCTGAAGCTATCTTGT

ATGAAATGTTAGCTTTCCTGATATTTAATAATATTTATTATGTTTGC

ATATAAGCTCAAAAAATTAATGCAAAAGTATACTTTACTCATGGTT

ATCTTCAGGTAAATATTAGTGGTTATGTTTAAAAGCCTGATTTTATA

TAGATGAAGTTGAGAAAAAAAAAGAGTATGGAAAGGTAAATTAGG

TCTTAGTCTTGATTCTGTTACCAGCTGTTTGACCTTGAGTAACTCTT

CACCCTTCAATGGGCCCCAGTTTGCTCCTCTATGAATTTTAAGGGGT

TGGACTAGTTGACAGACCAGGCCCCTTCCAAGTCTAACATTTCAAA

ATCCTAACATTCCAGGTTCTATCATCTTGATA

687
NON_CODING
TTGTATTTTGCATACTCAAGGTGAGAA

(UTR)

688
NON_CODING
GATCTACCATACCCATTGACTAACT

(UTR)

689
NON_CODING
GAGATACATCATCATATCACGGAAAG

(ncTRANSCRIPT)

690
NON_CODING
ATCAGCTTTGAGTGAACTTTGACAGAAG

(UTR)

691
NON_CODING
CCTGTACCCTTATGCAGAGCAAGCATTCCATCCTAAGTTATAAACT

(UTR)
ACAGTGATGTTTAATTTTGAAGCCAGGTCTACATTATTTAATTAATG

GCTTCAAAAGGTGGAGATGCACTTTATTTAATGTCTTTCCCTAGCTA

ATTCTTACTCTCACCTTAAATATGCTTTCTTGTTGCATATATGCACA

GATACACACACACACACACACGAAAATAAATAAATGTTCATATTCT

TCTGTTCAACAGACATTTATTTTCTCCTCTCCCTTGAATAAGAAAAT

AAGTTTTCCATTCCTATGAACTGTCTAATATCTTTCTATTACAGAAG

GGGAAACTGAGGCTGGGAAAGGCTAAATGACTTATC

692
NON_CODING
GTCCTCAGTGTACCACTACTTAGAGATATGTATCATAAAAATAAAA

(ncTRANSCRIPT)
TCTGTAAACCATAGGTAATGATTATATAAAATACATAATATTTTTC

AATTTTGAAAACTCTAATTGTCCATTCTTGCTTGACTCTACTATTAA

GTTTGAAAATAGTTACCTTCAAAGGCCAAGAGAATTCTATTTGAAG

CATGCTCTGTAAGTTGCTTCCTAACATCCTTGGACTGAGAAATT

693
NON_CODING
CTGGTTAATTAGCAATTTAAGACCAGAGCCAAATTATCCCAAGAGC

(ncTRANSCRIPT)
ATACATTCTTTTGGTTTTCCTAACTTTGTGAAAAAAATTGATGCAGC

TGTTTTTAACCCACGTTTTTATAGGACCTACTTCTTTGTAGATAACC

A

694
NON_CODING
TGATGCTGTCACTACCGTGGGAAATAAGATCTTT

(ncTRANSCRIPT)

695
NON_CODING
CACCTGACATGAACCGTGAGGATGTTGACTACGCAATCCGGAAAG

(ncTRANSCRIPT)
CTTTCCAAGTATGGAGTAATGTTACCCCCTTGAAATTCAGCAAGAT

TAACACAGGCATGG

696
NON_CODING
AGATAAACAAACTTCCAGTGACAAA

(ncTRANSCRIPT)

697
NON_CODING
TGCTTCAAGCCAATGCAAAAAGTTCATACATTATATTCCCTATTTCA

(UTR_ANTISENSE)
TTGTGTTTAGAATATATTATATTGTTTAAATGCCACTACCACAGTGT

AATTTTTTTTTTTTTAATACTGAATCTCTGGAATAATGGTAAGGTCA

AAATATATTGTATTGAGAGTTTAAAAATTAAGAGCAATTTTTAAAA

ATGTAACAAACATCTAAATATCTGACAATAAAATCTGAAATGCTGT

AACTTCAACATTAACTGCACCATCCAAATTCTTGTGACTTACGCATT

TTTGCCCAATTTAACCTTTCTGATGTTCCCCTGCCCCCAGACACCAT

AAATGCATTGTAA

698
NON_CODING
TTCCAGGACTGTCATAATGATCTGTACTTCC

(INTERGENIC)

699
NON_CODING
CTGCTGTGGTTTGTAAGAACTCATTGACTAACTCAAGGTCACAAAA

(INTERGENIC)
ATTTTCTCCTTTATTTTTTTCTAGACATTTTATAGCTTCAGGTTTTAT

ACTGAGGTCTATGATTTATTTGGGATTAATTCGACAAATGTAAATTT

GTCGAAAAGACTATTTTTCTTTACTAAATTGCTTTTGCACCTTTATC

ACCAATCAGTTGTCTGTATATTCATGGGATTATTTCTAAACTC

700
NON_CODING
ATTTACAGCTTGTAGCAATTATGTA

(UTR)

701
NON_CODING
CTACCATAAAGTCCGTAAGTGAATACAACGAATGTAATTGACATAA

(UTR)
TAATTGAAAATCATTGACTATACCTAAAATAGTTC

702
NON_CODING
GCTCTGGCTATATCAAATAAAAGTGTCAAGAGTGAGCATCCTTGCC

(ncTRANSCRIPT)
TTGTGCTGAATCACAAAGGAATACCTTTCAGTTTTTCTCCATTGATT

ATGATAGCAGTGGGCTTTTCACAGTGGGCTTTACT

703
NON_CODING
TCTTAGCATCCAATCTTATGGACCATTTTCATACAAAGCC

(INTRONIC)

704
NON_CODING
CTCCAACAATAAAGCACAGAGTGGAT

(UTR)

705
NON_CODING
TTAGATGTCATTGAATCCTTTTCAA

(UTR)

706
NON_CODING
TTCTTAAAGTTTGGCAATAAATCCA

(UTR)

707
NON_CODING
GTGGCCACATCATGCAAATATAGTCTCACCATTCCTAGG

(UTR)

708
NON_CODING
TCTTGGCAGAACTGCTCTATTGCTCAAGGAAGACTTAGTTTCTGGA

(INTERGENIC)
AATATTCCCCGGGTGAGTTAAGGGTTGTGTAAAAATGCAAGAATGG

AATACGAAATGATTTTCATTTTGATGGTTACTTATGAAGTTTTTGTG

TTCCGTAGAA

709
NON_CODING
CATTCATCTTTGAATAACGTCTCCTTGTTT

(UTR)

710
NON_CODING
CAGAGCCAGATCTTTAGACGTGATGGATTCCCAAGTTTCGTTCTTA

(INTRONIC)
AAATAGACAAACTGAGGCCAAGAGTGCACCAGCCTGCCAAGCACA

GACATGACACCTAAGGACTTTCCTCCCCTAAGTGTGTGGTTCTGGG

GAGCCAGCCTTCCTTTGTCCTTCATAACCCCAGTCACTGCCTTTCCA

GCCTTCTGCCAGGTCTGGGGCTCAGATGGAGATAAGCTTTTCACAG

AAGACCCTCACTCGAAAGATCCACCACTTATCTCCCATCTCCGACA

GTGCATG

711
NON_CODING
ATGTATTTTGTAGCAACTTCGATGGAGC

(CDS_ANTISENSE)

712
NON_CODING
CTGACACGACACTTTTCTGTGGTTTC

(CDS_ANTISENSE)

713
NON_CODING
GTACAATCACTACAACATGCTCTGCCACCCACTCCTTTTCCAGTGAC

(UTR)
ACTACTTGAGCCACACACTTTC

714
NON_CODING
CGTCTTTGGTCAGGAACTTTATAATGTGCTAT

(UTR)

715
NON_CODING
AGCAGCCTTGACAAAACGTTCCTGGAACTCA

(UTR)

716
NON_CODING
GCTATCCACAGCTTACAGCAATTTGATAAAATATACTTTTGTGAAC

(UTR)
AAAAATTGAGACATTTACATTTTCTCCCTATGTGGTCGCTCCAGACT

TGGGAAACTATTCATGAATATTTATATTGTATGGTAATATAGTTATT

GCACAAGTTC

717
NON_CODING
TTTGACTAGAATGTCGTATTTGAGGATATAAACCCATAGGTAATAA

(UTR)
ACCCACAGGTACT

718
NON_CODING
TGCAAAATAACGACTTATCTGCTTTTC

(INTRONIC)

719
NON_CODING
GCAATAGAAGACACGTCTAGCTTGAA

(INTRONIC)

720
NON_CODING
GAACCATTGGAGATACTCATTACTCTTTGAAGGCTTACAGTGGAAT

(INTRONIC)
GAATTCAAATACGACTTATTTGAGGAATTGAAGTTGACTTTATGGA

GCTGATAAGAATC

721
NON_CODING
AGCGACCACATAGGGAGAAAATGTAAATGTCTCAATTTTTGTTCAC

(UTR_ANTISENSE)
AAAAGTATATTTTATCAAATTGCTGTAAGCTGTGGATAGCTTAAAA

GAAAAAAAGTTTCCTGAAATCTGGGAAACAAGACATTTAAAGAAT

CAGCAAAATTTCAAATAAAAAATTATGAAAATATTATCCTCATTAG

TTCATTTAGTCCCATGAAATTAATTATTTTCTCTGCTTGATCTTGGT

GGACAGTTTCATGAAGCTGTCAGTTAGTTCATTAAAGTTTTGGAAA

TTCTCAGACAGTGCAGTGGTATCAGAAACTTGTATTCAAGAGTACA

GGTCAGA

722
NON_CODING
ATGCCTCATATTGTATCTAGATTGGTCTTAAACATGCTCTGCACTTC

(INTRONIC)
TCTGCCTTCATGGAAGACTTTTGCTGATATTTCCTTCACTTGATACA

CTTTTGGCTTTTCCACCCTCTCCCTGCCCCCAATTTCTGCTTGCCAG

AATAATATCTGTTCTTCTTTCATTCATTTATTTAACAACTATTGAGA

CACTGTTGTAGGTGCTTGGATACACCTAGTGAACA

723
NON_CODING
AAAGAAGTGAAGCAAACGGATGGGA

(INTRONIC)

724
NON_CODING
TTCTGATGCTGTATTTAACCACTATA

(ncTRANSCRIPT)

725
NON_CODING
CTGCCTCAGGGTAATCTGAATTTTCTATCTCAAGTTAGAGATTACTC

(INTERGENIC)
TTCACCCCTTCCCAAGCAGATATTAAAGTCTCTTATTCTGTTTTTTTC

CTTTAAAAAGTATCAGATCTGTCAAGAGTTGTTTCTTCAGAATCTTC

TATTGCCAAAAACTGTTCTTATAATCTATTTTATCATTCACTCACTT

TGTCACTGATTAACATATTAGCACCAAAGTTCAACCAATGCTTAC

726
NON_CODING
TTTGCAAAAGCACGGATGTGGATGA

(INTERGENIC)

727
NON_CODING
ATGTCCATGTCCATCTTAATGTCTTT

(INTRONIC)

728
NON_CODING
AGGTACTGAATGACTAGGAAACAGGAA

(ncTRANSCRIPT)

729
NON_CODING
GAGCACCTGATCTTCGGAGATGCCTG

(INTERGENIC)

730
NON_CODING
TCTGTGACAGTTGGTATTGTCAGTCTTTCACTAGAGATTTCAATGAG

(INTRONIC)
TTAAACATAAGCGACACTCAGTTCATTATTCTTAGTAATGAGGGAT

GAAGACAGGACATAAGCAAAGTGAATAACAAAAATAGAAATTTTA

TCCACAAAAAATCAATACCTCCTTTGCTCAGCTAATGTGCAATAGT

GATAGTCTAGACAAATTAAAGAAATTCCATTTTATTTTAAACACTC

TAGTTACTTTTGTGTAGTCTAACATATTGTACATATTAGGTACTCAC

TAAATCTCCTTTGATTGGTTTCCTTAGCCTTACTCTGAGATGTTTTAT

TCAGTTAACAAATGCTTACATAATGCTTGCAGTGAGC

731
NON_CODING
GACAGATCTTCTTGTGTTTAGTGAA

(INTRONIC)

732
NON_CODING
TAGGATAATTGGTTCTAGAATTGAATTCAAAAGT

(UTR)

733
NON_CODING
TTTTGGTAAGTGCTCAGGCAACCTG

(INTERGENIC)

734
NON_CODING
ATTGCATGAACACATATTTGCTGCCAGAAATAATTATTACATTGCC

(ncTRANSCRIPT)
TTCTTCATATTGAAAACTAACAGTTCTTAAAAGGGAAGCAGAGGTG

TTAAAGAGCTTGGTTACAATTTATTGCTAAGAGTTTGGACTTTACAT

TAGGAAGATAGCCTCTGAAATACAACG

735
NON_CODING
AATAATAATATTTAGGCATGAGCTCTT

(UTR)

736
NON_CODING
TGGTAATACGGGACTTTATTTGTGA

(INTRONIC)

737
NON_CODING
TAAGTAGGGAGTGGACTCCCTTCTC

(ncTRANSCRIPT)

738
NON_CODING
TGCCCTCTATAAACTTCGGACTGTGCACTCACATTAACAGTGTGTA

(INTRONIC)
AAAGGACTTGTTTCTTGTACACATTTGGCTAACATTAACTATACTAA

ATCTTTTCAAGCACCTGATGTAGTTTCTTTAATTATAGGTAGATTTG

GACATTTTTTGGATACATTTCGTGGCTGTTTAACTTCTTTCCTTTAA

ATTGACTGAATGGCTTTGTCCATTTTTCTATTGAGTCATTTCATTTTT

TTTCTGATTTGTTTGGATTTCTTTTTGTATAATTTATATTTTCCCTGG

ATAGTTGCAAGAAATTGTTAATAAATTGTTCTCCCTGGCTCCTTTCC

TGTGGTATATCCCTGGTTCCCATGTCGTTATCTCTCCTTACTGTCCTC

ATTTCGAAGGCACACTTTC

739
NON_CODING
GCAACACCTCTTCCTCTTATTGAAA

(INTRONIC_

ANTISENSE)

740
NON_CODING
GTAATTCGTATGCAAGAAGCTACAC

(UTR)

741
NON_CODING
ATTTAGGGATTAGTTACAGTTATGCTGTTTCGTAAAATTGGCATTTG

(INTRONIC)
ATTCTATATTTTATGCATAGATTTTTTTTAAAAGCACTCTTCTGTAG

AATTGCACTTAGACCA

742
NON_CODING
GCCTTCTTGATCTGGAAGTCAGAGG

(INTRONIC)

743
NON_CODING
TTTAGCATGAACTGGTGTTGAAATT

(INTERGENIC)

744
NON_CODING
AGATGAGCTGCTCAGACTCTACAGCATGACGACTACAATTTCTTTT

(UTR)
CATAAAACTTCTTCTCTTCTTGGAATTATTAATTCCTATCTGCTTCCT

AGCTGATAAAGCTTAGAAAAGGCAGTTATTCCTTCTTTCCAACCAG

CTTTGCTCGAGTTAGAA

745
NON_CODING
ACTTTACAGTCAGAATCAGACCACT

(INTRONIC)

746
NON_CODING
TGAGGACCTTGGTAATGTTTCTTCCTG

(CDS_ANTISENSE)

747
NON_CODING
TTGCTTTGGTGGAATATGTATGCTA

(ncTRANSCRIPT)

748
NON_CODING
TCACAACTCTATAAACCCAACCGAA

(INTERGENIC)

749
NON_CODING
AGATGAAACAACTGAGGGCCAAAAA

(CDS_ANTISENSE)

750
NON_CODING
GAGAATGAACTCCACCACTTACGAA

(ncTRANSCRIPT)

751
NON_CODING
ATGTCAGCTCCTTGTTTACCAATAA

(INTRONIC_

ANTISENSE)

752
NON_CODING
ACAACTATCTTAACTGCAAAACTTGTGTTCT

(INTRONIC)

753
NON_CODING
ATGGGAGTAGGAAAGCTAATCAAAAA

(INTRONIC)

754
NON_CODING
TAAATCTATAATATGGCTGGAGGCA

(UTR)

755
NON_CODING
GCTTCTCTCCAGACTTGGGCTTAAG

(UTR)

756
NON_CODING
AAAAGAAGAGTAGTCCAAGGTGTGG

(ncTRANSCRIPT)

757
NON_CODING
TTACTTAGTCTTCTATGTATAGCTATCAAGGA

(UTR)

758
NON_CODING
ATGCTGCAAAATGTACCAGTACCTG

(INTRONIC)

759
NON_CODING
ATGACTCTGACTAGCCAGCAGGAAG

(INTERGENIC)

760
NON_CODING
GCTGTCCTTTGTGTCAGCATCATGA

(INTRONIC)

761
NON_CODING
AAGTGAAGTTTGAAGTCTGCTCTCTGCAAAGAGGGTGGGAGTGGGT

(INTRONIC)
GGAGAAGAGGCTTGTTTTAAAAGCCAAAAACAGAAAGTAAAAAGA

AATGGGAAAGTAAAACCAAAGCAGCAAGTGACTCTCTTCTGATGT

GCACTTTTCATTTTTCTCCCCCACATTTCAGTGTTAGAAAGAAAACG

AGAGGAGCTAGGGAAAGAAGGAGTTGGGGACAGAAGACTAAGAT

TTCAACGTGAAATTCCATTTACAAAGGCTTTACTGCAAACAATAGC

TAATTTAGTCCTGTAAACATGCATTTATCATACATTTTAATTTTAAT

ATTAAAAATACTGCATGTAAATGTTCTGAACTAAAGGTAGATAGCA

ATATGTAGTTTGCCATAAAATGAATGCATGTCTTATTCTTTTCCATA

GTTCTTCATTAATGAGACTTGTAGTCAAGAATAGATTGAAGATACC

ATTCTCCTTGTGTAGTTCAAAAA

762
NON_CODING
GCACAGCACAGCTTGGGTTATCTGG

(INTERGENIC)

763
NON_CODING
ACCCTGCCCATTGGATGTTAGCTGA

(INTERGENIC)

764
NON_CODING
AAAATTTTATCATCTGGTCATGGTG

(INTRONIC)

765
NON_CODING
ATTTGGGACAGCTTTACAATGTTAT

(INTRONIC)

766
NON_CODING
TCAGGAACCTTTCAAAAATACATGC

(INTRONIC)

767
NON_CODING
CCCCTACCCTTTGTTCTCAGCAGCAAG

(INTERGENIC)

768
NON_CODING
GACACTGTGAGCTTGATACTGCTGG

(UTR_ANTISENSE)

769
NON_CODING
GAAACCAAATGGTGTGCCACAAATTAGGGAACACAAGCAAAC

(INTRONIC)

770
NON_CODING
GAATGATCCATCTTCCTTAAGGCTGCTACACCATAACTAGGAGCTT

(INTERGENIC)
TAAAAAAAAGGGGGGGGCATTTACTCTCTGAGGCACTCAAAAAAG

CACATGCTTTTAATTGAGGGATGGGGGTGACAATGGATCATTCTGT

TGATTTTAACTATCTCATATTTGTTAACAGCATCATTTCCATGGATA

GCTTTCTGAAAGACTGCCTATCCACTTAGAGGTGAGGAGAAGTAAT

AGGGGAGGAAACCCTGCCGAGCTGCAAAAAG

771
NON_CODING
GCCTAGGTGACCCAAAGTAATGGGA

(INTRONIC)

772
NON_CODING
CCTCCGCGCAATTCAGCTGCAGCTG

(INTERGENIC)

773
NON_CODING
CCAGCTCCACTGAAACAGGGGAAAT

(CDS_ANTISENSE)

774
NON_CODING
GGTGCCCTAACCACTTCCTGAAATCTGGCCTGATTTTTAATAGCTTT

(INTRONIC)
TACCTAAGTTCCTCAGATTCTCTGATTCATAGTTTTCAAAATATCTT

GTCTCCTATTTTTGTATATTGTTCTCGGCTTCTTCTGCATTTTAACTC

AAGTATAGGCAATTCTCACTATATTTACTGGA

775
NON_CODING
TGAATGCCATAGTAGTGAATGAATACT

(INTRONIC)

776
NON_CODING
CCTATATGGCATCGCAGTCTGCAAA

(INTRONIC)

777
NON_CODING
GTGGCTCTCAGACTTTACTAATCAT

(ncTRANSCRIPT)

778
NON_CODING
ACTTGCTATACATAAGATGATTCAC

(UTR)

779
NON_CODING
GTATGCTTATCTGTTTATCTTAGCCAAA

(INTRONIC)

780
NON_CODING
ATGCTGAAATACTTCTGCCTTTTAG

(INTRONIC)

781
NON_CODING
GTACTCATGACTCAACCACAGAAGA

(CDS_ANTISENSE)

782
NON_CODING
GCAGAAACGATGCAGTGGAGCATCAG

(INTRONIC_

ANTISENSE)

783
NON_CODING
ATGAATTCGGTTCCGTAAGTTTGAG

(INTRONIC)

784
NON_CODING
CTGTAAGAGTCAGAGCTTTCTGGGA

(INTRONIC)

785
NON_CODING
CTTGATGTGACAGAGTAGTGTGTTTTCAT

(UTR)

786
NON_CODING
CATAAAGAATGCACATGAACAGCAG

(INTERGENIC)

787
NON_CODING
ATGCTGTACCCCTCGGAGACAAATTCCACCCTCGAGTGCG

(INTERGENIC)

788
NON_CODING
GCATGTTCAGAATCTTGGATCCCTAAGTTCAATATATTGGACATATT

(INTRONIC)
TAGGAACTCTGGAAATTATGTTGTTTTCACATATCTAGTAACTTACT

AGATGAATCAGTAGATTTCATTAAAGTATATCTAATAACAGATAAT

TATGATGTACTTCTGGGTTGACATGCATGTCTCTCATTATCAGCTAT

CAGTATTAGTGTCATGCTTTGGAGACAGTTATCTTTTGAAGGTTTTG

GGGTTCTTATGAACCTCATTTTTCCCAGGAAGTTTCTGTAATTCCTC

CTATGCCTATTCTTGTCTTTTCTGTCTGCTTGCAGTGTAAGTTATTTA

GATCAGAGGCAATTATTTTTCAGGAAGAAAGAAATCATCAAGTGA

CACTCCTAAAGGCAGTA

789
NON_CODING
TTTGAAACAGGTGACTCTAGCCATG

(INTERGENIC)

790
NON_CODING
GGATGTTCGGAGACCATTTTTCCAA

(INTRONIC)

791
NON_CODING
TTCTGCTTCTGCTATAGGAGAGTGA

(INTRONIC)

792
NON_CODING
TGCATGTGCTTGTTGATACTCCGCA

(INTERGENIC)

793
NON_CODING
ATAAAACTGTCAGGCCCAAATAAAT

(INTERGENIC)

794
NON_CODING
GACTTTGAGACAAGCTTAGGCATCA

(INTRONIC)

795
NON_CODING
CTCCTCTGGCCTCTAATAGTCAATGATTGTGTAGCCATGCCTATCAG

(UTR)
TAAAAAGA

796
NON_CODING
GAATCAAAACAGACGAGCAAAAAGA

(CDS_ANTISENSE)

797
NON_CODING
TTGAAGCCAGCCTGAACAATGGCAG

(ncTRANSCRIPT)

798
NON_CODING
ATCTCTGGGGTGTTACAGAGACAAA

(INTRONIC)

799
NON_CODING
GATATTCAGAATTCAATTGCCAAGTGCCAAA

(INTRONIC)

800
NON_CODING
ATTTGCATCTTTAAGTTCTACATTCACTTC

(INTRONIC)

801
NON_CODING
AGAACTTCAGCCAAAGCATCTGAGA

(UTR)

802
NON_CODING
CTCAGGATCCCAACCTTTATGTATCAGTTTGCCCTCTTGTTGAATAT

(INTRONIC)
ATTTACTGTCCAGTGCTACTCCCTCTATCTGTGTGAAAAAATTATTT

CAAATTTCCACATCAGGAAAACATCCATGAATGCTTGCCAAGACAA

CCGGGAAAAAAACAGTAAGGTCATATTCATGACTGTAAAACCCTTG

TTTC

803
NON_CODING
TTCAAGTAGACCTAGAAGAGAGTTTTAAAAAACAAAACAATGTAA

(UTR)
GTAAAGGATATTTCTGAATCTTAAAATTCATCCCATGTGTGATCAT

AAACTCATAAAAATAATTTTAAGATGTCGGAAAAGGATACTTTGAT

TAAATAAAAACACTCATGGATATGTAAAAACTGTCAAGATTAAAAT

TTAATAGTTTCATTTATTTGTTATTTTATTTGTAAGAAATAGTGATG

AACAAAGATCCTTTTTCATACTGATACCTGGTTGTATATTATTTGAT

GCAACAGTTTTCTGAAATGATATTTCAAATTGCATCAAGAAATTAA

AATCATCTATCTGAGTAGTCAAAATACAAG

804
NON_CODING
TTATGTCAAAACATTTCCAGAGACT

(INTRONIC)

805
NON_CODING
GCAAAGCAGTTTAGCAATGACCAGATGTAATTCATTTTGGAGTTCT

(INTRONIC)
AAGTTTGAACTTAATCAATATGAACTTACAGCCATGGAAGAAGTGA

TTATCATTTGTTATTTGCTGGCACAAGAA

806
NON_CODING
GGGATAGTGAGGCATCGCAATGTAAGACTCGGGATTAGTACACAC

(UTR)
TTGTTGATTAATGGAAA

807
NON_CODING
TGTCACCTCTTAGTACAAAGCCATGCCAGACACTGCACCTACTCTG

(INTRONIC)
CACTCTAATGAGAACAATCCGGAAAGGATGATTTTCAAGGGAGAG

TGACCTCTTCCTGGAGATCTGAGGTTATGTTACAGTATTGTGGAGTT

TTGTTGCTTAAAATTCTCCTCCTGTCCTCACAGGCAATTTTGCTAGA

GTTGCAATCCTCACATTTG

808
NON_CODING
GATCCAGCAATTACAACGGAGTCAAAAATTAAACCGGACCATCTCT

(UTR)
CCAACT

809
NON_CODING
TGCCAAGGAGGCGTATTCTTCAATATTTGGAATAGACGTGTTCTC

(UTR)

810
NON
GTGCATACATTATGATACAGCCCTGATCTTTAAAAGGAGCAAAAAT

(INTRONIC_
CAGAGAATCGTATGTCTTAAAGAACTATTTCCTTACTTTTTTATGCT

ANTISENSE)
AGGTAATGCCCATGTGACAAACATGTAAATATTCATCAAAGACCAC

ATGTATATATTTTAAAGGCATTTTTTCTTCTCCCCAACTGTATGTAT

AGCTAGAATCTGCTTG

811
NON_CODING
ATTCTTTACTGAACTGTGATTTGACATT

(INTRONIC)

812
NON_CODING
GTTAGTGATATTAACAGCGAAAAGAGATTTTTGT

(INTRONIC)

813
NON_CODING
TTAAGTGAGGCATCTCAATTGCAAGATTTTCTCTGCATCGGTCAG

(INTRONIC)

814
NON_CODING
CTTCATGCTTAATACAAACACTTCTAATGGCTCATTGATTATAATGT

(INTRONIC)
ATTATCACATTTTATTTTATCCTCAGACATGATTGACTTTCTAAAGG

CTTGAATCAAA

815
NON_CODING
ATGGCAGGATTCAACATCTATTTGCTTTATAAGATATTGATAAAAA

(INTRONIC_
TGTATCTCATTCATAATGGTGTAGCAACTACTTTTTAATGGGGTTTT

ANTISENSE)
ACTATGCTCTTTTGTTTCCATTGGCTTTATAAATTAGGATTTGACTTT

GCTTTAATTACATGTTTTTAATTACCCAGTTATCTAGTTATCAAATG

AAAATGTTATTACTAATATAATTGGAACTCATAAAATGCTTAGCTG

816
NON_CODING
TTTCCTTATTTCATGATTGTGGCCATT

(INTRONIC)

817
NON_CODING
TTATGCAGATAAAACCTCCAGGTAGCAGGCTTCAGAGAGAATAGA

(INTRONIC)
TTATAAATGTTTCTTAGCAGACTTAAAAAGGTGCCAGAAGATCAGG

GAAAAGACCTGGAAAGGGAAAGGGAATCTCTATAGAATGTCAATT

ATCCTCACAAGAGATAGCTTTGTAGGGCCATTTCAAAATATATCAA

AGGAATATATTTTAGGGTAAAATACTTCAGTTTCTTTCAGGGCCTTC

TATGTGCCATATGATGCTGTACTAAAGTAAGGCTGGAATTT

818
NON_CODING
CTTCTGTTATCTCTTATTCCAGAGAAAAATCTGCTGTCACTAGATTA

(INTRONIC)
AATGCACTTTTTGAGTTGTCCTAATGACATCAGTTTGGTTTTCATTT

TGAAAGAATTAGGGCATCTGACATTTCAGCCTTATCATAGTCCATT

TTCAATT

819
NON_CODING
TGAGGTGGCTTTGCCATTTTATACCCATAATTAAATAAAAGGGCAA

(INTRONIC_
AATCCCCCCTGATAAATACCATGTTTATCATGGCACATAAAACTTT

ANTISENSE)
ATGGCAGAAAGCCAAGGCCAATTGACATATATATTTAAAGGTACC

ATGGAAAGTAAATGCTAACTCTGAATTTAAAACAGTGGGAAGATG

ATTAGTAAGAGTTGGTTTCTTGAAAAGGAATTGTTCTGGTAATAGT

CATCTTTAATGACTTCCACGGATTATTCAGTGTTTCTTTAGGGATAT

GCATAGGACACTGGTGCTTCAGTAGAAACCCCAGTTTTGGTGTATT

AAAGATACATCCATTCTTGACTGATCTTTAATCTAGAGTGTGGTTTT

AGCCAAGTCTTTGAATCTCATTTAGTC

820
NON_CODING
TTTAAGGTGAAATCTCTAATATTTATAAAAGTAGCAAAATAAATGC

(UTR)
ATAATTAAAATATATTTGGACATAACAGACTTGGAAGCAGATGATA

CAGACTTCTTTTTTTCATAATCAGGTTAGTGTAAGAAATTGCCATTT

GAAACAATCCATTTTGTAACTGAACCTTATGAAATATATGTATTTC

ATGGTACGTATTCTC

821
NON_CODING
TCACTGTGTAGAGAACATATATGCATAAACATAGGTCAATTATATG

(UTR)
TCTCCATTAGAA

822
NON_CODING
GCAACTTTTCCGTCAATCAAAAATGATTCTG

(INTRONIC)

823
NON_CODING
GGTAAAGGATAGACTCACATTTACAAGTAGTGAAGGTCCAAGAGT

(UTR)
TCTAAATACAGGAAATTTCTTAGGAACTCA

824
NON_CODING
CCTACCTCAGAGCTTCACATATATATATGAAAAAAAAAGTGCTTCA

(INTRONIC)
AATAACTAATAAGTTTAGGAAGTAGGCCTATCCTAAAGCACAAAA

ATATTTTATTTATGAGTAAAAAATATTTTTATAAGTACATAATTATT

TCAACAATATGTTACTTTTGTCATTTTTCCTACATATTCTTTTATATA

TTTTGAACTGTAGACATGTAGCATATTCTAGCACATTGCAGTAATG

ACAACT

825
NON_CODING
AAGGAAGATATTACTCTCATAATTCCATACTGGTGGAAACCTATCT

(INTRONIC)
GAGAATGTCTATTTCATTAATCCTCTTGAGTATGTTC

826
NON_CODING
TATTCTTAGGGCTTTTGTGTATGTCTGACTTGTTTTTAAATAACTTCC

(UTR)
TCAGCAATGCAGACCTTAATTTTTATATTTTTTTAAAGTAGCTAACA

TAGCAGTAGGCACTTAAGCATTTAGTCAATGATATTGGTAGAAATA

GTAAAATACATCCTTTAAATATATATCTAAGCATATATTTTAAAAG

GAGCAAAAATAAAACCAAAGTGTTAGTAAATTTTGATTTATTAGAT

ATTTTAGAAAAATAATAGAATTCTGAAGTTTTAAAAATGTCAGTAA

TTAATTTATTTTCATTTTCAGAAATATATGCATGCAGTTATGTTTTA

TTTGATTGTTGACTTAGGCTATGTCTGTATACAGTAACCA

827
NON_CODING
GAATATCACTACCTCAGGTTACGGTACACAGGCTATAATTGATGAT

(UTR)
GATG

828
NON_CODING
TCCTGTCCCTTGACCTTAACTCTGATGGTTCTTCAC

(UTR)

829
NON_CODING
TGGCGCCACTATACTGCTAAACCTATGCATGAAGGTAGTGACTAGG

(UTR_ANTISENSE)
ATGGAAATCTGTCAGTGCTACAAAAATATGTATGAACAAAATAATT

TTCACCCTTTGATAAAGCTACAAGATATAAAATTTAGAATACTTAT

ATAATTTCATACTAGATATGTGAAAAATATGCCATGCTAGAACCAT

CTTGTT

830
NON_CODING
CATTGAGAGATACAAAGCGTTTTCTAGAGAGTGTTTCT

(ncTRANSCRIPT)

831
NON_CODING
GTGACTATAGAGGCTAACAAGAATGGA

(ncTRANSCRIPT)

832
NON_CODING
GAGGCAGCCCTTTCTTATGCAGAAAATACAATACGCACTGCATGAG

(UTR)
AAGCTTGAGAGTGGATTCTAATCCAGGTCTGTCGACCTTGGATATC

ATGCATGTGGGAAGGTGGGTGTGGTGAGAAAAGTTTTAAGGCAAG

AGTAGATGGCCATGTTCAACTTTACAAAATTTCTTGGAAAACTGGC

AGTATTTTGAACTGCATCTTCTTTGGTACCGGAACCTGCAGAAACA

GTGTGAGAAATTAAGTCCTGGTTCACTGCGCAGTAGCAAAGATGGT

C

833
NON_CODING
GCTCCCATTTTTTGCACTGGAATTACTTGCCAAATGGCCTTTTCACC

(INTRONIC)
ATCTGAAATAGTTAATGTATTCACTTCTTAAATGAGCAAAAGTCTT

CAAACTATTAAGAAAGAGCCATAGACTGAGTGCAGGCACCAGTGT

GCTCTTATTACTGTGTCAATTAAATGAATGTATTTGAATGTTTGGAT

ACTTACCTCTGAATG

834
NON_CODING
CCTCTTACACATGACAAGTTTTGGCTTGTTGGTTTTTCAGAAGCGAA

(INTRONIC)
GAAATATGGCATTGAAAATGATGCTGAGTGTGAAGAAATGTAGAG

GACTCATTTTTGATCCCCCAGGGAGACCTATTTTTACTATAAATTTA

CTCCAATAATGAGATGTGTAGGAGGATTTACCATTACATAGTTTTA

ATACATTTCAGCGTCATTGGAGACTAAACATTTTCTTTCAGAGTAA

CTGATAGTTTCTAGCTACCTAAATAAGGATCTTTTCTAAATCTGACA

AGAAATTTTGAAAGTTTTTTCACAATGGCATTCTAGAGTCATCTCTA

GAATGATGATATTAGATATTAATCATTATTTTATAAAGAGAAGACT

TAATGAATACATCTGATGAATGCATTGGTTATAAGGCTAATAGTTT

TACATATAAGCTAGAAACAAAATGAGTCTGTTTGTGAAATTATCTC

CTCTACTCTAGTGGAAGAATCTGTAGTGAGATTACTAATAAAGGAC

TAATGTTTTATCATTTGATTTGTTCAGATGGGTAATGCAAAAAAAA

CTTTAGCCTTCTGTGAAGTAACCTTAGGA

835
NON_CODING
GTAAACAGATGTAATTAGAGACATTGGCTCTTTGTTTAGGCC

(UTR)

836
NON_CODING
TGAGGGTATCAGAACCAATACTGGAC

(UTR)

837
NON_CODING
CCCTGTAAAACCCTTGGCTTCTATGAAGGCCATTGAATAACTGCGA

(INTRONIC)
TATGCCTGTGAAAAATCACAAAAGGTGCAAAGTCCCCTCGCAATAA

AGATCAGTCACGATGAGATTTGCACCAATTGAACTTTTAAGATTGT

AAAATATTTTGTCTTGCAGAGCTGATGCATATCCATTAAAAAGTAT

ATCTTAGTGAGCCTTATCTTCAAGTTAGCAGCGAGAAGAGTAACAA

AAACGTGCCAATTTAAAATACTGAAATTCTGGGAAAATGTTTTACT

TATGAGTATTTCTTAGTATTGGGCTAGTGTGATAAAGATGGCAGCA

TGTTTTGATATCTACTCAGAAATTCATTTCACAAACGAAGATGTTTT

AGAGTTGGTGAACATACCTGGCCCATTACTGACAAAACCAATTACC

GTATTTATTGGTAATAGAGCTGTTTACAGGATGCTCACTGTAAAAA

GAAAGAGAAAGAAGAAAAAAAATCCTGCTTTTT

TTTTTTTATCTCTCTCTCTTTTGAAACAAGAGAACAATCCCATTCAC

ACATAGTAGCTGCCTTCTTTG

838
NON_CODING
GATCCTGCTATGATTCTTCACTGGGGGGAAAGAAGATACATTTAGA

(INTRONIC)
AAATTGGTTATCTCAGATTCTTAGTATGGTTTTAGTTAGTTAGTTTT

ACCACTTGGTAGAGTTAATGATTTGACAAATGACATTTGCTTCTTAT

TATCAGCCAGTTGGTTGCTAGCTTTAAAGA

839
NON_CODING
ACATATTTTCAAGTTGAATGTCTTCTGTTAATTTCTCTTTATTTTGTT

(INTRONIC)
TGCCAGTGAATATAGAACCTCTTTT

840
NON_CODING
CTTTTGAATTACAGAGATATAAATGAAGTATTATCTGTAAAAATTG

(UTR)
TTATAATTAGAGTTGTGATACAGAGTATATTTCCATTCAGACAATA

TATCATAAC

841
NON_CODING
TTTAGATGTTTAACTTGAACTGTTCTGAATT

(INTRONIC)

842
NON_CODING
TTCAATATTAGCAAGACAGCATGCCTTCAAATCAATCTGTAAAACT

(UTR)
AAGAAACTTAAATTTTAGTTCTTACTGCTTAATTCAAATAATAATTA

GTAAGCTAGCAAATAGTAATCTGTAAGCATAAGCTTATGCTTAAAT

TCAAGT

843
NON_CODING
CATTGCTGTAATCTAGTGAGGCATCTTGGACTTCTG

(ncTRANSCRIPT)

844
NON_CODING
TATATGCATCCTTTGACTTTGAATGGCTGCCATAATTGTTTACTGAG

(INTERGENIC)

845
NON_CODING
TGTCAAACAATGTGTAACTCCAGTTATACAAACATTACTGTATCTC

(ncTRANSCRIPT)
ATTGGGGATACGAAGCTCTACACACTTGAAGATGGTG

846
NON_CODING
GTCCAGACTTGGAGTACAAGTAATAAGAAGAATAAAACTTAATCC

(ncTRANSCRIPT)
CTTAAGTAGATTCACCATAAGTTAGCTCAGAGCAATTCCAGTGCAA

GTATGGTCTGTGATCC

847
NON_CODING
GCATTGGATTTACTAGACGAAAACCATACCTCTCTTCAATCAAAAT

(UTR)
GAAAACAAAGCAAATGAATACTGGACAGTCTTAACAATTTTATAA

GTTATAAAATGACTTTAGAGCACCCTCCTTCATTACTTTTGCAAAAA

CATACTGACTCAGGGCTCTTTTTTTCTTTTTGCATATGACAACTGTT

ACTAGAAATACAGGCTACTGGTTTTGCATAGATCATTCATCTTAATT

TTGGTACCAGTTAAAAATACAAATGTACTATATTGTAGTCATTTTA

AAGTACACAAAGGGCACAATCAAAATGAGATGCACTCATTTAAAT

CTGCATTCAGTGAATGTATTGGGAGAAAAATAGGTCTTGCAGGTTT

CCTTTTGAATTTTAAGTATCATAAATATTTTTAAAGTAAATAATACG

GGGTGTCAGTAATATCTGCAGAATGAATGCAGTCTTTCATGCTAAT

GAGTTAGTCTGGAAAAATAAAGTCTTATTTTCTATGTTTTATTCATA

GAAATGGAGTATTAATTTTTAATATTTTCACCATATGTGATAACAA

AGGATCTTTCATGAATGTCCAAGGGTAAGTCAGTATTAATTAATGC

TGTATTACAAGGCAATGCTACCTTCTTTATTCCCCCTTTGAACTACC

TTTGAAGTCACTATGAGCACATGGATAGAAATTTAACTTTTTTTTGT

AAAGCAAGCTTAAAATGTTTATGTATACATACCCAGCAACTTTTAT

AAATGTGTTAAACAATTTTACTGATTTTTATAATAAATATTTTGGTA

AGATTTTGAATAATATGAATTCAGGCAGATATACTAAACTGCTTTT

ATTTACTTGTTTAGAAAATTGTATATATATGTTTGTGTATCCTAACA

GCTGCTATGAA

848
NON_CODING
GCTTTGTAAATCAAACTGTGGACTAAATA

(INTRONIC)

849
NON_CODING
GCTGCTCTTCATTTGATTTCGAGGCAAG

(INTRONIC)

850
NON_CODING
TCTAGAAGGATTTATTGGCTTCATCAGACATAGGCTAGGATTCTCA

(INTRONIC)
CGGG

851
NON_CODING
AAGTGGCAGTACAACTGAGTATGGTG

(INTRONIC)

852
NON_CODING
CCATGGATTAGAAGCATTAGTTCTCAGTACTTGAAGACAAACTTCT

(CDS_ANTISENSE)
AAAAAGAAAATATATGCTCTGAACATCTGAAATGGGCTAGACTTTC

AAGTAAAATTGCTTCATTTCTCATTAACTGAAGAGCTATTGATCCA

AGTCATACTTGCCATTTAATGTAAATTATTTTTAAACTTTGCTGTAC

AAAACCATTAAGTG

853
NON_CODING
GAAAAAGGGGTATCAGTCTAATCTCATGGAGAAAAACTACTTGCA

(UTR)
AAAACTTCTTAAGAAGATGTCTTTTATTGTCTACAATGATTTCTAGT

CTTTAAAAACTGTGTTTGAGATTTGTTTTTAGGTTGGTCGCTAATGA

TGGCTGTATCTCCCTTCACTGTCTCTTCCTACATTACCACTACTACA

TGCTGGCAAAGGTG

854
NON_CODING
GATTGAAAGCCAGCTATTTGGTAATGTTTG

(INTRONIC)

855
NON_CODING
TTTTATGACCTAACAGCACAGATTGTGTT

(INTRONIC)

856
NON_CODING
TCATCTTTGCCTAAACAGAGATTCT

(INTRONIC)

857
NON_CODING
TCTGTAACAGTGATTCTCTTGGGTCATATAAAGGACTGAGTTATGG

(INTRONIC)
AGTTACCTACCCTCTTCGACTCATCTTTTAATTTGTCATAGAAAAAC

AACTGTTGTACATTGTGTTAAAAGTTAAATTCTATGGCCAGAGTGT

GATTTGGAAAAGAAAACTGAAGTAAGTTGGAAGCAGAGTGAAGAA

AATAACTCTGCCATTTTCTTCCAACTCACCCTACAGCATCTCTGTTT

TCCAGCCTCACTGGGTTAAGTCTTCAAATGTAGCCCTTTGCTTCTAA

GACAATCCCATGTTACAAAGCATCAATAATCCTCCTCTGAACATTT

TCCTCAAAAGTTCTAACTACAAAGCAGTTAGCCCTGATGTTCTGAT

AAAAGTCTAA

858
NON_CODING
CCTTAAGCTGCTCGATTTCTTAAAG

(INTRONIC)

859
NON_CODING
TGGTTACCAAAGGCAACAGTTGTTATCCAGTGGG

(INTERGENIC)

860
NON_CODING
TGGGTATCAGTGGATACACACGATGCAACAA

(INTERGENIC)

861
NON_CODING
AGAGAGGCAACACTTATTATCCACAGGGTAACAGTGGTTACCAGC

(INTERGENIC)
GATGCAATACTTATTATCCACCGGGTAACGGTGGTTACCAATGAGA

CAT

862
NON_CODING
GGCAACAACTATTATCCACCGTGTA

(INTERGENIC)

863
NON_CODING
GTACCATTGATTACCCATGAGACAATGCTTATTTTCCCCCGGGGAA

(INTERGENIC)
CAGTGGTTACCCTAGAGGCAATACTTATTATCCACAGGGTAACAGT

GATAACCCTAGAGGCAATACTTATTATCCACTGGGTAACAGTGGTT

ACCGACAAGGCAACACTTATTATCCAAAGGGCAACAGTGGTTACCC

AGGAGGAAACAGGTATTATCCACCG

864
NON_CODING
ACAGTCGTTATCTATGAGGCAGTACTTATTATCCACCTGGTTACAGT

(INTERGENIC)
GGTTACCTGGGAGGCAATGCTTATTATCCACCGGGTAACAGTGGTT

ACCCTCAAGGCAACAAGTATTATCCACCAGGTAACAGTGGTTACCC

TAGAGGCAACACTAATTATCCATTGGGTAACAGTGGTTACTCGCAA

GGCAACAATTATTATCCAGCAGGTAACAGTGGATACATGCGATGCA

ACAATTATTATCCACCGGGTAACAGTGCTTACCCGTGAGGCAACAC

TTATTATCCACGGGGTAACATTGATTACCCACAAGGCAATACTTAC

TATCCTCTGGGTAACAGTGCTTTC

865
NON_CODING
TAAACCAGGTATCAGTGGTTATGCATGAGGCGACACTTATTATTCA

(INTERGENIC)
C

866
NON_CODING
TAACATTTAGTATCCACTGGGTAAC

(INTERGENIC)

867
NON_CODING
TTACCCATGAGGCAGCAAATATTATTC

(INTERGENIC)

868
NON_CODING
TATCCACTGGGTCACAGTGCTTTTCCACGAGAGAATACTTATTATCC

(INTERGENIC)
AATGGGTAACAGTGGTTACCCATAAGTCGATACATATTATCCACCA

G

869
NON_CODING
TGCGTAACAGTGGTTACCAACAAGACAACACTTATTATCCACTGGG

(INTERGENIC)
TAACAATGGTTACCCACAAAACGTCACTTATTATCCACAGGGTAAC

AGTGGTTACCCACGAGGCAACACTTATTATCCATGCATTAACAGTT

GTTAC

870
NON_CODING
AGGATCCACTGGGTACCAATGGTTGCCCACGAGGCAATACTTACTA

(INTERGENIC)
TCCACTGGGTAACACTGGTTTCCCACGAGGCAACACTTTTTATCCA

CCAGATAACAGTGGCTACGCACGAGATAACACTTATTTTCCACAGG

GTAAGAATTGTTACCCACGACACAGCACTTATTATCAAGTGGGTAA

TACTGGTTACGCAAGAGGCAACACTTATTATAAACCGGGGAACAGT

GGTTACTCACAAGGCAATACTTATTATCCACAGGGTAACAGTTGTT

ACCCACGAGGCAATACTTATTATCCACTGGGTAACAGTGATCACCC

TAGAGGCAATACTTATTATCCACTGGGAAACAGTGGTTACCTACGA

GGCAACACTTATTATCCACAGGATAACAGTGGTTACCCATGAGGCA

ATACTTACTATCCACCAGGTAACAGTGGTTACCCATGAGGCAATAC

TTATTATCCACTGGGTAACAGTGACTACCCATGAGGCAACACTTAT

TATTGACCAGGTAACAGTGGTTACCCTAGAAGCAATACCTATTATC

CAACAGATAACAGTGGTTACCCATGCGGTAATACTTATTATCCAGT

GGGTAGCAGTGGTTACCCATAAGACAATCCTTATTATCCTCCGGGT

AACAGTGGTGACCAATGAGGCAATACTTAGTATCCACCGGGTACCA

ATGGTTACCCACGAGGCAATACTTACTATCCACCAGGTAACACTGG

TTTCCCACGAGGCGACACTTAATATCCACCGGGTCACAGTGGTTAC

CCATGAGGCAACACTTATTATCCACAGGGTAAGAGTTGTTACCCAC

GAGGCAACACTTATTATCCAGCGGGTAACACTGGTTACCCACGAGG

CAACACTTATTACAAACTGGATAACAGTGGTTTCCCACGAGGCAAT

ACTTATTATGCAGCAGATTACAGTGGTTACCCATGAGGCAATACTT

ATTATCCGCCAGGTAAGAGTGGTTACCCATGAGGCAATACTTATTA

TCAACTGGGTAACACTGGTTTCCCATGAGGCAACACTTATTATCCA

TCGGGTAACCGTGCTTACCCACAAGGCAACACTTATTATCCACATG

GTAACAGTGGTTACCAAGGAGGCAATACTTATTACGCATTGGGTAA

CAGTGGTTACCCACGAGGCAGTACTTTTTATCCACCGGGTAACAGT

GGTTACCCTAGAGGCAACACTTATTATCCATTGGGTAACAGTGGTT

ACCCTAAAGGCAACACTTATTATGCACCGGGTAACACCGGTTACCC

GTGAGGCAACTATTATTTTCCACTGGGTAACAGTGGTTAGCCACGA

GGCAACACGTATTATCCACCGGTTAACAGTGGTTACCCACGAGGCA

ACATTTGATATCCAGCAGATA

871
NON_CODING
ATCAGGCAAAAGTTAGTATCCAGCGG

(INTERGENIC)

872
NON_CODING
TTTCCTACGAGGCAATACATATTACCCAATGGGTAACAGTGGTAAC

(INTERGENIC)
CCACGAGGCAATACGTATTATCCACAGGGTAACAGTGGTTACCTAT

GAGGCAATACTTATTATCAACTGGTTAACAGTGGTATCCCATGAAG

C

873
NON_CODING
CCACGAGGCAATTCTTGTTATCCATAGG

(INTERGENIC)

874
NON_CODING
GGCCATACATATTATCCACCGGGTGACAGTGGTTACCCAAGAGGCA

(INTERGENIC)
ATACTTATTATCCATGTGGTAGAAGTGGTTGCCCATGAGGCAATAC

TTATTATCCACTGGGTAACAGTGGTTACCCAAGAGGCAATACTTAT

TATACACCCAGTAACAGTGGTTACCCACAGTGCAACACTTATTATC

CACTGGGTAACTGTGGTTACGCATGAGGCAACTCGTATTACCCACT

GGGAAACAGTGGTAACCCACGAGGCAATACGTATTATCCAACAGG

TAACAGTGGTTACCCACAAGGCCACACGTATTATCCACTGGGTAAC

AGTGGTTACCCACAAGGCAATACTTATTATCCAGTCATTAGAAGTG

GTTACCCA

875
NON_CODING
ATCAAGTTCACTAAAGCAGGAATGA

(INTRONIC)

876
NON_CODING
TTCTGGAGGAAACTTGTAATATTGGAGA

(INTRONIC)

877
NON_CODING
TTTAAGCAACAGTTTGACTGCATACAAAATTCCTGGGTCACATC

(INTERGENIC)

878
NON_CODING
TTCTCTACTGCAATGCTGAGGTCTCAGTAAATCGATTTTTGTCTGTG

(INTERGENIC)
CA

879
NON_CODING
GAGTGCTCACTCCATAAGACCCTTACATT

(ncTRANSCRIPT)

880
NON_CODING
TGTGTAACTGCACACGGCCTATCTCATCTGAATAAGGCCTTACTCTC

(ncTRANSCRIPT)
AGACCCCTTTTGCAGTACAGCAGGGGTGCTGATAACCAAGGCCCAT

TTTCCTGGCCTGTTATGTGTGTGATTATATTTGTCCAGGTTTCTGTGT

ACTAGACAAGGAAGCCTCCTCTGCCCCATCCCATCTACGCATAATC

TTTCTTT

881
NON_CODING
GTGCCAGCTCCATAAGAACCTTACATT

(ncTRANSCRIPT)

882
NON_CODING
CAACCATGCACCTTGGACATAAATGTGTGTAACTGCACATGGCCCA

(ncTRANSCRIPT)
TCCCATCTGAATAAGGTCCTACTCTCAGACCCCTTTTGCAGTACAGT

AGGTGTGCTGATAACCAAGGCCCCTCTTCCTGGCCTGTTAACGTAT

GTGATTATATTTGTCTGGGTTCCAGTGTATAAGACATG

883
NON_CODING
TGAGCATAGGCACTCACCTTGGACATGAATGTGCATAACTGCACAT

(ncTRANSCRIPT)
GGCCCATCCCATCTGAATAAGGTCCTACTCTCAGACCCTTTTTGCAG

TACAGCAGGGGTGCTGATCACCAAGGCCCCTTTTCCTGGCCTGTTA

TGTGTGTGATTATATTTGTTCCAGTTCCTGTGTAATAGACATGG

884
NON_CODING
TCCACTCCATATACCCTTACATTTGGACAAT

(ncTRANSCRIPT)

885
NON_CODING
CCCTCTCCATAAGACGCTTACGTTTGGA

(ncTRANSCRIPT)

886
NON_CODING
GCACCTTAGACATGGATTTGCATAACTACACACAGCTCAACCTATC

(ncTRANSCRIPT)
TGAATAAAATCCTACTCTCAGACCCCTTTTGCAGTACAGCAGGGGT

GCTGATCACCAAGGCCCTTTTTCCTGGCCTGGTATGCGTGTGATTAT

GTTTGTCCCGGTTCCTGTGTATTAGACATG

887
NON_CODING
GGAGTGCCCACTCCATAAGACTCTCACATTTG

(ncTRANSCRIPT)

888
NON_CODING
TTATTTGGAGAGTCTAGGTGCACAAT

(ncTRANSCRIPT)

889
NON_CODING
TTTCGTTGTATCCTGCCTGCCTAGCATCCAGTTCCTCCCCAGCCCTG

(ncTRANSCRIPT)
CTCCCAGCAAACCCCTAGTCTAGCCCCAGCCCTACTCCCACCCCGC

CCCAGCCCTGCCCCAGCCCCAGTCCCCTAACCCCCCAGCCCTAGCC

CCAGTCCCAGTCCTAGTTCCTCAGTCCCGCCCAGCTTCTCTCGAAAG

TCACTCTAATTTTCATTGATTCAGTGCTCAAAATAAGTTGTCCATTG

CTTATCCTATTATACTGGGATATTCCGTTTACCCTTGGCATTGCTGA

TCTTCAGTACTGACTCCTTGACCATTTTCAGTTAATGCATACAATCC

CATTTGTCTGTGATCTCAGGACAAAGAATTTCCTTACTCGGTACGTT

GAAGTTAGGGAATGTCAATTGAGAGCTTTCTATCAGAGCATTATTG

CCCACAATTTGAGTTACTTATCATTTTCTCGATCCCCTGCCCTTAAA

GGAGAAACCATTTCTCTGTCATTGCTTCTGTAGTCACAGTCCCAATT

TTGAGTAGTGATCTTTTCTTGTGTACTGTGTTGGCCACCTAAAACTC

TTTGCATTGAGTAAAATTCTAATTGCCAATAATCCTACCCATTGGAT

TAGACAGCACTCTGAACCCCATTTGCATTCAGCAGGGGGTCGCAGA

CAACCCGTCTTTTGTTGGACAGTTAAAATGCTCAGTCCCAATTGTCA

TAGCTTTGCCTATTAAACAAAGGCACCCTACTGCGCTTTTTGCTGTG

CTTCTGGAGAATCCTGCTGTTCTTGGACAATTAAAGAACAAAGTAG

TAATTGCTAATTGTCTCACCCATTAATCATGAAGACTACCAGTCGC

CCTTGCATTTGCCTTGAGGCAGCGCTGACTACCTGAGATTTAAGAG

TTTCTTAAATTATTGAGTAAAATCCCAATTATCCATAGTTCTGTTAG

TTACACTATGGCCTTTGCAAACATCTTTGCATAACAGCAGTGGGAC

TGACTCATTCTTAGAGCCCCTTCCCTTGGAATATTAATGGATACAAT

AGTAATTATTCATGGTTCTGCGTAACAGAGAAGACCCACTTATGTG

TATGCCTTTATCATTGCTCCTAGATAGTGTGACTACCTACCACCTT

GCATTAATATGTAAAACACTAATTGCCCATAGTCCCACTCATTAGT

CTAGGATGTCCTCTTTGCCATTGCTGCTGAGTTCTGACTACCCAAGT

TTCCTTCTCTTAAACAGTTGATATGCATAATTGCATATATTCATGGT

TCTGTGCAATAAAAATGGATTCTCACCCCATCCCACCTTCTGTGGG

ATGTTGCTAACGAGTGCAGATTATTCAATAACAGCTCTTGAACAGT

TAATTTGCACAGTTGCAATTGTCCAGAGTCCTGTCCATTAGAAAGG

GACTCTGTATCCTATTTGCACGCTACAATGTGGGCTGATCACCCAA

GGACTCTTCTTGTGCATTGATGTTCATAATTGTATTTGTCCACGATC

TTGTGCACTAACCCTTCCACTCCCTTTGTATTCCAGCAGGGGACCCT

TACTACTCAAGACCTCTGTACTAGGACAGTTTATGTGCACAATCCT

AATTGATTAGAACTGAGTCTTTTATATCAAGGTCCCTGCATCATCTT

TGCTTTACATCAAGAGGGTGCTGGTTACCTAATGCCCCTCCTCCAG

AAATTATTGATGTGCAAAATGCAATTTCCCTATCTGCTGTTAGTCTG

GGGTCTCATCCCCTCATATTCCTTTTGTCTTACAGCAGGGGGTACTT

GGGACTGTTAATGCGCATAATTGCAATTATGGTCTTTTCCATTAAAT

TAAGATCCCAACTGCTCACACCCTCTTAGCATTACAGTAGAGGGTG

CTAATCACAAGGACATTTCTTTTGTACTGTTAATGTGCTACTTGCAT

TTGTCCCTCTTCCTGTGCACTAAAGACCCCACTCACTTCCCTAGTGT

TCAGCAGTGGATGACCTCTAGTCAAGACCTTTGCACTAGGATAGTT

AATGTGAACCATGGCAACTGATCACAACAATGTCTTTCAGATCAGA

TCCATTTTATCCTCCTTGTTTTACAGCAAGGGATATTAATTACCTAT

GTTACCTTTCCCTGGGACTATGAATGTGCA

890
NON_CODING
GCCGTGGATACCTGCCTTTTAATTCTTTTTTATTCGCCCATCGGGGC

(ncTRANSCRIPT)
CGCGGATACCTGCTTTTTATTTTTTTTTCCTTAGCCCATCGGGGTAT

CGGATACCTGCTGATTCCCTTCCCCTCTGAACCCCCAACACTCTGGC

CCATCGGGGTGACGGATATCTGCTTTTTAAAAATTTTCTTTTTTTGG

CCCATCGGGGCTTCGGATACCTGCTTTTTTTTTTTTTATTTTTCCTTG

CCCATCGGGGCCTCGGATACCTGCTTTAATTTTTGTTTTTCTGGCCC

ATCGGGGCCGCGGATACCTGCTTTGATTTTTTTTTTTCATCGCCCAT

CGGTGCTTTTTATGGATGAAAAAATGTTGGTTTTGTGGGTTGTTGCA

CTCTCTGGAATATCTACACTTTTTTTTGCTGCTGATCATTTGGTGGT

GTGTGAGTGTACCTACCGCTTTGGCAGAGAATGACTCTGCAGTTAA

GCTAAGGGCGTGTTCAGATTGTGGAGGAAAAGTGGCCGCCATTTTA

GACTTGCCGCATAACTCGGCTTAGGGCTAGTC

891
NON_CODING
ATGGTGATTACTTTCTGTGGGGCTCGGAACTACATGCCCTAGGATA

(ncTRANSCRIPT)
TAAAAATGATGTTATCATTATAGAGTGCTCACAGAAGGAAATGAA

GTAATATAGGTGTGAGATCCAGACCAAAAGTCATTTAACAAGTTTA

TTCAGTGATGAAAACATGGGACAAATGGACTAATATAAGCGCAGTG

TACTAAGCTGAGTAGAGAGATAAAGTCCTGTCCAGAAGATACATG

CTTCCTGGCCTGATTGAGGAGATGGAAAATTTTTGCAAAAAACAAG

GTGTTGTGGTCTTCCATCCAGTTTCTTAGTGCTGATGATAAAAGTG

AATTAGACCCACCTTGACCTGGCCTACAGAAGTAAAGGAGTAAAA

ATAAATGCCTCAGGCGTGCTTTTTGATTCATTTGATAAACAAAGCA

TCTTTTATGTGGAATATACCATTCTGGGTCCTGAGGATAAGAGAGA

TGAGGGCATTAGATCACTGACAGCTGAAGATAGAAGAACATCTTTG

GTTTGATTGTTTAAATAATATTTCAATGCCTATTCTCTGCAAGGTAC

TATGTTTCGTAAATTAAATAGGTCTGGCCCAGAAGACCCACTCAAT

TGCCTTTGAGATTAAAAAAAAAAAAAAAAAGAAAGAAAAATGCAA

GTTTCTTTCAAAATAAAGAGACATTTTTCCTAGTTTCAGGAATCCCC

CAAATCACTTCCTCATTGGCTTAGTTTAAAGCCAGGAGACTGATAA

AAGGGCTCAGGGTTTGTTCTTTAATTCATTAACTAAACATTCTGCTT

TTATTACAGTTAAATGGTTCAAGATGTAACAACTAGTTTTAAAGGT

ATTTGCTCATTGGTCTGGCTTAGAGACAGGAAGACATATGAGCAAT

AAAAAAAAGATTCTTTTGCATTTACCAATTTAGTAAAAATTTATTA

AAACTGAATAAAGTGCTGTTCTTAAGTGCTTGAAAGACGTAAACCA

AAGTGCACTTTATCTCATTTATCTTATGGTGGAAACACAGGAACAA

ATTCTCTAAGAGACTGTGTTTCTTTAGTTGAGAAGAAACTTCATTGA

GTAGCTGTGATATGTTCGATACTAAGGAAAAACTAAACAGATCACC

TTTGACATGCGTTGTAGAGTGGGAATAAGAGAGGGCTTTTTATTTT

TTCGTTCATACGAGTATTGATGAAGATGATACTAAATGCTAAATGA

AATATATCTGCTCCAAAAGGCATTTATTCTGACTTGGAGATGCAAC

AAAAACACAAAAATGGAATGAAGTGATACTCTTCATCAAACAGAA

GTGACTGTTATCTCAACCATTTTGTTAAATCCTAAACAGAAAACAA

AAAAAATCATGACGAAAAGACACTTGCTTATTAATTGGCTTGGAAA

GTAGAATATAGGAGAAAGGTTACTGTTTATTTTTTTTCATGTATTCA

TTCATTCTACAAATATATTCGGGTGCCAATAGGTACTTGGTATAAG

GTTTTTGGCCCCAGAGACATGGGAAAAAAATGCATGCCTTCCCAGA

GAATGCCTAATACTTTCCTTTTGGCTTGTTTTCTTGTTAGGGGCATG

GCTTAGTCCCTAAATAACATTGTGTGGTTTAATTCCTACTCCGTATC

TCTTCTACCACTCTGGCCACTACGATAAGCAGGTA

892
NON_CODING
TGTGAACTCACTGTTAAAGGCACTGAAAATTTATCATATTTCATTTA

(ncTRANSCRIPT)
GCCACAGCCAAAAATAAGGCAATACCTATGTTAGCATTTTGTGAAC

TCTAAGGCACCA

893
NON_CODING
GGACTAAGCTTGTTGTGGTCACCTATAATGTGCCAGATACCATGCT

(ncTRANSCRIPT)
GGGTGCTAGAGCTACCAAAGGGGGAAAAGTATTCTCATAGAACAA

AAAATTTCAGAAAGGTGCATATTAAAGTGCTTTGTAAACTAAAGCA

TGATACAAATGTCAATGGGCTACATATTTATGAATGAATGAATGGA

TGAATGAATATTAAGTGCCTCTTACATACCAGCTATTTTGGGTACTG

TAAAATACAAGATTAATTCTCCTATGTAATAAGAGGAAAGTTTATC

CTCTATACTATTCAGATGTAAGGAATGATATATTGCTTAATTTTAAA

CAATCAAGACTTTACTGGTGAGGTTAAGTTAAATTATTACTGATAC

ATTTTTCCAGGTAACCAGGAAAGAGCTAGTATGAGGAAATGAAGT

AATAGATGTGAGATCCAGACCGAAAGTCACTTAATTCAGCTTGCGA

ATGTGCTTTCTA

894
NON_CODING
GGGGACAGCCTGAACTCCCTGCTCATAGTAGTGGCCAAATAATTTG

(ncTRANSCRIPT)
GTGGACTGTGCCAACGCTACTCCTGGGTTTAATACCCATCTCTAGG

CTTAAAGATGAGAGAACCTGGGACTGTTGAGCATGTTTAATACTTT

CCTTGATTTTTTTCTTCCTGTTTATGTGGGAAGTTGATTTAAATGAC

TGATAATGTGTATGAAAGCACTGTAAAACATAAGAGAAAAACCAA

TTAGTGTATTGGCAATCATGCAGTTAACATTTGAAAGTGCAGTGTA

AATTGTGAAGCATTATGTAAATCAGGGGTCCACAGTTTTTCTGTAA

GGGGTCAAATCATAAATACTTTAGACTGTGGGCCATATGGTTTCTG

TTACATATTTGTTTTTTAAACAACGTTTTTATAAGGTCAAAATCATT

CTTAGTTTTTGAGCCAATTGGATTTGGCCTGCTGTTCATAGCTTA

895
NON_CODING
TCTCAAGACTAACGGCCGGAATCTGGAGGCCCATGACCCAGAACC

(ncTRANSCRIPT)
CAGGAAGGATAGAAGCTTGAAGACCTGGGGAAATCCCAAGATGAG

AACCCTAAACCCTACCTCTTTTCTATTGTTTACACTTCTTACTCTTAG

ATATTTCCAGTTCTCCTGTTTATCTTTAAGCCTGATTCTTTTGAGATG

TACTTTTTGATGTTGCCGGTTACCTTTAGATTGACAGTATTATGCCT

GGGCCAGTCTTGAGCCAGCTTTAAATCACAGCTTTTACCTATTTGTT

AGGCTATAGTGTTTTGTAAACTTCTGTTTCTATTCACATCTTCTCCA

CTTGAGAGAGACACCAAAATCCAGTCAGTATCTAATCTGGCTTTTG

TTAACTTCCCTCAGGAGCAGACATTCATATA

896
NON_CODING
TGTCTCCTTTTTGGGTCACATGCTGTGTGCTTTTTGTCCTTTTCTTGT

(ncTRANSCRIPT)
TCTGTCTACCTCTCCTTTCTCTGCCTACCTCTCTTTTCTCTTTGTGAA

CTGTGATTATTTGTTACCCCTTCCCCTTCTCGTTCGTTTTAAATTTCA

CCTTTTTTCTGAGTCTGGCCTCCTTTCTGCTGTTTCTACTTTTTATCT

CACATTTCTCATTTCTGCATTTCCTTTCTGCCTCTCTTGGGCTATTCT

CTCTCTCCTCCCCTGCGTGCCTCAGCATCTCTTGCTGTTTGTGATTTT

CTATTTCAGTATTAATCTCTGTTGGCTTGTATTTGTTCTCTGCTTCTT

CCCTTTCTACTCACCTTTGAGTATTTCAGCCTCTTCATGAATCTATCT

CCCTCTCTTTGATTTCATGTAATCTCTCCTTAAATATTTCTTTGCATA

TGTGGGCAAGTGTACGTGTGTGTGTGTCATGTGTGGCAGAGGGGCT

TCCTAACCCCTGCCTGATAGGTGCAGAACGTCGGCTATCAGAGCAA

GCATTGTGGAGCGGTTCCTTATGCCAGGCTGCCATGTGAGATGATC

CAAGACCAAAACAAGGCCCTAGACTGCAGTAAAACCCAGAACTCA

AGTAGGGCAGAAGGTGGAAGGCTCATATGGATAGAAGGCCCAAAG

TATAAGACAGATGGTTTGAGACTTGAGACCCGAGGACTAAGATGG

AAAGCCCA

897
NON_CODING
TCATTGTTCCTATCTGCCAAATCATTATACTTCCTACAAGCAGTGCA

(ncTRANSCRIPT)
GAGAGCTGAGTCTTCAGCAGGTCCAAGAAATTTGAACACACTGAA

GGAAGTCAGCCTTCCCACCTGAAGATCAACATGCCTGGCACTCTAG

CACTTGAGGATA

898
NON_CODING
CCTCAGAAGAATAGGCTTGTTGTTTTACAGTGTTAGTGATCCATTCC

(ncTRANSCRIPT)
CTTTGACGATCCCTAGGTGGAGATGGGGCATGAGGATCCTCCAGGG

GAAAAGCTCACTACCACTGGGCAACAACCCTAGGTCAGGAGGTTCT

GTCAAGATACTTTCCTGGTCCCAGATAGG

899
NON_CODING
CCCATTGAAGATACCACGCTGCATGTGTCCTTAGTAGTCATGTCTCC

(ncTRANSCRIPT)
TTA

900
NON_CODING
AAGAATATTGTTTCTCGGAGAAGGATGTCAAAAGATCGGCCCAGCT

(ncTRANSCRIPT)
CAGGGAGCAGTTTGCCCTACTAGCTCCTCGGACAGCTGTAAAGAAG

AGTCTCTGGCTCTTTAGAATACT

901
NON_CODING
GGGTGCCCACTCCTTATGATCTTTACATTTGAACAGTTAATGTGAAT

(ncTRANSCRIPT)
AATTGCAGTTGTCCACAACCCTATCACTTCTAGGACCATTATACCTC

TTTTGCATTACTGTGGGGTATACTGTTTCCCTCCAAGGCCCCTTCTG

GTGGACTATCAACATATAATTGAAATTTTCTTTTGTCTTTGTCAGTA

GATTAAGGTCATACCCCATCACCTTTCCTTTGTAGTACAACAGGGT

GTCCTGATCAACCAAAGTCCTGTTGTTTTGGACTGTTAATATGTGCA

ATTACATTTGCTCCTGATCTGTGCACTAGATAAGGATCCTACCTACT

TTCTTAGTGTTTTTAGCAGGTAGTGCCCACTACTCAAGACTGTCACT

TGGAATGTTCATGTGCACAAACTCAATTCTCTAAGCATGTTCCTGTA

CCACCTTTGCTTTAGAGCAGGGGGATGATATTCACTAAGTGCCCCT

TCTTTTGGACTTAATATGCATTAATGCAATTGTCCACCTCTTCTTTT

AGACTAAGAGTTGATCTCCACATATTCCCCTTGCATCAGGGGCATG

TTAATTATGAATGAACCCTTTTCTTTTAATATTAATGTCATAATTGT

ATTTGTGGACCTGTGTAGGAGAAAAAGACCCTATGTTCCTCCCATT

ACCCTTTGGATTGCTGCTGAGAAGTGTTAACTACTCATAATCTCAG

CTCTTGGACAATTAATAGCATTAATAACAATTATCAAGGGCACTGA

TCATTAGATAAGACTCCTGCTTCCTCGTTGCTTACATCGGGGGTACT

GACCCACTAAGGCCCCTTGTACTGTTAATGTGAATATTTGCAATTAT

ATATGTCTCCTTCTGGTAGAGTGGGATATTATGCCCTAGTATCCCCT

TTGCATTACTGCAGGGGCTGCTGACTACTCAAAACTTCTCCTGGGA

CTGTTAATAGGCACAATGGCAGTTATCAATGGTTTTCTCCCTCCCTG

ACCTTGTTAAGCAAGCGCCCCACCCCACCCTTAGTTTCCCATGGCA

TAATAAAGTATAAGCATTGGAGTATTCCATGCACTTGTCTATCAAA

CAGTGGTCCATACTCCCAACCCTTTTGCATTGCGCCAGTGTGTAAA

ATCACAGGTAGCCATGGTGTCATGCTTTATATACGAAGTCTTCCCTC

TCTCTGCCCCTTGTGTGCCCTTGGCCCCTTTTTACAGACTATTGCTC

ACAATCTCAGGTGTCCATATTTGCAGCTATTAGGTAAGATTGTGCT

GTCTCCCTCTTCCCTTCCCTCTGCCCTGCCCCTTTTGCCTCTTTGCTG

GGTAATGTTGACCAGACAAGGCCCTTTCTCTTGGACTTAAACAATT

CTCAGTTGCACTTTCCTTGGTCCCACCCATTATACATGAACCCCTCT

ACTTCCTTTCGCATTGCTTCTGAGTATGCTGACTACCCAAAGCCCCT

TCTGTGTTATTAATAAACACAGTACTGATTGTCCCATTTTTCAGCCC

ATCAGTCCAAGATCTCCCTACCACTTTGGTGTGTTGGTGCAGTGTTG

ACTATGAAAAGCAGGCCTGAACTAGGTGGATAAGCCTTCACTCATT

TTCTTTCATTTATTAATGATCCTAGTTTCAATTATTGTCAGATTCTGG

GGACAAGAACCATTCTTGCCCACCTGTGTTACTGCTTTACTG

902
NON_CODING
TTTGCAGCAAAGTCACCCTTACAAAGAAGCTAATATGGAAACCACA

(UTR)
TGTAACTTAGCCAGACTATATTGTGTAGCTTCAAGAACTTGCAGTA

CATTACCAGCTGTGATTCTCCTGATAATTCAAGGGAGCTCAAAGTC

ACAAGAAGAAAAATGAAAGGAAAAAACAGCAGCCCTATTCAGAA

ATTGGTTTGAAGATGTAATTGCTCTAGTTTGGATTA

903
NON_CODING
ATGGTGGCTGTAAAACTAGGATCCCTGACGATTG

(UTR)

TABLE 7

Gene
Transcripts
Comparison

ACPP
ACPP-001(protein_coding)
PvsM

ACPP-005(retained_intron)
PvsM NvsM

ANK3
ANK3-021(retained_intron)
NvsP

AR
AR-001(protein_coding)
NvsM

AR-005(nonsense_mediated_decay)
PvsM NvsM

AR-203 (protein_coding)
NvsM

CD44
CD44-014(retained_intron)
NvsM

CHRAC1
CHRAC1-005(retained_intron)
NvsM

COL1A2
COL1A2-002(retained_intron)
NvsM

COL1A2-005(retained_intron)
NvsM

COL1A2-006(retained_intron)
NvsM

COL1A2-012(retained_intron)
NvsM

DLGAP1
DLGAP1-008(processed_transcript)
PvsM

DLGAP1-010(processed_transcript)
PvsM

DLGAP1-201(protein_coding)
PvsM NvsM

ETV6
ETV6-002(processed_transcript)
NvsM

ETV6-003(processed_transcript)
PvsM NvsM

ETV6-004(protein_coding)
NvsM

FBLN1
FBLN1-001(protein_coding)
PvsM NvsM

FBLN1-016(processed_transcript)
NvsM

FGFR1
FGFR1-005(retained_intron)
NvsM

FGFR2
FGFR2-008(processed_transcript)
ALL

FGFR2-016(protein_coding)
ALL

FGFR2-201(protein_coding)
PvsM NvsM

ILK
ILK-011(processed_transcript)
NvsM

ILK-012(processed_transcript)
NvsM

KHDRBS3
KHDRBS3-003(retained_intron)
PvsM

MYLK
MYLK-001(protein_coding)
NvsM

MYLK-014(retained_intron)
NvsM

PASK
PASK-015(retained_intron)
PvsM

PDLIM5
PDLIM5-010(protein_coding)
PvsM

PDLIM5-017(processed_transcript)
PvsM NvsM

POLR1C
POLR1C-002(retained_intron)
NvsM

ST6GAL1
ST6GAL1-021(retained_intron)
PvsM

THBS1
THBS1-001(protein_coding)
PvsM

THBS1-004(processed_transcript)
PvsM

THBS1-008(retained_intron)
PvsM

TABLE 8

Mean Fold Difference

Transcript
P vs N
M vs P
M vs N

TOP
ACOT11-001
0.79
0.77
0.61

AOX1-001
0.79
0.56
0.44

C19orf46-002
1.24*
1.23*
1.53*

C8orf84-001
0.76
0.75
0.57

COCH-202
0.76
0.83
0.63

CTA-55110.1-001
0.83
0.68
0.56

DMD-024
0.74
0.82
0.60

FGF10-002
0.83
0.64
0.53

FGFR2-008
0.76
0.79
0.60

FGFR2-016
0.74
0.67
0.49

GABRE-006
0.79
0.83
0.66

GNAL-001
0.82
0.69
0.57

GNAO1-002
0.78
0.75
0.58

HEATR8-006
0.80
0.80
0.64

ISL1-002
0.80
0.81
0.65

NR2F2-202
0.82
0.82
0.68

PCP4-004
0.81
0.72
0.58

PDE5A-005
0.74
0.79
0.59

PDZRN4-202
0.80
0.71
0.57

RSRC2-017
1.27*
1.28*
1.63*

TGM4-001
0.68
0.62
0.42

TSPAN2-001
0.80
0.77
0.61

Bottom
ABCC4-004
1.35*
0.81
N.A.

ALK-001
1.24*
0.83
N.A.

ATP1A1-002
1.23*
0.71
N.A.

NAMPT-006
1.34*
0.73
N.A.

NAMPT-007
1.75*
0.57
N.A.

RP11-627G23.1-004
1.38*
0.78
N.A.

TABLE 9

TS-PSRs
Genes

OR CI

OR CI

Classifier
OR
(95%)
P-value
OR
(95%)
P-value

KNN-positive
13
[2.5-99]
<0.005
3.8
[1.0-14.3]
0.05

Nomogram*
6.6
[2.3-20]
<0.001
7.9
[2.9-22.6]
<0.0001

TABLE 10

Variable
Categories
N (%)

Age
<70 yrs
132 (53)

≧70 yrs
119 (47)

Gender
Male
205 (82)

Female
46 (18)

Ethnicity
Caucasian
222 (88)

Other
29 (12)

Pathologic Stage
T2N0
62 (25)

T3N0
75 (30)

T4N0
25 (10)

Any T N1-3
89 (35)

Intravesical therapy
No
196 (78)

Yes
55 (22)

Adjuvant
No
150 (60)

chemotherapy
Yes
101 (40)

Age of FFPE blocks
<15 yrs
160 (64)

≧15 yrs
91 (36)

TABLE 11

Hazard ratio
95% CI
p-value

Gender
0.92
0.49-1.71
0.78

Age (<70 vs ≧70)
1.42
0.87-2.30
0.16

Ethnicity
0.89
0.42-1.88
0.75

T stage
2.46
1.05-5.72
0.04

Lymph nodes
3.37
2.07-5.49
<0.001

Lymphovascular invasion (LVI)
1.05
0.97-1.14
0.25

Adjuvant Chemotherapy
0.88
0.72-1.06
0.18

TABLE 12

Variable
Parameter
Training AUC
Testing AUC

Gender
M/F
0.48
0.56

Age
<70/≧70
0.51
0.48

Race
Caucasian/Other
0.49
0.54

Tumor Stage
1, 2, 3, 4
0.62
0.66

Node Status
Yes/No
0.66
0.65

LVI
Yes/No
0.64
0.63

Clinical Classifier 1
Logistic Model
0.73
0.71

Clinical Classifier 2
Cox model
0.72
0.72

TABLE 13

Genomic & Clinicopathologic
Hazard Ratio

Factors
(95% CI)
P value

GC*
2.20 (1.22-3.92)
0.00841

Age
1.55 (0.34-7.10)
0.58

Ethnicity
0.22 (0.01-3.46)
0.28

Gender
0.69 (0.11-4.40)
0.70

Pathological stage
1.02 (0.32-3.26)
0.97

Lymph node involvement
3.51 (0.76-16.25)
0.11

Lymphovascular invasion
2.90 (0.52-16.07)
0.22

Block age
0.99 (0.80-1.22)
0.93

Intravesical treatment
3.64 (0.64-20.64)
0.14

Adjuvant chemotherapy
4.31 (0.91-20.43)
0.07

*per 0.1 unit increment

TABLE 14

celfile
Batch
PatientId
AdjCTx
Age
Blockage
Gender
IV_Rx
LNI
LVI
OS_Event
OS_Event_Time

AA682-HuEx-
3
1646
0
69
18
male
0
1
1
1
10

1_0-st-v2-01-

1_118.CEL

AA629-HuEx-
2
1650
0
59
12
male
1
0
0
0
113

1_0-st-v2-01-

1_132.CEL

AA684-HuEx-
3
1652
0
69
19
female
0
0
1
1
19

1_0-st-v2-01-

1_142.CEL

AA736-HuEx-
6
1655
1
40
17
male
0
0
1
0
179

1_0-st-v2-02-

2_145.CEL

AA685-HuEx-
3
1657
1
57
15
female
0
1
1
1
10

1_0-st-v2-01-

1_157.CEL

AA739-HuEx-
6
1662
0
78
8
male
0
1
0
1
2

1_0-st-v2-02-

2_166.CEL

AA579-HuEx-
1
1678
0
72
10
male
0
0
1
1
5

1_0-st-v2-01-

1_220.CEL

AA636-HuEx-
6
1680
0
76
10
female
0
1
1
1
12

1_0-st-v2-01-

1_226.CEL

AA856-HuEx-
5
1691
1
68
10
male
0
1
NA
0
90

1_0-st-v2-01-

1_274.CEL

AA746-HuEx-
4
1697
1
49
16
female
0
0
1
1
21

1_0-st-v2-01-

1_292.CEL

AA694-HuEx-
3
1698
1
69
13
male
0
0
1
1
77

1_0-st-v2-01-

1_293.CEL

AA585-HuEx-
1
1699
0
89
9
male
0
0
NA
1
3

1_0-st-v2-01-

1_294.CEL

AA696-HuEx-
3
1702
1
77
9
male
0
1
1
1
9

1_0-st-v2-01-

1_299.CEL

AA697-HuEx-
3
1705
1
67
15
male
0
0
1
1
18

1_0-st-v2-01-

1_311.CEL

AA750-HuEx-
4
1712
1
68
10
male
0
1
1
0
83

1_0-st-v2-01-

1_343.CEL

AA643-HuEx-
6
1716
0
70
19
male
0
0
0
1
24

1_0-st-v2-01-

1_369.CEL

AA699-HuEx-
6
1717
1
50
10
male
1
1
1
0
90

1_0-st-v2-01-

1_373.CEL

AA753-HuEx-
4
1719
0
66
10
male
0
0
1
0
83

1_0-st-v2-01-

1_376.CEL

AA755-HuEx-
4
1723
0
72
10
male
0
0
NA
1
14

1_0-st-v2-01-

1_390.CEL

AA798-HuEx-
5
1731
0
70
12
male
1
0
NA
1
20

1_0-st-v2-01-

1_414.CEL

AA702-HuEx-
6
1733
0
65
19
male
0
0
1
1
42

1_0-st-v2-01-

1_420.CEL

AA704-HuEx-
6
1740
1
74
19
male
0
0
0
1
11

1_0-st-v2-01-

1_444.CEL

AA802-HuEx-
5
1747
0
72
14
male
1
0
0
0
130

1_0-st-v2-01-

1_469.CEL

AA762-HuEx-
4
1752
1
64
17
male
0
1
1
1
38

1_0-st-v2-01-

1_481.CEL

AA763-HuEx-
4
1753
1
48
8
male
1
0
0
0
69

1_0-st-v2-01-

1_485.CEL

AA594-HuEx-
1
1754
1
61
15
male
0
1
1
0
155

1_0-st-v2-01-

1_493.CEL

AA705-HuEx-
6
1756
1
66
9
male
0
0
0
0
73

1_0-st-v2-01-

1_506.CEL

AA597-HuEx-
6
1763
1
68
19
male
0
1
1
1
11

1_0-st-v2-01-

1_529_2.CEL

AA805-HuEx-
5
1768
1
58
13
male
0
1
1
1
18

1_0-st-v2-01-

1_560.CEL

AA766-HuEx-
4
1769
1
42
20
male
0
1
0
0
203

1_0-st-v2-01-

1_562.CEL

AA767-HuEx-
4
1771
1
54
9
female
0
1
1
1
11

1_0-st-v2-01-

1_569.CEL

AA806-HuEx-
5
1775
0
64
14
male
0
1
1
1
8

1_0-st-v2-01-

1_594.CEL

AA602-HuEx-
6
1785
0
71
17
male
0
0
0
0
169

1_0-st-v2-01-

1_623.CEL

AA771-HuEx-
4
1798
0
74
17
male
0
0
0
1
124

1_0-st-v2-01-

1_651.CEL

AA772-HuEx-
4
1799
0
48
9
male
0
0
0
0
76

1_0-st-v2-01-

1_652.CEL

AA808-HuEx-
5
1801
0
52
13
male
0
0
1
1
6

1_0-st-v2-01-

1_656.CEL

AA849-HuEx-
6
1802
0
85
15
male
0
0
0
1
15

1_0-st-v2-01-

1_664.CEL

AA774-HuEx-
4
1804
0
81
8
male
0
0
1
0
78

1_0-st-v2-01-

1_666.CEL

AA662-HuEx-
6
1814
0
55
14
male
1
0
0
0
143

1_0-st-v2-01-

1_703.CEL

AA607-HuEx-
1
1817
0
66
18
male
0
0
0
1
6

1_0-st-v2-01-

1_709.CEL

AA719-HuEx-
3
1822
0
72
8
female
0
0
0
0
69

1_0-st-v2-01-

1_726.CEL

AA721-HuEx-
3
1832
0
71
18
female
0
0
1
1
20

1_0-st-v2-01-

1_756.CEL

AA666-HuEx-
6
1834
1
63
11
male
0
0
NA
1
41

1_0-st-v2-01-

1_763.CEL

AA722-HuEx-
3
1837
0
49
16
male
0
0
0
0
159

1_0-st-v2-01-

1_777.CEL

AA780-HuEx-
4
1838
1
60
9
male
0
0
0
0
76

1_0-st-v2-01-

1_779.CEL

AA781-HuEx-
6
1842
0
47
14
male
0
0
0
1
18

1_0-st-v2-02-

2_800.CEL

AA667-HuEx-
6
1848
0
78
12
male
0
0
NA
0
112

1_0-st-v2-01-

1_826.CEL

AA619-HuEx-
1
1868
0
67
9
male
0
0
0
1
60

1_0-st-v2-01-

1_881.CEL

AA625-HuEx-
1
1887
0
79
13
female
0
1
1
0
120

1_0-st-v2-01-

1_956.CEL

AA732-HuEx-
6
1888
0
86
12
male
0
0
1
1
15

1_0-st-v2-01-

1_957.CEL

AA680-HuEx-
6
1889
1
56
17
male
0
1
1
1
172

1_0-st-v2-01-

1_958.CEL

AA733-HuEx-
6
1890
1
63
16
male
0
1
1
1
8

1_0-st-v2-01-

1_959.CEL

AA574-HuEx-
1
1647
0
67
17
male
0
0
0
1
66

1_0-st-v2-01-

1_120.CEL

AA628-HuEx-
2
1649
0
65
18
male
0
0
1
1
27

1_0-st-v2-01-

1_130.CEL

AA683-HuEx-
3
1651
0
70
9
female
0
0
0
1
11

1_0-st-v2-01-

1_135.CEL

AA575-HuEx-
1
1653
0
48
9
female
0
0
NA
1
3

1_0-st-v2-01-

1_143.CEL

AA630-HuEx-
2
1654
0
86
13
female
0
0
1
1
73

1_0-st-v2-01-

1_144.CEL

AA846-HuEx-
2
1658
0
67
16
male
0
1
1
1
68

1_0-st-v2-01-

1_159.CEL

AA576-HuEx-
1
1659
0
68
20
male
0
0
0
1
71

1_0-st-v2-01-

1_162.CEL

AA686-HuEx-
3
1661
1
64
17
male
0
0
0
0
149

1_0-st-v2-01-

1_165.CEL

AA687-HuEx-
3
1663
0
64
14
male
0
0
0
0
133

1_0-st-v2-01-

1_167.CEL

AA631-HuEx-
2
1665
1
52
18
male
0
1
0
1
15

1_0-st-v2-01-

1_173.CEL

AA577-HuEx-
1
1667
1
71
10
male
0
1
0
1
15

1_0-st-v2-01-

1_184.CEL

AA578-HuEx-
1
1668
0
54
9
male
0
1
1
1
13

1_0-st-v2-01-

1_186.CEL

AA632-HuEx-
2
1669
1
50
12
male
0
0
1
1
30

1_0-st-v2-01-

1_195.CEL

AA848-HuEx-
3
1670
1
62
12
male
1
0
1
0
107

1_0-st-v2-01-

1_198.CEL

AA689-HuEx-
3
1671
0
74
13
male
0
0
1
1
6

1_0-st-v2-01-

1_199.CEL

AA633-HuEx-
2
1672
0
83
15
male
1
0
0
1
31

1_0-st-v2-01-

1_203.CEL

AA690-HuEx-
3
1673
0
68
14
male
0
0
0
0
108

1_0-st-v2-01-

1_211.CEL

AA634-HuEx-
2
1674
0
93
16
male
0
0
1
1
13

1_0-st-v2-01-

1_213.CEL

AA691-HuEx-
3
1675
0
74
10
male
0
0
0
1
25

1_0-st-v2-01-

1_214.CEL

AA635-HuEx-
2
1676
0
74
19
male
1
1
1
1
78

1_0-st-v2-01-

1_218.CEL

AA692-HuEx-
3
1679
0
83
10
male
0
1
1
1
5

1_0-st-v2-01-

1_224.CEL

AA580-HuEx-
1
1681
0
58
17
male
0
0
0
1
45

1_0-st-v2-01-

1_227.CEL

AA637-HuEx-
2
1682
0
81
15
male
0
0
1
1
7

1_0-st-v2-01-

1_228.CEL

AA693-HuEx-
3
1683
0
71
10
male
1
0
1
1
25

1_0-st-v2-01-

1_230.CEL

AA581-HuEx-
1
1684
0
78
10
male
0
0
0
0
90

1_0-st-v2-01-

1_235.CEL

AA638-HuEx-
2
1688
1
64
15
female
0
1
1
1
55

1_0-st-v2-01-

1_258.CEL

AA639-HuEx-
2
1689
1
70
9
male
0
1
1
1
10

1_0-st-v2-01-

1_267.CEL

AA582-HuEx-
1
1690
1
57
16
male
1
1
0
1
19

1_0-st-v2-01-

1_272.CEL

AA640-HuEx-
2
1694
1
72
10
female
0
1
1
1
18

1_0-st-v2-01-

1_281.CEL

AA583-HuEx-
1
1695
1
71
19
male
1
1
1
1
24

1_0-st-v2-01-

1_284.CEL

AA584-HuEx-
1
1696
0
61
18
male
0
1
0
1
12

1_0-st-v2-01-

1_286.CEL

AA695-HuEx-
3
1700
1
73
9
male
0
1
NA
0
72

1_0-st-v2-01-

1_295.CEL

AA586-HuEx-
1
1701
1
71
9
male
0
0
NA
1
32

1_0-st-v2-01-

1_296.CEL

AA587-HuEx-
1
1704
0
73
19
male
0
0
0
1
175

1_0-st-v2-01-

1_309.CEL

AA641-HuEx-
2
1706
1
66
12
male
0
1
1
1
56

1_0-st-v2-01-

1_314.CEL

AA847-HuEx-
2
1709
0
68
11
male
0
0
0
1
29

1_0-st-v2-01-

1_338.CEL

AA698-HuEx-
3
1711
0
76
13
male
0
0
1
0
94

1_0-st-v2-01-

1_342.CEL

AA642-HuEx-
2
1715
0
44
11
female
0
0
1
1
21

1_0-st-v2-01-

1_368.CEL

AA588-HuEx-
2
1718
1
72
12
male
0
1
0
0
112

1_0-st-v2-01-

1_375.CEL

AA644-HuEx-
2
1721
0
73
11
male
0
0
0
1
47

1_0-st-v2-01-

1_382.CEL

AA700-HuEx-
3
1724
1
78
10
male
1
1
1
1
36

1_0-st-v2-01-

1_393.CEL

AA589-HuEx-
1
1725
1
51
13
male
1
0
0
0
126

1_0-st-v2-01-

1_396.CEL

AA701-HuEx-
3
1727
1
67
16
male
0
0
0
0
151

1_0-st-v2-01-

1_402.CEL

AA590-HuEx-
1
1736
0
78
13
male
0
0
1
1
7

1_0-st-v2-01-

1_430.CEL

AA591-HuEx-
1
1737
0
66
12
male
1
0
0
0
111

1_0-st-v2-01-

1_436.CEL

AA645-HuEx-
2
1738
1
55
12
female
0
1
0
0
105

1_0-st-v2-01-

1_437.CEL

AA703-HuEx-
3
1739
1
67
10
male
0
1
NA
1
50

1_0-st-v2-01-

1_441.CEL

AA646-HuEx-
2
1742
1
70
14
male
1
0
0
0
153

1_0-st-v2-01-

1_454.CEL

AA592-HuEx-
1
1743
1
68
17
male
0
0
NA
0
170

1_0-st-v2-01-

1_455.CEL

AA593-HuEx-
1
1748
0
75
14
female
0
0
0
0
112

1_0-st-v2-01-

1_475.CEL

AA647-HuEx-
2
1749
1
74
13
male
0
0
0
0
119

1_0-st-v2-01-

1_476.CEL

AA648-HuEx-
2
1750
0
60
14
male
0
0
0
0
132

1_0-st-v2-01-

1_477.CEL

AA649-HuEx-
2
1751
0
70
9
female
0
1
1
1
13

1_0-st-v2-01-

1_479.CEL

AA650-HuEx-
2
1755
0
81
14
male
1
0
0
1
60

1_0-st-v2-01-

1_504.CEL

AA651-HuEx-
2
1758
0
82
10
female
0
0
NA
1
17

1_0-st-v2-01-

1_510.CEL

AA595-HuEx-
1
1759
1
67
10
female
0
0
NA
0
97

1_0-st-v2-01-

1_512.CEL

AA706-HuEx-
3
1760
0
91
9
female
0
0
1
1
4

1_0-st-v2-01-

1_517.CEL

AA596-HuEx-
1
1762
1
47
9
male
0
1
1
0
76

1_0-st-v2-01-

1_528.CEL

AA845-HuEx-
2
1764
0
55
9
female
0
0
NA
0
69

1_0-st-v2-01-

1_547.CEL

AA598-HuEx-
1
1765
0
77
13
male
1
1
NA
1
4

1_0-st-v2-01-

1_552.CEL

AA707-HuEx-
3
1770
1
73
16
male
0
1
1
0
115

1_0-st-v2-01-

1_567.CEL

AA599-HuEx-
1
1772
0
67
10
male
0
1
0
1
19

1_0-st-v2-01-

1_579.CEL

AA600-HuEx-
1
1773
1
51
8
male
0
0
1
0
66

1_0-st-v2-01-

1_586.CEL

AA653-HuEx-
2
1774
0
76
8
male
1
0
0
1
36

1_0-st-v2-01-

1_591.CEL

AA654-HuEx-
2
1776
0
57
13
female
0
1
1
1
41

1_0-st-v2-01-

1_596.CEL

AA655-HuEx-
2
1777
0
75
19
male
0
0
0
1
128

1_0-st-v2-01-

1_597.CEL

AA656-HuEx-
2
1778
0
63
19
male
0
0
0
1
102

1_0-st-v2-01-

1_600.CEL

AA657-HuEx-
2
1779
0
78
12
male
1
0
0
0
99

1_0-st-v2-01-

1_608.CEL

AA601-HuEx-
1
1780
0
77
17
male
0
1
1
1
13

1_0-st-v2-01-

1_612.CEL

AA708-HuEx-
3
1781
0
77
17
male
0
1
1
1
2

1_0-st-v2-01-

1_616.CEL

AA709-HuEx-
3
1783
0
86
14
male
1
0
0
1
8

1_0-st-v2-01-

1_619.CEL

AA603-HuEx-
1
1786
0
66
14
male
1
0
0
0
127

1_0-st-v2-01-

1_626.CEL

AA658-HuEx-
2
1787
1
64
11
male
0
0
0
0
91

1_0-st-v2-01-

1_627.CEL

AA659-HuEx-
2
1788
0
74
15
male
0
0
1
1
11

1_0-st-v2-01-

1_630_2.CEL

AA604-HuEx-
1
1789
0
72
11
male
1
0
1
1
17

1_0-st-v2-01-

1_640.CEL

AA710-HuEx-
3
1791
0
65
12
male
1
0
1
0
107

1_0-st-v2-01-

1_643.CEL

AA660-HuEx-
2
1792
0
85
9
male
0
0
NA
1
14

1_0-st-v2-01-

1_644.CEL

AA711-HuEx-
3
1793
1
78
10
male
1
1
1
1
5

1_0-st-v2-01-

1_645.CEL

AA712-HuEx-
3
1794
1
65
12
female
0
0
0
0
108

1_0-st-v2-01-

1_646.CEL

AA713-HuEx-
3
1795
1
61
11
female
1
1
0
1
24

1_0-st-v2-01-

1_647.CEL

AA605-HuEx-
1
1796
0
77
17
male
0
0
0
1
69

1_0-st-v2-01-

1_648.CEL

AA714-HuEx-
3
1800
0
81
11
male
1
1
1
1
15

1_0-st-v2-01-

1_655.CEL

AA716-HuEx-
3
1805
0
67
18
male
0
0
0
0
168

1_0-st-v2-01-

1_668.CEL

AA661-HuEx-
2
1806
0
64
17
male
0
0
1
0
172

1_0-st-v2-01-

1_673.CEL

AA606-HuEx-
1
1809
0
68
12
male
1
0
NA
0
118

1_0-st-v2-01-

1_686.CEL

AA717-HuEx-
3
1811
1
63
9
female
0
1
1
1
10

1_0-st-v2-01-

1_691.CEL

AA718-HuEx-
3
1812
1
58
14
male
0
1
1
0
135

1_0-st-v2-01-

1_693.CEL

AA663-HuEx-
2
1816
0
74
13
male
0
0
0
1
7

1_0-st-v2-01-

1_708.CEL

AA608-HuEx-
1
1820
0
66
11
male
0
0
0
0
104

1_0-st-v2-01-

1_717.CEL

AA609-HuEx-
1
1821
0
67
15
female
0
0
0
1
37

1_0-st-v2-01-

1_722.CEL

AA610-HuEx-
1
1824
0
83
13
female
0
0
1
1
31

1_0-st-v2-01-

1_734.CEL

AA664-HuEx-
2
1825
1
61
8
male
0
1
NA
0
76

1_0-st-v2-01-

1_738.CEL

AA611-HuEx-
1
1826
1
69
11
male
1
1
1
1
13

1_0-st-v2-01-

1_740.CEL

AA665-HuEx-
2
1827
0
53
9
male
0
0
0
0
50

1_0-st-v2-01-

1_744.CEL

AA612-HuEx-
1
1829
1
70
8
male
0
1
1
1
47

1_0-st-v2-01-

1_750.CEL

AA720-HuEx-
3
1830
0
63
9
male
1
0
NA
0
87

1_0-st-v2-01-

1_752.CEL

AA613-HuEx-
1
1831
0
81
11
female
0
0
0
1
10

1_0-st-v2-01-

1_753.CEL

AA614-HuEx-
1
1835
0
49
16
male
0
0
0
0
129

1_0-st-v2-01-

1_767.CEL

AA615-HuEx-
1
1839
NA
65
12
male
0
1
1
1
16

1_0-st-v2-01-

1_781.CEL

AA723-HuEx-
3
1845
1
52
14
female
0
1
1
1
25

1_0-st-v2-01-

1_816.CEL

AA724-HuEx-
3
1847
0
78
18
male
1
0
NA
1
97

1_0-st-v2-01-

1_822.CEL

AA668-HuEx-
2
1849
1
77
9
male
1
1
1
1
10

1_0-st-v2-01-

1_827.CEL

AA669-HuEx-
2
1850
0
63
17
male
1
0
0
0
160

1_0-st-v2-01-

1_828.CEL

AA670-HuEx-
2
1851
1
50
9
male
0
1
1
1
25

1_0-st-v2-01-

1_832.CEL

AA616-HuEx-
1
1853
0
75
10
male
1
0
NA
0
90

1_0-st-v2-01-

1_842.CEL

AA671-HuEx-
2
1854
1
59
9
male
0
1
1
1
15

1_0-st-v2-01-

1_844.CEL

AA725-HuEx-
3
1855
0
75
13
male
0
0
1
0
110

1_0-st-v2-01-

1_846.CEL

AA672-HuEx-
2
1857
0
65
12
male
0
0
NA
1
19

1_0-st-v2-01-

1_850.CEL

AA617-HuEx-
1
1858
0
54
12
male
0
0
0
0
114

1_0-st-v2-01-

1_852.CEL

AA673-HuEx-
2
1860
0
68
10
female
0
0
1
1
3

1_0-st-v2-01-

1_857.CEL

AA618-HuEx-
1
1863
1
72
9
male
1
1
1
1
10

1_0-st-v2-01-

1_869.CEL

AA726-HuEx-
3
1864
1
61
9
male
1
0
0
0
92

1_0-st-v2-01-

1_872.CEL

AA674-HuEx-
2
1866
0
58
18
male
0
0
0
0
175

1_0-st-v2-01-

1_877.CEL

AA675-HuEx-
2
1867
1
66
18
male
1
0
0
0
174

1_0-st-v2-01-

1_878.CEL

AA727-HuEx-
3
1870
0
73
8
male
0
0
0
1
45

1_0-st-v2-01-

1_892.CEL

AA620-HuEx-
1
1871
0
76
15
male
0
0
1
1
22

1_0-st-v2-01-

1_894.CEL

AA728-HuEx-
3
1872
1
79
16
male
0
1
1
1
36

1_0-st-v2-01-

1_895.CEL

AA621-HuEx-
1
1873
1
66
9
female
0
1
1
1
11

1_0-st-v2-01-

1_902.CEL

AA676-HuEx-
2
1874
0
82
7
male
1
0
1
0
47

1_0-st-v2-01-

1_906.CEL

AA622-HuEx-
1
1875
1
52
16
male
0
0
0
0
130

1_0-st-v2-01-

1_907.CEL

AA677-HuEx-
2
1877
0
81
17
male
0
1
1
1
5

1_0-st-v2-01-

1_911.CEL

AA678-HuEx-
2
1878
1
66
20
male
0
1
1
1
32

1_0-st-v2-01-

1_914.CEL

AA729-HuEx-
3
1879
0
73
11
female
0
0
0
1
8

1_0-st-v2-01-

1_916.CEL

AA623-HuEx-
1
1881
0
85
17
male
0
0
1
1
5

1_0-st-v2-01-

1_924.CEL

AA730-HuEx-
3
1883
0
80
16
female
0
0
0
1
61

1_0-st-v2-01-

1_926.CEL

AA731-HuEx-
3
1884
1
70
18
male
0
1
1
1
11

1_0-st-v2-01-

1_928.CEL

AA679-HuEx-
2
1885
1
68
20
male
0
1
0
1
46

1_0-st-v2-01-

1_932.CEL

AA624-HuEx-
1
1886
0
76
9
male
0
0
0
0
76

1_0-st-v2-01-

1_951.CEL

AA681-HuEx-
2
1891
1
68
12
female
1
1
1
1
23

1_0-st-v2-01-

1_961.CEL

AA626-HuEx-
1
1892
0
69
9
male
0
1
0
1
3

1_0-st-v2-01-

1_963.CEL

AA734-HuEx-
3
1893
0
31
20
female
0
0
0
1
15

1_0-st-v2-01-

1_968.CEL

AA841-HuEx-
1
1894
0
66
8
male
0
0
0
0
71

1_0-st-v2-01-

1_983.CEL

AA735-HuEx-
3
1896
0
70
10
male
0
0
NA
0
102

1_0-st-v2-01-

1_887-A.CEL

AA790-HuEx-
5
1648
0
67
18
female
0
0
1
1
15

1_0-st-v2-01-

1_122.CEL

AA737-HuEx-
4
1656
0
66
19
female
1
0
1
1
32

1_0-st-v2-01-

1_155.CEL

AA738-HuEx-
4
1660
1
66
17
male
0
0
0
0
168

1_0-st-v2-01-

1_163.CEL

AA740-HuEx-
4
1664
1
68
9
male
0
1
1
1
6

1_0-st-v2-01-

1_168.CEL

AA741-HuEx-
4
1666
0
78
11
male
0
0
0
1
102

1_0-st-v2-01-

1_182.CEL

AA742-HuEx-
4
1677
0
68
9
female
0
1
1
1
19

1_0-st-v2-01-

1_219.CEL

AA743-HuEx-
4
1685
0
61
17
male
0
0
1
0
175

1_0-st-v2-01-

1_238.CEL

AA744-HuEx-
4
1686
1
55
12
male
0
1
1
1
14

1_0-st-v2-01-

1_240.CEL

AA745-HuEx-
4
1687
0
74
11
male
0
1
1
1
81

1_0-st-v2-01-

1_252.CEL

AA792-HuEx-
5
1692
1
71
10
male
0
0
0
1
24

1_0-st-v2-01-

1_276.CEL

AA857-HuEx-
5
1693
1
80
11
male
0
1
0
1
25

1_0-st-v2-01-

1_280.CEL

AA747-HuEx-
4
1703
1
71
19
male
1
0
0
1
57

1_0-st-v2-01-

1_306.CEL

AA748-HuEx-
4
1707
1
68
10
male
0
0
0
0
94

1_0-st-v2-01-

1_318.CEL

AA794-HuEx-
5
1708
0
65
14
male
0
0
0
0
131

1_0-st-v2-01-

1_337.CEL

AA749-HuEx-
4
1710
1
68
12
female
0
0
1
1
10

1_0-st-v2-01-

1_341.CEL

AA751-HuEx-
4
1713
1
80
16
male
1
0
0
1
11

1_0-st-v2-01-

1_352.CEL

AA752-HuEx-
4
1714
1
74
12
male
0
0
0
1
18

1_0-st-v2-01-

1_354.CEL

AA795-HuEx-
5
1720
0
71
11
male
0
1
1
1
28

1_0-st-v2-01-

1_377.CEL

AA754-HuEx-
4
1722
1
63
10
male
0
0
1
1
101

1_0-st-v2-01-

1_387.CEL

AA756-HuEx-
4
1726
0
53
9
male
0
0
1
1
13

1_0-st-v2-01-

1_397.CEL

AA757-HuEx-
4
1728
0
81
8
male
0
0
1
1
36

1_0-st-v2-01-

1_403.CEL

AA796-HuEx-
5
1729
1
55
12
male
0
0
0
0
107

1_0-st-v2-01-

1_411.CEL

AA797-HuEx-
5
1730
0
75
10
female
0
0
0
0
94

1_0-st-v2-01-

1_412.CEL

AA758-HuEx-
4
1732
0
60
12
male
0
0
0
0
112

1_0-st-v2-01-

1_419.CEL

AA799-HuEx-
5
1734
0
67
11
male
0
1
1
1
5

1_0-st-v2-01-

1_423.CEL

AA800-HuEx-
5
1736
1
69
17
female
0
0
1
0
157

1_0-st-v2-01-

1_431.CEL

AA759-HuEx-
4
1741
1
70
12
male
0
0
0
0
100

1_0-st-v2-01-

1_445.CEL

AA801-HuEx-
5
1744
0
79
9
male
1
1
NA
1
43

1_0-st-v2-01-

1_458.CEL

AA760-HuEx-
4
1745
1
60
18
male
0
0
1
1
26

1_0-st-v2-01-

1_459.CEL

AA761-HuEx-
4
1746
0
68
13
male
1
1
NA
0
111

1_0-st-v2-01-

1_467.CEL

AA764-HuEx-
4
1757
0
76
13
male
1
0
1
1
40

1_0-st-v2-01-

1_508.CEL

AA803-HuEx-
5
1761
NA
81
9
male
0
0
1
1
9

1_0-st-v2-01-

1_522.CEL

AA765-HuEx-
4
1766
1
58
15
male
0
1
1
1
20

1_0-st-v2-01-

1_557.CEL

AA804-HuEx-
5
1767
1
74
15
male
1
1
1
1
34

1_0-st-v2-01-

1_558.CEL

AA768-HuEx-
4
1782
0
71
20
male
0
0
0
1
10

1_0-st-v2-01-

1_618.CEL

AA769-HuEx-
4
1784
1
67
19
male
0
1
0
1
27

1_0-st-v2-01-

1_622.CEL

AA807-HuEx-
5
1790
0
67
14
male
1
0
1
1
21

1_0-st-v2-01-

1_641.CEL

AA770-HuEx-
4
1797
1
70
18
male
0
1
1
1
60

1_0-st-v2-01-

1_649.CEL

AA773-HuEx-
4
1803
1
70
9
male
0
1
1
1
16

1_0-st-v2-01-

1_665.CEL

AA775-HuEx-
4
1807
1
74
9
female
0
1
1
1
27

1_0-st-v2-01-

1_676.CEL

AA809-HuEx-
5
1808
0
73
14
female
0
0
0
1
100

1_0-st-v2-01-

1_685.CEL

AA810-HuEx-
5
1810
0
76
9
male
0
0
1
1
4

1_0-st-v2-01-

1_690.CEL

AA852-HuEx-
4
1813
0
72
12
female
0
0
NA
0
119

1_0-st-v2-01-

1_695.CEL

AA811-HuEx-
5
1815
0
46
16
male
1
0
1
1
39

1_0-st-v2-01-

1_707.CEL

AA777-HuEx-
4
1818
0
62
11
male
0
0
0
1
37

1_0-st-v2-01-

1_713.CEL

AA778-HuEx-
4
1819
0
59
9
male
1
1
1
1
51

1_0-st-v2-01-

1_716.CEL

celfile
P-Stage
Race
Rec_Event
Rec_Event_Time
qc.10.20.pass
qc.15.20.pass
qc.20.25.pass

AA682-HuEx-
11
non-
1
9
0
0
0

1_0-st-v2-01-

white

1_118.CEL

AA629-HuEx-
12
non-
0
113
0
0
0

1_0-st-v2-01-

white

1_132.CEL

AA684-HuEx-
13
white
0
19
1
0
0

1_0-st-v2-01-

1_142.CEL

AA736-HuEx-
12
white
0
179
0
0
0

1_0-st-v2-02-

2_145.CEL

AA685-HuEx-
14
white
1
9
0
0
0

1_0-st-v2-01-

1_157.CEL

AA739-HuEx-
11
white
0
2
1
0
0

1_0-st-v2-02-

2_166.CEL

AA579-HuEx-
13
white
1
3
0
0
0

1_0-st-v2-01-

1_220.CEL

AA636-HuEx-
12
white
0
12
0
0
0

1_0-st-v2-01-

1_226.CEL

AA856-HuEx-
11
white
0
90
0
0
0

1_0-st-v2-01-

1_274.CEL

AA746-HuEx-
14
white
1
3
0
0
0

1_0-st-v2-01-

1_292.CEL

AA694-HuEx-
13
white
1
63
0
0
0

1_0-st-v2-01-

1_293.CEL

AA585-HuEx-
13
white
0
3
0
0
0

1_0-st-v2-01-

1_294.CEL

AA696-HuEx-
13
white
1
7
0
0
0

1_0-st-v2-01-

1_299.CEL

AA697-HuEx-
12
white
1
12
1
0
0

1_0-st-v2-01-

1_311.CEL

AA750-HuEx-
12
white
0
83
1
0
0

1_0-st-v2-01-

1_343.CEL

AA643-HuEx-
13
white
1
14
1
1
0

1_0-st-v2-01-

1_369.CEL

AA699-HuEx-
12
white
0
90
0
0
0

1_0-st-v2-01-

1_373.CEL

AA753-HuEx-
12
white
0
83
0
0
0

1_0-st-v2-01-

1_376.CEL

AA755-HuEx-
12
white
0
14
0
0
0

1_0-st-v2-01-

1_390.CEL

AA798-HuEx-
12
white
1
6
0
0
0

1_0-st-v2-01-

1_414.CEL

AA702-HuEx-
12
white
0
42
1
1
1

1_0-st-v2-01-

1_420.CEL

AA704-HuEx-
13
white
1
8
0
0
0

1_0-st-v2-01-

1_444.CEL

AA802-HuEx-
12
white
0
130
0
0
0

1_0-st-v2-01-

1_469.CEL

AA762-HuEx-
12
white
1
8
1
0
0

1_0-st-v2-01-

1_481.CEL

AA763-HuEx-
12
white
0
69
0
0
0

1_0-st-v2-01-

1_485.CEL

AA594-HuEx-
12
white
0
155
0
0
0

1_0-st-v2-01-

1_493.CEL

AA705-HuEx-
12
non-
0
73
1
1
1

1_0-st-v2-01-

white

1_506.CEL

AA597-HuEx-
13
white
1
8
1
1
1

1_0-st-v2-01-

1_529_2.CEL

AA805-HuEx-
12
white
1
13
0
0
0

1_0-st-v2-01-

1_560.CEL

AA766-HuEx-
11
white
0
203
1
0
0

1_0-st-v2-01-

1_562.CEL

AA767-HuEx-
13
white
1
9
0
0
0

1_0-st-v2-01-

1_569.CEL

AA806-HuEx-
14
white
0
8
0
0
0

1_0-st-v2-01-

1_594.CEL

AA602-HuEx-
12
white
0
169
0
0
0

1_0-st-v2-01-

1_623.CEL

AA771-HuEx-
13
white
0
124
1
0
0

1_0-st-v2-01-

1_651.CEL

AA772-HuEx-
12
white
0
76
1
0
0

1_0-st-v2-01-

1_652.CEL

AA808-HuEx-
13
non-
0
6
0
0
0

1_0-st-v2-01-

white

1_656.CEL

AA849-HuEx-
13
white
1
12
1
1
1

1_0-st-v2-01-

1_664.CEL

AA774-HuEx-
13
white
0
78
0
0
0

1_0-st-v2-01-

1_666.CEL

AA662-HuEx-
12
white
0
143
1
0
0

1_0-st-v2-01-

1_703.CEL

AA607-HuEx-
14
white
0
6
0
0
0

1_0-st-v2-01-

1_709.CEL

AA719-HuEx-
13
non-
0
69
1
0
0

1_0-st-v2-01-

white

1_726.CEL

AA721-HuEx-
13
non-
0
20
0
0
0

1_0-st-v2-01-

white

1_756.CEL

AA666-HuEx-
13
white
1
20
1
1
0

1_0-st-v2-01-

1_763.CEL

AA722-HuEx-
12
white
0
159
0
0
0

1_0-st-v2-01-

1_777.CEL

AA780-HuEx-
13
white
0
76
0
0
0

1_0-st-v2-01-

1_779.CEL

AA781-HuEx-
12
white
0
18
1
1
0

1_0-st-v2-02-

2_800.CEL

AA667-HuEx-
12
white
0
112
1
1
1

1_0-st-v2-01-

1_826.CEL

AA619-HuEx-
12
white
0
60
0
0
0

1_0-st-v2-01-

1_881.CEL

AA625-HuEx-
13
white
0
120
0
0
0

1_0-st-v2-01-

1_956.CEL

AA732-HuEx-
14
white
1
13
1
1
1

1_0-st-v2-01-

1_957.CEL

AA680-HuEx-
12
white
0
172
0
0
0

1_0-st-v2-01-

1_958.CEL

AA733-HuEx-
13
white
1
7
1
1
0

1_0-st-v2-01-

1_959.CEL

AA574-HuEx-
14
white
1
56
1
1
0

1_0-st-v2-01-

1_120.CEL

AA628-HuEx-
13
white
0
27
1
1
0

1_0-st-v2-01-

1_130.CEL

AA683-HuEx-
14
white
0
11
1
1
1

1_0-st-v2-01-

1_135.CEL

AA575-HuEx-
13
white
0
3
1
1
1

1_0-st-v2-01-

1_143.CEL

AA630-HuEx-
13
non-
0
73
1
1
0

1_0-st-v2-01-

white

1_144.CEL

AA846-HuEx-
13
non-
0
68
1
1
1

1_0-st-v2-01-

white

1_159.CEL

AA576-HuEx-
14
white
0
71
1
1
1

1_0-st-v2-01-

1_162.CEL

AA686-HuEx-
12
white
0
149
1
1
1

1_0-st-v2-01-

1_165.CEL

AA687-HuEx-
12
non-
0
133
1
1
1

1_0-st-v2-01-

white

1_167.CEL

AA631-HuEx-
11
white
1
14
1
1
0

1_0-st-v2-01-

1_173.CEL

AA577-HuEx-
13
white
1
14
1
1
1

1_0-st-v2-01-

1_184.CEL

AA578-HuEx-
13
white
1
4
1
1
1

1_0-st-v2-01-

1_186.CEL

AA632-HuEx-
14
white
1
24
1
1
1

1_0-st-v2-01-

1_195.CEL

AA848-HuEx-
14
white
0
107
1
1
1

1_0-st-v2-01-

1_198.CEL

AA689-HuEx-
13
white
1
4
1
1
1

1_0-st-v2-01-

1_199.CEL

AA633-HuEx-
13
white
0
31
1
1
1

1_0-st-v2-01-

1_203.CEL

AA690-HuEx-
13
white
0
108
1
1
0

1_0-st-v2-01-

1_211.CEL

AA634-HuEx-
14
white
1
7
1
1
1

1_0-st-v2-01-

1_213.CEL

AA691-HuEx-
12
white
0
25
1
1
1

1_0-st-v2-01-

1_214.CEL

AA635-HuEx-
13
white
0
78
1
1
1

1_0-st-v2-01-

1_218.CEL

AA692-HuEx-
13
white
0
5
1
1
1

1_0-st-v2-01-

1_224.CEL

AA580-HuEx-
13
white
0
45
1
1
0

1_0-st-v2-01-

1_227.CEL

AA637-HuEx-
13
white
1
7
1
1
1

1_0-st-v2-01-

1_228.CEL

AA693-HuEx-
12
white
1
18
1
1
1

1_0-st-v2-01-

1_230.CEL

AA581-HuEx-
14
white
0
90
1
1
1

1_0-st-v2-01-

1_235.CEL

AA638-HuEx-
13
white
1
37
1
1
1

1_0-st-v2-01-

1_258.CEL

AA639-HuEx-
13
white
0
10
1
1
1

1_0-st-v2-01-

1_267.CEL

AA582-HuEx-
12
white
1
8
1
1
1

1_0-st-v2-01-

1_272.CEL

AA640-HuEx-
13
white
1
12
1
1
1

1_0-st-v2-01-

1_281.CEL

AA583-HuEx-
13
white
1
23
1
1
0

1_0-st-v2-01-

1_284.CEL

AA584-HuEx-
14
white
1
7
1
1
0

1_0-st-v2-01-

1_286.CEL

AA695-HuEx-
13
white
0
72
1
1
0

1_0-st-v2-01-

1_295.CEL

AA586-HuEx-
13
white
1
20
1
1
1

1_0-st-v2-01-

1_296.CEL

AA587-HuEx-
12
white
0
175
1
1
0

1_0-st-v2-01-

1_309.CEL

AA641-HuEx-
12
white
1
50
1
1
1

1_0-st-v2-01-

1_314.CEL

AA847-HuEx-
12
white
1
24
1
1
1

1_0-st-v2-01-

1_338.CEL

AA698-HuEx-
13
white
0
94
1
1
1

1_0-st-v2-01-

1_342.CEL

AA642-HuEx-
12
white
1
12
1
1
1

1_0-st-v2-01-

1_368.CEL

AA588-HuEx-
11
white
0
112
1
1
1

1_0-st-v2-01-

1_375.CEL

AA644-HuEx-
13
white
1
35
1
1
1

1_0-st-v2-01-

1_382.CEL

AA700-HuEx-
13
white
1
18
1
1
1

1_0-st-v2-01-

1_393.CEL

AA589-HuEx-
14
white
1
70
1
1
1

1_0-st-v2-01-

1_396.CEL

AA701-HuEx-
14
white
0
151
1
1
1

1_0-st-v2-01-

1_402.CEL

AA590-HuEx-
13
white
1
3
1
1
1

1_0-st-v2-01-

1_430.CEL

AA591-HuEx-
12
white
0
111
1
1
1

1_0-st-v2-01-

1_436.CEL

AA645-HuEx-
13
white
0
105
1
1
1

1_0-st-v2-01-

1_437.CEL

AA703-HuEx-
14
white
1
40
1
1
1

1_0-st-v2-01-

1_441.CEL

AA646-HuEx-
14
white
0
153
1
1
1

1_0-st-v2-01-

1_454.CEL

AA592-HuEx-
12
white
0
170
1
1
1

1_0-st-v2-01-

1_455.CEL

AA593-HuEx-
12
non-
0
112
1
1
1

1_0-st-v2-01-

white

1_475.CEL

AA647-HuEx-
13
white
0
119
1
1
1

1_0-st-v2-01-

1_476.CEL

AA648-HuEx-
13
white
0
132
1
1
1

1_0-st-v2-01-

1_477.CEL

AA649-HuEx-
13
white
1
4
1
1
1

1_0-st-v2-01-

1_479.CEL

AA650-HuEx-
12
white
1
48
1
1
1

1_0-st-v2-01-

1_504.CEL

AA651-HuEx-
13
white
0
17
1
1
1

1_0-st-v2-01-

1_510.CEL

AA595-HuEx-
13
non-
0
97
1
1
1

1_0-st-v2-01-

white

1_512.CEL

AA706-HuEx-
13
white
1
4
1
1
1

1_0-st-v2-01-

1_517.CEL

AA596-HuEx-
12
white
1
4
1
1
0

1_0-st-v2-01-

1_528.CEL

AA845-HuEx-
12
non-
0
69
1
1
1

1_0-st-v2-01-

white

1_547.CEL

AA598-HuEx-
14
white
1
3
1
1
1

1_0-st-v2-01-

1_552.CEL

AA707-HuEx-
13
white
0
115
1
1
1

1_0-st-v2-01-

1_567.CEL

AA599-HuEx-
11
white
0
19
1
1
1

1_0-st-v2-01-

1_579.CEL

AA600-HuEx-
13
white
0
66
1
1
1

1_0-st-v2-01-

1_586.CEL

AA653-HuEx-
12
white
0
36
1
1
1

1_0-st-v2-01-

1_591.CEL

AA654-HuEx-
14
white
1
23
1
1
1

1_0-st-v2-01-

1_596.CEL

AA655-HuEx-
12
white
0
128
1
1
1

1_0-st-v2-01-

1_597.CEL

AA656-HuEx-
13
white
1
18
1
1
1

1_0-st-v2-01-

1_600.CEL

AA657-HuEx-
14
white
1
65
1
1
1

1_0-st-v2-01-

1_608.CEL

AA601-HuEx-
13
white
0
13
1
1
1

1_0-st-v2-01-

1_612.CEL

AA708-HuEx-
14
white
0
2
1
1
1

1_0-st-v2-01-

1_616.CEL

AA709-HuEx-
13
white
0
8
1
1
1

1_0-st-v2-01-

1_619.CEL

AA603-HuEx-
14
white
0
127
1
1
0

1_0-st-v2-01-

1_626.CEL

AA658-HuEx-
13
white
0
91
1
1
1

1_0-st-v2-01-

1_627.CEL

AA659-HuEx-
12
white
0
11
1
1
1

1_0-st-v2-01-

1_630_2.CEL

AA604-HuEx-
12
non-
1
15
1
1
1

1_0-st-v2-01-

white

1_640.CEL

AA710-HuEx-
12
white
0
107
1
1
1

1_0-st-v2-01-

1_643.CEL

AA660-HuEx-
13
white
1
8
1
1
1

1_0-st-v2-01-

1_644.CEL

AA711-HuEx-
14
white
1
4
1
1
1

1_0-st-v2-01-

1_645.CEL

AA712-HuEx-
13
white
0
108
1
1
1

1_0-st-v2-01-

1_646.CEL

AA713-HuEx-
12
non-
1
16
1
1
1

1_0-st-v2-01-

white

1_647.CEL

AA605-HuEx-
14
white
0
69
1
1
1

1_0-st-v2-01-

1_648.CEL

AA714-HuEx-
11
white
1
15
1
1
1

1_0-st-v2-01-

1_655.CEL

AA716-HuEx-
13
white
0
168
1
1
0

1_0-st-v2-01-

1_668.CEL

AA661-HuEx-
13
white
0
172
1
1
1

1_0-st-v2-01-

1_673.CEL

AA606-HuEx-
13
white
0
118
1
1
1

1_0-st-v2-01-

1_686.CEL

AA717-HuEx-
13
non-
1
8
1
1
1

1_0-st-v2-01-

white

1_691.CEL

AA718-HuEx-
12
white
0
135
1
1
1

1_0-st-v2-01-

1_693.CEL

AA663-HuEx-
12
white
0
7
1
1
1

1_0-st-v2-01-

1_708.CEL

AA608-HuEx-
12
white
1
76
1
1
1

1_0-st-v2-01-

1_717.CEL

AA609-HuEx-
13
white
1
34
1
1
1

1_0-st-v2-01-

1_722.CEL

AA610-HuEx-
13
white
0
31
1
1
1

1_0-st-v2-01-

1_734.CEL

AA664-HuEx-
14
white
1
63
1
1
1

1_0-st-v2-01-

1_738.CEL

AA611-HuEx-
14
white
1
12
1
1
1

1_0-st-v2-01-

1_740.CEL

AA665-HuEx-
13
non-
0
50
1
1
1

1_0-st-v2-01-

white

1_744.CEL

AA612-HuEx-
13
white
1
12
1
1
1

1_0-st-v2-01-

1_750.CEL

AA720-HuEx-
12
white
1
4
1
1
1

1_0-st-v2-01-

1_752.CEL

AA613-HuEx-
12
white
0
10
1
1
1

1_0-st-v2-01-

1_753.CEL

AA614-HuEx-
12
white
0
129
1
1
1

1_0-st-v2-01-

1_767.CEL

AA615-HuEx-
12
white
1
11
1
1
1

1_0-st-v2-01-

1_781.CEL

AA723-HuEx-
12
white
1
7
1
1
1

1_0-st-v2-01-

1_816.CEL

AA724-HuEx-
14
white
1
44
1
1
1

1_0-st-v2-01-

1_822.CEL

AA668-HuEx-
14
non-
1
8
1
1
1

1_0-st-v2-01-

white

1_827.CEL

AA669-HuEx-
12
white
0
160
1
1
1

1_0-st-v2-01-

1_828.CEL

AA670-HuEx-
12
white
1
14
1
1
1

1_0-st-v2-01-

1_832.CEL

AA616-HuEx-
12
white
0
90
1
1
1

1_0-st-v2-01-

1_842.CEL

AA671-HuEx-
13
non-
1
11
1
1
1

1_0-st-v2-01-

white

1_844.CEL

AA725-HuEx-
12
white
0
110
1
1
1

1_0-st-v2-01-

1_846.CEL

AA672-HuEx-
14
white
0
19
1
1
1

1_0-st-v2-01-

1_850.CEL

AA617-HuEx-
12
white
0
114
1
1
0

1_0-st-v2-01-

1_852.CEL

AA673-HuEx-
13
white
0
3
1
1
1

1_0-st-v2-01-

1_857.CEL

AA618-HuEx-
14
white
1
3
1
1
1

1_0-st-v2-01-

1_869.CEL

AA726-HuEx-
13
white
0
92
1
1
1

1_0-st-v2-01-

1_872.CEL

AA674-HuEx-
12
white
0
175
1
1
1

1_0-st-v2-01-

1_877.CEL

AA675-HuEx-
14
white
0
174
1
1
1

1_0-st-v2-01-

1_878.CEL

AA727-HuEx-
13
white
1
16
1
1
1

1_0-st-v2-01-

1_892.CEL

AA620-HuEx-
14
white
1
11
1
1
1

1_0-st-v2-01-

1_894.CEL

AA728-HuEx-
12
white
1
33
1
1
0

1_0-st-v2-01-

1_895.CEL

AA621-HuEx-
13
non-
1
9
1
1
1

1_0-st-v2-01-

white

1_902.CEL

AA676-HuEx-
12
white
0
47
1
1
1

1_0-st-v2-01-

1_906.CEL

AA622-HuEx-
13
white
0
130
1
1
0

1_0-st-v2-01-

1_907.CEL

AA677-HuEx-
14
white
1
5
1
1
1

1_0-st-v2-01-

1_911.CEL

AA678-HuEx-
13
white
1
12
1
1
1

1_0-st-v2-01-

1_914.CEL

AA729-HuEx-
13
non-
0
8
1
1
1

1_0-st-v2-01-

white

1_916.CEL

AA623-HuEx-
13
white
1
5
1
1
1

1_0-st-v2-01-

1_924.CEL

AA730-HuEx-
12
white
0
61
1
1
0

1_0-st-v2-01-

1_926.CEL

AA731-HuEx-
14
white
1
10
1
1
1

1_0-st-v2-01-

1_928.CEL

AA679-HuEx-
14
non-
1
32
1
1
1

1_0-st-v2-01-

white

1_932.CEL

AA624-HuEx-
13
white
0
76
1
1
1

1_0-st-v2-01-

1_951.CEL

AA681-HuEx-
11
white
0
23
1
1
1

1_0-st-v2-01-

1_961.CEL

AA626-HuEx-
14
white
0
3
1
1
1

1_0-st-v2-01-

1_963.CEL

AA734-HuEx-
13
white
1
3
1
1
1

1_0-st-v2-01-

1_968.CEL

AA841-HuEx-
12
non-
0
71
1
1
1

1_0-st-v2-01-

white

1_983.CEL

AA735-HuEx-
12
white
0
102
1
1
1

1_0-st-v2-01-

1_887-A.CEL

AA790-HuEx-
13
white
0
15
1
1
0

1_0-st-v2-01-

1_122.CEL

AA737-HuEx-
13
white
1
9
1
1
0

1_0-st-v2-01-

1_155.CEL

AA738-HuEx-
12
white
0
168
1
1
1

1_0-st-v2-01-

1_163.CEL

AA740-HuEx-
13
white
0
6
1
1
0

1_0-st-v2-01-

1_168.CEL

AA741-HuEx-
13
white
0
102
1
1
1

1_0-st-v2-01-

1_182.CEL

AA742-HuEx-
13
white
1
6
1
1
0

1_0-st-v2-01-

1_219.CEL

AA743-HuEx-
13
white
0
175
1
1
1

1_0-st-v2-01-

1_238.CEL

AA744-HuEx-
14
non-
1
12
1
1
0

1_0-st-v2-01-

white

1_240.CEL

AA745-HuEx-
13
white
0
81
1
1
1

1_0-st-v2-01-

1_252.CEL

AA792-HuEx-
13
non-
1
15
1
1
0

1_0-st-v2-01-

white

1_276.CEL

AA857-HuEx-
13
white
1
25
1
1
1

1_0-st-v2-01-

1_280.CEL

AA747-HuEx-
13
white
1
57
1
1
1

1_0-st-v2-01-

1_306.CEL

AA748-HuEx-
13
white
0
94
1
1
1

1_0-st-v2-01-

1_318.CEL

AA794-HuEx-
12
white
0
131
1
1
0

1_0-st-v2-01-

1_337.CEL

AA749-HuEx-
13
white
1
6
1
1
1

1_0-st-v2-01-

1_341.CEL

AA751-HuEx-
13
white
1
7
1
1
0

1_0-st-v2-01-

1_352.CEL

AA752-HuEx-
13
white
1
12
1
1
1

1_0-st-v2-01-

1_354.CEL

AA795-HuEx-
14
white
1
20
1
1
1

1_0-st-v2-01-

1_377.CEL

AA754-HuEx-
12
non-
1
90
1
1
0

1_0-st-v2-01-

white

1_387.CEL

AA756-HuEx-
13
white
0
13
1
1
1

1_0-st-v2-01-

1_397.CEL

AA757-HuEx-
13
white
0
36
1
1
0

1_0-st-v2-01-

1_403.CEL

AA796-HuEx-
13
white
0
107
1
1
1

1_0-st-v2-01-

1_411.CEL

AA797-HuEx-
13
white
0
94
1
1
1

1_0-st-v2-01-

1_412.CEL

AA758-HuEx-
12
white
0
112
1
1
1

1_0-st-v2-01-

1_419.CEL

AA799-HuEx-
14
white
1
5
1
1
1

1_0-st-v2-01-

1_423.CEL

AA800-HuEx-
13
white
0
157
1
1
1

1_0-st-v2-01-

1_431.CEL

AA759-HuEx-
14
non-
0
100
1
1
1

1_0-st-v2-01-

white

1_445.CEL

AA801-HuEx-
14
non-
1
12
1
1
1

1_0-st-v2-01-

white

1_458.CEL

AA760-HuEx-
13
white
0
26
1
1
1

1_0-st-v2-01-

1_459.CEL

AA761-HuEx-
12
white
0
111
1
1
1

1_0-st-v2-01-

1_467.CEL

AA764-HuEx-
13
white
1
26
1
1
0

1_0-st-v2-01-

1_508.CEL

AA803-HuEx-
14
white
0
9
1
1
1

1_0-st-v2-01-

1_522.CEL

AA765-HuEx-
14
white
1
16
1
1
0

1_0-st-v2-01-

1_557.CEL

AA804-HuEx-
14
white
1
28
1
1
1

1_0-st-v2-01-

1_558.CEL

AA768-HuEx-
12
white
1
4
1
1
0

1_0-st-v2-01-

1_618.CEL

AA769-HuEx-
11
white
1
23
1
1
1

1_0-st-v2-01-

1_622.CEL

AA807-HuEx-
13
white
1
11
1
1
1

1_0-st-v2-01-

1_641.CEL

AA770-HuEx-
13
white
0
60
1
1
1

1_0-st-v2-01-

1_649.CEL

AA773-HuEx-
13
white
1
7
1
1
1

1_0-st-v2-01-

1_665.CEL

AA775-HuEx-
13
white
1
15
1
1
1

1_0-st-v2-01-

1_676.CEL

AA809-HuEx-
13
white
0
100
1
1
0

1_0-st-v2-01-

1_685.CEL

AA810-HuEx-
14
white
1
4
1
1
1

1_0-st-v2-01-

1_690.CEL

AA852-HuEx-
12
non-
0
119
1
1
0

1_0-st-v2-01-

white

1_695.CEL

AA811-HuEx-
12
white
0
39
1
1
1

1_0-st-v2-01-

1_707.CEL

AA777-HuEx-
12
non-
1
14
1
1
0

1_0-st-v2-01-

white

1_713.CEL

AA778-HuEx-
T4
white
1
8
1
1
0

1_0-st-v2-01-

1_716.CEL

Percent

celfile
qc.20.30.pass
qc.30.40.pass
Present
Set
GC
GCC

AA682-HuEx-
0
0
9.65862
NA
NA
NA

1_0-st-v2-01-

1_118.CEL

AA629-HuEx-
0
0
16.4473
NA
NA
NA

1_0-st-v2-01-

1_132.CEL

AA684-HuEx-
0
0
10.8961
NA
NA
NA

1_0-st-v2-01-

1_142.CEL

AA736-HuEx-
0
0
7.92935
NA
NA
NA

1_0-st-v2-02-

2_145.CEL

AA685-HuEx-
0
0
8.87618
NA
NA
NA

1_0-st-v2-01-

1_157.CEL

AA739-HuEx-
0
0
12.3508
NA
NA
NA

1_0-st-v2-02-

2_166.CEL

AA579-HuEx-
0
0
42.4653
NA
NA
NA

1_0-st-v2-01-

1_220.CEL

AA636-HuEx-
0
0
9.35561
NA
NA
NA

1_0-st-v2-01-

1_226.CEL

AA856-HuEx-
0
0
27.2338
NA
NA
NA

1_0-st-v2-01-

1_274.CEL

AA746-HuEx-
0
0
9.0163
NA
NA
NA

1_0-st-v2-01-

1_292.CEL

AA694-HuEx-
0
0
13.7011
NA
NA
NA

1_0-st-v2-01-

1_293.CEL

AA585-HuEx-
0
0
50.6811
NA
NA
NA

1_0-st-v2-01-

1_294.CEL

AA696-HuEx-
0
0
39.3282
NA
NA
NA

1_0-st-v2-01-

1_299.CEL

AA697-HuEx-
0
0
13.9019
NA
NA
NA

1_0-st-v2-01-

1_311.CEL

AA750-HuEx-
0
0
14.9777
NA
NA
NA

1_0-st-v2-01-

1_343.CEL

AA643-HuEx-
0
0
16.8539
NA
NA
NA

1_0-st-v2-01-

1_369.CEL

AA699-HuEx-
0
0
10.7207
NA
NA
NA

1_0-st-v2-01-

1_373.CEL

AA753-HuEx-
0
0
6.79608
NA
NA
NA

1_0-st-v2-01-

1_376.CEL

AA755-HuEx-
0
0
16.6886
NA
NA
NA

1_0-st-v2-01-

1_390.CEL

AA798-HuEx-
0
0
12.711
NA
NA
NA

1_0-st-v2-01-

1_414.CEL

AA702-HuEx-
1
0
27.9437
NA
NA
NA

1_0-st-v2-01-

1_420.CEL

AA704-HuEx-
0
0
6.87799
NA
NA
NA

1_0-st-v2-01-

1_444.CEL

AA802-HuEx-
0
0
18.9741
NA
NA
NA

1_0-st-v2-01-

1_469.CEL

AA762-HuEx-
0
0
13.4185
NA
NA
NA

1_0-st-v2-01-

1_481.CEL

AA763-HuEx-
0
0
36.6512
NA
NA
NA

1_0-st-v2-01-

1_485.CEL

AA594-HuEx-
0
0
11.2307
NA
NA
NA

1_0-st-v2-01-

1_493.CEL

AA705-HuEx-
1
0
29.4208
NA
NA
NA

1_0-st-v2-01-

1_506.CEL

AA597-HuEx-
1
0
20.2292
NA
NA
NA

1_0-st-v2-01-

1_529_2.CEL

AA805-HuEx-
0
0
19.9348
NA
NA
NA

1_0-st-v2-01-

1_560.CEL

AA766-HuEx-
0
0
10.8515
NA
NA
NA

1_0-st-v2-01-

1_562.CEL

AA767-HuEx-
0
0
44.9042
NA
NA
NA

1_0-st-v2-01-

1_569.CEL

AA806-HuEx-
0
0
35.3453
NA
NA
NA

1_0-st-v2-01-

1_594.CEL

AA602-HuEx-
0
0
5.91103
NA
NA
NA

1_0-st-v2-01-

1_623.CEL

AA771-HuEx-
0
0
14.8189
NA
NA
NA

1_0-st-v2-01-

1_651.CEL

AA772-HuEx-
0
0
14.828
NA
NA
NA

1_0-st-v2-01-

1_652.CEL

AA808-HuEx-
0
0
16.3954
NA
NA
NA

1_0-st-v2-01-

1_656.CEL

AA849-HuEx-
1
0
20.492
NA
NA
NA

1_0-st-v2-01-

1_664.CEL

AA774-HuEx-
0
0
42.3303
NA
NA
NA

1_0-st-v2-01-

1_666.CEL

AA662-HuEx-
0
0
10.1421
NA
NA
NA

1_0-st-v2-01-

1_703.CEL

AA607-HuEx-
0
0
19.8998
NA
NA
NA

1_0-st-v2-01-

1_709.CEL

AA719-HuEx-
0
0
14.0108
NA
NA
NA

1_0-st-v2-01-

1_726.CEL

AA721-HuEx-
0
0
19.172
NA
NA
NA

1_0-st-v2-01-

1_756.CEL

AA666-HuEx-
0
0
16.328
NA
NA
NA

1_0-st-v2-01-

1_763.CEL

AA722-HuEx-
0
0
7.71556
NA
NA
NA

1_0-st-v2-01-

1_777.CEL

AA780-HuEx-
0
0
34.3543
NA
NA
NA

1_0-st-v2-01-

1_779.CEL

AA781-HuEx-
0
0
15.2955
NA
NA
NA

1_0-st-v2-02-

2_800.CEL

AA667-HuEx-
1
0
23.8016
NA
NA
NA

1_0-st-v2-01-

1_826.CEL

AA619-HuEx-
0
0
38.3754
NA
NA
NA

1_0-st-v2-01-

1_881.CEL

AA625-HuEx-
0
0
15.5688
NA
NA
NA

1_0-st-v2-01-

1_956.CEL

AA732-HuEx-
1
0
25.7658
NA
NA
NA

1_0-st-v2-01-

1_957.CEL

AA680-HuEx-
0
0
5.83528
NA
NA
NA

1_0-st-v2-01-

1_958.CEL

AA733-HuEx-
0
0
19.708
NA
NA
NA

1_0-st-v2-01-

1_959.CEL

AA574-HuEx-
0
0
18.3853
trn
0.714286
0.498007

1_0-st-v2-01-

1_120.CEL

AA628-HuEx-
0
0
17.1642
trn
0.571429
0.566006

1_0-st-v2-01-

1_130.CEL

AA683-HuEx-
0
0
28.4031
trn
0.333333
0.12917

1_0-st-v2-01-

1_135.CEL

AA575-HuEx-
1
1
33.2624
trn
0.428571
NA

1_0-st-v2-01-

1_143.CEL

AA630-HuEx-
0
0
19.9707
trn
0.619048
0.470843

1_0-st-v2-01-

1_144.CEL

AA846-HuEx-
1
1
32.0879
trn
0.761905
0.90905

1_0-st-v2-01-

1_159.CEL

AA576-HuEx-
1
0
23.9048
trn
0.285714
0.111354

1_0-st-v2-01-

1_162.CEL

AA686-HuEx-
1
0
26.9212
trn
0.285714
0.123361

1_0-st-v2-01-

1_165.CEL

AA687-HuEx-
1
1
82.5709
trn
0.238095
0.100865

1_0-st-v2-01-

1_167.CEL

AA631-HuEx-
0
0
19.8189
trn
0.285714
0.395288

1_0-st-v2-01-

1_173.CEL

AA577-HuEx-
1
0
25.9857
trn
0.571429
0.594805

1_0-st-v2-01-

1_184.CEL

AA578-HuEx-
1
1
40.6266
trn
0.666667
0.902539

1_0-st-v2-01-

1_186.CEL

AA632-HuEx-
1
1
86.5852
trn
0.428571
0.505144

1_0-st-v2-01-

1_195.CEL

AA848-HuEx-
1
1
80.4682
trn
0.190476
0.303659

1_0-st-v2-01-

1_198.CEL

AA689-HuEx-
1
0
25.311
trn
0.285714
0.204983

1_0-st-v2-01-

1_199.CEL

AA633-HuEx-
1
0
26.1989
trn
0.285714
0.132257

1_0-st-v2-01-

1_203.CEL

AA690-HuEx-
0
0
15.5687
trn
0.619048
0.379821

1_0-st-v2-01-

1_211.CEL

AA634-HuEx-
1
0
21.8405
trn
0.714286
0.533344

1_0-st-v2-01-

1_213.CEL

AA691-HuEx-
1
1
81.409
trn
0.190476
0.062719

1_0-st-v2-01-

1_214.CEL

AA635-HuEx-
1
1
43.8173
trn
0.47619
0.797366

1_0-st-v2-01-

1_218.CEL

AA692-HuEx-
1
0
20.0017
trn
0.47619
0.617298

1_0-st-v2-01-

1_224.CEL

AA580-HuEx-
0
0
19.7804
trn
0.285714
0.143443

1_0-st-v2-01-

1_227.CEL

AA637-HuEx-
1
1
81.9929
trn
0.380952
0.248786

1_0-st-v2-01-

1_228.CEL

AA693-HuEx-
1
0
26.0979
trn
0.380952
0.454064

1_0-st-v2-01-

1_230.CEL

AA581-HuEx-
1
1
41.302
trn
0.238095
0.069548

1_0-st-v2-01-

1_235.CEL

AA638-HuEx-
1
0
29.038
trn
0.666667
0.873885

1_0-st-v2-01-

1_258.CEL

AA639-HuEx-
1
1
85.1116
trn
0.52381
0.746824

1_0-st-v2-01-

1_267.CEL

AA582-HuEx-
1
0
24.8041
trn
0.428571
0.677148

1_0-st-v2-01-

1_272.CEL

AA640-HuEx-
1
0
26.1611
trn
0.619048
0.814131

1_0-st-v2-01-

1_281.CEL

AA583-HuEx-
0
0
17.5976
trn
0.333333
0.685014

1_0-st-v2-01-

1_284.CEL

AA584-HuEx-
0
0
19.1203
trn
0.428571
0.498466

1_0-st-v2-01-

1_286.CEL

AA695-HuEx-
0
0
18.3506
trn
0.619048
NA

1_0-st-v2-01-

1_295.CEL

AA586-HuEx-
1
1
34.6682
trn
0.571429
NA

1_0-st-v2-01-

1_296.CEL

AA587-HuEx-
0
0
19.3913
trn
0.285714
0.097794

1_0-st-v2-01-

1_309.CEL

AA641-HuEx-
1
1
41.2583
trn
0.428571
0.67796

1_0-st-v2-01-

1_314.CEL

AA847-HuEx-
1
1
41.036
trn
0619048
0.379821

1_0-st-v2-01-

1_338.CEL

AA698-HuEx-
1
0
27.0603
trn
0.666667
0.598666

1_0-st-v2-01-

1_342.CEL

AA642-HuEx-
1
1
38.6051
trn
0.666667
0.790496

1_0-st-v2-01-

1_368.CEL

AA588-HuEx-
1
1
30.4825
trn
0.428571
0.419424

1_0-st-v2-01-

1_375.CEL

AA644-HuEx-
1
1
36.1872
trn
0.619048
0346308

1_0-st-v2-01-

1_382.CEL

AA700-HuEx-
1
1
35.3889
trn
0.47619
0.777976

1_0-st-v2-01-

1_393.CEL

AA589-HuEx-
1
1
30.6512
trn
0.380952
0.377586

1_0-st-v2-01-

1_396.CEL

AA701-HuEx-
1
0
26.802
trn
0.333333
0.139276

1_0-st-v2-01-

1_402.CEL

AA590-HuEx-
1
0
21.0747
trn
0.714286
0.638435

1_0-st-v2-01-

1_430.CEL

AA591-HuEx-
1
1
33.7772
trn
0.333333
0.238398

1_0-st-v2-01-

1_436.CEL

AA645-HuEx-
1
1
39.2918
trn
0.571429
0.70012

1_0-st-v2-01-

1_437.CEL

AA703-HuEx-
1
0
20.3831
trn
0.809524
NA

1_0-st-v2-01-

1_441.CEL

AA646-HuEx-
1
1
36.2867
trn
0.285714
0.181807

1_0-st-v2-01-

1_454.CEL

AA592-HuEx-
1
0
27.4093
trn
0.333333
NA

1_0-st-v2-01-

1_455.CEL

AA593-HuEx-
1
1
31.6379
trn
0.285714
0.092794

1_0-st-v2-01-

1_475.CEL

AA647-HuEx-
1
1
43.307
trn
0.47619
0.206802

1_0-st-v2-01-

1_476.CEL

AA648-HuEx-
1
1
30.9764
trn
0.428571
0.237764

1_0-st-v2-01-

1_477.CEL

AA649-HuEx-
1
1
42.7328
trn
0.428571
0.652129

1_0-st-v2-01-

1_479.CEL

AA650-HuEx-
1
0
27.342
trn
0.285714
0.139056

1_0-st-v2-01-

1_504.CEL

AA651-HuEx-
1
1
36.3301
trn
0.571429
NA

1_0-st-v2-01-

1_510.CEL

AA595-HuEx-
0
0
26.5464
trn
0.52381
NA

1_0-st-v2-01-

1_512.CEL

AA706-HuEx-
1
1
36.0797
trn
0.714286
0.547747

1_0-st-v2-01-

1_517.CEL

AA596-HuEx-
0
0
15.7068
trn
0.47619
0.820845

1_0-st-v2-01-

1_528.CEL

AA845-HuEx-
0
0
25.0048
trn
0.47619
NA

1_0-st-v2-01-

1_547.CEL

AA598-HuEx-
1
1
36.0621
trn
0.47619
NA

1_0-st-v2-01-

1_552.CEL

AA707-HuEx-
1
0
25.8532
trn
0.380952
0.578042

1_0-st-v2-01-

1_567.CEL

AA599-HuEx-
1
1
38.0983
trn
0.619048
0.674044

1_0-st-v2-01-

1_579.CEL

AA600-HuEx-
1
0
24.5212
trn
0.333333
0.386568

1_0-st-v2-01-

1_586.CEL

AA653-HuEx-
1
0
37.2906
trn
0.285714
0.157341

1_0-st-v2-01-

1_591.CEL

AA654-HuEx-
1
1
41.2555
trn
0.666667
0.894613

1_0-st-v2-01-

1_596.CEL

AA655-HuEx-
1
1
33.7621
trn
0.285714
0.092794

1_0-st-v2-01-

1_597.CEL

AA656-HuEx-
1
0
23.2758
trn
0.761905
0.582902

1_0-st-v2-01-

1_600.CEL

AA657-HuEx-
1
0
27.325
trn
0.619048
0.4627

1_0-st-v2-01-

1_608.CEL

AA601-HuEx-
1
0
21.2178
trn
0.428571
0.604777

1_0-st-v2-01-

1_612.CEL

AA708-HuEx-
1
0
23.2791
trn
0.666667
0.826174

1_0-st-v2-01-

1_616.CEL

AA709-HuEx-
1
0
28.8194
trn
0.52381
0.302628

1_0-st-v2-01-

1_619.CEL

AA603-HuEx-
0
0
16.4245
trn
0.571429
0.492965

1_0-st-v2-01-

1_626.CEL

AA658-HuEx-
1
1
39.2526
trn
0.52381
0.304144

1_0-st-v2-01-

1_627.CEL

AA659-HuEx-
1
0
21.7147
trn
0.47619
0.389655

1_0-st-v2-01-

1_630_2.CEL

AA604-HuEx-
1
0
25.4692
trn
0.666667
0.758916

1_0-st-v2-01-

1_640.CEL

AA710-HuEx-
1
0
26.8504
trn
0.142857
0.24166

1_0-st-v2-01-

1_643.CEL

AA660-HuEx-
1
1
33.4575
trn
0.714286
NA

1_0-st-v2-01-

1_644.CEL

AA711-HuEx-
1
1
34.7123
trn
0.761905
0.931752

1_0-st-v2-01-

1_645.CEL

AA712-HuEx-
1
1
81.0272
trn
0.238095
0.098265

1_0-st-v2-01-

1_646.CEL

AA713-HuEx-
1
0
22.7
trn
0.285714
0.486177

1_0-st-v2-01-

1_647.CEL

AA605-HuEx-
1
0
23.5929
trn
0.095238
0.037519

1_0-st-v2-01-

1_648.CEL

AA714-HuEx-
1
1
87.2293
trn
0.571429
0.834829

1_0-st-v2-01-

1_655.CEL

AA716-HuEx-
0
0
17.8395
trn
0.142857
0.061342

1_0-st-v2-01-

1_668.CEL

AA661-HuEx-
1
1
88.4027
trn
0.47619
0.460393

1_0-st-v2-01-

1_673.CEL

AA606-HuEx-
1
0
24.6641
trn
0.47619
NA

1_0-st-v2-01-

1_686.CEL

AA717-HuEx-
1
1
87.6296
trn
0.619048
0.850444

1_0-st-v2-01-

1_691.CEL

AA718-HuEx-
1
0
22.3175
trn
0.333333
0.627867

1_0-st-v2-01-

1_693.CEL

AA663-HuEx-
1
1
85.2171
trn
0.333333
0.116673

1_0-st-v2-01-

1_708.CEL

AA608-HuEx-
1
1
82.3636
trn
0.619048
0.393576

1_0-st-v2-01-

1_717.CEL

AA609-HuEx-
1
1
80.2338
trn
0.809524
0.609537

1_0-st-v2-01-

1_722.CEL

AA610-HuEx-
1
0
20.643
trn
0.666667
0.549068

1_0-st-v2-01-

1_734.CEL

AA664-HuEx-
1
1
51.905
trn
0.333333
NA

1_0-st-v2-01-

1_738.CEL

AA611-HuEx-
1
0
88.6613
trn
0.619048
0.899792

1_0-st-v2-01-

1_740.CEL

AA665-HuEx-
1
1
40.653
trn
0.47619
0.324031

1_0-st-v2-01-

1_744.CEL

AA612-HuEx-
1
0
22.8988
trn
0.714286
0.879576

1_0-st-v2-01-

1_750.CEL

AA720-HuEx-
1
1
87.5981
trn
0.47619
NA

1_0-st-v2-01-

1_752.CEL

AA613-HuEx-
1
0
20.9361
trn
0.095238
0.038548

1_0-st-v2-01-

1_753.CEL

AA614-HuEx-
1
0
25.7961
trn
0.666667
0.571351

1_0-st-v2-01-

1_767.CEL

AA615-HuEx-
1
1
90.9451
trn
0.571429
0.810524

1_0-st-v2-01-

1_781.CEL

AA723-HuEx-
1
1
84.859
trn
0.619048
0.886662

1_0-st-v2-01-

1_816.CEL

AA724-HuEx-
1
0
27.8093
trn
0.714286
NA

1_0-st-v2-01-

1_822.CEL

AA668-HuEx-
1
0
24.3978
trn
0.380952
0.696265

1_0-st-v2-01-

1_827.CEL

AA669-HuEx-
1
1
33.7277
trn
0.285714
0.213972

1_0-st-v2-01-

1_828.CEL

AA670-HuEx-
1
0
29.3556
trn
0.333333
0.680285

1_0-st-v2-01-

1_832.CEL

AA616-HuEx-
1
0
22.0787
trn
0.590909
NA

1_0-st-v2-01-

1_842.CEL

AA671-HuEx-
1
1
49.7328
trn
0.333333
0.621067

1_0-st-v2-01-

1_844.CEL

AA725-HuEx-
1
0
26.3833
trn
0.238095
0.166436

1_0-st-v2-01-

1_846.CEL

AA672-HuEx-
1
1
42.364
trn
0.714286
NA

1_0-st-v2-01-

1_850.CEL

AA617-HuEx-
0
0
19.5706
trn
0.333333
0.190879

1_0-st-v2-01-

1_852.CEL

AA673-HuEx-
1
1
81.4528
trn
0.333333
0.277928

1_0-st-v2-01-

1_857.CEL

AA618-HuEx-
1
1
44.3887
trn
0.619048
0.89167

1_0-st-v2-01-

1_869.CEL

AA726-HuEx-
1
1
87.5466
trn
0.619048
0.585047

1_0-st-v2-01-

1_872.CEL

AA674-HuEx-
1
0
29.6863
trn
0.619048
0.45009

1_0-st-v2-01-

1_877.CEL

AA675-HuEx-
1
1
85.1356
trn
0.285714
0.199704

1_0-st-v2-01-

1_878.CEL

AA727-HuEx-
1
1
87.4076
trn
0.380952
0.145711

1_0-st-v2-01-

1_892.CEL

AA620-HuEx-
1
0
20.6594
trn
0.714286
0.651713

1_0-st-v2-01-

1_894.CEL

AA728-HuEx-
0
0
16.4462
trn
0.428571
0.590833

1_0-st-v2-01-

1_895.CEL

AA621-HuEx-
1
1
81.6921
trn
0.761905
0.911419

1_0-st-v2-01-

1_902.CEL

AA676-HuEx-
1
1
87.3924
trn
0.380952
0.376774

1_0-st-v2-01-

1_906.CEL

AA622-HuEx-
0
0
16.2054
trn
0.238095
0.137093

1_0-st-v2-01-

1_907.CEL

AA677-HuEx-
1
0
29.8555
trn
0.363636
0.500079

1_0-st-v2-01-

1_911.CEL

AA678-HuEx-
1
0
23.862
trn
0.380952
0.62662

1_0-st-v2-01-

1_914.CEL

AA729-HuEx-
1
1
36.9093
trn
0.380952
0.145711

1_0-st-v2-01-

1_916.CEL

AA623-HuEx-
1
0
27.7507
trn
0.285714
0.157834

1_0-st-v2-01-

1_924.CEL

AA730-HuEx-
0
0
19.4855
trn
0.285714
0.081287

1_0-st-v2-01-

1_926.CEL

AA731-HuEx-
1
0
25.638
trn
0.380952
0.599106

1_0-st-v2-01-

1_928.CEL

AA679-HuEx-
1
0
22.014
trn
0.52381
0.560748

1_0-st-v2-01-

1_932.CEL

AA624-HuEx-
1
1
87.6194
trn
0.380952
0.135211

1_0-st-v2-01-

1_951.CEL

AA681-HuEx-
1
1
38.9953
trn
0.333333
0.703477

1_0-st-v2-01-

1_961.CEL

AA626-HuEx-
1
1
38.754
trn
0.272727
0.272899

1_0-st-v2-01-

1_963.CEL

AA734-HuEx-
1
1
33.0472
trn
0.333333
0.314901

1_0-st-v2-01-

1_968.CEL

AA841-HuEx-
1
1
43.1947
trn
0.52381
0.29201

1_0-st-v2-01-

1_983.CEL

AA735-HuEx-
1
1
38.5059
trn
0.190476
NA

1_0-st-v2-01-

1_887-A.CEL

AA790-HuEx-
0
0
16.4732
tst
0.619048
0.606886

1_0-st-v2-01-

1_122.CEL

AA737-HuEx-
0
0
19.4884
tst
0.666667
0.789306

1_0-st-v2-01-

1_155.CEL

AA738-HuEx-
1
0
24.3258
tst
0.333333
0.142789

1_0-st-v2-01-

1_163.CEL

AA740-HuEx-
0
0
18.8438
tst
0.47619
0.71363

1_0-st-v2-01-

1_168.CEL

AA741-HuEx-
1
1
90.9373
tst
0.333333
0.10524

1_0-st-v2-01-

1_182.CEL

AA742-HuEx-
0
0
16.0158
tst
0.47619
0.71363

1_0-st-v2-01-

1_219.CEL

AA743-HuEx-
1
0
27.8375
tst
0.285714
0.273203

1_0-st-v2-01-

1_238.CEL

AA744-HuEx-
0
0
16.0941
tst
0.714286
0.918596

1_0-st-v2-01-

1_240.CEL

AA745-HuEx-
1
0
25.5354
tst
0.428571
0.625371

1_0-st-v2-01-

1_252.CEL

AA792-HuEx-
0
0
20.2035
tst
0.47619
0.221437

1_0-st-v2-01-

1_276.CEL

AA857-HuEx-
1
1
38.0538
tst
0.47619
0.418125

1_0-st-v2-01-

1_280.CEL

AA747-HuEx-
1
0
27.0322
tst
0.333333
0.213076

1_0-st-v2-01-

1_306.CEL

AA748-HuEx-
1
0
20.3642
tst
0.333333
0.135836

1_0-st-v2-01-

1_318.CEL

AA794-HuEx-
0
0
16.2502
tst
0.238095
0.098265

1_0-st-v2-01-

1_337.CEL

AA749-HuEx-
1
1
42.8271
tst
0.380952
0.325613

1_0-st-v2-01-

1_341.CEL

AA751-HuEx-
0
0
18.6045
tst
0.619048
0.448316

1_0-st-v2-01-

1_352.CEL

AA752-HuEx-
1
1
31.3183
tst
0.52381
0.246448

1_0-st-v2-01-

1_354.CEL

AA795-HuEx-
0
0
28.1588
tst
0.666667
0.849764

1_0-st-v2-01-

1_377.CEL

AA754-HuEx-
0
0
16.18
tst
0.333333
0.307941

1_0-st-v2-01-

1_387.CEL

AA756-HuEx-
1
1
38.3485
tst
0.285714
0.3216

1_0-st-v2-01-

1_397.CEL

AA757-HuEx-
0
0
17.2699
tst
0.333333
0.208867

1_0-st-v2-01-

1_403.CEL

AA796-HuEx-
1
0
27.0163
tst
0.238095
0.127122

1_0-st-v2-01-

1_411.CEL

AA797-HuEx-
1
1
31.3986
tst
0.47619
0.202085

1_0-st-v2-01-

1_412.CEL

AA758-HuEx-
1
1
40.5702
tst
0.333333
0.165438

1_0-st-v2-01-

1_419.CEL

AA799-HuEx-
1
1
33.0817
tst
0.428571
0.671596

1_0-st-v2-01-

1_423.CEL

AA800-HuEx-
1
1
36.4681
tst
0.238095
0.191995

1_0-st-v2-01-

1_431.CEL

AA759-HuEx-
1
0
27.702
tst
0.52381
0.268618

1_0-st-v2-01-

1_445.CEL

AA801-HuEx-
1
1
83.1966
tst
0.52381
NA

1_0-st-v2-01-

1_458.CEL

AA760-HuEx-
1
0
26.771
tst
0.428571
0.433048

1_0-st-v2-01-

1_459.CEL

AA761-HuEx-
1
0
21.3604
tst
0.47619
NA

1_0-st-v2-01-

1_467.CEL

AA764-HuEx-
0
0
19.7612
tst
0.190476
0.225144

1_0-st-v2-01-

1_508.CEL

AA803-HuEx-
1
1
87.0205
tst
0.428571
0.293503

1_0-st-v2-01-

1_522.CEL

AA765-HuEx-
0
0
17.5484
tst
0.571429
0.839756

1_0-st-v2-01-

1_557.CEL

AA804-HuEx-
1
0
26.8291
tst
0.761905
0.938769

1_0-st-v2-01-

1_558.CEL

AA768-HuEx-
0
0
17.0565
tst
0.52381
0.262959

1_0-st-v2-01-

1_618.CEL

AA769-HuEx-
1
0
21.1354
tst
0.285714
0.29731

1_0-st-v2-01-

1_622.CEL

AA807-HuEx-
1
0
29.0506
tst
0.809524
0.877799

1_0-st-v2-01-

1_641.CEL

AA770-HuEx-
1
0
29.6962
tst
0.190476
0.376383

1_0-st-v2-01-

1_649.CEL

AA773-HuEx-
1
0
21.4004
tst
0.380952
0.599106

1_0-st-v2-01-

1_665.CEL

AA775-HuEx-
1
0
26.7034
tst
0.47619
0.676794

1_0-st-v2-01-

1_676.CEL

AA809-HuEx-
0
0
17.675
tst
0.52381
0.251874

1_0-st-v2-01-

1_685.CEL

AA810-HuEx-
1
1
87.4839
tst
0.47619
0.375953

1_0-st-v2-01-

1_690.CEL

AA852-HuEx-
0
0
18.8276
tst
0.333333
NA

1_0-st-v2-01-

1_695.CEL

AA811-HuEx-
1
0
22.2554
tst
0.47619
0.729893

1_0-st-v2-01-

1_707.CEL

AA777-HuEx-
0
0
16.3117
tst
0.666667
0.477605

1_0-st-v2-01-

1_713.CEL

AA778-HuEx-
0
0
15.5155
tst
0.52381
0.884073

1_0-st-v2-01-

1_716.CEL

TABLE 15

Probe set ID
Category
Gene Symbol

3337703
CODING
PPP6R3

3326487
CODING
EHF

3160006
CODING
SMARCA2

3576730
CODING
TC2N

2365991
CODING
MPZL1

3536951
CODING
KTN1

3147328
CODING
UBR5

2852379
CODING
ZFR

3331573
CODING
CTNND1

3463598
CODING
PPP1R12A

2703240
CODING
KPNA4

3974728
CODING
USP9X

3887661
CODING
NCOA3

2758874
CODING
CYTL1

2823854
CODING
WDR36

2975719
CODING
BCLAF1

2458376
CODING
PARP1; ENAH

3754530
CODING
ACACA

3757658
CODING
KAT2A

3659319
CODING
LONP2

3463528
CODING
PAWR

2799051
CODING
SLC6A19

2554001
CODING
PNPT1

3012438
CODING
AKAP9

4024378
CODING
CDR1

3165799
CODING
IFT74

2555411
CODING
USP34

3536996
CODING
KTN1

2669750
CODING
SCN10A

3148620
CODING
EIF3E

3851902
NON_CODING (CDS_ANTISENSE)
CALR

2651515
NON_CODING (CDS_ANTISENSE)
MECOM

3111306
NON_CODING (CDS_ANTISENSE)
RSPO2

2669316
NON_CODING (CDS_ANTISENSE)
GOLGA4

3560055
NON_CODING (CDS_ANTISENSE)
AKAP6

3484750
NON_CODING (CDS_ANTISENSE)
N4BP2L2

2651521
NON_CODING (CDS_ANTISENSE)
MECOM

3002694
NON_CODING (INTRONIC)
EGFR

3384586
NON_CODING (INTRONIC)
DLG2

3986003
NON_CODING (INTRONIC)
IL1RAPL2

3476549
NON_CODING (INTRONIC)
NCOR2

3875037
NON_CODING (INTRONIC)
RP5-828H9.1

3524631
NON_CODING (INTRONIC)
ARGLU1

3384580
NON_CODING (INTRONIC)
DLG2

3932938
NON_CODING (INTRONIC)
TMPRSS3;

AL773572.7

3581867
NON_CODING (INTRONIC)
IGHG3

3253347
NON_CODING (ncTRANSCRIPT)
RP11-428P16.2

2956494
NON_CODING (ncTRANSCRIPT)
CYP2AC1P

2705151
NON_CODING (UTR)
RPL22L1

3666869
NON_CODING (UTR)
NFAT5

2318755
NON_CODING (UTR)
PARK7

3969511
NON_CODING (UTR)
OFD1

3719123
NON_CODING (UTR)
ZNHIT3

3421223
NON_CODING (UTR)
NUP107

3739125
NON_CODING (UTR)
FN3KRP

2553585
NON_CODING (UTR)
RTN4

2405285
NON_CODING (UTR)
TMEM54

2473624
NON_CODING (UTR)
RAB10

3593171
NON_CODING (UTR)
DUT

2663553
NON_CODING (UTR)
NUP210

2874688
NON_CODING (UTR)
HINT1

3628924
NON_CODING (UTR)
FAM96A

3066770
NON_CODING (UTR)
SYPL1

3936897
NON_CODING (UTR)
MRPL40

3505453
NON_CODING (UTR)
MIPEP

3368555
NON_CODING (UTR)
CSTF3

3985635
NON_CODING (UTR)
TCEAL4

3816402
NON_CODING (UTR)
OAZ1

2361095
NON_CODING (UTR)
MSTO1;

RP11-243J18.3;

DAP3

2451873
NON_CODING (UTR)
ETNK2

2414960
NON_CODING (UTR)
TACSTD2

3005357
NON_CODING (UTR)
CRCP

3776446
NON_CODING (UTR)
MYL12A

3260965
NON_CODING (UTR)
LZTS2

3619236
NON_CODING (UTR)
BMF

3454547
NON_CODING (UTR_ANTISENSE)
METTL7A

2735017
NON_CODING (UTR_ANTISENSE)
SPARCL1

3061144
NON_CODING (UTR_ANTISENSE)
ANKIB1

2710217
NON_CODING (UTR_ANTISENSE)
LPP

3005652
NON_CODING (UTR_ANTISENSE)
GS1-124K5.12

3854371
NON_CODING (UTR_ANTISENSE)
MRPL34

3337703
CODING
PPP6R3

TABLE 16

Machine

Learning
Feature Selection

Standardization
AUC
AUC

Algorithm
Method
# Features Selected
Method
Training
Testing

Naive Bayes
Ranking based on
Top 20
Percentile
0.81
0.73

(NB)
Median Fold

Rank

Difference

K-Nearest
Ranking based on
Top 12
Z-score
0.72
0.73

Neighbours
Median Fold

(KNN)
Difference and

Random Forest-based

Gini Importance

Generalized
Ranking by Area
2 based on random
none
0.77
0.74

Linear
Under the ROC curve
selection within the

Model
(AUC)
top 100

(GLM)

N.A.
Ranking by Area
1 based on random
none
0.69
0.71

Under the ROC curve
selection within the

(AUC)
top 100

TABLE 17

SEQ ID

NO.:
Probe set ID
Gene
Classifier(s)
Chromosome
Start
End
Strand

353
3337703
PPP6R3
NB20
chr11
68355451
68355475
1

354
3326487
EHF
KNN12, NB20
chr11
34673110
34673157
1

355
3160006
SMARCA2
KNN12, NB20
chr9
2073575
2073599
1

356
3576730
TC2N
NB20
chr14
92278706
92278866
−1

357
2365991
MPZL1
NB20
chr1
167757129
167757158
1

358
3536951
KTN1
NB20
chr14
56108443
56108473
1

359
3147328
UBR5
NB20
chr8
103269860
103269932
−1

360
2852379
ZFR
NB20
chr5
32417753
32417779
−1

361
3331573
CTNND1
KNN12, NB20
chr11
57577586
57577659
1

366
2758874
CYTL1
KNN12
chr4
5016922
5016946
−1

369
2458376
ENAH
KNN12
chr1
225692693
225692726
−1

383
3851902
CALR
NB20
chr19
13050901
13050963
−1

384
2651515
MECOM
NB20
chr3
169003654
169003734
1

385
3111306
RSPO2
NB20
chr8
109084359
109084383
1

387
3560055
AKAP6
KNN12
chr14
32985209
32985233
−1

390
2886458
chr5-:
KNN12, NB20
chr5
168794202
168794226
−1

168794202-168794226

391
2537212
chr2-: 343842-343866
NB20
chr2
343842
343866
−1

397
3002694
EGFR
NB20
chr7
55163823
55163847
1

398
3384586
DLG2
KNN12, NB20
chr11
83467292
83467316
−1

399
3986003
IL1RAPL2
KNN12, NB20
chrX
104682956
104682980
1

410
3666869
NFAT5
NB20
chr16
69738402
69738519
1

411
2318755
PARK7
NB20
chr1
8045210
8045305
1

421
2874688
HINT1
KNN12
chr5
130495094
130495120
−1

422
3628924
FAM96A
KNN12
chr15
64364822
64365114
−1

434
3260965
LZTS2
KNN12
chr10
102762254
102762278
1

436
3454547
METTL7A
NB20
chr12
51324677
51324701
−1

458
2704702
MECOM
SINGLE_PSR,
chr3
169245434
169245479
−1

GLM2

459
3286471
HNRNPA3P1
GLM2
chr10
44285533
44285567
−1

TABLE 18

Machine

Learning
Feature Selection
# Features
Standardization
AUC
AUC

Algorithm
Method
Selected
Method
Training
Testing

Support Vector
Ranking by Area
Top 20
None
0.95
0.75

Machine (SVM)
Under the ROC

curve (AUC)

Support Vector
Ranking by Area
Top 11
None
0.96
0.8

Machine (SVM)
Under the ROC

curve (AUC)

Support Vector
Ranking by Area
Top 5
None
0.98
0.78

Machine (SVM)
Under the ROC

curve (AUC)

Generalized
Ranking by Area
2 based on
None
0.86
0.79

Linear Model
Under the ROC
random

(GLM)
curve (AUC)
selection

within the top

100

TABLE 19

SEQ ID
Probe set

NO.:
ID
Gene
Classifier(s)
Chromosome
Start
End
Strand

460
3648760
SHISA9
SVM11,
chr16
12996183
12996441
1

SVM20

461
2461946
GNG4
SVM11,
chr1
235715432
235715511
−1

SVM5,

SVM20

462
2790629
FGA
SVM11,
chr4
155505296
155505833
−1

SVM5,

SVM20

463
3074872
PTN
SVM11,
chr7
136935982
136936125
−1

SVM5,

SVM20

464
3558478
STXBP6
SVM11,
chr14
25443877
25444024
−1

SVM5,

SVM20

465
2420621
LPAR3
SVM20
chr1
85279570
85279820
−1

466
2914697
SH3BGRL2
SVM20
chr6
80341180
80341219
1

467
3501746
ARHGEF7
SVM20
chr13
111955366
111955393
1

468
3648824
SHISA9
SVM20
chr16
13297252
13297396
1

469
3750877
KIAA0100
SVM20
chr17
26942687
26942797
−1

470
3276127
chr10-:
GLM2
chr10
7129102
7129152
−1

7129102-7129152

471
3648839
chr16+:
SVM11,
chr16
13333744
13333834
1

13333744-13333834
SVM20

472
3558521
STXBP6
SVM11,
chr14
25349924
25350138
−1

GLM2,

SVM20

473
3558522
STXBP6
SVM11,
chr14
25350244
25350281
−1

SVM5,

SVM20

474
2461975
GNG4
SVM20
chr1
235807028
235807056
−1

475
3648778
SHISA9
SVM20
chr16
13053399
13053481
1

476
3648792
SHISA9
SVM20
chr16
13156216
13156254
1

477
3091419
EPHX2
SVM11,
chr8
27369439
27369789
1

SVM20

478
2461940
GNG4
SVM11,
chr1
235711039
235711691
−1

SVM20

479
2461962
GNG4
SVM11,
chr1
235758756
235758792
−1

SVM20

480
3558502
STXBP6
SVM20
chr14
25518570
25518806
−1

TABLE 20

# ICE Blocks per
Comparison

comparison, per
Normal vs.
Primary vs.
Normal vs.
GS6 vs
BCR vs

correlation threshold
Primary
Metastasis
Metastasis
GS7+
non-BCR

Correlation
0.9
7675 (3580)
8853 (3503)
12978 (5785)
7864 (545)
7873 (506)

Threshold

0.8
17288 (7019)
17773 (5622)
24433 (8445)
17415 (875)
17378 (1090)

0.7
27434 (8625)
29120 (6729)
44999 (10642)
28103 (1225)
28068 (1423)

0.6
46626 (11180)
50840 (8152)
71519 (14561)
49170 (1612)
48994 (2177)

TABLE 21A

# ICE Blocks per

comparison, per
Normal versus Primary

correlation
CDS
Intronic
Intergenic
Antisense

All Other

threshold
Only
Only
Only
Only
Multigene
Combinations

Correlation
0.9
2310
245
34
26
33
932

Threshold
0.8
3196
586
118
96
189
2834

0.7
2677
799
249
242
430
4228

0.6
2248
1026
649
532
992
5733

TABLE 21B

# ICE Blocks per

comparison, per
Primary versus Metastasis

correlation
CDS
Intronic
Intergenic
Antisense

All Other

threshold
Only
Only
Only
Only
Multigene
Combinations

Correlation
0.9
2058
253
32
28
43
1089

Threshold
0.8
2055
567
76
82
163
2679

0.7
1728
677
144
185
408
3587

0.6
1489
718
324
378
808
4435

TABLE 21C

# ICE Blocks per

comparison, per
Primary versus Metastasis

correlation
CDS
Intronic
Intergenic
Antisense

All Other

threshold
Only
Only
Only
Only
Multigene
Combinations

Correlation
0.9
2058
253
32
28
43
1089

Threshold
0.8
2055
567
76
82
163
2679

0.7
1728
677
144
185
408
3587

0.6
1489
718
324
378
808
4435

TABLE 21D

# ICE Blocks per

comparison, per
Normal versus Metastasis

correlation
CDS
Intronic
Intergenic
Antisense

All Other

threshold
Only
Only
Only
Only
Multigene
Combinations

Correlation
0.9
3064
386
61
46
82
2146

Threshold
0.8
2561
771
181
186
388
4358

0.7
2103
1018
486
495
956
5584

0.6
1685
1464
1125
1204
1987
7096

TABLE 21E

# ICE Blocks per

comparison, per
GS6 versus GS7+

correlation
CDS
Intronic
Intergenic
Antisense

All Other

threshold
Only
Only
Only
Only
Multigene
Combinations

Correlation
0.9
285
45
10
3
14
188

Threshold
0.8
287
77
28
16
55
412

0.7
298
126
39
41
105
616

0.6
267
147
77
89
174
858

TABLE 21F

# ICE Blocks per

comparison, per
BCR versus Non-BCR

correlation
CDS
Intronic
Intergenic
Antisense

All Other

threshold
Only
Only
Only
Only
Multigene
Combinations

Correlation
0.9
213
112
11
5
11
154

Threshold
0.8
305
277
18
16
47
427

0.7
241
320
55
54
129
624

0.6
225
367
199
151
273
962

TABLE 22

ICE

Category

Block
Wilcoxon
Chromosomal
# of
Overlapping
(Composition

ID
P-value
Coordinates
Genes
Genes
%)
PSRs
Probe Set ID(s)

Block_2190
0.000002
chr14: 25325143 . . . 25326345; −
1
STXBP6;
CODING
2
3558448; 3558449

(100%);

Block_4398
0.000005
chr20: 52612441 . . . 52674693; −
1
BCAS1;
CODING
3
3910385; 3910393;

(100%);

3910394

Block_5988
0.000015
chr5: 120022459 . . . 120022612; +
1
PRR16;
UTR (100%);
2
2825939; 2825940

Block_6655
0.000033
chr7: 136935982 . . . 136938338; −
1
PTN;
CODING
2
3074872; 3074873

(100%);

Block_5987
0.000044
chr5: 120021701 . . . 120022162; +
1
PRR16;
CODING
2
2825937; 2825938

(100%);

Block_331
0.000049
chr1: 169483568 . . . 169551730; −
1
F5;
CODING
25
2443374; 2443375;

(100%);

2443378; 2443381;

2443382; 2443383;

2443384; 2443385;

2443388; 2443389;

2443391; 2443392;

2443393; 2443395;

2443396; 2443397;

2443398; 2443399;

2443400; 2443403;

2443404; 2443405;

2443406; 2443407;

2443412

Block_7716
0.000074
chrX: 16142105 . . . 16175029; +
2
GRPR;
CODING
4
3970026; 3970034;

RP11-
(50%);

3970036; 3970039

431J24.2;
INTRONIC_AS

(50%);

Block_6372
0.000087
chr6: 38800098 . . . 38831738; +
1
DNAH8;
CODING
13
2905993; 2905995;

(100%);

2905996; 2905997;

2905999; 2906000;

2906001; 2906002;

2906003; 2906004;

2906005; 2906010;

2906012

Block_4271
0.000112
chr2: 219676945 . . . 219679977; +
1
CYP27A1;
CODING
7
2528108; 2528110;

(85.71%); UTR

2528111; 2528112;

(14.28%);

2528113; 2528115;

2528118

Block_4397
0.000132
chr20: 52574002 . . . 52601991; −
1
BCAS1;
CODING
3
3910367; 3910373;

(100%);

3910378

Block_5000
0.000132
chr3: 3886073 . . . 3890904; +
2
LRRN1;
INTRONIC_AS
5
2608321; 2608324;

SUMF1;
(40%);

2608326; 2608331;

CODING

2608332

(20%); UTR

(40%);

Block_1039
0.00014
chr10: 43609044 . . . 43610087; +
1
RET;
CODING
2
3243869; 3243870

(100%);

Block_3838
0.000197
chr2: 100484261 . . . 100509150; −
1
AFF3;
INTRONIC
2
2567082; 2567086

(100%);

Block_7796
0.000205
chrX: 105153170 . . . 105156727; +
1
NRK;
CODING
2
3986120; 3986121

(100%);

Block_5986
0.000209
chr5: 119801697 . . . 119998479; +
1
PRR16;
UTR (16.66%);
6
2825917; 2825921;

INTRONIC

2825922; 2825923;

(83.33%);

2825928; 2825932

Block_1733
0.000213
chr12: 103234188 . . . 103249107; −
1
PAH;
CODING
3
3468486; 3468494;

(100%);

3468504

Block_3839
0.000218
chr2: 100667261 . . . 100690911; −
1
AFF3;
INTRONIC
2
2567016; 2567024

(100%);

Block_6879
0.000218
chr8: 22570904 . . . 22582442; −
1
PEBP4;
CODING
2
3127612; 3127614

(100%);

Block_413
0.00025
chr1: 235712540 . . . 235715511; −
1
GNG4;
CODING
4
2461942; 2461944;

(25%); UTR

2461945; 2461946

(75%);

Block_4396
0.00027
chr20: 52571654 . . . 52574704; −
1
BCAS1;
INTRONIC
2
3910366; 3910368

(100%);

Block_7431
0.000292
chr9: 96069125 . . . 96069401; +
1
WNK2;
ncTRANSCRIPT
2
3179784; 3179785

(100%);

Block_1146
0.000309
chr10: 123779283 . . . 123781483; +
1
TACC2;
ncTRANSCRIPT
2
3268069; 3268071

(50%);

UTR (50%);

Block_7640
0.000315
chrX: 106959080 . . . 106959334; −
1
TSC22D3;
CODING
2
4017408; 4017410

(50%); UTR

(50%);

Block_6371
0.000328
chr6: 38783258 . . . 38783411; +
1
DNAH8;
CODING
2
2905985; 2905986

(100%);

Block_1735
0.000361
chr12: 103306570 . . . 103306674; −
1
PAH;
CODING
2
3468531; 4053738

(100%);

Block_4308
0.000428
chr2: 242135147 . . . 242164581; +
1
ANO7;
CODING
24
2536222; 2536226;

(91.66%); UTR

2536228; 2536229;

(8.33%);

2536231; 2536232;

2536233; 2536234;

2536235; 2536236;

2536237; 2536238;

2536240; 2536241;

2536243; 2536245;

2536248; 2536249;

2536252; 2536253;

2536256; 2536260;

2536261; 2536262

Block_3836
0.000436
chr2: 100377851 . . . 100400837; −
1
AFF3;
INTRONIC
2
2566945; 2566952

(100%);

Block_6570
0.000497
chr7: 37946647 . . . 37956059; −
1
SFRP4;
CODING
9
3046448; 3046449;

(66.66%); UTR

3046450; 3046457;

(33.33%);

3046459; 3046460;

3046461; 3046462;

3046465

Block_1532
0.000507
chr11: 114311909 . . . 114320545; +
1
REXO2;
CODING
6
3349958; 3349959;

(33.33%);

3349966; 3349970;

INTRONIC

3349975; 3349979

(66.66%);

Block_2087
0.000507
chr13: 24464154 . . . 24465613; +
1
RP11-
ncTRANSCRIPT
2
3481518; 3481519

45B20.3;
(100%);

Block_2922
0.000536
chr16: 81047741 . . . 81065037; +
1
CENPN;
CODING
10
3670638; 3670639;

(80%); UTR

3670641; 3670644;

(10%);

3670645; 3670650;

INTRONIC

3670659; 3670660;

(10%);

3670661; 3670666

Block_3281
0.000588
chr17: 65027167 . . . 65028692; +
2
CACNG4;
CODING
2
3732138; 3732139

AC005544.1;
(50%); UTR

(50%);

Block_5080
0.000657
chr3: 53528861 . . . 53847736; +
1
CACNA1D;
ncTRANSCRIPT
91
2624389; 2624393;

(1.09%);

2624394; 2624395;

CODING

2624397; 2624398;

(49.45%); UTR

2624399; 2624400;

(2.19%);

2624401; 2624402;

INTRONIC

2624403; 2624404;

(47.25%);

2624405; 2624406;

2624407; 2624408;

2624529; 2624531;

2624533; 2624537;

2624411; 2624412;

2624413; 2624415;

2624416; 2624417;

2624421; 2624422;

2624424; 2624426;

2624427; 2624428;

2624429; 2624430;

2624432; 2624434;

2624435; 2624438;

2624439; 2624440;

2624441; 2624442;

2624443; 2624444;

2624446; 2624453;

2624458; 2624459;

2624460; 2624461;

2624462; 2624465;

2624466; 2624467;

2624470; 2624472;

2624473; 2624475;

2624477; 2624479;

2624480; 2624481;

2624482; 2624484;

2624485; 2624487;

2624488; 2624490;

2624491; 2624492;

2624493; 2624494;

2624495; 2624496;

2624499; 2624500;

2624501; 2624502;

2624503; 2624504;

2624505; 2624507;

2624508; 2624511;

2624512; 2624515;

2624516; 2624518;

2624519; 2624526;

2624527

Block_6033
0.000669
chr5: 149357733 . . . 149361471; +
1
SLC26A2;
CODING
2
2835310; 2835314

(50%); UTR

(50%);

Block_1566
0.000733
chr11: 129722378 . . . 129729817; +
1
TMEM45B;
CODING
7
3356054; 3356055;

(85.71%); UTR

3356056; 3356058;

(14.28%);

3356061; 3356063;

3356066

Block_1222
0.000746
chr11: 30601825 . . . 30602041; −
1
MPPED2;
CODING
2
3367741; 3367743

(50%); UTR

(50%);

Block_2090
0.00076
chr13: 26145795 . . . 26156094; +
1
ATP8A2;
CODING
3
3482326; 3482335;

(100%);

3482336

Block_4334
0.000774
chr20: 10619700 . . . 10620579; −
1
JAG1;
CODING
3
3897508; 3897509;

(33.33%); UTR

3897512

(66.66%);

Block_2162
0.000788
chr13: 111932910 . . . 111938586; +
1
ARHGEF7;
CODING
2
3501728; 3501736

(100%);

Block_2628
0.000788
chr15: 74005696 . . . 74005846; +
1
CD276;
UTR (100%);
2
3601259; 3601260

Block_5303
0.000803
chr4: 80898781 . . . 80905088; −
1
ANTXR2;
CODING
3
2775016; 2775017;

(100%);

2775018;

Block_213
0.000832
chr1: 85277703 . . . 85279820; −
1
LPAR3;
CODING
3
2420617; 2420619;

(33.33%); UTR

2420621

(66.66%);

Block_773
0.000863
chr1: 220870275 . . . 220872267; +
1
C1orf115;
UTR (100%);
2
2381258; 2381260

Block_3219
0.000927
chr17: 40932892 . . . 40945698; +
1
WNK4;
CODING
8
3722087; 3722090;

(100%);

3722094; 3722095;

3722100; 3722101;

3722105; 3722106

Block_7722
0.001069
chrX: 18643259 . . . 18646559; +
1
CDKL5;
CODING
2
3970693; 3970698

(100%);

Block_5415
0.001107
chr4: 170016681 . . . 170017797; −
1
SH3RF1;
CODING
3
2793150; 2793151;

(66.66%); UTR

2793152

(33.33%);

Block_6420
0.001127
chr6: 80383340 . . . 80406282; +
1
SH3BGRL2;
CODING
2
2914706; 2914708

(100%);

Block_6142
0.001147
chr6: 38890758 . . . 38901026; −
1
RP1-
ncTRANSCRIPT
7
2952718; 2952719;

207H1.3;
(85.71%);

2952720; 2952721;

INTRONIC

2952723; 2952724;

(14.28%);

2952725

Block_3837
0.001188
chr2: 100426047 . . . 100692345; −
1
AFF3;
CODING
61
2566957; 2566960;

(6.55%);

2566961; 2566965;

ncTRANSCRIPT

2566966; 2566971;

(3.27%);

2567075; 2567076;

INTRONIC

2567084; 2567063;

(90.16%);

2566976; 2567087;

2567088; 2566977;

2567064; 2567097;

2567067; 2567069;

2567101; 2567103;

2567071; 2566979;

2566982; 2566983;

2566984; 2566985;

2567105; 2567111;

2567113; 2567115;

2567106; 2566987;

2566988; 2566991;

2566993; 2566994;

2566996; 2566997;

2567121; 2566998;

2567125; 2567000;

2567001; 2567002;

2567003; 2567005;

2567007; 2567008;

2567010; 2567011;

2567012; 2567013;

2567014; 2567015;

2567017; 2567018;

2567019; 2567020;

2567022; 2567023;

2567127

Block_1378
0.001391
chr 11: 134022950 . . . 134052868; −
1
NCAPD3;
ncTRANSCRIPT
11
3399552; 3399554;

(45.45%);

3399556; 3399558;

INTRONIC

3399559; 3399560;

(54.54%);

3399561; 3399568;

3399575; 3399578;

3399582

Block_3834
0.001415
chr2: 100199328 . . . 100318709; −
1
AFF3;
CODING
22
2566873; 2566875;

(22.72%); UTR

2566880; 2566885;

(4.54%);

2566886; 2566888;

INTRONIC

2566893; 2566898;

(72.72%);

2566900; 2566902;

2566905; 2566906;

2566908; 2566910;

2566911; 2566912;

2566915; 2566919;

2566920; 2566922;

2566924; 2566929

Block_4395
0.001569
chr20: 52560335 . . . 52561534; −
1
BCAS1;
CODING
2
3910362; 3910363

(50%); UTR

(50%);

Block_6520
0.001624
chr6: 160770298 . . . 160864773; +
2
AL591069.1;
ncTRANSCRIPT
29
2934526; 2934527;

SLC22A3;
(3.44%);

2934531; 2934533;

CODING

2934535; 2934580;

(27.58%);

2934582; 2934585;

INTRONIC

2934586; 2934536;

(68.96%);

2934537; 2934538;

2934539; 2934541;

2934543; 2934545;

2934547; 2934548;

2934549; 2934550;

2934551; 2934554;

2934556; 2934557;

2934558; 2934559;

2934560; 2934561;

2934562

Block_3917
0.001652
chr2: 178762785 . . . 178769891; −
1
PDE11A;
CODING
2
2589116; 2589118

(100%);

Block_3752
0.001681
chr2: 42662806 . . . 42670619; −
1
KCNG3;
INTERGENIC
2
2550177; 2550178

(50%); UTR

(50%);

Block_7162
0.001739
chr9: 3262938 . . . 3271101; −
1
RFX3;
CODING
2
3196865; 3196873

(100%);

Block_5975
0.001769
chr5: 113698875 . . . 113699698; +
1
KCNN2;
CODING
2
2824632; 2824635

(100%);

Block_6604
0.001769
chr7: 87907478 . . . 87920296; −
1
STEAP4;
CODING
12
3060339; 3060340;

(75%); UTR

3060341; 3060342;

(25%);

3060343; 3060344;

3060347; 3060348;

3060350; 3060351;

3060352; 3060353

Block_4200
0.0018
chr2: 181852076 . . . 181894023; +
1
UBE2E3;
CODING
5
2518175; 2518178;

(20%);

2518179; 2518180;

INTRONIC

2518184

(80%);

Block_4201
0.0018
chr2: 181920432 . . . 181924616; +
1
UBE2E3;
INTRONIC
3
2518192; 2518193;

(100%);

2518197

Block_3913
0.001926
chr2: 178528594 . . . 178540212; −
1
PDE11A;
CODING
2
2589038; 2589043

(100%);

Block_5936
0.001926
chr5: 79361251 . . . 79378964; +
1
THBS4;
CODING
10
2817602; 2817603;

(100%);

2817605; 2817606;

2817609; 2817611;

2817614; 2817615;

2817620; 2817621

Block_3916
0.001959
chr2: 178681582 . . . 178705094; −
1
PDE11A;
CODING
3
2589101; 2589102;

(100%);

2589105

Block_4125
0.001959
chr2: 101541626 . . . 101564800; +
1
NPAS2;
CODING
4
2496436; 2496440;

(100%);

2496446; 2496448

Block_2925
0.001992
chr16: 84479997 . . . 84485677; +
1
ATP2C2;
CODING
2
3671768; 3671774

(100%);

Block_874
0.002026
chr10: 33545282 . . . 33559775; −
1
NRP1;
CODING
3
3284370; 3284373;

(100%);

3284377

Block_4971
0.002061
chr3: 184910469 . . . 184922544; −
1
EHHADH;
CODING
3
2708726; 2708727;

(100%);

2708733

Block_2216
0.002131
chr14: 51379747 . . . 51387339; −
1
PYGL;
CODING
2
3564224; 3564231

(100%);

Block_6886
0.002131
chr8: 27317314 . . . 27336535; −
1
CHRNA2;
CODING
10
3129025; 3129030;

(60%); UTR

3129034; 3129038;

(40%);

3129039; 3129040;

3129044; 3129045;

3129046; 3129047

Block_1533
0.002167
chr11: 114311389 . . . 114314645; +
1
REXO2;
CODING
2
3349956; 3349963

(100%);

Block 4336
0.002167
chr20: 10632779 . . . 10644662; −
1
JAG1;
CODING
4
3897552; 3897558;

(100%);

3897559; 3897568

Block_4349
0.002167
chr20: 20596706 . . . 20621488; −
1
RALGAPA2;
CODING
5
3900218; 3900220;

(100%);

3900228; 3900233;

3900235

Block_1576
0.002204
chr11: 134147231 . . . 134188819; +
1
GLB1L3;
CODING
13
3357348; 3357349;

(100%);

3357360; 3357363;

3357369; 3357370;

3357371; 3357375;

3357382; 3357383;

3357384; 3357386;

3357387

Block_3611
0.002204
chr19: 32080316 . . . 32084433; +
0

INTERGENIC
2
3828710; 3828717

(100%);

Block_1649
0.002241
chr12: 44913789 . . . 44915959; −
1
NELL2;
CODING
2
3451835; 3451838

(100%);

Block_5976
0.002317
chr5: 113740155 . . . 113740553; +
1
KCNN2;
CODING
2
2824643; 2824644

(100%);

Block_1964
0.002476
chr12: 121134218 . . . 121137627; +
1
MLEC;
CODING
2
3434542; 3434546

(50%); UTR

(50%);

Block_2762
0.002476
chr16: 56701878 . . . 56701935; −
1
MT1G;
CODING
2
3693007; 3693008

(50%); UTR

(50%);

Block_4864
0.002476
chr3: 116058173 . . . 116094106; −
1
LSAMP;
INTRONIC
3
2690112; 2690113;

(100%);

2690118

Block_829
0.002559
chr1: 247712494 . . . 247739511; +
1
C1orf150;
CODING
3
2390125; 2390128;

(66.66%); UTR

2390134

(33.33%);

Block_2311
0.002602
chr14: 38054451 . . . 38055847; +
0

INTERGENIC
4
3533031; 3533035;

(100%);

3533037; 3533039

Block_2822
0.002602
chr16: 8875186 . . . 8878061; +
1
ABAT;
CODING
5
3647480; 3647481;

(20%); UTR

3647483; 3647484;

(80%);

3647485

Block_5310
0.002602
chr4: 82026968 . . . 82031699; −
1
PRKG2;
CODING
2
2775219; 2775221

(100%);

Block_7638
0.002602
chrX: 106957270 . . . 106960029; −
1
TSC22D3;
CODING
6
4017398; 4017399;

(50%); UTR

4017400; 4017403;

(50%);

4017409; 4017414

Block_1652
0.002645
chr12: 45168545 . . . 45173801; −
1
NELL2;
CODING
4
3451885; 3451888;

(100%);

3451889; 3451891

Block_1917
0.002645
chr12: 81528607 . . . 81545849; +
1
ACSS3;
CODING
4
3424233; 3424234;

(100%);

3424243; 3424244

Block_1933
0.002779
chr12: 102113921 . . . 102117625; +
1
CHPT1;
CODING
2
3428698; 3428702

(100%);

Block_3096
0.002872
chr17: 74622431 . . . 74625201; −
1
ST6GALNAC1;
CODING
6
3771721; 3771722;

(100%);

3771723; 3771725;

3771726; 3771727

Block_3273
0.002872
chr17: 59093209 . . . 59112144; +
1
BCAS3;
CODING
2
3729624; 3729628

(100%);

Block_3832
0.002872
chr2: 100165334 . . . 100170892; −
1
AFF3;
CODING
4
2566847; 2566849;

(50%); UTR

2566850; 2566851

(50%);

Block_6032
0.002872
chr5: 149357507 . . . 149366444; +
1
SLC26A2;
CODING
7
2835309; 2835311;

(57.14%); UTR

2835312; 2835313;

(42.85%);

2835315; 2835316;

2835317

Block_214
0.003016
chr1: 85331090 . . . 85331666; −
1
LPAR3;
CODING
2
2420633; 2420635

(100%);

Block_4670
0.003016
chr22: 32480910 . . . 32482314; +
1
SLC5A1;
CODING
2
3943253; 3943255

(100%);

Block_5621
0.003016
chr4: 159812601 . . . 159828286; +
1
FNIP2;
CODING
6
2749669; 2749671;

(50%); UTR

2749675; 2749676;

(50%);

2749677; 2749678

Block_7835
0.003016
chrX: 152770164 . . . 152773851; +
1
BGN;
CODING
6
3995642; 3995651;

(100%);

3995654; 3995657;

3995659; 3995661

Block_4022
0.003115
chr2: 1718308 . . . 11721346; +
1
GREB1;
UTR (50%);
2
2469846; 2469850

INTRONIC

(50%);

Block_6521
0.003218
chr6: 160866011 . . . 160868068; +
1
SLC22A3;
INTRONIC
3
2934564; 2934565;

(100%);

2934567

Block_4344
0.003271
chr20: 20475772 . . . 20507004; −
1
RALGAPA2;
CODING
7
3900137; 3900143;

(100%);

3900149; 3900150;

3900152; 3900154;

3900156

Block_3505
0.003324
chr19: 15297695 . . . 15302661; −
1
NOTCH3;
CODING
5
3853157; 3853158;

(100%);

3853159; 3853161;

3853166

Block_4335
0.003324
chr20: 10621471 . . . 10630262; −
1
JAG1;
CODING
16
3897514; 3897515;

(100%);

3897516; 3897517;

3897518; 3897519;

3897520; 3897527;

3897529; 3897531;

3897533; 3897535;

3897536; 3897537;

3897539; 3897540

Block_3168
0.003433
chr17: 7945688 . . . 7951882; +
1
ALOX15B;
CODING
11
3709424; 3709426;

(100%);

3709428; 3709429;

3709430; 3709432;

3709433; 3709435;

3709437; 3709438;

3709440

Block_456
0.003433
chr1: 19981582 . . . 19984800; +
1
NBL1;
CODING
3
2323777; 2323778;

(66.66%); UTR

2323782

(33.33%);

Block_1377
0.003489
chr11: 134022430 . . . 134095174; −
1
NCAPD3;
CODING
42
3399550; 3399551;

(90.47%); UTR

3399553; 3399555;

(7.14%);

3399562; 3399563;

INTRONIC

3399565; 3399566;

(2.38%);

3399567; 3399569;

3399570; 3399571;

3399572; 3399573;

3399574; 3399576;

3399577; 3399579;

3399580; 3399581;

3399583; 3399584;

3399585; 3399587;

3399588; 3399589;

3399590; 3399591;

3399592; 3399593;

3399594; 3399595;

3399597; 3399598;

3399600; 3399601;

3399602; 3399603;

3399605; 3399606;

3399607; 3399613

Block_1505
0.003545
chr11: 92085296 . . . 92088273; +
1
FAT3;
CODING
3
3344438; 3344439;

(100%);

3344440

Block_4671
0.003545
chr22: 32498039 . . . 32507284; +
1
SLC5A1;
CODING
5
3943258; 3943259;

(60%); UTR

3943261; 3943263;

(40%);

3943265

Block_743
0.003603
chr1: 203275102 . . . 203275613; +
1
BTG2;
INTRONIC
3
2375667; 2375668;

(100%);

2375670

Block_4306
0.003661
chr2: 241404507 . . . 241405065; +
1
GPC1;
CODING
2
2535800; 2535802

(100%);

Block_6592
0.003661
chr7: 80546027 . . . 80548317; −
1
SEMA3C;
CODING
2
3058814; 3058816

(50%); UTR

(50%);

Block_4345
0.003841
chr20: 20486102 . . . 20517400; −
1
RALGAPA2;
CODING
5
3900146; 3900151;

(100%);

3900155; 3900164;

3900167

Block_1651
0.003902
chr12: 45059307 . . . 45097550; −
1
NELL2;
CODING
2
3451868; 3451874

(100%);

Block_7859
0.003902
chrY: 14799855 . . . 14802344; +
1
TTTY15;
ncTRANSCRIPT
2
4030072; 4030074

(100%);

Block_2091
0.003965
chr13: 26411312 . . . 26434996; +
1
ATP8A2;
CODING
2
3482379; 3482386

(100%);

Block_4935
0.003965
chr3: 142567065 . . . 142567284; −
1
PCOLCE2;
CODING
2
2699027; 2699028

(100%);

Block_1366
0.004093
chr11: 124617431 . . . 124619754; −
1
VSIG2;
CODING
2
3396086; 3396095

(100%);

Block_1999
0.004093
chr13: 38158866 . . . 38162106; −
1
POSTN;
CODING
2
3510099; 3510102

(100%);

Block_2897
0.004158
chr16: 67202953 . . . 67203210; +
1
HSF4;
CODING
2
3665255; 3665257;

(100%);

Block_3442
0.004292
chr18: 56585564 . . . 56587447; +
1
ZNF532;
CODING
3
3790379; 3790380;

(100%);

3790381

Block_5409
0.004429
chr4: 159046177 . . . 159048546; −
1
FAM198B;
UTR (100%);
4
2791422; 2791423;

2791424; 2791425

Block_6505
0.004429
chr7: 87910829 . . . 87912896; −
1
STEAP4;
UTR (50%);
2
3060345; 3060349

INTRONIC

(50%);

Block_7860
0.004429
chrY: 14838600 . . . 14968421; +
1
USP9Y;
CODING
18
4030087; 4030096;

(100%);

4030104; 4030112;

4030113; 4030115;

4030116; 4030119;

4030120; 4030125;

4030126; 4030127;

4030128; 4030134;

4030144; 4030146;

4030149; 4030153

Block_873
0.004429
chr10: 33491851 . . . 33515213; −
1
NRP1;
CODING
4
3284334; 3284341;

(100%);

3284346; 3284351

Block_1221
0.004499
chr11: 30443973 . . . 30517053; −
1
MPPED2;
CODING
12
3367684; 3367688;

(16.66%);

3367691; 3367693;

ncTRANSCRIPT

3367696; 3367697;

(8.33%);

3367702; 3367706;

INTRONIC

3367707; 3367710;

(75%)

3367712; 3367714

Block_3512
0.004499
chr19: 18893864 . . . 18897074; −
1
COMP;
CODING
2
3855221; 3855230

(100%);

Block_3914
0.00457
chr2: 178565861 . . . 178592888; −
1
PDE11A;
CODING
4
2589055; 2589058;

(100%);

2589064; 2589065

Block_5309
0.00457
chr4: 80992745 . . . 80993659; −
1
ANTXR2;
CODING
2
2775042; 2775043

(100%);

Block_3446
0.004716
chr18: 56819806 . . . 56824879; +
1
SEC11C;
CODING
2
3790485; 3790494

(100%);

Block_453
0.004716
chr1: 16332765 . . . 16333026; +
1
C1orf64;
CODING
2
2322216; 2322218

(50%); UTR

(50%);

Block_169
0.00479
chr1: 53373542 . . . 53377448; −
1
ECHDC2;
CODING
2
2413055; 2413058

(100%);

Block_3443
0.00479
chr18: 56623078 . . . 56648694; +
1
ZNF532;
INTRONIC
6
3790396; 3790398;

(100%);

3790399; 3790401;

3790403; 3790404

Block_5081
0.00479
chr3: 53736689 . . . 53753808; +
1
CACNA1D;
CODING
3
2624448; 2624454;

(100%);

2624457

Block_4829
0.004865
chr3: 86988621 . . . 87039865; −
1
VGLL3;
CODING
17
2684857; 2684831;

(58.82%); UTR

2684832; 2684833;

(41.17%);

2684835; 2684859;

2684861; 2684863;

2684865; 2684867;

2684869; 2684871;

2684873; 2684877;

2684879; 2684881;

2684883

Block_886
0.004942
chr10: 61551607 . . . 61572483; −
1
CCDC6;
CODING
7
3290791; 3290792;

(71.42%); UTR

3290796; 3290799;

(28.57%);

3290802; 3290803;

3290807

Block_1330
0.005019
chr11: 106555201 . . . 106558073; −
1
GUCY1A2;
UTR (100%);
2
3389670; 3389672

Block_3835
0.005019
chr2: 100372047 . . . 100415240; −
1
AFF3;
INTRONIC
5
2566941; 2566942;

(100%);

2566948; 2566949;

2566955

Block_481
0.005019
chr1: 27676149 . . . 27677810; +
1
SYTL1;
CODING
3
2327014; 2327022;

(100%);

2327025

Block_3688
0.005098
chr19: 55315113 . . . 55315146; +
1
KIR2DL4;
CODING
2
3841790; 4052980

(100%);

Block_4342
0.005098
chr20: 20370667 . . . 20373784; −
1
RALGAPA2;
CODING
3
3900089; 3900090;

(33.33%); UTR

3900092

(66.66%);

Block_6457
0.005098
chr6: 138657744 . . . 138658255; +
1
KIAA1244;
UTR (100%);
3
2927694; 2927695;

2927696

Block_5167
0.005178
chr3: 156170688 . . . 156192603; +
1
KCNAB1;
CODING
3
2649038; 2649044;

(100%);

2649051

Block_5620
0.005178
chr4: 159772477 . . . 159790535; +
1
FNIP2;
CODING
9
2749639; 2749640;

(100%);

2749644; 2749646;

2749647; 2749648;

2749650; 2749651;

2749652

Block_1250
0.005258
chr11: 61290559 . . . 61291972; −
1
SYT7;
CODING
3
3375403; 3375404;

(100%);

3375405;

Block_2089
0.005258
chr13: 26104137 . . . 26163815; +
1
ATP8A2;
CODING
12
3482305; 3482309;

(100%);

3482310; 3482313;

3482314; 3482316;

3482319; 3482321;

3482322; 3482330;

3482333; 3482337

Block_3915
0.005258
chr2: 178621229 . . . 178630397; −
1
PDE11A;
INTRONIC
3
2589079; 2589083;

(100%);

2589089

Block_5060
0.00534
chr3: 48289117 . . . 48312089; +
1
ZNF589;
CODING
9
2621590; 2621598;

(55.55%); UTR

2621602; 2621603;

(44.44%);

2621604; 2621606;

2621607; 2621608;

2621609

Block_5619
0.00534
chr4: 159750328 . . . 159754780; +
1
FNIP2;
CODING
4
2749625; 2749626;

(100%);

2749627; 2749629

Block_2896
0.005508
chr16: 67199438 . . . 67201057; +
1
HSF4;
ncTRANSCRIPT
5
3665235; 3665240;

(20%);

3665244; 3665245;

CODING

3665246

(80%);

Block_3964
0.005508
chr2: 204309603 . . . 204313496; −
1
RAPH1;
CODING
3
2595578; 2595581;

(100%);

2595583

Block_4025
0.00568
chr2: 13872471 . . . 13926374; +
2
NCRNA00276;
INTRONIC
2
2470336; 2470352

AC016730.1;
(50%);

INTRONIC_AS

(50%);

Block_4861
0.00568
chr3: 115524258 . . . 115529246; −
1
LSAMP;
CODING
5
2690021; 2690022;

(20%);

2690023; 2690025;

INTERGENIC

2690027

(60%); UTR

(20%);

Block_1220
0.005947
chr11: 30431953 . . . 30439165; −
1
MPPED2;
CODING
4
3367675; 3367676;

(75%); UTR

3367679; 3367680

(25%);

Block_736
0.005947
chr1: 201285703 . . . 201293641; +
1
PKP1;
CODING
4
2374622; 2374628;

(100%);

2374629; 2374631

Block_7442
0.005947
chr9: 101589035 . . . 101611356; +
1
GALNT12;
CODING
5
3181611; 3181614;

(100%);

3181620; 3181622;

3181628;

Block_7533
0.005947
chrX: 1505524 . . . 1506210; −
1
SLC25A6;
CODING
3
3997378; 4033179;

(100%);

4033181

Block_1251
0.006039
chr11: 61295389 . . . 61300540; −
1
SYT7;
CODING
2
3375406; 3375409

(100%);

Block_3169
0.006039
chr17: 7960222 . . . 7966722; +
0

INTERGENIC
6
3709445; 3709446;

(100%);

3709448; 3709451;

3709453; 3709455

Block_3903
0.006039
chr2: 169094505 . . . 169097430; −
1
STK39;
INTRONIC
2
2585794; 2585796

(100%);

Block_1997
0.006132
chr13: 38154719 . . . 38164537; −
1
POSTN;
CODING
3
3510096; 3510097;

(100%);

3510103

Block_4863
0.006226
chr3: 115984267 . . . 116001005; −
1
LSAMP;
INTRONIC
3
2690278; 2690273;

(100%);

2690288

Block_7046
0.006226
chr8: 27358443 . . . 27380016; +
1
EPHX2;
CODING
6
3091408; 3091410;

(100%);

3091412; 3091414;

3091418; 3091427

Block_3912
0.006321
chr2: 178493807 . . . 178494276; −
1
PDE11A;
CODING
2
2589025; 2589028

(50%); UTR

(50%);

Block_4194
0.006321
chr2: 173885368 . . . 173891966; +
1
RAPGEF4;
CODING
2
2515897; 2515902

(100%);

Block_4979
0.006418
chr3: 189674965 . . . 189681873; −
1
LEPREL1;
CODING
4
2710476; 2710477;

(75%); UTR

2710483; 2710484

(25%);

Block_5977
0.006615
chr5: 113798749 . . . 113808838; +
1
KCNN2;
CODING
3
2824655; 2824656;

(100%);

2824657

Block_1534
0.006818
chr11: 114315278 . . . 114320629; +
1
REXO2;
CODING
3
3349968; 3349972;

(100%);

3349980

Block_4346
0.006818
chr20: 20552104 . . . 20563856; −
1
RALGAPA2;
CODING
3
3900185; 3900187;

(100%);

3900191

Block_1731
0.006922
chr12: 102173985 . . . 102190536; −
1
GNPTAB;
CODING
3
3468148; 3468152;

(100%);

3468159;

Block_4980
0.006922
chr3: 189689680 . . . 189713231; −
1
LEPREL1;
CODING
12
2710494; 2710495;

(100%);

2710496; 2710498;

2710502; 2710503;

2710504; 2710505;

2710506; 2710509;

2710510; 2710511

Block_5661
0.007241
chr5: 29476852 . . . 29477004; −
0

INTERGENIC
2
2851724; 2851725

(100%);

Block_7425
0.007241
chr9: 90301466 . . . 90312118; +
1
DAPK1;
CODING
2
3177954; 3177956

(100%);

Block_5416
0.00735
chr4: 170037444 . . . 170043285; −
1
SH3RF1;
CODING
3
2793155; 2793156;

(100%);

2793159

Block_5567
0.007461
chr4: 108866136 . . . 108873298; +
1
CYP2U1;
CODING
6
2738706; 2738707;

(66.66%); UTR

2738708; 2738712;

(33.33%);

2738714; 2738715

Block_6698
0.007461
chr7: 12620691 . . . 12691507; +
1
SCIN;
CODING
9
2990415; 2990418;

(100%);

2990420; 2990421;

2990424; 2990425;

2990427; 2990430;

2990431

Block_4865
0.007573
chr3: 116123466 . . . 116161481; −
1
LSAMP;
INTRONIC
5
2690300; 2690302;

(100%);

2690304; 2690131;

2690132

Block_6522
0.007573
chr6: 160868751 . . . 160872088; +
1
SLC22A3;
CODING
2
2934572; 2934575

(100%);

Block_2419
0.007687
chr15: 23006467 . . . 23014513; −
1
NIPA2;
CODING
2
3613310: 3613312

(100%);

Block_6164
0.007687
chr6: 55739210 . . . 55740206; −
1
BMP5;
CODING
4
2958199; 2958200;

(75%); UTR

2958201; 2958202

(25%);

Block_2128
0.007919
chr13: 76379046 . . . 76382387; +
1
LMO7;
INTRONIC
3
3494196; 3494197;

(100%);

3494206

Block_3455
0.007919
chr19: 282756 . . . 287715; −
1
PPAP2C;
CODING
2
3844475; 3844477

(100%);

Block_4347
0.007919
chr20: 20582326 . . . 20586044; −
1
RALGAPA2;
CODING
2
3900205; 3900207

(100%);

Block_5364
0.008158
chr4: 120442102 . . . 120528393; −
1
PDE5A;
CODING
11
2783626; 2783629;

(100%);

2783637; 2783638;

2783644; 2783650;

2783652; 2783654;

2783659; 2783662;

2783663

Block_7048
0.008279
chr8: 27398133 . . . 27402173; +
1
EPHX2;
CODING
2
3091435; 3091442

(50%); UTR

(50%);

Block_1772
0.008528
chr12: 118470966 . . . 118480761; −
1
WSB2;
CODING
5
3473729; 3473732;

(80%); UTR

3473735; 3473736;

(20%);

3473739

Block_3445
0.008528
chr18: 56820014 . . . 56824583; +
1
SEC11C;
UTR (20%);
5
3790486; 3790487;

INTRONIC

3790489; 3790492;

(80%);

3790493

Block_1105
0.008654
chr10: 102732697 . . . 102737466; +
1
SEMA4G;
CODING
2
3260899; 3260903

(100%);

Block_1978
0.008654
chr13: 24254773 . . . 24280276; −
0

INTERGENIC
8
3505432; 3505434;

(100%);

3505436; 3505438;

3505440; 3505442;

3505444; 3505446

Block_5305
0.008782
chr4: 80929675 . . . 80954689; −
1
ANTXR2;
CODING
3
2775023; 2775024;

(100%);

2775031

Block_1998
0.008912
chr13: 38158126 . . . 38166301; −
1
POSTN;
CODING
4
3510098; 3510100;

(100%);

3510101; 3510105

Block_4944
0.008912
chr3: 148895685 . . . 148939500; −
1
CP;
CODING
9
2700263; 2700272;

(100%);

2700276; 2700284;

2700287; 2700288;

2700289; 2700292;

2700300;

Block_3901
0.009044
chr2: 168920012 . . . 168921891; −
1
STK39;
CODING
2
2585735; 2585736;

(100%);

Block_4348
0.009044
chr20: 20591959 . . . 20601268; −
1
RALGAPA2;
CODING
3
3900211; 3900212;

(100%);

3900221

Block_6149
0.009044
chr6: 46821609 . . . 46836749; −
1
GPR116;
CODING
7
2955866; 2955877;

(85.71%); UTR

2955879; 2955881;

(14.28%);

2955884; 2955885;

2955887

Block_1274
0.009177
chr11: 65197863 . . . 65204294; −
1
NEAT1;
ncTRANSCRIPT_AS
3
3377621; 3377623;

(100%);

3377630

Block_1797
0.009177
chr12: 125398337 . . . 125399059; −
1
UBC;
UTR (100%);
5
3476772; 3476773;

3476774; 3476775;

3476776

Block_461
0.009177
chr1: 24766662 . . . 24799256; +
1
NIPAL3;
CODING
12
2325438; 2325443;

(83.33%); UTR

2325444; 2325445;

(16.66%);

2325448; 2325449;

2325452; 2325453;

2325457; 2325462;

2325463; 2325464

Block_5410
0.009177
chr4: 159052021 . . . 159091694; −
1
FAM198B;
CODING
3
2791428; 2791433;

(100%);

2791438

Block_6449
0.009177
chr6: 132190499 . . . 132196962; +
1
ENPP1;
CODING
2
2925975; 2925979

(100%);

Block_2924
0.009312
chr16: 84449114 . . . 84482221; +
1
ATP2C2;
CODING
4
3671751; 3671757;

(100%);

3671766; 3671769

Block_6700
0.009312
chr7: 16815929 . . . 16823939; +
1
TSPAN13;
CODING
5
2991161; 2991163;

(80%); UTR

2991164; 2991165;

(20%);

2991172

Block_7016
0.009312
chr8: 424465 . . . 424757; +
0

INTERGENIC
2
3082591; 3082592;

(100%);

Block_6140
0.009449
chr6: 38643851 . . . 38649827; −
1
GLO1;
CODING
3
2952681; 2952683;

(66.66%); UTR

2952684

(33.33%);

Block_2442
0.009588
chr15: 42445498 . . . 42446391; −
1
PLA2G4F;
CODING
2
3620449; 3620451

(100%);

Block_2631
0.009588
chr15: 75108788 . . . 75123934; +
2
CPLX3;
CODING
20
3601898; 3601899;

LMAN1L;
(55%); UTR

3601900; 3601902;

(25%);

3601904; 3601906;

INTRONIC

3601909; 3601911;

(20%);

3601912; 3601919;

3601921; 3601914;

3601923; 3601926;

3601933; 3601934;

3601936; 3601937;

3601939; 3601940

Block_124
0.009728
chr1: 25573295 . . . 25573974; −
1
C1orf63;
CODING
3
2402129; 2402130;

(33.33%); UTR

2402134

(66.66%);

Block_1788
0.009728
chr12: 123212329 . . . 123213804; −
1
GPR81;
UTR (100%);
2
3475776; 3475778

Block_2163
0.009871
chr13: 111940732 . . . 111953191; +
1
ARHGEF7;
CODING
2
3501737; 3501744

(100%);

Block_4943
0.009871
chr3: 148896342 . . . 148897449; −
1
CP;
CODING
2
2700265; 2700267

(100%);

Block_7740
0.009871
chrX: 43571128 . . . 43605327; +
1
MAOA;
CODING
14
4055670; 4055678;

(92.85%); UTR

4055680; 3975248;

(7.14%);

4055682; 3975250;

3975251; 4055686;

3975252; 3975253;

3975256; 3975258;

3975259; 3975260

Block_5168
0.010015
chr3: 156249230 . . . 156254535; +
1
KCNAB1;
CODING
2
2649070; 2649077

(100%);

Block_5185
0.010015
chr3: 175165052 . . . 175293963; +
1
NAALADL2;
CODING
4
2653186; 2653187;

(100%);

2653188; 2653192

Block_462
0.010161
chr1: 24840908 . . . 24867125; +
1
RCAN3;
INTERGENIC
7
2325485; 2325490;

(14.28%);

2325491; 2325494;

CODING

2325497; 2325498;

(57.14%); UTR

2325499

(28.57%);

Block_3672
0.01061
chr19: 52462246 . . . 52469039; +
1
AC011460.1;
INTRONIC
4
3839986; 3839988;

(100%);

3839990; 3839992

Block_4273
0.01061
chr2: 220283450 . . . 220283756; +
1
DES;
CODING
2
2528481; 2528482

(100%);

Block_3289
0.010764
chr17: 66038430 . . . 66039426; +
1
KPNA2;
CODING
5
3732630; 4041134;

(100%);

3732632; 3732633;

4041130

Block_5774
0.010764
chr5: 132163477 . . . 132164924; −
1
SHROOM1;
INTRONIC
2
2875520; 2875521

(100%);

Block_2489
0.01092
chr15: 59428644 . . . 59450551; −
1
MYO1E;
CODING
2
3626828; 3626837

(50%); UTR

(50%);

Block_6910
0.01092
chr8: 42033008 . . . 42050729; −
1
PLAT;
CODING
13
3133235; 3133236;

(84.61%); UTR

3133241; 3133242;

(15.38%);

3133244; 3133248;

3133252; 3133254;

3133257; 3133259;

3133260; 3133263;

3133264

Block_5841
0.011077
chr5: 176981427 . . . 176981459; −
1
FAM193B;
CODING
2
2888991; 2889081

(100%);

Block_6141
0.011077
chr6: 38644052 . . . 38650635; −
1
GLO1;
CODING
3
2952682; 2952685;

(66.66%); UTR

2952686

(33.33%);

Block_326
0.011237
chr1: 163112906 . . . 163122506; −
1
RGS5;
CODING
7
2441391; 2441393;

(42.85%); UTR

2441394; 2441395;

(57.14%);

2441396; 2441398;

2441399

Block_1996
0.011399
chr13: 38137470 . . . 38138697; −
1
POSTN;
CODING
2
3510070; 3510072

(100%);

Block_2083
0.011399
chr13: 24157611 . . . 24190183; +
1
TNFRSF19;
CODING
5
3481424; 3481425;

(60%);

3481429; 3481433;

ncTRANSCRIPT

3481434

(20%);

INTRONIC

(20%);

Block_2847
0.011399
chr16: 28506463 . . . 28506488; +
1
APOBR;
CODING
2
3686631; 3686648

(100%);

Block_5302
0.011399
chr4: 80887049 . . . 80896290; −
1
ANTXR2;
INTRONIC
2
2775010; 2775011

(100%);

Block_607
0.011563
chr1: 110211967 . . . 110214138; +
1
GSTM2;
CODING
4
2350963; 2350964;

(100%);

2350971; 2350973

Block_6224
0.011563
chr6: 110932448 . . . 110991713; −
1
CDK19;
CODING
10
2969474; 2969475;

(80%); UTR

2969476; 2969479;

(20%);

2969485; 2969488;

2969489; 2969493;

2969496; 2969499

Block_3268
0.011729
chr17: 57724893 . . . 57733355; +
1
CLTC;
CODING
2
3729179; 3729186

(100%);

Block_4504
0.011729
chr21: 29897038 . . . 29922984; −
1
AF131217.1;
INTRONIC
2
3927867; 3927875

(100%);

Block_1960
0.011897
chr12: 119631512 . . . 119632155; +
1
HSPB8;
CODING
2
3434022; 3434023

(50%); UTR

(50%);

Block_228
0.011897
chr1: 94995124 . . . 95006762; −
1
F3;
ncTRANSCRIPT
8
2423915; 2423916;

(12.5%);

2423918; 2423920;

CODING

2423923; 2423928;

(62.5%); UTR

2423929; 2423930

(12.5%);

INTRONIC

(12.5%);

Block_7829
0.011897
chrX: 135288595 . . . 135292180; +
1
FHL1;
CODING
6
3992433; 3992434;

(100%);

3992435; 3992439;

3992440; 3992448

Block_4343
0.012239
chr20: 20469819 . . . 20475748; −
1
RALGAPA2;
INTRONIC
4
3900130; 3900133;

(100%);

3900135; 3900136

Block_7167
0.012239
chr9: 5335054 . . . 5339746; −
1
RLN1;
CODING
4
3197515; 3197516;

(75%); UTR

3197518; 3197520

(25%);

Block_2732
0.012414
chr16: 28123180 . . . 28123325; −
1
XPO6;
CODING
2
3686351; 3686352

(100%);

Block_4075
0.012414
chr2: 47604162 . . . 47606139; +
1
EPCAM;
CODING
2
2480978; 2480980

(100%);

Block_7007
0.012414
chr8: 144695086 . . . 144697077; −
1
TSTA3;
CODING
6
3157663; 3157665;

(100%);

3157670; 3157671;

3157674; 3157675

Block_969
0.012414
chr10: 100219332 . . . 100249939; −
1
HPSE2;
CODING
2
3302888; 3302896

(100%);

Block_3397
0.01259
chr18: 13574674 . . . 13585570; +
1
C18orf1;
INTRONIC
2
3780272; 3780043

(100%);

Block_1932
0.012769
chr12: 102011150 . . . 102079590; +
1
MYBPC1;
CODING
36
3428611; 3428612;

(69.44%); UTR

3428613; 3428617;

(2.77%);

3428619; 3428620;

INTRONIC

3428623; 3428624;

(27.77%);

3428625; 3428626;

3428627; 3428628;

3428629; 3428630;

3428631; 3428634;

3428635; 3428636;

3428637; 3428638;

3428639; 3428640;

3428641; 3428642;

3428643; 3428644;

3428646; 3428647;

3428648; 3428650;

3428651; 3428654;

3428655; 3428659;

3428665; 3428666;

Block_6157
0.012769
chr6: 49695711 . . . 49704193; −
1
CRISP3;
CODING
8
2956567; 2956568;

(87.5%); UTR

2956569; 2956571;

(12.5%);

2956572; 2956573;

2956574; 2956575

Block_5365
0.01295
chr4: 121954556 . . . 121966964; −
1
C4orf31;
CODING
3
2783896; 2783898;

(33.33%);

2783906

INTERGENIC

(33.33%); UTR

(33.33%);

Block_1650
0.013134
chr12: 44902385 . . . 44926477; −
1
NELL2;
CODING
3
3451832; 3451841;

(66.66%); UTR

3451843

(33.33%);

Block_3093
0.013134
chr17: 74139170 . . . 74158083; −
1
RNF157;
CODING
8
3771400; 3771403;

(62.5%); UTR

3771404; 3771411;

(37.5%);

3771416; 3771419;

3771421; 3771424

Block_5093
0.013134
chr3: 68057255 . . . 68057279; +
1
FAM19A1;
INTRONIC
2
2628487; 4047275

(100%);

Block_7271
0.013134
chr9: 114190325 . . . 114199375; −
1
KIAA0368;
CODING
3
3220621; 3220627;

(100%);

3220629

Block_243
0.013319
chr1: 110282086 . . . 110282515; −
1
GSTM3;
CODING
2
2427224; 2427226

(100%);

Block_5281
0.013507
chr4: 66465162 . . . 66468022; −
1
EPHA5;
CODING
3
2771409; 2771411;

(66.66%);

2771412

INTRONIC

(33.33%);

Block_931
0.013507
chr10: 81319697 . . . 81319724; −
1
SFTPA2;
ncTRANSCRIPT
2
3297075; 3297138

(100%);

Block_4894
0.013698
chr3: 123452947 . . . 123456357; −
1
MYLK;
CODING
2
2692532; 2692536

(100%);

Block_1775
0.013891
chr12: 118636857 . . . 118639157; −
1
TAOK3;
CODING
2
3473836; 3473838

(100%);

Block_2881
0.013891
chr16: 56692595 . . . 56693058; +
1
MT1F;
CODING
2
3662206; 3662208

(100%);

Block_5117
0.013891
chr3: 121603566 . . . 121604258; +
1
EAF2;
INTRONIC
2
2638711; 2638712;

(100%);

Block_5827
0.013891
chr5: 176919406 . . . 176919436; −
1
PDLIM7;
CODING
2
2888869; 2889110

(100%);

Block_7423
0.013891
chr9: 90254565 . . . 90261474; +
1
DAPK1;
CODING
7
3177926; 3177928;

(100%);

3177929; 3177930;

3177932; 3177933;

3177934

Block_2772
0.014086
chr16: 66651699 . . . 66655784; −
1
CMTM4;
UTR (100%);
4
3695162; 3695163;

3695166; 3695167

Block_3936
0.014086
chr2: 180306906 . . . 180409688; −
1
ZNF385B;
ncTRANSCRIPT
13
2590020; 2590021;

(7.69%);

2590022; 2590027;

CODING

2590028; 2590029;

(53.84%); UTR

2590033; 2590034;

(23.07%);

2590038; 2590039;

INTRONIC

2590129; 2590044;

(15.38%);

2590045

Block_6264
0.014086
chr6: 136888801 . . . 136926464; −
1
MAP3K5;
CODING
6
2975883; 2975891;

(100%);

2975893; 2975896;

2975900; 2975901

Block_4719
0.014283
chr22: 48088744 . . . 48107002; +
1
RP11-
INTRONIC
2
3949444; 3949447

191L9.4;
(100%);

Block_6353
0.014283
chr6: 31785240 . . . 31797461; +
2
HSPA1B;
CODING
2
2902713; 2902730

HSPA1A;
(100%);

Block_6562
0.014283
chr7: 27234981 . . . 27237774; −
1
HOXA13;
UTR (100%);
2
3042998; 3043001

Block_6873
0.014283
chr8: 19315164 . . . 19315317; −
1
CSGALN
CODING
2
3126531; 3126532

ACT1;
(50%); UTR

(50%);

Block_7134
0.014283
chr8: 104709474 . . . 104778764; +
1
RIMS2;
CODING
3
3110435; 3110437;

(100%);

3110438

Block_7639
0.014283
chrX: 106957605 . . . 106957732; −
1
TSC22D3;
UTR (100%);
2
4017401; 4017402

Block_3675
0.014483
chr19: 53945049 . . . 53945553; +
1
CTD-
ncTRANSCRIPT
2
3840864; 3840869

2224J9.2;
(100%);

Block_6206
0.014483
chr6: 94066465 . . . 94068123; −
1
EPHA7;
CODING
2
2965235; 2965237

(100%);

Block_2713
0.014686
chr16: 15797034 . . . 15950855; −
1
MYH11;
CODING
43
3682029; 3682030;

(97.67%); UTR

3682034; 3682035;

(2.32%);

3682037; 3682041;

3682042; 3682043;

3682044; 3682045;

3682046; 3682047;

3682049; 3682050;

3682052; 3682054;

3682057; 3682062;

3682066; 3682067;

3682068; 3682071;

3682072; 3682076;

3682078; 3682079;

3682080; 3682082;

3682083; 3682084;

3682086; 3682091;

3682092; 3682094;

3682099; 3682103;

3682107; 3682109;

3682113; 3682118;

3682122; 3682129;

368213

Block_3833
0.014686
chr2: 100175340 . . . 100185376; −
1
AFF3;
CODING
3
2566859; 2566862;

(100%);

2566863

Block_5016
0.014686
chr3: 19295194 . . . 19322810; +
1
KCNH8;
CODING
2
2613308; 2613316

(100%);

Block_6327
0.014686
chr6: 16279026 . . . 16290811; +
1
GMPR;
CODING
3
2896566; 2896570;

(100%);

2896575

Block_2885
0.014891
chr16: 56972888 . . . 56975332; +
1
HERPUD1;
INTRONIC
2
3662400; 3662405

(100%);

Block_2888
0.014891
chr16: 57159781 . . . 57168720; +
1
CPNE2;
CODING
2
3662570; 3662575

(100%);

Block_1339
0.015098
chr11: 111779401 . . . 111782388; −
1
CRYAB;
CODING
4
3391171; 3391173;

(75%); UTR

3391176; 3391181

(25%);

Block_6265
0.015308
chr6: 136934261 . . . 136944102; −
1
MAP3K5;
CODING
2
2975904; 297590

(100%);

Block_4088
0.015521
chr2: 61333740 . . . 61335484; +
1
KIAA1841;
CODING
2
2484488; 2484489

(100%);

Block_6205
0.015736
chr6: 93953170 . . . 93982106; −
1
EPHA7;
CODING
9
2965209; 2965210;

(100%);

2965211; 2965214;

2965218; 2965219;

2965222; 2965223;

2965224

Block_6418
0.015736
chr6: 76591424 . . . 76617955; +
1
MYO6;
CODING
14
2914115; 2914118;

(14.28%);

2914123; 2914124;

INTRONIC

2914125; 2914126;

(85.71%);

2914128; 2914130;

2914131; 2914134;

2914135; 2914136;

2914137; 2914139

Block_709
0.015736
chr1: 183079624 . . . 183111896; +
1
LAMC1;
CODING
15
2371095; 2371102;

(100%);

2371106; 2371107;

2371111; 2371115;

2371118; 2371120;

2371121; 2371122;

2371123; 2371124;

2371128; 2371132;

2371136

Block_5054
0.015953
chr3: 44926817 . . . 44955803; +
1
TGM4;
ncTRANSCRIPT
24
2620356; 2620357;

(4.16%);

2620358; 2620359;

CODING

2620360; 2620361;

(79.16%); UTR

2620362; 2620364;

(8.33%);

2620366; 2620367;

INTRONIC

2620368; 2620371;

(8.33%);

2620373; 2620374;

2620375; 2620376;

2620381; 2620382;

2620384; 2620386;

2620387; 2620388;

2620389; 2620390

Block_2887
0.016174
chr16: 57155009 . . . 57155672; +
1
CPNE2;
CODING
2
3662564; 3662565

(100%);

Block_4137
0.016174
chr2: 111556187 . . . 111562970; +
1
ACOXL;
CODING
3
2500189; 2500190;

(100%);

2500193

Block_1980
0.016397
chr13: 24334264 . . . 24334353; −
1
MIPEP;
CODING
2
3505466; 3505467

(100%);

Block_6665
0.016851
chr7: 148701024 . . . 148716114; −
1
PDIA4;
CODING
8
3078437; 3078440;

(100%);

3078441; 3078445;

3078446; 3078447;

3078449; 3078453

Block_1982
0.017083
chr13: 24384023 . . . 24460604; −
1
MIPEP;
CODING
13
3505485; 3505494;

(100%);

3505495; 3505497;

3505499; 3505500;

3505504; 3505505;

3505506; 3505507;

3505508; 3505512;

3505517

Block_2679
0.017083
chr15: 101422111 . . . 101422244; +
1
ALDH1A3;
INTRONIC
2
3611631; 3611632

(100%);

Block_3008
0.017317
chr17: 38545810 . . . 38546338; −
1
TOP2A;
CODING
2
3756196; 3756197

(100%);

Block_5308
0.017317
chr4: 80957129 . . . 80976604; −
1
ANTXR2;
CODING
3
2775032; 2775037;

(100%);

2775038

Block_6709
0.017317
chr7: 27224759 . . . 27225870; +
1
HOXA11-
ncTRANSCRIPT
5
2994152; 2994154;

AS1;
(100%);

2994156; 2994159;

2994160

Block_2215
0.017553
chr14: 51378696 . . . 51382203; −
1
PYGL;
CODING
3
3564220; 3564225;

(100%);

3564227

Block_6943
0.017553
chr8: 73978218 . . . 73982163; −
1
C8orf84;
CODING
4
3140490; 3140491;

(50%); UTR

3140492; 3140493

(50%);

Block_2491
0.017793
chr15: 59480325 . . . 59497655; −
1
MYO1E;
CODING
4
3626865; 3626867;

(100%);

3626869; 3626871

Block_6591
0.017793
chr7: 80372319 . . . 80456803; −
1
SEMA3C;
CODING
16
3058760; 3058761;

(87.5%); UTR

3058762; 3058766;

(12.5%);

3058768; 3058773;

3058778; 3058780;

3058784; 3058786;

3058787; 3058788;

3058789; 3058790;

3058794; 3058796

Block_7430
0.017793
chr9: 96026229 . . . 96031027; +
1
WNK2;
CODING
2
3179747; 3179752

(100%);

Block_1045
0.018281
chr10: 51555733 . . . 51556843; +
1
MSMB;
CODING
2
3246411; 3246412

(100%);

Block_2756
0.018281
chr16: 54953317 . . . 54954239; −
1
CRNDE;
ncTRANSCRIPT
2
3692520; 3692521

(50%);

INTRONIC

(50%);

Block_363
0.018281
chr1: 203310039 . . . 203317324; −
1
FMOD;
CODING
10
2451698; 2451699;

(40%); UTR

2451700; 2451701;

(60%);

2451702; 2451703;

2451704; 2451710;

2451711; 2451712

Block_370
0.018281
chr1: 205627208 . . . 205634013; −
1
SLC45A3;
CODING
8
2452616; 2452617;

(75%); UTR

2452618; 2452619;

(25%);

2452621; 2452622;

2452623; 2452624

Block_6688
0.018281
chr7: 2565880 . . . 2566535; +
1
LFNG;
CODING
2
2987566; 2987568

(100%);

Block_7188
0.018281
chr9: 35682105 . . . 35689177; −
1
TPM2;
CODING
6
3204723; 3204730;

(100%);

3204734; 3204737;

3204739; 3204740

Block_1179
0.018529
chr11: 2016621 . . . 2017401; −
1
H19;
ncTRANSCRIPT
4
3359080; 3359084;

(100%);

3359085; 3359087

Block_7359
0.018529
chr9: 140375422 . . . 140389574; −
1
PNPLA7;
CODING
3
3231051; 3231059;

(100%);

3231063

Block_1193
0.018781
chr11: 6653316 . . . 6661474; −
1
DCHS1;
CODING
2
3361093; 3361099

(100%);

Block_1474
0.018781
chr11: 65194527 . . . 65211475; +
1
NEAT1;
ncTRANSCRIPT
8
3335225; 3335227;

(100%);

3335229; 3335231;

3335233; 3335235;

3335239; 3335240

Block_2362
0.018781
chr14: 68113486 . . . 68115462; +
1
ARG2;
INTRONIC
2
3541413; 3541416

(100%);

Block_2983
0.019035
chr17: 26958501 . . . 26966660; −
1
KIAA0100;
CODING
5
3750898; 3750901;

(100%);

3750909; 3750911;

3750917

Block_6098
0.019035
chr6: 24666778 . . . 24666965; −
1
TDP2;
CODING
2
2945667; 2945670

(100%);

Block_6856
0.019035
chr7: 155100327 . . . 155101637; +
1
INSIG1;
UTR (100%);
2
3033258; 3033259

Block_1040
0.019292
chr10: 43615579 . . . 43622087; +
1
RET;
CODING
3
3243877; 3243878;

(100%);

3243881

Block_1047
0.019292
chr10: 51562272 . . . 51562497; +
1
MSMB;
CODING
2
3246417; 3246418

(50%); UTR

(50%);

Block_4023
0.019292
chr2: 11724711 . . . 11731961; +
1
GREB1;
UTR (50%);
2
2469853; 2469863

INTRONIC

(50%);

Block_1044
0.019553
chr10: 51532298 . . . 51535286; +
2
TIMM23B;
ncTRANSCRIPT
4
3246373; 3246408;

RP11-
(50%);

3246374; 3246376

481A12.2;
INTRONIC

(50%);

Block_2252
0.019553
chr14: 76446944 . . . 76447361; −
1
TGFB3;
CODING
2
3572536; 3572538

(50%); UTR

(50%);

Block_2547
0.019553
chr15: 90328249 . . . 90349999; −
1
ANPEP;
CODING
25
3638608; 3638609;

(92%); UTR

3638610; 3638611;

(8%);

3638612; 3638614;

3638615; 3638616;

3638622; 3638623;

3638624; 3638625;

3638631; 3638633;

3638635; 3638637;

3638639; 3638640;

3638641; 3638643;

3638644; 3638645;

3638646; 3638648;

3638649

Block_4065
0.019553
chr2: 39944177 . . . 39944970; +
1
TMEM178;
CODING
3
2478298; 2478299;

(33.33%); UTR

2478300

(66.66%);

Block_4351
0.019553
chr20: 20634174 . . . 20661443; −
1
RALGAPA2;
CODING
2
3900240; 3900249

(100%);

Block_5767
0.019553
chr5: 121405764 . . . 121406282; −
1
LOX;
CODING
3
2872855; 2872856;

(100%);

2872857

Block_1076
0.019816
chr10: 77453352 . . . 77454380; +
1
C10orf11;
INTRONIC
2
3252742; 3252954

(100%);

Block_3269
0.020083
chr17: 57741220 . . . 57763148; +
1
CLTC;
CODING
16
3729191; 3729193;

(100%);

3729194; 3729195;

3729196; 3729199;

3729201; 3729202;

3729206; 3729207;

3729208; 3729209;

3729210; 3729213;

3729216; 3729218

Block_3886
0.020083
chr2: 162883071 . . . 162891670; −
1
DPP4;
UTR (33.33%);
3
2584060; 2584063;

INTRONIC

2584065

(66.66%);

Block_5453
0.020083
chr4: 15839733 . . . 15852471; +
1
CD38;
INTERGENIC
5
2719689; 2719692;

(20%);

2719694; 2719695;

CODING

2719696

(60%); UTR

(20%);

Block_1965
0.020352
chr12: 121138015 . . . 121138614; +
1
MLEC;
UTR (100%);
2
3434547; 3434548

Block_3245
0.020352
chr17: 45753775 . . . 45754478; +
1
KPNB1;
CODING
2
3724808; 3724810

(100%);

Block_6543
0.020352
chr7: 6502772 . . . 6505843; −
1
KDELR2;
CODING
2
3037394; 3037396

(100%);

Block_7361
0.020625
chr9: 140437902 . . . 140444736; −
1
PNPLA7;
CODING
4
3231109; 3231112;

(75%); UTR

3231115; 3231117

(25%);

Block_7520
0.020625
chrX: 229408 . . . 229432; −
1
GTPBP6;
ncTRANSCRIPT
2
3997098; 4032902

(100%);

Block_1934
0.020901
chr12: 104335273 . . . 104336343; +
1
HSP90B1;
CODING
4
3429327; 3429329;

(100%);

3429330; 3429331

Block_6150
0.020901
chr6: 46846004 . . . 46851982; −
1
GPR116;
CODING
5
2955898; 2955900;

(100%);

2955904; 2955908;

2955911

Block_6388
0.020901
chr6: 44752539 . . . 44800262; +
1
SUPT3H;
INTRONIC_AS
3
2908668; 2908682;

(33.33%);

2908684

INTERGENIC

(33.33%);

CODING_AS

(33.33%);

Block_1716
0.02118
chr12: 81655761 . . . 81661862; −
1
PPFIA2;
CODING
2
3463825; 3463833

(100%);

Block_1734
0.02118
chr12: 103238114 . . . 103246723; −
1
PAH;
CODING
3
3468493; 3468497;

(100%);

3468501

Block_330
0.02118
chr1: 169434441 . . . 169446972; −
1
SLC19A2;
CODING
7
2443338; 2443339;

(85.71%); UTR

2443342; 2443344;

(14.28%);

2443345; 2443351;

2443352

Block_1239
0.021462
chr11: 49175403 . . . 49229959; −
1
FOLH1;
CODING
3
3372906; 3372936;

(100%);

3372937

Block_2310
0.021462
chr14: 38033662 . . . 38058763; +
0

INTERGENIC
4
3533021; 3533028;

(100%);

3533041; 3533045

Block_3594
0.021748
chr19: 15729440 . . . 15730475; +
1
CYP4F8;
CODING
2
3823269; 3823272

(50%);

INTRONIC

(50%);

Block_6355
0.021748
chr6: 31901946 . . . 31903811; +
1
C2;
CODING
2
2902816; 2902819

(100%);

Block_6731
0.021748
chr7: 56130382 . . . 56131617; +
1
CCT6A;
CODING
3
3003220; 3003225;

(33.33%); UTR

3003226

(66.66%);

Block_2492
0.022037
chr15: 59506427 . . . 59506888; −
1
MYO1E;
CODING
2
3626878; 3626879

(100%);

Block_4981
0.022037
chr3: 189787406 . . . 189823386; −
1
LEPREL1;
INTRONIC
2
2710531; 2710536

(100%);

Block_6399
0.022037
chr6: 57311563 . . . 57324709; +
1
PRIM2;
INTRONIC
2
2911450; 2911483

(100%);

Block_7292
0.022037
chr9: 128000931 . . . 128003092; −
1
HSPA5;
CODING
4
3225407; 3225408;

(100%);

3225411; 3225416

Block_3621
0.022329
chr19: 35611982 . . . 35613858; +
1
FXYD3;
CODING
3
3830179; 3830181;

(100%);

3830183

Block_7270
0.022329
chr9: 114176751 . . . 114182394; −
1
KIAA0368;
CODING
4
3220599; 3220601;

(100%);

3220603; 3220609

Block_4024
0.022625
chr2: 13749190 . . . 13929969; +
3
NCRNA00276;
INTERGENIC
15
2470320; 2470321;

AC016730.1;
(53.33%);

2470322; 2470323;

AC092635.1;
ncTRANSCRIPT

2470324; 2470325;

(20%);

2470328; 2470330;

INTRONIC

2470331; 2470333;

(6.66%);

2470334; 2470335;

INTRONIC_AS

2470344; 2470346;

(20%);

2470354

Block_5388
0.022625
chr4: 143326360 . . . 143383879; −
1
INPP4B;
CODING
6
2787554; 2787555;

(66.66%); UTR

2787562; 2787563;

(33.33%);

2787564; 2787567

Block_6426
0.022625
chr6: 88210238 . . . 88218297; +
1
SLC35A1;
CODING
5
2916360; 2916361;

(100%);

2916363; 2916365;

2916372

Block_2538
0.022924
chr15: 76254177 . . . 76301622; −
1
NRG4;
CODING
3
3633708; 3633710;

(66.66%); UTR

3633715

(33.33%);

Block_7429
0.022924
chr9: 95993221 . . . 96000589; +
1
WNK2;
CODING
3
3179723; 3179725;

(100%);

3179726

Block_1325
0.023226
chr11: 102269452 . . . 102272423; −
1
TMEM123;
CODING
2
3388634; 3388639

(50%); UTR

(50%);

Block_2166
0.023226
chr13: 113751561 . . . 113752679; +
2
MCF2L;
CODING
2
3502390; 3502391

AL137002.1;
(50%); UTR

(50%);

Block_2361
0.023226
chr14: 68086731 . . . 68118330; +
1
ARG2;
CODING
8
3541396; 3541398;

(87.5%); UTR

3541407; 3541412;

(12.5%);

3541414; 3541415;

3541420; 3541421

Block_2982
0.023226
chr17: 26948047 . . . 26962543; −
1
KIAA0100;
CODING
6
3750892; 3750900;

(100%);

3750904; 3750905;

3750907; 3750910

Block_4388
0.023226
chr20: 48122492 . . . 48160955; −
1
PTGIS;
CODING
5
3908938; 3908939;

(60%);

3908943; 3908951;

INTERGENIC

3908952

(20%); UTR

(20%);

Block_4862
0.023226
chr3: 115561318 . . . 115571410; −
1
LSAMP;
CODING
2
2690039; 2690041

(100%);

Block_4905
0.023226
chr3: 129123093 . . . 129137223; −
1
C3orf25;
CODING
2
2694763; 2694771

(100%);

Block_1408
0.023532
chr11: 17304338 . . . 17352512; +
1
NUCB2;
CODING
12
3322265; 3322271;

(91.66%); UTR

3322272; 3322276;

(8.33%);

3322277; 3322278;

3322279; 3322280;

3322281; 3322283;

3322287; 3322289

Block_2086
0.023532
chr13: 24289383 . . . 24309286; +
1
MIPEP;
INTERGENIC
11
3481477; 3481478;

(72.72%);

3481487; 3481489;

ncTRANSCRIPT_AS

3481491; 3481493;

(18.18%);

3481479; 3481475;

INTRONIC_AS

3481480; 3481481;

(9.09%);

3481495

Block_2821
0.023532
chr16: 8839879 . . . 8862784; +
1
ABAT;
CODING
6
3647456; 3647459;

(100%);

3647462; 3647467;

3647468; 3647472

Block_5471
0.023532
chr4: 41395354 . . . 41395449; +
1
LIMCH1;
INTRONIC
2
2725082; 2725083

(100%);

Block_745
0.023532
chr1: 203311379 . . . 203316520; +
1
FMOD;
ncTRANSCRIPT_AS
2
2375681; 2375682

(50%);

INTRONIC_AS

(50%);

Block_2886
0.023842
chr16: 56975974 . . . 56977926; +
1
HERPUD1;
INTERGENIC
2
3662406; 3662413

(50%);

INTRONIC

(50%);

Block_4945
0.023842
chr3: 149086852 . . . 149095329; −
1
TM4SF1;
CODING
5
2700368; 2700372;

(80%); UTR

2700374; 2700376;

(20%);

2700379

Block_6419
0.023842
chr6: 76604531 . . . 76626280; +
1
MYO6;
CODING
8
2914127; 2914129;

(62.5%); UTR

2914138; 2914140;

(37.5%);

2914146; 2914147;

2914148; 2914149

Block_6154
0.024155
chr6: 47251674 . . . 47252155; −
1
TNFRSF21;
CODING
2
2956076; 2956077

(100%);

Block_1388
0.024471
chr11: 4730763 . . . 4740320; +
2
AC103710.1;
CODING
4
3318188; 3318189;

MMP26;
(25%);

3318226; 3318229

INTRONIC

(75%);

Block_2898
0.024471
chr16: 67203603 . . . 67203747; +
1
HSF4;
CODING
2
3665259; 3665260

(100%);

Block_4522
0.024791
chr21: 39858595 . . . 39862882; −
1
ERG;
INTRONIC
2
3931864; 3931914

(100%);

Block_5306
0.024791
chr4: 80918912 . . . 80949988; −
1
ANTXR2;
INTRONIC
3
2775059; 2775027;

(100%);

2775028

Block_2184
0.025443
chr14: 23816393 . . . 23816935; −
1
SLC22A17;
CODING
2
3557354; 3557358

(100%);

Block_2254
0.025443
chr14: 80666635 . . . 80668673; −
1
DIO2;
UTR (100%);
2
3573882; 3573883

Block_2435
0.025443
chr15: 37217501 . . . 37225462; −
1
MEIS2;
INTRONIC
2
3618372; 3618379

(100%);

Block_2648
0.025774
chr15: 86212981 . . . 86228071; +
1
AKAP13;
CODING
3
3606399; 3606405;

(100%);

3606409

Block_3540
0.025774
chr19: 51410040 . . . 51412584; −
1
KLK4;
CODING
7
3868736; 3868737;

(85.71%); UTR

3868738; 3868740;

(14.28%);

3868741; 3868743;

3868745

Block_3894
0.025774
chr2: 166737190 . . . 166758405; −
1
TTC21B;
CODING
4
2585261; 2585265;

(100%);

2585273; 2585274

Block_4572
0.025774
chr21: 42648718 . . . 42652968; +
0

INTERGENIC
2
3921988; 3921989

(100%);

Block_1981
0.026108
chr13: 24348459 . . . 24352051; −
1
MIPEP;
INTRONIC
3
3505475; 3505477;

(100%);

3505478

Block_2146
0.026108
chr13: 99099031 . . . 99100596; +
1
FARP1;
CODING
2
3498038; 3498041

(50%); UTR

(50%);

Block_5418
0.026108
chr4: 170137651 . . . 170167646; −
1
SH3RF1;
INTRONIC
2
2793179; 2793181

(100%);

Block_1963
0.026447
chr12: 121132919 . . . 121134161; +
1
MLEC;
CODING
2
3434539; 3434541

(100%);

Block_6398
0.026447
chr6: 57270903 . . . 57311752; +
1
PRIM2;
ncTRANSCRIPT
7
2911447; 2911470;

(14.28%);

2911448; 2911473;

INTRONIC

2911475; 2911451;

(85.71%);

2911452

Block_1159
0.026789
chr10: 125726574 . . . 125726620; +
0

INTERGENIC
2
3311091; 4038113

(100%);

Block_182
0.026789
chr1: 59246516 . . . 59249254; −
1
JUN;
CODING
9
2415086; 2415088;

(33.33%); UTR

2415090; 2415091;

(66.66%);

2415093; 2415094;

2415096; 2415098;

2415099

Block_2594
0.026789
chr15: 57745886 . . . 57754067; +
1
CGNL1;
CODING
2
3595336; 3595342

(100%);

Block_2880
0.026789
chr16: 56667710 . . . 56678081; +
4
MT1JP;
ncTRANSCRIPT
5
3662156; 3662163;

MT1DP;
(20%);

3662122; 3662124;

MT1M;
CODING

3662175

MT1A;
(80%);

Block_3661
0.026789
chr19: 49699887 . . . 49703683; +
1
TRPM4;
CODING
2
3838347; 3838348

(100%);

Block_5184
0.026789
chr3: 174951778 . . . 174974294; +
1
NAALADL2;
CODING
3
2653162; 2653163;

(100%);

2653164

Block_241
0.027135
chr1: 110276731 . . . 110279596; −
1
GSTM3;
UTR (100%);
2
2427209; 2427213

Block_2441
0.027135
chr15: 42437997 . . . 42439930; −
1
PLA2G4F;
CODING
3
3620436; 3620439;

(100%);

3620441

Block_3238
0.027135
chr17: 44828869 . . . 44832729; +
1
NSF;
CODING
2
3724262; 3724264

(100%);

Block_6472
0.027135
chr6: 144904413 . . . 144904734; +
1
UTRN;
CODING
2
2929285; 2929286

(50%); UTR

(50%);

Block_6883
0.027135
chr8: 26611808 . . . 26614843; −
1
ADRA1A;
CODING
2
3128825; 3128829

(50%);

INTRONIC

(50%);

Block_7532
0.027135
chrX: 1505179 . . . 1505423; −
1
SLC25A6;
UTR (100%);
2
3997377; 4033178

Block_1298
0.027485
chr11: 72468829 . . . 72470411; −
1
STARD10;
CODING
2
3381326; 3381331

(100%);

Block_3532
0.027839
chr19: 46280628 . . . 46281019; −
1
DMPK;
CODING
2
3865653; 3865654

(100%);

Block_6942
0.027839
chr8: 72211297 . . . 72246402; −
1
EYA1;
CODING
6
3140094; 3140095;

(100%);

3140101; 3140103;

3140106; 3140109

Block_7269
0.027839
chr9: 114151836 . . . 114170935; −
1
KIAA0368;
CODING
4
3220569; 3220571;

(100%);

3220577; 3220589

Block_751
0.027839
chr1: 207497909 . . . 207504583; +
1
CD55;
CODING
3
2377239; 2377242;

(100%);

2377245

Block_1050
0.028197
chr10: 60559972 . . . 60573731; +
1
BICC1;
CODING
2
3247880; 3247887

(100%);

Block_7720
0.028197
chrX: 18597972 . . . 18606218; +
1
CDKL5;
CODING
2
3970672; 3970676

(100%);

Block_7402
0.028925
chr9: 71080046 . . . 71114251; +
1
PGM5;
CODING
4
3173537; 3173540;

(100%);

3173541; 3173543

Block_1619
0.029294
chr12: 16703175 . . . 16713472; −
1
LMO3;
CODING
3
3446141; 3446142;

(66.66%); UTR

3446145

(33.33%);

Block_4177
0.029294
chr2: 160082200 . . . 160087326; +
1
TANC1;
CODING
2
2512182; 2512191

(100%);

Block_4425
0.029294
chr20: 21312923 . . . 21329067; +
1
XRN2;
CODING
4
3879487; 3879492;

(100%);

3879498; 3879506

Block_7830
0.029294
chrX: 135289915 . . . 135291372; +
1
FHL1;
INTRONIC
2
3992437; 3992441

(100%);

Block_1575
0.029668
chr11: 134130954 . . . 134131239; +
1
ACAD8;
CODING
2
3357326; 3357327

(100%);

Block_2381
0.030046
chr14: 88553185 . . . 88560834; +
0

INTERGENIC
2
3547415; 3547424

(100%);

Block_5332
0.030046
chr4: 89199385 . . . 89199620; −
1
PPM1K;
CODING
2
2777363; 2777364;

(100%);

Block_6163
0.030046
chr6: 55618961 . . . 55620476; −
1
BMP5;
CODING
2
2958174; 2958176

(50%); UTR

(50%);

Block_6705
0.030046
chr7: 23286477 . . . 23314622; +
1
GPNMB;
CODING
10
2992816; 2992825;

(90%); UTR

2992827; 2992831;

(10%);

2992832; 2992840;

2992842; 2992845;

2992847; 2992848

Block_6774
0.030046
chr7: 99159637 . . . 99167388; +
1
ZNF655;
CODING
5
3014911; 3014912;

(20%);

3014913; 3014917;

ncTRANSCRIPT

3014954

(20%);

UTR (20%);

INTRONIC

(40%);

Block_7531
0.030046
chrX: 1505060 . . . 1505127; −
1
SLC25A6;
UTR (100%);
2
3997376; 4033177

Block_1730
0.030428
chr12: 102153818 . . . 102164296; −
1
GNPTAB;
CODING
9
3468120; 3468121;

(100%);

3468122; 3468123;

3468126; 3468131;

3468134; 3468135;

3468136

Block_4021
0.030428
chr2: 11680067 . . . 11782662; +
1
GREB1;
CODING
33
2469828; 2469836;

(87.87%); UTR

2469837; 2469841;

(12.12%);

2469849; 2469857;

2469861; 2469865;

2469866; 2469867;

2469868; 2469869;

2469870; 2469874;

2469876; 2469877;

2469880; 2469881;

2469882; 2469884;

2469887; 2469889;

2469891; 2469892;

2469893; 2469894;

2469896; 2469897;

2469898; 2469899;

2469900; 2469901;

2469902

Block_4231
0.030428
chr2: 198948634 . . . 198950883; +
1
PLCL1;
CODING
2
2521607; 2521608

(100%);

Block_265
0.030814
chr1: 144892521 . . . 144892549; −
1
PDE4DIP;
CODING
2
2431960; 4042079

(100%);

Block_6777
0.030814
chr7: 99169519 . . . 99170579; +
1
ZNF655;
CODING
2
3014924; 3014928

(50%);

INTRONIC

(50%);

Block_2757
0.031205
chr16: 55844435 . . . 55855323; −
1
CES1;
CODING
6
3692709; 3661846;

(100%);

3692711; 3661834;

3661831; 3692722

Block_4573
0.031205
chr21: 42694866 . . . 42729633; +
1
FAM3B;
CODING
8
3922003; 3922012;

(87.5%); UTR

3922017; 3922023;

(12.5%);

3922027; 3922028;

3922031; 3922032

Block_6941
0.031205
chr8: 72156865 . . . 72182058; −
1
EYA1;
CODING
2
3140079; 3140083

(100%);

Block_3246
0.0316
chr17: 45755412 . . . 45755765; +
1
KPNB1;
CODING
2
3724811; 3724812

(100%);

Block_5372
0.0316
chr4: 138451013 . . . 138453177; −
1
PCDH18;
CODING
4
4047508; 2786238;

(100%);

2786239; 4047511

Block_710
0.0316
chr1: 183077411 . . . 183087270; +
1
LAMC1;
CODING
3
2371094; 2371103;

(100%);

2371108

Block_1362
0.031998
chr11: 122929505 . . . 122930647; −
1
HSPA8;
CODING
4
3395428; 3395433;

(100%);

3395438; 3395439

Block_139
0.031998
chr1: 38041207 . . . 38042091; −
1
GNL2;
CODING
2
2407202; 2407204

(100%);

Block_242
0.031998
chr1: 110280148 . . . 110280790; −
1
GSTM3;
CODING
2
2427219; 2427222

(100%);

Block_2615
0.031998
chr15: 69855990 . . . 69863685; +
1
AC100826.1;
ncTRANSCRIPT
5
3599886; 3599887;

(80%);

3599888; 3599890;

INTRONIC

3599891

(20%);

Block_6882
0.031998
chr8: 23540117 . . . 23540330; −
1
NKX3-1;
CODING
3
3127991; 3127992;

(100%);

3127994

Block_1049
0.032402
chr10: 60553245 . . . 60556259; +
1
BICC1;
CODING
2
3247875; 3247877

(100%);

Block_2731
0.032402
chr16: 28109882 . . . 28137158; −
1
XPO6;
CODING
8
3686341; 3686343;

(100%);

3686347; 3686348;

3686349; 3686353;

3686356; 3686361

Block_7314
0.032402
chr9: 136230241 . . . 136230349; −
1
SURF4;
CODING
2
3228678; 4051970

(100%);

Block_3435
0.032809
chr18: 56054957 . . . 56057598; +
1
NEDD4L;
INTRONIC
7
3790090; 3790091;

(100%);

3790092; 3790094;

3790095; 3790097;

3790098

Block_4064
0.032809
chr2: 39931241 . . . 39931334; +
1
TMEM178;
CODING
2
2478287; 2478288

(100%);

Block_4333
0.032809
chr20: 6090960 . . . 6096685; −
1
FERMT1;
CODING
2
3896652; 3896654

(100%);

Block_1256
0.033221
chr11: 62303454 . . . 62304039; −
1
AHNAK;
CODING
2
3375784; 3375785

(50%); UTR

(50%);

Block_5294
0.033221
chr4: 76846890 . . . 76861308; −
1
NAAA;
CODING
2
2773891; 2773897

(100%);

Block_6360
0.033638
chr6: 32868955 . . . 32870947; +
1
AL669918.1;
ncTRANSCRIPT
2
2903325; 2903327

(100%);

Block_6542
0.033638
chr7: 6210524 . . . 6210945; −
1
CYTH3;
CODING
2
3037270; 3037272

(100%);

Block_1389
0.034059
chr11: 4788501 . . . 5009539; +
6
OR51F2;
CODING
13
3318193; 3318195;

OR51A8P;
(46.15%);

3318240; 3318241;

OR51H2P;
ncTRANSCRIPT

3318242; 3318200;

OR51T1;
(38.46%);

3318205; 3318206;

MMP26;
UTR (7.69%);

3318210; 3318211;

OR51N1P;
INTRONIC

3318246; 3318247;

(7.69%);

3318215

Block_876
0.034059
chr10: 43881590 . . . 43882061; −
1
HNRNPF;
UTR (100%);
2
3286289; 3286290

Block_2883
0.034484
chr16: 56968915 . . . 56970561; +
1
HERPUD1;
INTRONIC
3
3662392; 3662394;

(100%);

3662396

Block_2971
0.034484
chr17: 17398026 . . . 17399476; −
1
RASD1;
CODING
5
3747795; 3747796;

(80%); UTR

3747797; 3747799;

(20%);

3747801

Block_5102
0.034484
chr3: 105243191 . . . 105266352; +
1
ALCAM;
CODING
5
2634545; 2634550;

(100%);

2634552; 2634561;

2634562

Block_5301
0.034484
chr4: 80825530 . . . 80828621; −
1
ANTXR2;
CODING
6
2774995; 2774996;

(16.66%); UTR

2774997; 2774999;

(83.33%);

2775000; 2775001

Block_5537
0.034484
chr4: 89588558 . . . 89602441; +
1
HERC3;
CODING
3
2735499; 2735503;

(100%);

2735510

Block_3902
0.035348
chr2: 168986056 . . . 168997267; −
1
STK39;
CODING
2
2585761; 2585766

(100%);

Block_1515
0.035787
chr11: 108010817 . . . 108017045; +
1
ACAT1;
CODING
2
3347636; 3347644

(100%);

Block_158
0.035787
chr1: 51768040 . . . 51768245; −
1
TTC39A;
CODING
2
2412328; 2412330

(100%);

Block_168
0.035787
chr1: 53363109 . . . 53370744; −
1
ECHDC2;
CODING
3
2413037; 2413040;

(100%);

2413044

Block_3529
0.035787
chr19: 45016075 . . . 45029277; −
1
CEACAM20;
ncTRANSCRIPT
8
3864953; 3864956;

(100%);

3864957; 3864959;

3864961; 3864962;

3864964; 3864967

Block_6470
0.035787
chr6: 144835069 . . . 144872213; +
1
UTRN;
CODING
5
2929254; 2929260;

(100%);

2929262; 2929268;

2929274

Block_7040
0.035787
chr8: 26265556 . . . 26265860; +
1
BNIP3L;
CODING
2
3091030; 3091031

(100%);

Block_2735
0.036231
chr16: 28493570 . . . 28493624; −
1
CLN3;
INTRONIC
2
3654751; 3654816

(100%);

Block_6533
0.036231
chr6: 168351907 . . . 168352865; +
1
MLLT4;
CODING
2
2936935; 2936937

(100%);

Block_881
0.036231
chr10: 46969414 . . . 46969439; −
1
SYT15;
CODING
2
3287392; 4038216

(100%);

Block_5436
0.036679
chr4: 187516851 . . . 187557363; −
1
FAT1;
CODING
17
2797405; 2797407;

(100%);

2797408; 2797410;

2797411; 2797414;

2797415; 2797418;

2797423; 2797426;

2797427; 2797430;

2797433; 2797435;

2797437; 2797438;

2797446

Block_5623
0.036679
chr4: 165691596 . . . 165722585; +
1
RP11-
ncTRANSCRIPT
2
2750414; 2750417

294O2.2;
(100%);

Block_3420
0.037132
chr18: 48581190 . . . 48586286; +
1
SMAD4;
CODING
2
3788324; 3788330

(100%);

Block_3937
0.037132
chr2: 181436457 . . . 181469005; −
1
AC009478.1;
ncTRANSCRIPT
2
2590308; 2590313

(50%);

INTRONIC

(50%);

Block_5634
0.037132
chr4: 174109607 . . . 174135233; +
1
GALNT7;
INTRONIC
2
2751944; 2751947

(100%);

Block_5681
0.03759
chr5: 40760621 . . . 40767760; −
1
AC008810.1;
CODING
4
2854740; 2854741;

(50%); UTR

2854743; 2854749

(50%);

Block_6148
0.03759
chr6: 46669622 . . . 46690628; −
2
TDRD6;
CODING
13
2955823; 2955825;

PLA2G7;
(76.92%);

2955826; 2955830;

UTR_AS

2955835; 2955836;

(15.38%);

2955837; 2955838;

CODING_AS

2955839; 2955840;

(7.69%);

2955841; 2955842;

2955844

Block_6211
0.03759
chr6: 99853979 . . . 99857124; −
1
SFRS18;
CODING
2
2966275; 2966279

(100%);

Block_1312
0.038053
chr11: 85445044 . . . 85469138; −
1
SYTL2;
CODING
6
3385111; 3385113;

(83.33%); UTR

3385114; 3385117;

(16.66%);

3385121; 3385123

Block_2926
0.038053
chr16: 84495374 . . . 84497337; +
1
ATP2C2;
CODING
2
3671793; 3671798

(100%);

Block_4520
0.038053
chr21: 39752360 . . . 39852761; −
1
ERG;
ncTRANSCRIPT
65
3931784; 3931785;

(4.61%);

3931786; 3931787;

CODING

3931788; 3931789;

(20%); UTR

3931790; 3931791;

(13.84%);

3931792; 3931793;

INTRONIC

3931794; 3931796;

(61.53%);

3931798; 3931799;

3931800; 3931801;

3931802; 3931803;

3931804; 3931806;

3931807; 3931808;

3931809; 3931810;

3931811; 3931813;

3931814; 3931815;

3931816; 3931817;

3931818; 3931819;

3931820; 3931821;

3931822; 3931824;

3931827; 3931828;

3931829; 3931830;

3931831; 3931832;

3931833; 3931835;

3931836; 3931837;

3931838; 3931840;

3931841; 3931843;

3931844; 3931845;

3931846; 3931848;

3931849; 3931851;

3931852; 3931853;

3931854; 3931856;

3931857; 3931858;

3931859; 3931861;

3931862

Block_6855
0.038053
chr7: 155093280 . . . 155100014; +
1
INSIG1;
CODING
4
3033244; 3033247;

(100%);

3033249; 3033256

Block_7161
0.038053
chr9: 3223306 . . . 3228889; −
1
RFX3;
CODING
2
3196843; 3196849

(50%); UTR

(50%);

Block_2625
0.03852
chr15: 73028188 . . . 73029911; +
1
BBS4;
CODING
3
3600996; 3600997;

(100%);

3600999

Block_7163
0.03852
chr9: 3277354 . . . 3301613; −
1
RFX3;
CODING
4
3196877; 3196878;

(100%);

3196879; 3196881

Block_34
0.038993
chr1: 2336552 . . . 2337237; −
1
PEX10;
CODING
2
2392426; 2392427

(50%); UTR

(50%);

Block_4718
0.038993
chr22: 48031017 . . . 48082931; +
1
RP11-
ncTRANSCRIPT
3
3949433; 3949438;

191L9.4;
(100%);

3949440

Block_6477
0.038993
chr6: 145142024 . . . 145157563; +
1
UTRN;
CODING
3
2929340; 2929344;

(100%);

2929351

Block_3502
0.03947
chr19: 13050901 . . . 13051160; −
1
CALR;
CODING_AS
2
3851902; 3851903

(100%);

Block_5414
0.03947
chr4: 169919358 . . . 169928001; −
1
CBR4;
CODING
3
2793091; 2793093;

(33.33%);

2793098

INTRONIC

(66.66%);

Block_7223
0.03947
chr9: 93983092 . . . 93983273; −
1
AUH;
CODING
2
3214385; 3214386

(100%);

Block_2127
0.039952
chr13: 76374862 . . . 76378658; +
1
LMO7;
CODING
2
3494192; 3494194

(100%);

Block_3580
0.039952
chr19: 11210844 . . . 11213743; +
1
LDLR;
INTRONIC
2
3821020; 3821024

(100%);

Block_4531
0.039952
chr21: 42839814 . . . 42841274; −
1
TMPRSS2;
ncTRANSCRIPT
3
3933046; 3933048;

(66.66%);

3933049

INTRONIC

(33.33%);

Block_5329
0.039952
chr4: 88261689 . . . 88293951; −
1
HSD17B11;
CODING
2
2777078; 2777086

(100%);

Block_5367
0.039952
chr4: 122590800 . . . 122592788; −
1
ANXA5;
CODING
2
2784046; 2784049

(100%);

Block_1456
0.040439
chr11: 58385590 . . . 58387157; +
1
ZFP91;
UTR (100%);
2
3331770; 3331771

Block_376
0.040439
chr1: 216824320 . . . 216850671; −
1
ESRRG;
CODING
2
2455970; 2455975

(100%);

Block_7353
0.040439
chr9: 140356003 . . . 140357262; −
1
PNPLA7;
CODING
6
3231020; 4051802;

(100%);

3231024; 4051804;

3231029; 4051807

Block_1476
0.040932
chr11: 65273777 . . . 65273907; +
1
MALAT1;
ncTRANSCRIPT
2
3335195; 3335196

(100%);

Block_5017
0.040932
chr3: 19389238 . . . 19498406; +
1
KCNH8;
CODING
6
2613328; 2613336;

(100%);

2613337; 2613340;

2613342; 2613344

Block_6874
0.040932
chr8: 19325762 . . . 19339547; −
1
CSGALNACT1;
INTRONIC
4
3126537; 3126539;

(100%);

3126540; 3126543

Block_258
0.041429
chr1: 120295908 . . . 120307209; −
1
HMGCS2;
CODING
9
2431038; 2431042;

(100%);

2431044; 2431047;

2431050; 2431051;

2431056; 2431057;

2431058

Block_3899
0.041429
chr2: 168825060 . . . 168864496; −
1
STK39;
INTRONIC
2
2585709; 2585717

(100%);

Block_4051
0.041429
chr2: 30748528 . . . 30785140; +
1
LCLAT1;
CODING
2
2475742; 2475748

(100%);

Block_5419
0.041429
chr4: 170190133 . . . 170190434; −
1
SH3RF1;
CODING
2
2793189; 2793190

(50%); UTR

(50%);

Block_6180
0.041429
chr6: 75822940 . . . 75902036; −
1
COL12A1;
CODING
40
2961207; 2961209;

(100%);

2961210; 2961211;

2961218; 2961222;

2961224; 2961225;

2961227; 2961229;

2961230; 2961231;

2961232; 2961233;

2961234; 2961237;

2961239; 2961240;

2961242; 2961244;

2961247; 2961248;

2961251; 2961252;

2961253; 2961254;

2961256; 2961257;

2961258; 2961259;

2961260; 2961261;

2961263; 2961264;

2961266; 2961267;

2961268; 2961270;

2961271; 2961273

Block_7008
0.041429
chr8: 144698291 . . . 144698872; −
1
TSTA3;
CODING
2
3157677; 3157679

(100%);

Block_5786
0.041931
chr5: 140907177 . . . 140908450; −
1
DIAPH1;
CODING
3
2878674; 2878677;

(100%);

2878678

Block_7480
0.041931
chr9: 133339512 . . . 133342185; +
1
ASS1;
CODING
2
3191541; 3191544

(100%);

Block_1345
0.042439
chr11: 117708078 . . . 117708992; −
1
FXYD6;
CODING
2
3393486; 3393487

(50%); UTR

(50%);

Block_4268
0.042439
chr2: 219204527 . . . 219208304; +
1
PNKD;
CODING
2
2527695; 2527701

(100%);

Block_6945
0.042439
chr8: 74705646 . . . 74722855; −
1
UBE2W;
CODING
3
3140775; 3140777;

(33.33%); UTR

3140784

(66.66%);

Block_7232
0.042439
chr9: 95043034 . . . 95050521; −
1
IARS;
CODING
4
3214728; 3214733;

(100%);

3214735; 3214738

Block_7440
0.042439
chr9: 100823070 . . . 100840627; +
1
NANS;
CODING
3
3181467; 3181476;

(100%);

3181477

Block_2098
0.042952
chr13: 32749690 . . . 32759246; +
1
FRY;
CODING
2
3484547; 3484554

(100%);

Block_3188
0.042952
chr17: 28770823 . . . 28794571; +
1
CPD;
CODING
13
3716448; 3716452;

(61.53%); UTR

3716456; 3716462;

(38.46%);

3716464; 3716465;

3716467; 3716468;

3716469; 3716470;

3716471; 3716472;

3716473

Block_3884
0.042952
chr2: 162849805 . . . 162851512; −
1
DPP4;
CODING
2
2584026; 2584027

(100%);

Block_4710
0.042952
chr22: 45914565 . . . 45921519; +
1
FBLN1;
CODING
2
3948657; 3948663

(100%);

Block_5278
0.042952
chr4: 52890189 . . . 52896012; −
1
SGCB;
CODING
2
2768987; 2768991

(100%);

Block_5417
0.042952
chr4: 170057497 . . . 170077777; −
1
SH3RF1;
CODING
3
2793167; 2793171;

(100%);

2793172

Block_5452
0.042952
chr4: 15780104 . . . 15826604; +
1
CD38;
CODING
4
2719662; 2719664;

(100%);

2719672; 2719679

Block_6207
0.042952
chr6: 94120488 . . . 94124485; −
1
EPHA7;
CODING
2
2965246; 2965247

(100%);

Block_1773
0.043469
chr12: 118588359 . . . 118588947; −
1
TAOK3;
CODING
3
3473806; 3473807;

(66.66%); UTR

3473808

(33.33%);

Block_298
0.043469
chr1: 154557366 . . . 154558321; −
1
ADAR;
CODING
2
2436758; 2436762

(100%);

Block_4455
0.043469
chr20: 37174997 . . . 37199484; +
1
RALGAPB;
CODING
5
3884695; 3884701;

(100%);

3884707; 3884708;

3884716

Block_5162
0.043469
chr3: 153973294 . . . 153975253; +
1
ARHGEF26;
CODING
2
2648576; 2648579

(50%); UTR

(50%);

Block_5625
0.043469
chr4: 166301254 . . . 166375499; +
1
CPE;
CODING
16
2750634; 2750635;

(6.25%); UTR

2750636; 2750638;

(12.5%);

2750639; 2750640;

INTRONIC

2750642; 2750643;

(81.25%);

2750680; 2750646;

2750647; 2750649;

2750650; 2750653;

2750655; 2750659

Block_6152
0.043469
chr6: 47199596 . . . 47199895; −
1
TNFRSF21;
UTR (100%);
2
2956054; 2956055

Block_7360
0.043469
chr9: 140403604 . . . 140404196; −
1
PNPLA7;
CODING
2
3231080; 3231081

(50%);

INTRONIC

(50%);

Block_3670
0.043993
chr19: 51380028 . . . 51380127; +
1
KLK2;
INTRONIC
2
3839576; 3839577

(100%);

Block_4440
0.043993
chr20: 32232190 . . . 32236720; +
1
CBFA2T2;
CODING
3
3882597; 3882598;

(33.33%); UTR

3882603

(66.66%);

Block_6431
0.043993
chr6: 106967344 . . . 106975345; +
1
AIM1;
CODING
5
2919813; 2919814;

(100%);

2919815; 2919816;

2919820

Block_7138
0.043993
chr8: 120255695 . . . 120257606; +
1
MAL2;
ncTRANSCRIPT
3
3113192; 3113193;

(100%);

3113194

Block_7231
0.043993
chr9: 95013006 . . . 95033327; −
1
IARS;
CODING
7
3214701; 3214708;

(100%);

3214713; 3214714;

3214716; 3214719;

3214721

Block_7316
0.043993
chr9: 136231716 . . . 136231744; −
1
SURF4;
CODING
2
3228682; 4051974

(100%);

Block_374
0.044521
chr1: 207102212 . . . 207112808; −
1
PIGR;
CODING
11
2453007; 2453010;

(90.90%); UTR

2453011; 2453012;

(9.09%);

2453013; 2453015;

2453016; 2453018;

2453019; 2453020;

2453021

Block_4771
0.044521
chr3: 49062361 . . . 49062661; −
1
IMPDH2;
CODING
2
2673881; 2673882

(100%);

Block_6871
0.044521
chr8: 19261989 . . . 19277968; −
1
CSGALNACT1;
CODING
5
3126508; 3126509;

(80%); UTR

3126514; 3126520;

(20%);

3126522

Block_3592
0.045055
chr19: 13264023 . . . 13264647; +
1
IER2;
CODING
2
3822220; 3822222

(100%);

Block_5515
0.045055
chr4: 79475596 . . . 79503433; +
1
ANXA3;
CODING
2
2732851; 2732860

(50%); UTR

(50%);

Block_6620
0.045055
chr7: 99267347 . . . 99272139; −
1
CYP3A5;
ncTRANSCRIPT
3
3063437; 3063444;

(66.66%);

3063447

INTRONIC

(33.33%);

Block_1688
0.045594
chr12: 57648708 . . . 57650291; −
1
R3HDM2;
CODING
2
3458457; 3458461

(100%);

Block_2958
0.045594
chr17: 4175402 . . . 4186127; −
1
UBE2G1;
UTR (100%);
2
3742072; 3742078

Block_698
0.045594
chr1: 178408557 . . . 178421750; +
1
RASAL2;
CODING
4
2369197; 2369198;

(100%);

2369199; 2369205

Block_7807
0.045594
chrX: 107923910 . . . 107923944; +
1
COL4A5;
CODING
2
3986840; 4055605

(100%);

Block_2251
0.046139
chr14: 76424744 . . . 76448197; −
1
TGFB3;
INTERGENIC
11
3572518; 3572524;

(9.09%);

3572528; 3572529;

CODING

3572533; 3572534;

(45.45%); UTR

3572539; 3572540;

(45.45%);

3572541; 3572542;

3572543

Block_3274
0.046139
chr17: 59479110 . . . 59480539; +
1
TBX2;
CODING
2
3729850; 3729852

(100%);

Block_412
0.046139
chr1: 235643382 . . . 235658086; −
1
B3GALNT2;
CODING
3
2461913; 2461914;

(100%);

2461921

Block_2884
0.046689
chr16: 56969154 . . . 56977753; +
1
HERPUD1;
CODING
10
3662393; 3662395;

(80%); UTR

3662397; 3662401;

(20%);

3662402; 3662403;

3662407; 3662408;

3662411; 3662412

Block_3707
0.046689
chr2: 10580851 . . . 10585351; −
1
ODC1;
CODING
12
2540164; 2540166;

(91.66%); UTR

2540167; 2540169;

(8.33%);

2540171; 2540172;

2540173; 2540174;

2540175; 2540176;

2540178; 2540180

Block_6266
0.046689
chr6: 136990497 . . . 137041697; −
1
MAP3K5;
CODING
6
2975930; 2975936;

(100%);

2975938; 2975939;

2975940; 2975946

Block_947
0.046689
chr10: 95185842 . . . 95191270; −
1
MYOF;
CODING
2
3300707; 3300708

(100%);

Block_2092
0.047245
chr13: 26434339 . . . 26436545; +
1
ATP8A2;
CODING
2
3482385; 3482388

(100%);

Block_6585
0.047245
chr7: 51095830 . . . 51098577; −
1
COBL;
CODING
3
3050639; 3050644;

(100%);

3050648

Block_7357
0.047245
chr9: 140358830 . . . 140358908; −
1
PNPLA7;
CODING
2
3231037; 4051814

(100%);

Block_1824
0.047806
chr12: 12037385 . . . 12047640; +
1
ETV6;
CODING
3
3405156; 3405162;

(100%);

3405164

Block_439
0.047806
chr1: 11888539 . . . 11889339; +
1
CLCN6;
CODING
3
2320500; 2320501;

(100%);

2320502

Block_6029
0.047806
chr5: 148804031 . . . 148811072; +
1
RP11-
INTERGENIC
8
2835105; 2835106;

394O4.2;
(50%);

2835107; 2835108;

ncTRANSCRIPT

2835111; 2835120;

(50%);

2835124; 2835127

Block_6466
0.047806
chr6: 144724259 . . . 144768883; +
1
UTRN;
CODING
8
2929201; 2929208;

(100%);

2929210; 2929214;

2929215; 2929216;

2929223; 2929227

Block_1059
0.048945
chr10: 70728765 . . . 70741336; +
1
DDX21;
CODING
5
3250074; 3250076;

(100%);

3250079; 3250084;

3250086

Block_7268
0.048945
chr9: 114128562 . . . 114137482; −
1
KIAA0368;
CODING
4
3220517; 3220518;

(100%);

3220524; 3220527

Block_7643
0.048945
chrX: 114345684 . . . 114357459; −
1
LRCH2;
CODING
2
4018756; 4018762

(50%); UTR

(50%);

Block_2984
0.049523
chr17: 26966940 . . . 26969094; −
1
KIAA0100;
CODING
3
3750919; 3750921;

(100%);

3750923

TABLE 23

ICE

Category

Block
Wilcoxon
Chromosomal
# of
Overlapping
(Composition
# of

ID
P-value
Coordinates
Genes
Genes
%)
PSRs
Probe set ID(s)

Block_6592
0.000072
chr7: 37946647 . . . 37956059; −
1
SFRP4;
CODING
9
3046448; 3046449;

(66.66%); UTR

3046450; 3046457;

(33.33%);

3046459; 3046460;

3046461; 3046462;

3046465;

Block_4226
0.000089
chr2: 189863400 . . . 189867071; +
1
COL3A1;
CODING
2
2519614; 2519620;

(100%);

Block_4627
0.000116
chr22: 29191774 . . . 29195014; −
1
XBP1;
ncTRANSCRIPT
3
3956596; 3956601;

(33.33%);

3956603;

INTRONIC

(66.66%);

Block_6930
0.000183
chr8: 48649878 . . . 48650049; −
1
CEBPD;
CODING
2
3134023; 3134024;

(100%);

Block_7113
0.00028
chr8: 75737169 . . . 75767196; +
1
PI15;
CODING
16
3103704; 3103705;

(43.75%); UTR

3103706; 3103707;

(43.75%);

3103708; 3103710;

INTRONIC

3103712; 3103713;

(12.5%);

3103714; 3103715;

3103717; 3103718;

3103720; 3103721;

3103725; 3103726;

Block_5470
0.000286
chr4: 15839733 . . . 15852471; +
1
CD38;
INTERGENIC
5
2719689; 2719692;

(20%);

2719694; 2719695;

CODING

2719696;

(60%); UTR

(20%);

Block_5155
0.000299
chr3: 132043108 . . . 132068493; +
1
ACPP;
ncTRANSCRIPT
13
2642733; 2642735;

(15.38%);

2642738; 2642739;

INTRONIC

2642740; 2642741;

(84.61%);

2642744; 2642745;

2642746; 2642747;

2642748; 2642750;

2642753;

Block_3531
0.000313
chr19: 39897525 . . . 39899806; −
1
ZFP36;
UTR_AS
4
3862010; 3862011;

(25%);

3862006; 3862007;

CODING_AS

(75%);

Block_1992
0.00032
chr13: 38158126 . . . 38166301; −
1
POSTN;
CODING
4
3510098; 3510100;

(100%);

3510101; 3510105;

Block_4227
0.000372
chr2: 189867682 . . . 189873745; +
1
COL3A1;
CODING
7
2519621; 2519623;

(100%);

2519628; 2519629;

2519634; 2519637;

2519644;

Block_5813
0.000424
chr5: 148880617 . . . 148880811; −
1
CTB-
ncTRANSCRIPT
2
2880917; 2880918;

89H12.4;
(100%);

Block_6391
0.000433
chr6: 38840803 . . . 38841129; +
1
DNAH8;
CODING
2
2906020; 2906021;

(100%);

Block_5469
0.000452
chr4: 15780104 . . . 15826604; +
1
CD38;
CODING
4
2719662; 2719664;

(100%);

2719672; 2719679;

Block_1127
0.000595
chr10: 114710550 . . . 114711012; +
1
TCF7L2;
CODING
2
3264623; 3264624;

(100%);

Block_6388
0.000634
chr6: 38783258 . . . 38783411; +
1
DNAH8;
CODING
2
2905985; 2905986;

(100%);

Block_3521
0.000718
chr19: 18893864 . . . 18897074; −
1
COMP;
CODING
2
3855221; 3855230;

(100%);

Block_2375
0.000812
chr14: 88553185 . . . 88560834; +
0

INTERGENIC
2
3547415; 3547424;

(100%);

Block_6389
0.000829
chr6: 38800098 . . . 38831738; +
1
DNAH8;
CODING
14
2905993; 2905995;

(100%);

2905996; 2905997;

2905999; 2906000;

2906001; 2906002;

2906003; 2906004;

2906005; 2906006;

2906010; 2906012;

Block_2896
0.000846
chr16: 67202953 . . . 67203210; +
1
HSF4;
CODING
2
3665255; 3665257;

(100%);

Block_1579
0.000882
chr12: 3718615 . . . 3753793; −
1
EFCAB4B;
CODING
10
3440929; 3440999;

(60%);

3441000; 3440930;

INTRONIC

3440936; 3440938;

(40%);

3440941; 3440942;

3440951; 3440952;

Block_3687
0.000918
chr19: 53945049 . . . 53945553; +
1
CTD-
ncTRANSCRIPT
2
3840864; 3840869;

2224J9.2;
(100%);

Block_3688
0.000957
chr19: 53957950 . . . 53961428; +
1
ZNF761;
ncTRANSCRIPT
6
3840917; 3840921;

(100%);

3840923; 3840935;

3840937; 3840939;

Block_939
0.000996
chr10: 88820216 . . . 88820346; −
1
GLUD1;
ncTRANSCRIPT
2
3298991; 4038370;

(100%);

Block_4225
0.001058
chr2: 189839219 . . . 189861926; +
1
COL3A1;
CODING
15
2519583; 2519585;

(100%);

2519586; 2519588;

2519589; 2519590;

2519595; 2519596;

2519598; 2519599;

2519601; 2519602;

2519604; 2519605;

2519610;

Block_3653
0.001147
chr19: 41223728 . . . 41231316; +
1
ITPKC;
CODING
5
3833738; 3833739;

(80%);

3833740; 3833741;

INTRONIC

3833743;

(20%);

Block_7267
0.001147
chr9: 99370376 . . . 99375212; −
1
CDC14B;
INTRONIC
2
3216428; 3216429;

(100%);

Block_1991
0.00117
chr13: 38154719 . . . 38164537; −
1
POSTN;
CODING
3
3510096; 3510097;

(100%);

3510103;

Block_3042
0.001292
chr17: 48262881 . . . 48277296; −
1
COL1A1;
CODING
39
3762204; 3762206;

(100%);

3762207; 3762208;

3762210; 3762211;

3762212; 3762215;

3762216; 3762217;

3762218; 3762220;

3762221; 3762222;

3762223; 3762225;

3762226; 3762227;

3762228; 3762229;

3762234; 3762235;

3762236; 3762238;

3762241; 3762242;

3762243; 3762244;

3762245; 3762246;

3762249; 3762252;

3762253; 3762254;

3762256; 3762257;

3762263; 3762264;

3762268;

Block_6371
0.001345
chr6: 31785240 . . . 31797461; +
2
HSPA1B;
CODING
2
2902713; 2902730;

HSPA1A;
(100%);

Block_5279
0.001372
chr4: 40592576 . . . 40629213; −
1
RBM47;
INTRONIC
4
2766856; 2766859;

(100%);

2766860; 2766861;

Block_3023
0.001455
chr17: 40538906 . . . 40539322; −
1
STAT3;
INTRONIC
2
3757901; 3757902;

(100%);

Block_4139
0.001455
chr2: 101541626 . . . 101564800; +
1
NPAS2;
CODING
4
2496436; 2496440;

(100%);

2496446; 2496448;

Block_2374
0.001605
chr14: 88550504 . . . 88559014; +
0

INTERGENIC
5
3547412; 3547413;

(100%);

3547419; 3547420;

3547422;

Block_3981
0.001605
chr2: 208628777 . . . 208631527; −
1
FZD5;
UTR (100%);
4
2596768; 2596769;

2596771; 2596775;

Block_7365
0.001636
chr9: 140354426 . . . 140354842; −
1
PNPLA7;
UTR (100%);
2
3231011; 4051791;

Block_6370
0.001701
chr6: 31795534 . . . 31795716; +
1
HSPA1B;
CODING
2
2902726; 2902727;

(100%);

Block_6484
0.001701
chr6: 144635551 . . . 144635647; +
1
UTRN;
INTRONIC
2
2929396; 2929397;

(100%);

Block_6152
0.001947
chr6: 35545311 . . . 35555083; −
1
FKBP5;
INTRONIC
2
2951580; 2951584;

(100%);

Block_1926
0.001985
chr12: 102011150 . . . 102079590; +
1
MYBPC1;
CODING
36
3428611; 3428612;

(69.44%); UTR

3428613; 3428617;

(2.77%);

3428619; 3428620;

INTRONIC

3428623; 3428624;

(27.77%);

3428625; 3428626;

3428627; 3428628;

3428629; 3428630;

3428631; 3428634;

3428635; 3428636;

3428637; 3428638;

3428639; 3428640;

3428641; 3428642;

3428643; 3428644;

3428646; 3428647;

3428648; 3428650;

3428651; 3428654;

3428655; 3428659;

3428665; 3428666;

Block_4322
0.002062
chr2: 242135147 . . . 242164581; +
1
ANO7;
CODING
24
2536222; 2536226;

(91.66%); UTR

2536228; 2536229;

(8.33%);

2536231; 2536232;

2536233; 2536234;

2536235; 2536236;

2536237; 2536238;

2536240; 2536241;

2536243; 2536245;

2536248; 2536249;

2536252; 2536253;

2536256; 2536260;

2536261; 2536262;

Block_3449
0.002102
chr18: 56647020 . . . 56648694; +
1
ZNF532;
INTRONIC
3
3790402; 3790403;

(100%);

3790404;

Block_1427
0.002268
chr11: 35166517 . . . 35193320; +
1
CD44;
INTRONIC
2
3326642; 3326650;

(100%);

Block_3648
0.002312
chr19: 39897722 . . . 39899906; +
1
ZFP36;
CODING
8
3832980; 3832981;

(25%); UTR

3832982; 3832984;

(12.5%);

3832985; 3832986;

INTRONIC

3832987; 3832988;

(62.5%);

Block_2832
0.002493
chr16: 19433756 . . . 19439293; +
1
TMC5;
INTRONIC
2
3650948; 3650949;

(100%);

Block_5745
0.002493
chr5: 86688587 . . . 86688721; −
1
CCNH;
ncTRANSCRIPT
2
2865880; 2865881;

(100%);

Block_2304
0.002588
chr14: 38054451 . . . 38055847; +
0

INTERGENIC
4
3533031; 3533035;

(100%);

3533037; 3533039;

Block_1993
0.002637
chr13: 38158866 . . . 38162106; −
1
POSTN;
CODING
2
3510099; 3510102;

(100%);

Block_6649
0.002687
chr7: 105893270 . . . 105922863; −
1
NAMPT;
ncTRANSCRIPT
20
3066831; 3066833;

(20%);

3066836; 3066837;

INTRONIC

3066838; 3066839;

(80%);

3066840; 3066841;

3066843; 3066844;

3066846; 3066847;

3066848; 3066849;

3066850; 3066853;

3066854; 3066859;

3066861; 3066862;

Block_2897
0.002738
chr16: 67203603 . . . 67203747; +
1
HSF4;
CODING
2
3665259; 3665260;

(100%);

Block_5232
0.002789
chr3: 186759705 . . . 186769256; +
1
ST6GAL1;
ncTRANSCRIPT
3
2656853; 2656859;

(66.66%);

2656860;

UTR (33.33%);

Block_1128
0.002895
chr10: 114723487 . . . 114732026; +
1
TCF7L2;
INTRONIC
2
3264632; 3264636;

(100%);

Block_2631
0.002895
chr15: 78557858 . . . 78567151; +
1
DNAJA4;
ncTRANSCRIPT
3
3603257; 3603266;

(66.66%);

3603267;

CODING

(33.33%);

Block_3099
0.002949
chr17: 76354002 . . . 76355176; −
1
SOCS3;
CODING
4
3772289; 3772290;

(25%); UTR

3772292; 3772293;

(75%);

Block_3597
0.003118
chr19: 12902599 . . . 12904034; +
1
JUNB;
CODING
3
3821896; 3821898;

(66.66%) UTR

3821899;

(33.33%);

Block_3448
0.003176
chr18: 56623078 . . . 56646570; +
1
ZNF532;
INTRONIC
4
3790396; 3790398;

(100%);

3790399; 3790401;

Block_1429
0.003234
chr11: 35211649 . . . 35229188; +
1
CD44;
ncTRANSCRIPT
15
3326671; 3326672;

(40%);

3326674; 3326676;

INTRONIC

3326677; 3326679;

(60%);

3326680; 3326681;

3326684; 3326692;

3326695; 3326701;

3326703; 3326704;

3326708;

Block_2471
0.003234
chr15: 55543544 . . . 55562575; −
1
RAB27A;
UTR (50%);
2
3625289; 3625295;

INTRONIC

(50%);

Block_2895
0.003294
chr16: 67199438 . . . 67201057; +
1
HSF4;
ncTRANSCRIPT
5
3665235; 3665240;

(20%);

3665244; 3665245;

CODING

3665246;

(80%);

Block_1542
0.003355
chr11: 118379852 . . . 118380821; +
1
MLL;
CODING
2
3351445; 3351446;

(100%);

Block_1185
0.003481
chr11: 3800418 . . . 3803305; −
1
NUP98;
CODING
2
3359982; 3359983;

(100%);

Block_3591
0.003481
chr19: 11210844 . . . 11213743; +
1
LDLR;
INTRONIC
2
3821020; 3821024;

(100%);

Block_4284
0.003676
chr2: 219676945 . . . 219679977; +
1
CYP27A1;
CODING
7
2528108; 2528110;

(85.71%); UTR

2528111; 2528112;

(14.28%);

2528113; 2528115;

2528118;

Block_834
0.003676
chr1: 247712494 . . . 247739511; +
1
C1orf150;
CODING
3
2390125; 2390128;

(66.66%); UTR

2390134;

(33.33%);

Block_1825
0.003743
chr12: 13350040 . . . 13366545; +
2
EMP1;
UTR (50%);
6
3405757; 3405758;

AC079628.1;
INTRONIC

3405760; 3405766;

(50%);

3405770; 3405772;

Block_3512
0.003812
chr19: 15297695 . . . 15302661; −
1
NOTCH3;
CODING
5
3853157; 3853158;

(100%);

3853159; 3853161;

3853166;

Block_4229
0.003812
chr2: 189875001 . . . 189877194; +
1
COL3A1;
CODING
5
2519649; 2519652;

(60%); UTR

2519656; 2519657;

(40%);

2519658;

Block_5137
0.003812
chr3: 121603566 . . . 121604258; +
1
EAF2;
INTRONIC
2
2638711; 2638712;

(100%);

Block_5780
0.003952
chr5: 115146858 . . . 115148955; −
1
CDO1;
CODING
2
2871912; 2871914;

(100%);

Block_5954
0.003952
chr5: 82785957 . . . 82786199; +
1
VCAN;
CODING
2
2818532; 2818533;

(100%);

Block_1472
0.004024
chr11: 65273777 . . . 65273907; +
1
MALAT1;
ncTRANSCRIPT
2
3335195; 3335196;

(100%);

Block_5764
0.004024
chr5: 95243613 . . . 95288598; −
1
ELL2;
ncTRANSCRIPT
12
2867907; 2867915;

(8.33%);

2867916; 2867924;

INTRONIC

2867925; 2867926;

(91.66%);

2867930; 2867931;

2867932; 2867934;

2867940; 2867941;

Block_7742
0.004097
chrX: 23802057 . . . 23803407; +
1
SAT1;
ncTRANSCRIPT
5
3971816; 3971817;

(40%);

3971818; 3971820;

CODING

3971821;

(20%); UTR

(40%);

Block_5765
0.004247
chr5: 95257267 . . . 95259483; −
1
ELL2;
INTRONIC
4
2867919; 2867921;

(100%);

2867922; 2867923;

Block_6642
0.004247
chr7: 99267347 . . . 99272139; −
1
CYP3A5;
ncTRANSCRIPT
3
3063437; 3063444;

(66.66%);

3063447;

INTRONIC

(33.33%);

Block_4409
0.004324
chr20: 52560335 . . . 52561534; −
1
BCAS1;
CODING
2
3910362; 3910363;

(50%); UTR

(50%);

Block_7846
0.004324
chrX: 152770164 . . . 152773851; +
1
BGN;
CODING
6
3995642; 3995651;

(100%);

3995654; 3995657;

3995659; 3995661;

Block_5950
0.004402
chr5: 79361251 . . . 79378964; +
1
THBS4;
CODING
10
2817602; 2817603;

(100%);

2817605; 2817606;

2817609; 2817611;

2817614; 2817615;

2817620; 2817621;

Block_3980
0.004482
chr2: 208627560 . . . 208629500; −
1
FZD5;
UTR (100%);
3
2596764; 2596765;

2596772;

Block_7065
0.004482
chr8: 27398133 . . . 27402173; +
1
EPHX2;
CODING
2
3091435; 3091442;

(50%); UTR

(50%);

Block_2156
0.004562
chr13: 111940732 . . . 111953191; +
1
ARHGEF7;
CODING
2
3501737; 3501744;

(100%);

Block_2613
0.004644
chr15: 71803346 . . . 71808234; +
1
THSD4;
INTRONIC
2
3600358; 3600361;

(100%);

Block_4875
0.004644
chr3: 114412375 . . . 114429160; −
1
ZBTB20;
UTR (100%);
2
2689628; 2689631;

Block_1342
0.004727
chr11: 116914101 . . . 116935147; −
1
SIK3;
INTRONIC
4
3393111; 3393112;

(100%);

3393115; 3393116;

Block_2614
0.004727
chr15: 71839666 . . . 71889637; +
1
THSD4;
CODING
8
3600365; 3600366;

(12.5%); UTR

3600482; 3600486;

(12.5%);

3600368; 3600478;

INTRONIC

3600371; 3600372;

(75%);

Block_2658
0.004727
chr15: 93482832 . . . 93486203; +
1
CHD2;
CODING
2
3609197; 3609200;

(100%);

Block_3283
0.004727
chr17: 65027167 . . . 65028692; +
2
CACNG4;
CODING
2
3732138; 3732139;

AC005544.1;
(50%); UTR

(50%);

Block_2002
0.004812
chr13: 45113061 . . . 45146842; −
1
TSC22D1;
INTRONIC
7
3512332; 3512337;

(100%);

3512338; 3512339;

3512341; 3512342;

3512344;

Block_2833
0.004812
chr16: 19441750 . . . 19460940; +
1
TMC5;
CODING
5
3650950; 3650954;

(60%); UTR

3650955; 3650957;

(40%);

3650958;

Block_847
0.004812
chr10: 7392799 . . . 7409508; −
1
SFMBT2;
INTRONIC
3
3276296; 3276241;

(100%);

3276242;

Block_1469
0.004898
chr11: 65191129 . . . 65191996; +
1
NEAT1;
ncTRANSCRIPT
2
3335211; 3335215;

(100%);

Block_853
0.004986
chr10: 18874889 . . . 18903446; −
1
NSUN6;
CODING
2
3280258; 3280265;

(100%);

Block_3682
0.005074
chr19: 51380028 . . . 51380127; +
1
KLK2;
INTRONIC
2
3839576; 3839577;

(100%);

Block_3573
0.005165
chr19: 2476367 . . . 2477960; +
1
GADD45B;
CODING
4
3816512; 3816515;

(75%); UTR

3816519; 3816524;

(25%);

Block_4876
0.005165
chr3: 114435628 . . . 114450706; −
1
ZBTB20;
INTRONIC
2
2689633; 2689638;

(100%);

Block 6288
0.005165
chr6: 143251252 . . . 143252058; −
1
HIVEP2;
INTRONIC
2
2977329; 2977355;

(100%);

Block_6338
0.005165
chr6: 10556781 . . . 10566189; +
1
GCNT2;
CODING
2
2894601; 2894610;

(50%);

INTRONIC

(50%);

Block_7142
0.005256
chr8: 102506747 . . . 102518399; +
1
GRHL2;
INTRONIC
2
3109702; 3109705;

(100%);

Block_1361
0.005349
chr11: 122932160 . . . 122932410; −
1
HSPA8;
UTR (100%);
2
3395451; 3395452;

Block_2612
0.00554
chr15: 71716691 . . . 71716939; +
1
THSD4;
INTRONIC
2
3600342; 3600343;

(100%);

Block_6390
0.00554
chr6: 38828265 . . . 38834650; +
1
DNAH8;
CODING
2
2906008; 2906016;

(100%);

Block_748
0.00554
chr1: 203275102 . . . 203275613; +
1
BTG2;
INTRONIC
3
2375667; 2375668;

(100%);

2375670;

Block_1894
0.005638
chr12: 69019900 . . . 69035432; +
1
RAP1B;
INTRONIC
2
3421126; 3421130;

(100%);

Block_3970
0.005638
chr2: 201719352 . . . 201719803; −
1
CLK1;
CODING
2
2594506; 2594508;

(100%);

Block_6229
0.005737
chr6: 99860469 . . . 99860591; −
1
SFRS18;
CODING
2
2966287; 2966288;

(100%);

Block_2303
0.005838
chr14: 38033662 . . . 38058763; +
0

INTERGENIC
4
3533021; 3533028;

(100%);

3533041; 3533045;

Block_7715
0.005838
chrX: 2619960 . . . 2620197; +
1
CD99;
INTRONIC
2
3966874; 4028497;

(100%);

Block_3508
0.006044
chr19: 12902622 . . . 12904019; −
1
JUNB;
CODING_AS
2
3851771; 3851773;

(100%);

Block_6785
0.006044
chr7: 94028361 . . . 94059882; +
1
COL1A2;
CODING
41
3013083; 3013086;

(97.56%); UTR

3013095; 3013096;

(2.43%);

3013098; 3013102;

3013103; 3013105;

3013106; 3013107;

3013109; 3013110;

3013111; 3013113;

3013114; 3013115;

3013116; 3013118;

3013119; 3013120;

3013124; 3013125;

3013127; 3013128;

3013129; 3013130;

3013135; 3013137;

3013139; 3013141;

3013142; 3013143;

3013146; 3013148;

3013151; 3013155;

3013156; 3013157;

3013158; 3013160;

3013161;

Block_1771
0.00615
chr12: 118636857 . . . 118639157; −
1
TAOK3;
CODING
2
3473836; 3473838;

(100%);

Block_4997
0.00615
chr3: 187460081 . . . 187461297; −
1
BCL6;
INTRONIC
3
2709837; 2709817;

(100%);

2709839;

Block_2787
0.006477
chr16: 72984427 . . . 72992414; −
1
ZFHX3;
CODING
2
3698340; 3698347;

(100%);

Block_4873
0.006477
chr3: 114353933 . . . 114405567; −
1
ZBTB20;
INTRONIC
6
2689789; 2689794;

(100%);

2689798; 2689807;

2689809; 2689776;

Block_1826
0.006589
chr12: 13364471 . . . 13366481; +
1
EMP1;
CODING
2
3405769; 3405771;

(100%);

Block_6318
0.006589
chr6: 160103692 . . . 160113602; −
1
SOD2;
CODING
5
2982328; 2982330;

(20%); UTR

2982332; 2982333;

(40%);

2982335;

INTRONIC

(40%);

Block_4898
0.006703
chr3: 120389279 . . . 120401114; −
1
HGD;
CODING
6
2691446; 4047079;

(66.66%); UTR

2691452; 4047076;

(33.33%);

2691462; 4047071;

Block_5788
0.006819
chr5: 131820117 . . . 131822522; −
1
IRF1;
CODING
2
2875353; 2875362;

(100%);

Block_5824
0.006819
chr5: 151041302 . . . 151054230; −
1
SPARC;
CODING
13
2882119; 2882120;

(53.84%); UTR

2882121; 2882122;

(46.15%);

2882123; 2882125;

2882128; 2882131;

2882133; 2882137;

2882139; 2882142;

2882143;

Block_1145
0.006937
chr10: 123779283 . . . 123781483; +
1
TACC2;
ncTRANSCRIPT
2
3268069; 3268071;

(50%);

UTR (50%);

Block_3689
0.007178
chr19: 53959452 . . . 53959887; +
1
ZNF761;
ncTRANSCRIPT
2
3840925; 3840931;

(100%);

Block_4535
0.007301
chr21: 36252858 . . . 36260789; −
1
RUNX1;
CODING
3
3930427; 3930435;

(33.33%); UTR

3930438;

(66.66%);

Block_5434
0.007682
chr4: 170137651 . . . 170167646; −
1
SH3RF1;
INTRONIC
2
2793179; 2793181;

(100%);

Block_4228
0.007946
chr2: 189873814 . . . 189875606; +
1
COL3A1;
CODING
3
2519645; 2519648;

(100%);

2519653;

Block_4973
0.007946
chr3: 156865888 . . . 156874463; −
1
CCNL1;
ncTRANSCRIPT
14
2702330; 2702333;

(35.71%);

2702335; 2702342;

CODING

2702344; 2702345;

(28.57%); UTR

2702346; 2702348;

(21.42%);

2702352; 2702355;

INTRONIC

2702356; 2702357;

(14.28%);

2702358; 2702359;

Block_5911
0.007946
chr5: 60648670 . . . 60667704; +
1
ZSWIM6;
INTRONIC
4
2811300; 2811301;

(100%);

2811302; 2811303;

Block_2183
0.008081
chr14: 25325143 . . . 25326345; −
1
STXBP6;
CODING
2
3558448; 3558449;

(100%);

Block_3447
0.008218
chr18: 56585564 . . . 56587447; +
1
ZNF532;
CODING
3
3790379; 3790380;

(100%);

3790381;

Block_5955
0.008218
chr5: 82832827 . . . 82876595; +
1
VCAN;
CODING
9
2818559; 2818561;

(88.88%); UTR

2818568; 2818571;

(11.11%);

2818572; 2818573;

2818577; 2818578;

2818582;

Block_4412
0.008499
chr20: 52612441 . . . 52674693; −
1
BCAS1;
CODING
3
3910385; 3910393;

(100%);

3910394;

Block_4770
0.008499
chr3: 39183443 . . . 39186746; −
1
CSRNP1;
CODING
4
2669932; 2669935;

(75%); UTR

2669936; 2669937;

(25%);

Block_3590
0.008642
chr19: 11210938 . . . 11241992; +
1
LDLR;
CODING
17
3821022; 3821023;

(100%);

3821026; 3821029;

3821031; 3821034;

3821035; 3821036;

3821037; 3821041;

3821042; 3821044;

3821045; 3821046;

3821048; 3821052;

3821054;

Block_6089
0.008642
chr6: 2116070 . . . 2117790; −
1
GMDS;
CODING
2
2938767; 2938771;

(100%);

Block_7272
0.008936
chr9: 110248037 . . . 110250537; −
1
KLF4;
CODING
4
3219229; 3219230;

(100%);

3219233; 3219235;

Block_1183
0.009085
chr 11: 3792978 . . . 3793149; −
1
NUP98;
CODING
2
3359975; 3359977;

(100%);

Block_1990
0.009085
chr13: 38137470 . . . 38138697; −
1
POSTN;
CODING
2
3510070; 3510072;

(100%);

Block_4411
0.009085
chr20: 52574002 . . . 52601991; −
1
BCAS1;
CODING
3
3910367; 3910373;

(100%);

3910378;

Block_6454
0.009085
chr6: 108942915 . . . 108943132; +
1
FOXO3;
INTRONIC
2
2920517; 2920518;

(100%);

Block_6540
0.009085
chr6: 160770298 . . . 160864773; +
2
AL591069.1;
ncTRANSCRIPT
29
2934526; 2934527;

SLC22A3;
(3.44%);

2934531; 2934533;

CODING

2934535; 2934580;

(27.58%);

2934582; 2934585;

INTRONIC

2934586; 2934536;

(68.96%);

2934537; 2934538;

2934539; 2934541;

2934543; 2934545;

2934547; 2934548;

2934549; 2934550;

2934551; 2934554;

2934556; 2934557;

2934558; 2934559;

2934560; 2934561;

2934562;

Block_7064
0.009085
chr8: 27382879 . . . 27399020; +
1
EPHX2;
CODING
3
3091429; 3091433;

(100%);

3091436;

Block_5912
0.009238
chr5: 60699060 . . . 60705963; +
1
ZSWIM6;
INTRONIC
2
2811311; 2811314;

(100%);

Block_6369
0.009238
chr6: 31785537 . . . 31785681; +
1
HSPA1A;
UTR (100%);
2
2902715; 2902716;

Block_2101
0.009392
chr13: 41890982 . . . 41891060; +
1
NAA16;
CODING
2
3486890; 3486891;

(100%);

Block_6340
0.009392
chr6: 10697570 . . . 10707720; +
1
PAK1IP1;
CODING
6
2894670; 2894671;

(100%);

2894673; 2894676;

2894677; 2894681;

Block_6407
0.009392
chr6: 44752539 . . . 44800262; +
1
SUPT3H;
INTRONIC_AS
3
2908668; 2908682;

(33.33%);

2908684;

INTERGENIC

(33.33%);

CODING_AS

(33.33%);

Block_7862
0.009392
chrY: 21186129 . . . 21189006; −
1
NCRNA00185;
INTRONIC
2
4035800; 4035801;

(100%);

Block_1729
0.009708
chr12: 103234188 . . . 103249107; −
1
PAH;
CODING
3
3468486; 3468494;

(100%);

3468504;

Block_4002
0.009708
chr2: 227657803 . . . 227659434; −
1
IRS1;
INTRONIC
2
2602032; 2602033;

(100%);

Block_6542
0.009708
chr6: 160868751 . . . 160872088; +
1
SLC22A3;
CODING
2
2934572; 2934575;

(100%);

Block_1913
0.009869
chr12: 93774378 . . . 93775567; +
1
NUDT4;
INTRONIC
3
3426176; 3426178;

(100%);

3426180;

Block_4996
0.010033
chr3: 187457927 . . . 187458752; −
1
BCL6;
INTRONIC
2
2709814; 2709815;

(100%);

Block_6541
0.010033
chr6: 160866011 . . . 160868068; +
1
SLC22A3;
INTRONIC
3
2934564; 2934565;

(100%);

2934567;

Block_7019
0.010033
chr8: 135820790 . . . 135827602; −
0

INTERGENIC
2
3154820; 3154823;

(100%);

Block_5230
0.010199
chr3: 186696431 . . . 186720502; +
1
ST6GAL1;
UTR (33.33%);
3
2656910; 2656906;

INTRONIC

2656846;

(66.66%);

Block_4974
0.010368
chr3: 156866425 . . . 156867848; −
1
CCNL1;
ncTRANSCRIPT
2
2702334; 2702341;

(100%);

Block_5229
0.010368
chr3: 186656184 . . . 186662034; +
1
ST6GAL1;
INTRONIC
2
2656876; 2656884;

(100%);

Block_2155
0.010539
chr13: 111932910 . . . 111938586; +
1
ARHGEF7;
CODING
2
3501728; 3501736;

(100%);

Block_5727
0.010539
chr5: 68588077 . . . 68595899; −
1
CCDC125;
CODING
2
2860627; 2860632;

(100%);

Block_4872
0.010713
chr3: 114311442 . . . 114318066; −
1
ZBTB20;
INTRONIC
3
2689598; 2689599;

(100%);

2689601;

Block_6449
0.010713
chr6: 106967344 . . . 106975345; +
1
AIM1;
CODING
5
2919813; 2919814;

(100%);

2919815; 2919816;

2919820;

Block_1048
0.010889
chr10: 51550046 . . . 51562146; +
1
MSMB;
ncTRANSCRIPT
9
3246410; 3246413;

(22.22%);

3246427; 3246428;

INTRONIC

3246429; 3246430;

(77.77%);

3246431; 3246414;

3246415;

Block_2517
0.010889
chr15: 66072454 . . . 66076243; −
1
DENND4A;
INTRONIC
2
3629917; 3629918;

(100%);

Block_6088
0.010889
chr6: 1930342 . . . 1961193; −
1
GMDS;
CODING
3
2938731; 2938739;

(100%);

2938741;

Block_7063
0.010889
chr8: 27358443 . . . 27380016; +
1
EPHX2;
CODING
6
3091408; 3091410;

(100%);

3091412; 3091414;

3091418; 3091427;

Block_6674
0.011068
chr7: 130764976 . . . 130789833; −
1
AC058791.2;
ncTRANSCRIPT
6
3072944; 3072948;

(16.66%);

3072856; 3072860;

INTRONIC

3072861; 3072863;

(83.33%);

Block_7280
0.011068
chr9: 112963294 . . . 112963740; −
1
C9orf152;
CODING
3
3220143; 3220147;

(100%);

3220149;

Block_1093
0.011249
chr10: 93702200 . . . 93713592; +
1
BTAF1;
CODING
2
3257953; 3257956;

(100%);

Block_1783
0.011249
chr12: 123212329 . . . 123213804; −
1
GPR81;
UTR (100%);
2
3475776; 3475778;

Block_1262
0.011433
chr11: 62559948 . . . 62563808; −
1
NXF1;
CODING
5
3376159; 3376162;

(100%);

3376163; 3376165;

3376169;

Block_2366
0.011433
chr14: 73572725 . . . 73572938; +
1
RBM25;
CODING
2
3543443; 3543444;

(100%);

Block_4428
0.01162
chr20: 6004032 . . . 6005887; +
1
CRLS1;
ncTRANSCRIPT
2
3875259; 3875261;

(50%);

INTRONIC

(50%);

Block_4828
0.01162
chr3: 71080277 . . . 71088814; −
1
FOXP1;
INTRONIC
4
2681951; 2681956;

(100%);

2681814; 2681815;

Block_7607
0.01181
chrX: 67413739 . . . 67518927; −
1
OPHN1;
CODING
6
4011226; 4011231;

(100%);

4011234; 4011241;

4011242; 4011244;

Block_2003
0.012595
chr13: 45147330 . . . 45150071; −
1
TSC22D1;
CODING
8
3512345; 3512347;

(100%);

3512348; 3512350;

3512351; 3512352;

3512354; 3512355;

Block_4410
0.012595
chr20: 52571654 . . . 52574704; −
1
BCAS1;
INTRONIC
2
3910366; 3910368;

(100%);

Block_6796
0.012595
chr7: 99169875 . . . 99170304; +
1
ZNF655;
CODING
2
3014925; 3014926;

(100%);

Block_1572
0.012798
chr11: 134147231 . . . 134188819; +
1
GLB1L3;
CODING
13
3357348; 3357349;

(100%);

3357360; 3357363;

3357369; 3357370;

3357371; 3357375;

3357382; 3357383;

3357384; 3357386;

3357387;

Block_4895
0.013005
chr3: 120363705 . . . 120364125; −
1
HGD;
INTRONIC
2
2691410; 4047097;

(100%);

Block_6178
0.013005
chr6: 53200331 . . . 53207275; −
2
ELOVL5;
ncTRANSCRIPT
4
2957648; 2957651;

RP3-
(50%);

2957653; 2957655;

483K16.2;
INTRONIC

(50%);

Block_6315
0.013005
chr6: 159216475 . . . 159227934; −
1
EZR;
INTRONIC
2
2981955; 2981961;

(100%);

Block_4534
0.013214
chr21: 36238786 . . . 36251434; −
1
RUNX1;
INTRONIC
5
3930422; 3930512;

(100%);

3930426; 3930520;

3930522;

Block_7689
0.013214
chrX: 138182745 . . . 138221675; −
1
FGF13;
INTRONIC
2
4024065; 4023962;

(100%);

Block_1415
0.013426
chr11: 32953313 . . . 32976949; +
1
QSER1;
CODING
4
3325783; 3325784;

(100%);

3325787; 3325791;

Block_6641
0.013426
chr7: 99250225 . . . 99260505; −
1
CYP3A5;
CODING
2
3063412; 3063422;

(100%);

Block_1720
0.013641
chr12: 93959391 . . . 93960697; −
1
AC025260.2;
ncTRANSCRIPT
2
3465863; 3465865;

(50%);

INTRONIC

(50%);

Block_1827
0.01386
chr12: 13366615 . . . 13369004; +
1
EMP1;
CODING
3
3405774; 3405777;

(66.66%); UTR

3405778;

(33.33%);

Block_7001
0.01386
chr8: 116555732 . . . 116584992; −
1
TRPS1;
INTRONIC
3
3149560; 3149563;

(100%);

3149566;

Block_4874
0.014081
chr3: 114406132 . . . 114412366; −
1
ZBTB20;
ncTRANSCRIPT
5
2689618; 2689620;

(20%);

2689621; 2689622;

INTRONIC

2689627;

(80%);

Block_6329
0.014081
chr6: 170594681 . . . 170595380; −
1
DLL1;
CODING
2
2986376; 2986377;

(100%);

Block_3850
0.014305
chr2: 100484261 . . . 100509150; −
1
AFF3;
INTRONIC
2
2567082; 2567086;

(100%);

Block_1603
0.014533
chr12: 10856860 . . . 10871920; −
1
CSDA;
ncTRANSCRIPT
12
3444265; 3444266;

(50%);

3444274; 3444275;

INTRONIC

3444280; 3444281;

(50%);

3444283; 3444286;

3444287; 3444288;

3444289; 3444291;

Block_3511
0.014533
chr19: 14626171 . . . 14627750; −
1
DNAJB1;
CODING
3
3852788; 3852789;

(33.33%); UTR

3852793;

(66.66%);

Block_4890
0.014533
chr3: 120347285 . . . 120347311; −
1
HGD;
CODING
2
2691370; 4047116;

(100%);

Block_2332
0.014998
chr14: 60398687 . . . 60411444; +
1
LRRC9;
ncTRANSCRIPT
2
3538417; 3538420;

(100%);

Block_5744
0.014998
chr5: 86682116 . . . 86683398; −
1
RASA1;
INTRONIC_AS
2
2865872; 2865875;

(100%);

Block_601
0.014998
chr1: 104076371 . . . 104078044; +
1
RNPC3;
CODING
3
2349363; 2349364;

(100%);

2349365;

Block_7215
0.014998
chr9: 73021937 . . . 73022490; −
1
KLF9;
INTRONIC
2
3209008; 3209009;

(100%);

Block_7366
0.014998
chr9: 140354863 . . . 140355186; −
1
PNPLA7;
CODING
4
3231012; 4051792;

(100%);

3231015; 4051795;

Block_7376
0.014998
chr9: 140437902 . . . 140444736; −
1
PNPLA7;
CODING
4
3231109; 3231112;

(75%); UTR

3231115; 3231117;

(25%);

Block_7441
0.015235
chr9: 92219943 . . . 92220976; +
1
GADD45G;
CODING
5
3178680; 3178681;

(80%); UTR

3178683; 3178685;

(20%);

3178687;

Block_5812
0.015476
chr5: 148876962 . . . 148929959; −
2
CTB-
CODING
21
2880949; 2880951;

89H12.4;
(9.52%);

2880958; 2880960;

CSNK1A1;
ncTRANSCRIPT

2880964; 2880968;

(14.28%);

2880973; 2880983;

UTR (9.52%);

2880985; 2880889;

INTRONIC

2880890; 2880987;

(66.66%);

2880892; 2880893;

2880896; 2880901;

2880989; 2880991;

2880993; 2880995;

2880997;

Block_6536
0.015476
chr6: 160174501 . . . 160176484; +
1
WTAP;
CODING
2
2934120; 2934122;

(100%);

Block_4877
0.015719
chr3: 114455332 . . . 114550610; −
1
ZBTB20;
UTR (9.09%);
11
2689639; 2689640;

INTRONIC

2689641; 2689647;

(90.90%);

2689655; 2689824;

2689825; 2689826;

2689829; 2689658;

2689838;

Block_4892
0.015719
chr3: 120352074 . . . 120352166; −
1
HGD;
CODING
2
2691378; 4047112;

(100%);

Block_5014
0.015719
chr3: 196118688 . . . 196120490; −
1
UBXN7;
CODING
2
2712875; 2712876;

(100%);

Block_7724
0.015719
chrX: 2653716 . . . 2653766; +
1
CD99;
INTRONIC
2
3966880; 4028503;

(100%);

Block_4041
0.015967
chr2: 14775429 . . . 14775897; +
1
FAM84A;
UTR (100%);
2
2470490; 2470491;

Block_4759
0.016217
chr3: 27490249 . . . 27493978; −
1
SLC4A7;
CODING
2
2666957; 2666959;

(100%);

Block_5187
0.016217
chr3: 156395446 . . . 156424304; +
1
TIPARP;
CODING
12
2649140; 2649141;

(75%); UTR

2649142; 2649149;

(25%);

2649150; 2649151;

2649152; 2649154;

2649155; 2649156;

2649158; 2649160;

Block_5728
0.016217
chr5: 68581172 . . . 68599751; −
1
CCDC125;
CODING
2
2860623; 2860634;

(100%);

Block_2879
0.016472
chr16: 56667710 . . . 56678081; +
4
MT1JP;
ncTRANSCRIPT
5
3662156; 3662163;

MT1DP;
(20%);

3662122; 3662124;

MT1M;
CODING

3662175;

MT1A;
(80%);

Block_3849
0.016472
chr2: 100426047 . . . 100692345; −
1
AFF3;
CODING
61
2566957; 2566960;

(6.55%);

2566961; 2566965;

ncTRANSCRIPT

2566966; 2566971;

(3.27%);

2567075; 2567076;

INTRONIC

2567084; 2567063;

(90.16%);

2566976; 2567087;

2567088; 2566977;

2567064; 2567097;

2567067; 2567069;

2567101; 2567103;

2567071; 2566979;

2566982; 2566983;

2566984; 2566985;

2567105; 2567111;

2567113; 2567115;

2567106; 2566987;

2566988; 2566991;

2566993; 2566994;

2566996; 2566997;

2567121; 2566998;

2567125; 2567000;

2567001; 2567002;

2567003; 2567005;

2567007; 2567008;

2567010; 2567011;

2567012; 2567013;

2567014; 2567015;

2567017; 2567018;

2567019; 2567020;

2567022; 2567023;

2567127;

Block_4871
0.016472
chr3: 114304388 . . . 114307096; −
1
ZBTB20;
INTRONIC
2
2689592; 2689595;

(100%);

Block_7460
0.016472
chr9: 102594989 . . . 102628250; +
1
NR4A3;
CODING
5
3182004; 3182005;

(80%); UTR

3182010; 3182012;

(20%);

3182015;

Block_7690
0.016472
chrX: 138283258 . . . 138284475; −
1
FGF13;
INTRONIC
2
4023972; 4023973;

(100%);

Block_5769
0.016729
chr5: 98208150 . . . 98209408; −
1
CHD1;
CODING
2
2868550; 2868554;

(100%);

Block_6035
0.01699
chr5: 142273810 . . . 142281592; +
1
ARHGAP26;
CODING
2
2833347; 2833348;

(100%);

Block_2057
0.017255
chr13: 107220269 . . . 107220463; −
1
ARGLU1;
UTR (100%);
2
3524638; 3524639;

Block_4034
0.017255
chr2: 10133339 . . . 10136095; +
1
GRHL1;
CODING
2
2469190; 2469193;

(100%);

Block_5298
0.017255
chr4: 66465162 . . . 66468022; −
1
EPHA5;
CODING
3
2771409; 2771411;

(66.66%);

2771412;

INTRONIC

(33.33%);

Block_6483
0.017255
chr6: 144615778 . . . 144641963; +
1
UTRN;
INTRONIC
4
2929179; 2929184;

(100%);

2929185; 2929186;

Block_2666
0.017523
chr15: 99256649 . . . 99277206; +
1
IGF1R;
INTRONIC
2
3610818; 3610825;

(100%);

Block_2758
0.017523
chr16: 56701878 . . . 56701935; −
1
MT1G;
CODING
2
3693007; 3693008;

(50%); UTR

(50%);

Block_4893
0.017523
chr3: 120357311 . . . 120357397; −
1
HGD;
CODING
2
2691386; 4047108;

(100%);

Block_6349
0.017523
chr6: 18392721 . . . 18401507; +
1
RNF144B;
INTRONIC
2
2897184; 2897227;

(100%);

Block_7144
0.017523
chr8: 102593447 . . . 102596253; +
1
GRHL2;
INTRONIC
2
3109729; 3109731;

(100%);

Block_1049
0.017795
chr10: 51562272 . . . 51562497; +
1
MSMB;
CODING
2
3246417; 3246418;

(50%); UTR

(50%);

Block_2195
0.017795
chr14: 38041009 . . . 38048612; −
0

INTERGENIC
2
3561714; 3561715;

(100%);

Block_2885
0.017795
chr16: 56975974 . . . 56977926; +
1
HERPUD1;
INTERGENIC
2
3662406; 3662413;

(50%);

INTRONIC

(50%);

Block_3767
0.017795
chr2: 43793837 . . . 43793938; −
1
THADA;
CODING
2
2550679; 2550680;

(100%);

Block_3865
0.017795
chr2: 121999944 . . . 122005845; −
1
TFCP2L1;
CODING
3
2573617; 2573621;

(100%);

2573622;

Block_5763
0.017795
chr5: 95242076 . . . 95243501; −
1
ELL2;
INTRONIC
2
2867901; 2867906;

(100%);

Block_6151
0.017795
chr6: 35542614 . . . 35588051; −
1
FKBP5;
CODING
10
2951575; 2951576;

(80%); UTR

2951579; 2951581;

(20%);

2951583; 2951587;

2951589; 2951593;

2951595; 2951596;

Block_1340
0.018071
chr11: 115219890 . . . 115222358; −
1
CADM1;
INTRONIC
2
3392454; 3392441;

(100%);

Block_3892
0.018071
chr2: 160303401 . . . 160304888; −
1
BAZ2B;
CODING
2
2583084; 2583085;

(100%);

Block_4924
0.018071
chr3: 129123093 . . . 129137223; −
1
C3orf25;
CODING
2
2694763; 2694771;

(100%);

Block_626
0.018071
chr1: 116933667 . . . 116939211; +
1
ATP1A1;
INTRONIC
4
2353509; 2353512;

(100%);

2353513; 2353517;

Block_3422
0.018351
chr18: 39623725 . . . 39629533; +
1
PIK3C3;
CODING
2
3786127; 3786129;

(100%);

Block_3756
0.018351
chr2: 38975252 . . . 38976820; −
1
SRSF7;
CODING
5
2548982; 2548985;

(80%); UTR

2548989; 2548990;

(20%);

2548993;

Block_4870
0.018351
chr3: 114214433 . . . 114219034; −
1
ZBTB20;
INTRONIC
5
2689743; 2689744;

(100%);

2689754; 2689756;

2689758;

Block_5617
0.018634
chr4: 148786000 . . . 148787937; +
1
ARHGAP10;
CODING
2
2746731; 2746736;

(100%);

Block_5746
0.018634
chr5: 86686709 . . . 86690299; −
2
CCNH;
CODING
2
2865878; 2865887;

RASA1;
(50%);

UTR_AS

(50%);

Block_7422
0.018634
chr9: 75773460 . . . 75785150; +
1
ANXA1;
CODING
11
3174830; 3174831;

(90.90%); UTR

3174835; 3174838;

(9.09%);

3174840; 3174845;

3174847; 3174850;

3174853; 3174856;

3174857;

Block_3565
0.019212
chr19: 863256 . . . 863423; +
1
CFD;
UTR (100%);
2
3815252; 3815253;

Block_4878
0.019507
chr3: 114465255 . . . 114510905; −
1
ZBTB20;
ncTRANSCRIPT
7
2689643; 2689645;

(14.28%);

2689646; 2689649;

INTRONIC

2689651; 2689654;

(85.71%);

2689656;

Block_5523
0.019507
chr4: 77512391 . . . 77515089; +
1
SHROOM3;
INTRONIC
2
2732215; 2732103;

(100%);

Block_7219
0.019507
chr9: 74360664 . . . 74362413; −
1
TMEM2;
INTRONIC
2
3209449; 3209451;

(100%);

Block_745
0.019507
chr1: 201980268 . . . 201985198; +
1
ELF3;
CODING
9
2375017; 2375020;

(77.77%); UTR

2375022; 2375027;

(22.22%);

2375028; 2375031;

2375033; 2375034;

2375035;

Block_4894
0.019805
chr3: 120357401 . . . 120369669; −
1
HGD;
ncTRANSCRIPT
22
2691388; 4047107;

(9.09%);

2691394; 4047105;

CODING

2691396; 4047104;

(63.63%);

2691400; 4047102;

INTRONIC

2691404; 4047100;

(27.27%);

2691406; 4047099;

2691408; 4047098;

2691414; 4047095;

2691416; 4047094;

2691418; 4047093;

2691420; 4047092;

Block_7368
0.019805
chr9: 140356003 . . . 140357262; −
1
PNPLA7;
CODING
6
3231020; 4051802;

(100%);

3231024; 4051804;

3231029; 4051807;

Block_1146
0.020108
chr10: 123988023 . . . 123990167; +
1
TACC2;
CODING
3
3268174; 3268175;

(33.33%);

3268178;

INTRONIC

(66.66%);

Block_1818
0.020108
chr12: 11836357 . . . 11863628; +
1
ETV6;
INTRONIC
4
3405070; 3405071;

(100%);

3405073; 3405079;

Block_2437
0.020415
chr15: 42445498 . . . 42446391; −
1
PLA2G4F;
CODING
2
3620449; 3620451;

(100%);

Block_334
0.020415
chr1: 169693470 . . . 169702101; −
1
SELE;
CODING
11
2443481; 2443482;

(81.81%); UTR

2443486; 2443489;

(18.18%);

2443490; 2443492;

2443494; 2443495;

2443496; 2443499;

2443501;

Block_7796
0.020415
chrX: 70775823 . . . 70776629; +
1
OGT;
CODING
2
3981142; 3981144;

(100%);

Block_1893
0.020726
chr12: 69006519 . . . 69013759; +
1
RAP1B;
INTRONIC
3
3421121; 3421122;

(100%);

3421123;

Block_5714
0.020726
chr5: 58481017 . . . 58511763; −
1
PDE4D;
CODING
4
2858211; 2858215;

(100%);

2858221; 2858222;

Block_5866
0.020726
chr5: 180278404 . . . 180278437; −
1
ZFP62;
CODING
2
2890930; 4047645;

(100%);

Block_4533
0.02104
chr21: 36193606 . . . 36197820; −
1
RUNX1;
UTR (50%);
2
3930392; 3930397;

INTRONIC

(50%);

Block_6626
0.02104
chr7: 87910829 . . . 87912896; −
1
STEAP4;
UTR (50%);
2
3060345; 3060349;

INTRONIC

(50%);

Block_3539
0.02136
chr19: 45016075 . . . 45029277; −
1
CEACAM20;
ncTRANSCRIPT
8
3864953; 3864956;

(100%);

3864957; 3864959;

3864961; 3864962;

3864964; 3864967;

Block_3551
0.02136
chr19: 51410040 . . . 51412584; −
1
KLK4;
CODING
7
3868736; 3868737;

(85.71%); UTR

3868738; 3868740;

(14.28%);

3868741; 3868743;

3868745;

Block_3894
0.02136
chr2: 160885361 . . . 160898634; −
1
PLA2R1;
CODING
3
2583439; 2583441;

(100%);

2583443;

Block_2870
0.021683
chr16: 53260310 . . . 53269212; +
1
CHD9;
CODING
2
3660920; 3660927;

(100%);

Block_6631
0.021683
chr7: 95213206 . . . 95224446; −
1
PDK4;
CODING
12
3062083; 3062084;

(91.66%); UTR

3062087; 3062089;

(8.33%);

3062091; 3062096;

3062099; 3062100;

3062102; 3062103;

3062105; 3062108;

Block_5961
0.02201
chr5: 95087958 . . . 95103870; +
1
RHOBTB3;
CODING
3
2820942; 2820947;

(100%);

2820954;

Block_6316
0.02201
chr6: 159222851 . . . 159229779; −
1
EZR;
INTRONIC
2
2981957; 2981963;

(100%);

Block_6886
0.02201
chr8: 17573279 . . . 17612789; −
1
MTUS1;
CODING
4
3125964; 3125967;

(100%);

3125973; 3125975;

Block_1915
0.022342
chr12: 93968968 . . . 93969774; +
1
SOCS2;
UTR (100%);
4
3426279; 3426280;

3426281; 3426282;

Block_1263
0.022679
chr11: 62568586 . . . 62571024; −
1
NXF1;
CODING
3
3376178; 3376180;

(100%);

3376187;

Block_7564
0.022679
chrX: 11369976 . . . 11398542; −
1
ARHGAP6;
INTRONIC
2
3999693; 3999639;

(100%);

Block_7605
0.022679
chrX: 67272384 . . . 67284017; −
1
OPHN1;
CODING
2
4011206; 4011209;

(100%);

Block_4815
0.023019
chr3: 64630315 . . . 64636668; −
1
ADAMTS9;
UTR (50%);
2
2680133; 2680139;

INTRONIC

(50%);

Block_5174
0.023019
chr3: 150128646 . . . 150129079; +
1
TSC22D2;
CODING
2
2647664; 2647665;

(100%);

Block_3214
0.023364
chr17: 39969468 . . . 39976700; +
1
FKBP10;
CODING
5
3721456; 3721461;

(100%);

3721462; 3721465;

3721472;

Block_3456
0.023714
chr18: 59958780 . . . 59972846; +
1
KIAA1468;
CODING
4
3791229; 3791231;

(75%); UTR

3791236; 3791237;

(25%);

Block_4141
0.024068
chr2: 102781282 . . . 102792104; +
1
IL1R1;
CODING
7
2497000; 2497001;

(100%);

2497002; 2497004;

2497007; 2497010;

2497012;

Block_6797
0.024068
chr7: 99169519 . . . 99170579; +
1
ZNF655;
CODING
2
3014924; 3014928;

(50%);

INTRONIC

(50%);

Block_7300
0.024068
chr9: 124124355 . . . 124128420; −
1
STOM;
UTR (50%);
2
3223950; 3223954;

INTRONIC

(50%);

Block_1042
0.024426
chr10: 43615579 . . . 43622087; +
1
RET;
CODING
3
3243877; 3243878;

(100%);

3243881;

Block_3534
0.024426
chr19: 40540451 . . . 40540826; −
1
ZNF780B;
CODING
2
3862345; 3862347;

(100%);

Block_3668
0.024426
chr19: 49377023 . . . 49378997; +
1
PPP1R15A;
CODING
4
3838008; 3838010;

(100%);

3838011; 3838013;

Block_4625
0.024426
chr22: 29190562 . . . 29191698; −
1
XBP1;
CODING
3
3956591; 3956593;

(66.66%); UTR

3956594;

(33.33%);

Block_6815
0.024426
chr7: 104749510 . . . 104750810; +
1
MLL5;
CODING
2
3017637; 3017638;

(100%);

Block_37
0.024789
chr1: 8072266 . . . 8082267; −
1
ERRFI1;
ncTRANSCRIPT
10
2395182; 2395184;

(10%);

2395187; 2395188;

CODING

2395189; 2395190;

(30%); UTR

2395191; 2395192;

(30%);

2395193; 2395195;

INTRONIC

(30%);

Block_5138
0.024789
chr3: 121615255 . . . 121660380; +
1
SLC15A2;
CODING
21
2638732; 2638733;

(90.47%); UTR

2638734; 2638735;

(9.52%);

2638737; 2638738;

2638742; 2638743;

2638744; 2638745;

2638746; 2638749;

2638750; 2638751;

2638754; 2638756;

2638757; 2638758;

2638760; 2638761;

2638762;

Block_6328
0.024789
chr6: 169616207 . . . 169620400; −
1
THBS2;
CODING
6
2985811; 2985812;

(16.66%); UTR

2985813; 2985814;

(83.33%);

2985815; 2985816;

Block_6611
0.024789
chr7: 75721390 . . . 75729255; −
1
AC005077.12;
ncTRANSCRIPT
2
3057596; 3057600;

(100%);

Block_940
0.024789
chr10: 88848954 . . . 88853651; −
1
GLUD1;
UTR (60%);
5
3299016; 4038350;

INTRONIC

3299019; 3299020;

(40%);

3299022;

Block_2230
0.025157
chr14: 69421708 . . . 69430379; −
1
ACTN1;
INTRONIC
2
3569890; 3569894;

(100%);

Block_2275
0.025157
chr14: 102548195 . . . 102552551; −
1
HSP90AA1;
INTRONIC
6
3580183; 3580189;

(100%);

3580195; 3580199;

3580201; 3580206;

Block_2928
0.025157
chr16: 84910468 . . . 84914235; +
1
CRISPLD2;
INTRONIC
2
3671967; 3671971;

(100%);

Block_7374
0.025157
chr9: 140375422 . . . 140389574; −
1
PNPLA7;
CODING
3
3231051; 3231059;

(100%);

3231063;

Block_949
0.025157
chr10: 95066684 . . . 95066750; −
1
MYOF;
CODING
2
3300605; 3300606;

(50%); UTR

(50%);

Block_2931
0.02553
chr16: 89758258 . . . 89759855; +
1
CDK10;
CODING
3
3674319; 3674324;

(100%);

3674326;

Block_4003
0.02553
chr2: 227661614 . . . 227662290; −
1
IRS1;
CODING
2
2602044; 2602045;

(100%);

Block_4979
0.02553
chr3: 160803580 . . . 160804455; −
1
B3GALNT1;
CODING
2
2703388; 2703390;

(100%);

Block_2786
0.025907
chr16: 72827353 . . . 72832458; −
1
ZFHX3;
CODING
2
3698277; 3698282;

(100%);

Block_3856
0.025907
chr2: 106005706 . . . 106013825; −
1
FHL2;
INTRONIC
2
2568719; 2568727;

(100%);

Block_5618
0.025907
chr4: 148800406 . . . 148834290; +
1
ARHGAP10;
CODING
2
2746744; 2746753;

(100%);

Block_6861
0.025907
chr7: 139083359 . . . 139090458; +
1
LUC7L2;
CODING
3
3027013; 3027014;

(100%);

3027015;

Block_2302
0.026289
chr14: 38038123 . . . 38038868; +
0

INTERGENIC
2
3533022; 3533023;

(100%);

Block_5707
0.026289
chr5: 54786572 . . . 54830000; −
1
PPAP2A;
INTRONIC
8
2857242; 2857264;

(100%);

2857273; 2857275;

2857280; 2857282;

2857246; 2857254;

Block_3672
0.026677
chr19: 49606718 . . . 49606842; +
1
SNRNP70;
UTR (100%);
2
3838212; 3838213;

Block_4626
0.026677
chr22: 29192148 . . . 29195118; −
1
XBP1;
CODING
3
3956598; 3956600;

(100%);

3956604;

Block_4994
0.026677
chr3: 185643370 . . . 185644451; −
1
TRA2B;
CODING
2
2709093; 2709095;

(100%);

Block_7143
0.026677
chr8: 102555510 . . . 102565001; +
1
GRHL2;
CODING
2
3109712; 3109716;

(100%);

Block_3735
0.027069
chr2: 24535214 . . . 24536392; −
1
ITSN2;
CODING
2
2544325; 2544328;

(100%);

Block_3847
0.027069
chr2: 100372047 . . . 100415240; −
1
AFF3;
INTRONIC
5
2566941; 2566942;

(100%);

2566948; 2566949;

2566955;

Block_4519
0.027069
chr21: 29811695 . . . 29818793; −
1
AF131217.1;
ncTRANSCRIPT
4
3927812; 3927814;

(50%);

3927818; 3927819;

INTERGENIC

(50%);

Block_5170
0.027069
chr3: 141596514 . . . 141622381; +
1
ATP1B3;
INTRONIC
6
2645770; 2645771;

(100%);

2645775; 2645776;

2645777; 2645780;

Block_1940
0.027465
chr12: 110720638 . . . 110723521; +
1
ATP2A2;
INTRONIC
2
3431489; 3431491;

(100%);

Block_5676
0.027465
chr5: 29476852 . . . 29477004; −
0

INTERGENIC
2
2851724; 2851725;

(100%);

Block_6897
0.027465
chr8: 22570904 . . . 22582442; −
1
PEBP4;
CODING
2
3127612; 3127614;

(100%);

Block_1730
0.027867
chr12: 103238114 . . . 103246723; −
1
PAH;
CODING
3
3468493; 3468497;

(100%);

3468501;

Block_1770
0.027867
chr12: 118597975 . . . 118610428; −
1
TAOK3;
CODING
2
3473817; 3473823;

(100%);

Block_5708
0.027867
chr5: 55243448 . . . 55246076; −
1
IL6ST;
CODING
4
2857431; 2857432;

(25%);

2857433; 2857435;

ncTRANSCRIPT

(25%);

INTRONIC

(50%);

Block_7242
0.027867
chr9: 94180062 . . . 94184577; −
1
NFIL3;
INTRONIC
2
3214459; 3214464;

(100%);

Block_1097
0.028274
chr10: 93753461 . . . 93756275; +
1
BTAF1;
CODING
3
3257988; 3257990;

(100%);

3257991;

Block_1270
0.028274
chr11: 64536711 . . . 64540977; −
1
SF1;
CODING
3
3377068; 3377069;

(100%);

3377075;

Block_1523
0.028274
chr11: 114028398 . . . 114028592; +
1
ZBTB16;
INTRONIC
2
3349769; 3349770;

(100%);

Block_2882
0.028274
chr16: 56968915 . . . 56970561; +
1
HERPUD1;
INTRONIC
3
3662392; 3662394;

(100%);

3662396;

Block_2912
0.028274
chr16: 69727019 . . . 69727890; +
1
NFAT5;
CODING
3
3666854; 3666855;

(100%);

3666860;

Block_5233
0.028274
chr3: 186790651 . . . 186795948; +
1
ST6GAL1;
CODING
5
2656865; 2656867;

(80%); UTR

2656868; 2656869;

(20%);

2656870;

Block_5487
0.028274
chr4: 40104120 . . . 40104817; +
1
N4BP2;
CODING
2
2724618; 2724619;

(100%);

Block_7363
0.028274
chr9: 140350912 . . . 140350938; −
1
NELF;
CODING
2
3231002; 4051780;

(100%);

Block_7688
0.028274
chrX: 138158562 . . . 138160882; −
1
FGF13;
INTRONIC
2
4024012; 4023960;

(100%);

Block_7633
0.028686
chrX: 76938144 . . . 76938170; −
1
ATRX;
CODING
2
4013275; 4055301;

(100%);

Block_1337
0.029104
chr11: 111779401 . . . 111782388; −
1
CRYAB;
CODING
4
3391171; 3391173;

(75%); UTR

3391176; 3391181;

(25%);

Block_3757
0.029104
chr2: 38976048 . . . 38976240; −
1
SRSF7;
UTR (100%);
2
2548987; 2548988;

Block_5217
0.029104
chr3: 182987375 . . . 182988389; +
1
B3GNT5;
CODING
4
2654979; 2654980;

(75%); UTR

2654981; 2654983;

(25%);

Block_7420
0.029104
chr9: 72912918 . . . 72915067; +
1
SMC5;
CODING
2
3174237; 3174238;

(100%);

Block_7630
0.029104
chrX: 76912053 . . . 76912120; −
1
ATRX;
CODING
2
4013266; 4055308;

(100%);

Block_2316
0.029526
chr14: 52794058 . . . 52794156; +
1
PTGER2;
CODING
2
3535798; 3535799;

(100%);

Block_2728
0.029526
chr16: 28123180 . . . 28123325; −
1
XPO6;
CODING
2
3686351; 3686352;

(100%);

Block_2900
0.029526
chr16: 68155896 . . . 68160503; +
1
NFATC3;
CODING
5
3666049; 3666050;

(100%);

3666052; 3666053;

3666055;

Block_5704
0.029526
chr5: 54721975 . . . 54822340; −
1
PPAP2A;
CODING
25
2857212; 2857213;

(4%);

2857218; 2857219;

INTRONIC

2857221; 2857222;

(96%);

2857224; 2857226;

2857227; 2857231;

2857232; 2857238;

2857240; 2857241;

2857243; 2857244;

2857269; 2857271;

2857277; 2857284;

2857267; 2857247;

2857248; 2857249;

2857250;

Block_5989
0.029526
chr5: 113698875 . . . 113699698; +
1
KCNN2;
CODING
2
2824632; 2824635;

(100%);

Block_6904
0.029526
chr8: 27317314 . . . 27336535; −
1
CHRNA2;
CODING
10
3129025; 3129030;

(60%); UTR

3129034; 3129038;

(40%);

3129039; 3129040;

3129044; 3129045;

3129046; 3129047;

Block_2245
0.029954
chr14: 76424744 . . . 76448197; −
1
TGFB3;
INTERGENIC
11
3572518; 3572524;

(9.09%);

3572528; 3572529;

CODING

3572533; 3572534;

(45.45%); UTR

3572539; 3572540;

(45.45%);

3572541; 3572542;

3572543;

Block_6439
0.029954
chr6: 80383340 . . . 80406282; +
1
SH3BGRL2;
CODING
2
2914706; 2914708;

(100%);

Block_6719
0.029954
chr7: 12620691 . . . 12691507; +
1
SCIN;
CODING
9
2990415; 2990418;

(100%);

2990420; 2990421;

2990424; 2990425;

2990427; 2990430;

2990431;

Block_1375
0.030387
chr11: 134022430 . . . 134095174; −
1
NCAPD3;
CODING
42
3399550; 3399551;

(90.47%); UTR

3399553; 3399555;

(7.14%);

3399562; 3399563;

INTRONIC

3399565; 3399566;

(2.38%);

3399567; 3399569;

3399570; 3399571;

3399572; 3399573;

3399574; 3399576;

3399577; 3399579;

3399580; 3399581;

3399583; 3399584;

3399585; 3399587;

3399588; 3399589;

3399590; 3399591;

3399592; 3399593;

3399594; 3399595;

3399597; 3399598;

3399600; 3399601;

3399602; 3399603;

3399605; 3399606;

3399607; 3399613;

Block_2444
0.030387
chr15: 42730835 . . . 42737120; −
1
ZFP106;
CODING
3
3620619; 3620620;

(100%);

3620629;

Block_3525
0.030387
chr19: 23543094 . . . 23545314; −
1
ZNF91;
CODING
2
3857111; 3857120;

(100%);

Block_3864
0.030387
chr2: 121989436 . . . 121995260; −
1
TFCP2L1;
CODING
3
2573607; 2573609;

(100%);

2573613;

Block_5724
0.030387
chr5: 59683251 . . . 59770534; −
1
PDE4D;
INTRONIC
9
2858550; 2858561;

(100%);

2858551; 2858552;

2858565; 2858431;

2858567; 2858575;

2858577;

Block_2138
0.030825
chr13: 99098380 . . . 99099024; +
1
FARP1;
CODING
2
3498035; 3498037;

(100%);

Block_2878
0.030825
chr16: 56642626 . . . 56643147; +
1
MT2A;
INTRONIC
3
3662111; 3662112;

(100%);

3662115;

Block_6479
0.030825
chr6: 144070122 . . . 144075017; +
1
PHACTR2;
CODING
2
2928962; 2928964;

(100%);

Block_1627
0.031269
chr12: 26755308 . . . 26755636; −
1
ITPR2;
CODING
2
3448289; 3448290;

(100%);

Block_3754
0.031269
chr2: 38973291 . . . 38973876; −
1
SRSF7;
CODING
2
2548976; 2548978;

(100%);

Block_5751
0.031269
chr5: 90667505 . . . 90675837; −
1
ARRDC3;
ncTRANSCRIPT
6
2866739; 2866710;

(33.33%);

2866715; 2866719;

INTRONIC

2866723; 2866741;

(66.66%);

Block_612
0.031269
chr1: 110211967 . . . 110214138; +
1
GSTM2;
CODING
4
2350963; 2350964;

(100%);

2350971; 2350973;

Block_6189
0.031269
chr6: 56479851 . . . 56507576; −
1
DST;
CODING
27
2958476; 2958479;

(96.29%); UTR

2958484; 2958485;

(3.70%);

2958486; 2958487;

2958488; 2958489;

2958490; 2958491;

2958493; 2958494;

2958496; 2958497;

2958498; 2958500;

2958501; 2958502;

2958505; 2958506;

2958507; 2958508;

2958509; 2958510;

2958511; 2958512;

2958513;

Block_906
0.031269
chr10: 64988219 . . . 65015457; −
1
JMJD1C;
INTRONIC
4
3291839; 3291736;

(100%);

3291737; 3291741;

Block_1499
0.031719
chr11: 82878465 . . . 82878887; +
1
PCF11;
CODING
2
3342544; 3342545;

(100%);

Block_2187
0.031719
chr14: 30374876 . . . 30385713; −
1
PRKD1;
INTRONIC
2
3559283; 3559284;

(100%);

Block_3198
0.031719
chr17: 32583269 . . . 32584108; +
1
CCL2;
CODING
3
3718173; 3718175;

(66.66%); UTR

3718176;

(33.33%);

Block_4897
0.031719
chr3: 120370215 . . . 120370855; −
1
HGD;
INTRONIC
2
2691428; 4047088;

(100%);

Block_6093
0.031719
chr6: 3270435 . . . 3287296; −
1
SLC22A23;
CODING
4
2939302; 2939303;

(50%); UTR

2939307; 2939313;

(50%);

Block_6632
0.031719
chr7: 95215175 . . . 95216702; −
1
PDK4;
INTRONIC
2
3062085; 3062088;

(100%);

Block_2121
0.032173
chr13: 76379046 . . . 76379380; +
1
LMO7;
INTRONIC
2
3494196; 3494197;

(100%);

Block_460
0.032173
chr1: 19981582 . . . 19984800; +
1
NBL1;
CODING
3
2323777; 2323778;

(66.66%); UTR

2323782;

(33.33%);

Block_5035
0.032173
chr3: 19190143 . . . 19190250; +
1
KCNH8;
CODING
2
2613294; 2613295;

(50%); UTR

(50%);

Block_5642
0.032173
chr4: 166301254 . . . 166375499; +
1
CPE;
CODING
16
2750634; 2750635;

(6.25%); UTR

2750636; 2750638;

(12.5%);

2750639; 2750640;

INTRONIC

2750642; 2750643;

(81.25%);

2750680; 2750646;

2750647; 2750649;

2750650; 2750653;

2750655; 2750659;

Block_5909
0.032173
chr5: 56526692 . . . 56531821; +
1
GPBP1;
CODING
2
2810484; 2810487;

(100%);

Block_7744
0.032173
chrX: 23803557 . . . 23803771; +
1
SAT1;
ncTRANSCRIPT
2
3971823; 3971825;

(50%);

INTRONIC

(50%);

Block_4816
0.032634
chr3: 64666890 . . . 64672644; −
1
ADAMTS9;
CODING
4
2680160; 2680168;

(100%);

2680170; 2680172;

Block_5167
0.032634
chr3: 140251178 . . . 140275496; +
1
CLSTN2;
CODING
2
2645167; 2645174;

(100%);

Block_5713
0.032634
chr5: 58442688 . . . 58450083; −
1
PDE4D;
INTRONIC
3
2858190; 2858192;

(100%);

2858194;

Block_5967
0.032634
chr5: 96215443 . . . 96222457; +
1
ERAP2;
CODING
2
2821370; 2821373;

(100%);

Block_2001
0.0331
chr13: 45048688 . . . 45053829; −
1
TSC22D1;
INTRONIC
2
3512320; 3512324;

(100%);

Block_2049
0.0331
chr13: 95873854 . . . 95889452; −
1
ABCC4;
INTRONIC
5
3521282; 3521283;

(100%);

3521284; 3521286;

3521293;

Block_6693
0.0331
chr7: 151864248 . . . 151873818; −
1
MLL3;
CODING
4
3080082; 3080086;

(100%);

3080088; 3080089;

Block_7514
0.0331
chr9: 136333151 . . . 136333198; +
1
C9orf7;
INTRONIC
2
3193029; 4050936;

(100%);

Block_127
0.033572
chr1: 25573295 . . . 25573974; −
1
C1orf63;
CODING
3
2402129; 2402130;

(33.33%); UTR

2402134;

(66.66%);

Block_4749
0.033572
chr3: 18427936 . . . 18438764; −
1
SATB1;
CODING
3
2665227; 2665231;

(100%);

2665233;

Block_5231
0.033572
chr3: 186760464 . . . 186769107; +
1
ST6GAL1;
CODING
3
2656855; 2656857;

(66.66%); UTR

2656858;

(33.33%);

Block_6269
0.033572
chr6: 132617405 . . . 132618041; −
1
MOXD1;
UTR (100%);
2
2974428; 2974429;

Block_852
0.033572
chr10: 18837090 . . . 18840876; −
1
NSUN6;
CODING
2
3280249; 3280253;

(100%);

Block_2202
0.034049
chr14: 50296082 . . . 50298964; −
1
NEMF;
CODING
3
3563511; 3563512;

(100%);

3563514;

Block_3855
0.034049
chr2: 106002513 . . . 106013154; −
1
FHL2;
CODING
2
2568717; 2568725;

(50%);

INTRONIC

(50%);

Block_4103
0.034049
chr2: 61333740 . . . 61335484; +
1
KIAA1841;
CODING
2
2484488; 2484489;

(100%);

Block_4837
0.034049
chr3: 71622652 . . . 71629752; −
2
RP11-
ncTRANSCRIPT
2
2682247; 2682249;

154H23.1;
(50%);

FOXP1;
INTRONIC

(50%);

Block_6424
0.034049
chr6: 71125002 . . . 71264155; +
2
RNU7-
ncTRANSCRIPT
30
2912782; 2912787;

48P;
(6.66%);

2912788; 2912795;

FAM135A;
CODING

2912802; 2912803;

(63.33%); UTR

2912806; 2912808;

(3.33%);

2912809; 2912813;

INTRONIC

2912814; 2912815;

(26.66%);

2912816; 2912817;

2912818; 2912819;

2912820; 2912822;

2912824; 2912828;

2912829; 2912831;

2912832; 2912833;

2912838; 2912839;

2912841; 2912842;

2912847; 2912849;

Block_6453
0.034049
chr6: 108938446 . . . 108942121; +
1
FOXO3;
INTRONIC
2
2920510; 2920512;

(100%);

Block_1077
0.034532
chr10: 77453352 . . . 77454380; +
1
C10orf11;
INTRONIC
2
3252742; 3252954;

(100%);

Block_1250
0.034532
chr11: 61295389 . . . 61300540; −
1
SYT7;
CODING
2
3375406; 3375409;

(100%);

Block_6190
0.034532
chr6: 56503045 . . . 56504056; −
1
DST;
ncTRANSCRIPT
2
2958503; 2958504;

(50%);

INTRONIC

(50%);

Block_6677
0.034532
chr7: 136935982 . . . 136938338; −
1
PTN;
CODING
2
3074872; 3074873;

(100%);

Block_911
0.034532
chr10: 70276866 . . . 70276996; −
1
SLC25A16;
UTR (100%);
2
3292763; 3292764;

Block_993
0.034532
chr10: 118687375 . . . 118704523; −
1
KIAA1598;
CODING
2
3308529; 3308533;

(100%);

Block_3168
0.035021
chr17: 7945688 . . . 7951882; +
1
ALOX15B;
CODING
11
3709424; 3709426;

(100%);

3709428; 3709429;

3709430; 3709432;

3709433; 3709435;

3709437; 3709438;

3709440;

Block_3739
0.035021
chr2: 31749837 . . . 31754527; −
1
SRD5A2;
ncTRANSCRIPT
3
2547235; 2547237;

(100%);

2547238;

Block_3049
0.035516
chr17: 56492694 . . . 56494638; −
1
RNF43;
INTERGENIC
4
3764435; 3764437;

(25%);

3764438; 3764441;

CODING

(25%); UTR

(50%);

Block_5576
0.035516
chr4: 106474899 . . . 106477521; +
1
ARHGEF38;
INTRONIC
4
2738247; 2738268;

(100%);

2738270; 2738248;

Block_5770
0.035516
chr5: 98224781 . . . 98231958; −
1
CHD1;
CODING
4
2868574; 2868577;

(100%);

2868578; 2868580;

Block_7247
0.035516
chr9: 95146567 . . . 95155495; −
1
OGN;
CODING
6
3214802; 3214803;

(50%); UTR

3214804; 3214806;

(50%);

3214807; 3214810;

Block_7810
0.035516
chrX: 105153170 . . . 105156727; +
1
NRK;
CODING
2
3986120; 3986121;

(100%);

Block_1289
0.036017
chr11: 70824339 . . . 70830068; −
1
SHANK2;
CODING
2
3380586; 3380591;

(100%);

Block_2429
0.036017
chr15: 37195097 . . . 37210290; −
1
MEIS2;
INTRONIC
3
3618360; 3618366;

(100%);

3618367;

Block_2493
0.036017
chr15: 60677881 . . . 60688620; −
1
ANXA2;
INTRONIC
9
3627332; 3627334;

(100%);

3627336; 3627341;

3627343; 3627345;

3627346; 3627348;

3627349;

Block_3679
0.036017
chr19: 51359727 . . . 51362135; +
1
KLK3;
UTR (50%);
2
3839545; 3839552;

INTRONIC

(50%);

Block_6037
0.036017
chr5: 145843146 . . . 145843355; +
1
TCERG1;
CODING
2
2834115; 2834117;

(100%);

Block_7373
0.036017
chr9: 140361786 . . . 140361907; −
1
PNPLA7;
CODING
2
3231040; 4051817;

(100%);

Block_2894
0.036524
chr16: 67159862 . . . 67178779; +
1
C16orf70;
CODING
6
3665168; 3665171;

(100%);

3665173; 3665177;

3665179; 3665183;

Block_3987
0.036524
chr2: 216226027 . . . 216299511; −
1
FN1;
CODING
57
2598267; 2598268;

(94.73%); UTR

2598269; 2598270;

(5.26%);

2598271; 2598273;

2598276; 2598277;

2598280; 2598281;

2598284; 2598286;

2598288; 2598289;

2598290; 2598294;

2598296; 2598299;

2598301; 2598302;

2598304; 2598306;

2598307; 2598308;

2598310; 2598313;

2598314; 2598318;

2598321; 2598324;

2598325; 2598328;

2598329; 2598330;

2598331; 2598334;

2598335; 2598338;

2598339; 2598340;

2598342; 2598344;

2598346; 2598352;

2598353; 2598354;

2598356; 2598357;

2598358; 2598360;

2598362; 2598363;

2598367; 2598371;

2598372; 2598373;

2598374;

Block_416
0.036524
chr1: 235712540 . . . 235715511; −
1
GNG4;
CODING
4
2461942; 2461944;

(25%); UTR

2461945; 2461946;

(75%);

Block_7241
0.036524
chr9: 94171357 . . . 94172980; −
1
NFIL3;
CODING
3
3214452; 3214453;

(33.33%); UTR

3214454;

(66.66%);

Block_1192
0.037037
chr11: 8132291 . . . 8148335; −
1
RIC3;
CODING
2
3361638; 3361639;

(100%);

Block_1431
0.037037
chr11: 35226060 . . . 35227773; +
1
CD44;
CODING
2
3326700; 3326705;

(100%);

Block_1945
0.037037
chr12: 111558155 . . . 111620438; +
1
CUX2;
INTRONIC
3
3431789; 3431792;

(100%);

3431795;

Block_2436
0.037037
chr15: 42437997 . . . 42439930; −
1
PLA2G4F;
CODING
3
3620436; 3620439;

(100%);

3620441;

Block_6629
0.037037
chr7: 92354966 . . . 92355105; −
1
CDK6;
CODING
2
3061361; 3061362;

(100%);

Block_7095
0.037037
chr8: 42798476 . . . 42805590; +
1
HOOK3;
CODING
2
3096385; 3096387;

(100%);

Block_3287
0.037557
chr17: 65941696 . . . 65941965; +
1
BPTF;
CODING
2
3732514; 3732516;

(100%);

Block_5073
0.037557
chr3: 42678445 . . . 42687432; +
1
NKTR;
CODING
3
2619384; 2619390;

(100%);

2619399;

Block_4586
0.038082
chr21: 42541819 . . . 42601866; +
1
BACE2;
INTRONIC
8
3921943; 3921944;

(100%);

3921945; 3921949;

3921950; 3921951;

3921991; 3921961;

Block_7687
0.038082
chrX: 138063436 . . . 138104840; −
1
FGF13;
INTRONIC
4
4024021; 4024027;

(100%);

4024008; 4024011;

Block_7797
0.038082
chrX: 70782986 . . . 70784559; +
1
OGT;
CODING
3
3981153; 3981154;

(100%);

3981155;

Block_7864
0.038082
chrY: 21903642 . . . 21905110; −
1
KDM5D;
CODING
2
4036111; 4036113;

(100%);

Block_328
0.038614
chr1: 163112906 . . . 163122506; −
1
RGS5;
CODING
7
2441391; 2441393;

(42.85%); UTR

2441394; 2441395;

(57.14%);

2441396; 2441398;

2441399;

Block_5056
0.038614
chr3: 37356931 . . . 37360665; +
1
GOLGA4;
CODING
2
2617089; 2617093;

(100%);

Block_7459
0.038614
chr9: 102590326 . . . 102590574; +
1
NR4A3;
CODING
2
3181993; 3181994;

(100%);

Block_1339
0.039152
chr11: 115211747 . . . 115213046; −
1
CADM1;
INTRONIC
2
3392448; 3392450;

(100%);

Block_1817
0.039152
chr12: 11805464 . . . 11817168; +
1
ETV6;
INTRONIC
3
3405046; 3405051;

(100%);

3405055;

Block_6459
0.039152
chr6: 116431503 . . . 116431626; +
1
NT5DC1;
INTRONIC
2
2922530; 2922531;

(100%);

Block_7222
0.039152
chr9: 74978264 . . . 74978497; −
1
ZFAND5;
UTR (50%);
2
3209642; 3209643;

INTRONIC

(50%);

Block_3275
0.039696
chr17: 59093209 . . . 59112144; +
1
BCAS3;
CODING
2
3729624; 3729628;

(100%);

Block_6094
0.039696
chr6: 3304594 . . . 3307353; −
1
SLC22A23;
INTRONIC
2
2939326; 2939328;

(100%);

Block_7372
0.039696
chr9: 140358830 . . . 140358908; −
1
PNPLA7;
CODING
2
3231037; 4051814;

(100%);

Block_7611
0.039696
chrX: 73434306 . . . 73442101; −
0

INTERGENIC
2
4012764; 4012770;

(100%);

Block_260
0.040246
chr1: 120295908 . . . 120307209; −
1
HMGCS2;
CODING
9
2431038; 2431042;

(100%);

2431044; 2431047;

2431050; 2431051;

2431056; 2431057;

2431058;

Block_7111
0.040246
chr8: 70570914 . . . 70572224; +
1
SULF1;
UTR (100%);
2
3102461; 3102463;

Block_1095
0.040803
chr10: 93722326 . . . 93723946; +
1
BTAF1;
CODING
2
3257967; 3257969;

(100%);

Block_6228
0.040803
chr6: 99853979 . . . 99857124; −
1
SFRS18;
CODING
2
2966275; 2966279;

(100%);

Block_1954
0.041367
chr12: 119631512 . . . 119632155; +
1
HSPB8;
CODING
2
3434022; 3434023;

(50%); UTR

(50%);

Block_2868
0.041367
chr16: 48395568 . . . 48396210; +
1
SIAH1;
CODING_AS
3
3659376; 3659377;

(100%);

3659378;

Block_5186
0.041367
chr3: 156249230 . . . 156254535; +
1
KCNAB1;
CODING
2
2649070; 2649077;

(100%);

Block_5326
0.041367
chr4: 80992745 . . . 80993659; −
1
ANTXR2;
CODING
2
2775042; 2775043;

(100%);

Block_749
0.041367
chr1: 203276405 . . . 203277831; +
1
BTG2;
CODING
3
2375671; 2375672;

(33.33%); UTR

2375673;

(66.66%);

Block_186
0.041937
chr1: 59247791 . . . 59248778; −
1
JUN;
CODING
2
2415092; 2415095;

(50%); UTR

(50%);

Block_161
0.042514
chr1: 51768040 . . . 51768245; −
1
TTC39A;
CODING
2
2412328; 2412330;

(100%);

Block_2076
0.042514
chr13: 24157611 . . . 24190183; +
1
TNFRSF19;
CODING
5
3481424; 3481425;

(60%);

3481429; 3481433;

ncTRANSCRIPT

3481434;

(20%);

INTRONIC

(20%);

Block_3220
0.042514
chr17: 40932892 . . . 40945698; +
1
WNK4;
CODING
8
3722087; 3722090;

(100%);

3722094; 3722095;

3722100; 3722101;

3722105; 3722106;

Block_3425
0.042514
chr18: 48581190 . . . 48586286; +
1
SMAD4;
CODING
2
3788324; 3788330;

(100%);

Block_3684
0.042514
chr19: 52462246 . . . 52469039; +
1
AC011460.1;
INTRONIC
4
3839986; 3839988;

(100%);

3839990; 3839992;

Block_4891
0.042514
chr3: 120351994 . . . 120352038; −
1
HGD;
CODING
2
2691376; 4047113;

(100%);

Block_5619
0.042514
chr4: 148860985 . . . 148876520; +
1
ARHGAP10;
CODING
3
2746763; 2746767;

(100%);

2746769;

Block_5894
0.042514
chr5: 38886367 . . . 38906492; +
1
OSMR;
UTR (33.33%);
3
2807398; 2807399;

INTRONIC

2807405;

(66.66%);

Block_3009
0.043098
chr17: 39079241 . . . 39084827; −
1
KRT23;
CODING
4
3756593; 3756596;

(100%);

3756602; 3756603;

Block_5560
0.043098
chr4: 95507630 . . . 95508222; +
1
PDLIM5;
CODING
3
2736395; 2736396;

(33.33%);

2736397;

INTRONIC

(66.66%);

Block_5893
0.043098
chr5: 38883930 . . . 38886253; +
1
OSMR;
CODING
2
2807390; 2807396;

(100%);

Block_2154
0.043688
chr13: 111896260 . . . 111920011; +
1
ARHGEF7;
CODING
2
3501707; 3501714;

(100%);

Block_2385
0.043688
chr14: 95081422 . . . 95084915; +
1
SERPINA3;
ncTRANSCRIPT
3
3549773; 3549776;

(100%);

3549777;

Block_3557
0.043688
chr19: 52568528 . . . 52579356; −
1
ZNF841;
CODING
4
3869431; 3869432;

(100%);

3869434; 3869435;

Block_3704
0.043688
chr19: 57802283 . . . 57804159; +
1
ZNF460;
CODING
3
3843164; 3843166;

(66.66%); UTR

3843168;

(33.33%);

Block_4020
0.043688
chr2: 239176702 . . . 239180131; −
1
PER2;
CODING
2
2605780; 2605784;

(100%);

Block_6547
0.043688
chr6: 168272897 . . . 168281196; +
1
MLLT4;
CODING
6
2936868; 4048405;

(100%);

2936869; 4048403;

4048399; 2936875;

Block_665
0.043688
chr1: 156100418 . . . 156106788; +
1
LMNA;
CODING
7
2361313; 2361314;

(100%);

2361316; 2361317;

2361320; 2361322;

2361325;

Block_1064
0.044285
chr10: 71119734 . . . 71128378; +
1
HK1;
CODING
2
3250324; 3250327;

(100%);

Block_4291
0.044285
chr2: 223758226 . . . 223772451; +
1
ACSL3;
INTRONIC
2
2529553; 2529557;

(100%);

Block_5884
0.044285
chr5: 14602311 . . . 14607558; +
1
FAM105A;
CODING
2
2802711; 2802714;

(100%);

Block_1047
0.044889
chr10: 51555733 . . . 51556843; +
1
MSMB;
CODING
2
3246411; 3246412;

(100%);

Block_2056
0.044889
chr13: 107211047 . . . 107211667; −
1
ARGLU1;
CODING
2
3524631; 3524633;

(50%);

INTRONIC

(50%);

Block_6885
0.044889
chr8: 17503466 . . . 17507465; −
1
MTUS1;
CODING
3
3125921; 3125923;

(100%);

3125925;

Block_7779
0.044889
chrX: 53114856 . . . 53115271; +
1
TSPYL2;
CODING
2
3978189; 3978190;

(100%);

Block_1046
0.0455
chr10: 51532298 . . . 51535286; +
2
TIMM23B;
ncTRANSCRIPT
4
3246373; 3246408;

RP11-
(50%);

3246374; 3246376;

481A12.2;
INTRONIC

(50%);

Block_3683
0.0455
chr19: 51380495 . . . 51381606; +
1
KLK2;
INTRONIC
2
3839580; 3839583;

(100%);

Block_377
0.0455
chr1: 207102212 . . . 207112808; −
1
PIGR;
CODING
11
2453007; 2453010;

(90.90%); UTR

2453011; 2453012;

(9.09%);

2453013; 2453015;

2453016; 2453018;

2453019; 2453020;

2453021;

Block_4748
0.0455
chr3: 17413596 . . . 17425454; −
1
TBC1D5;
CODING
5
2664953; 2664954;

(100%);

2664955; 2664956;

2664957;

Block_6405
0.0455
chr6: 44216514 . . . 44217722; +
1
HSP90AB1;
INTRONIC
2
2908484; 2908490;

(100%);

Block_181
0.046118
chr1: 57025279 . . . 57038895; −
1
PPAP2B;
INTRONIC
2
2414403; 2414411;

(100%);

Block_2341
0.046118
chr14: 64444642 . . . 64447421; +
1
SYNE2;
CODING
2
3539761; 3539763;

(100%);

Block_2611
0.046118
chr15: 71574554 . . . 71586847; +
1
THSD4;
INTRONIC
2
3600324; 3600327;

(100%);

Block_3692
0.046118
chr19: 54080729 . . . 54081190; +
1
ZNF331;
CODING
2
3840996; 3840998;

(100%);

Block_7486
0.046118
chr9: 130914205 . . . 130914547; +
1
LCN2;
CODING
2
3190204; 3190205;

(100%);

Block_1338
0.046743
chr11: 115099833 . . . 115111135; −
1
CADM1;
CODING
3
3392393; 3392394;

(100%);

3392398;

Block_2668
0.046743
chr15: 99372148 . . . 99385603; +
1
IGF1R;
INTRONIC
3
3610947; 3610951;

(100%);

3610955;

Block_2766
0.046743
chr16: 65005837 . . . 65022233; −
1
CDH11;
CODING
3
3694677; 3694684;

(100%);

3694691;

Block_4086
0.046743
chr2: 46529640 . . . 46533141; +
1
EPAS1;
INTRONIC
2
2480399; 2480401;

(100%);

Block_4584
0.046743
chr21: 40179160 . . . 40196766; +
1
ETS2;
CODING
22
3921087; 3921088;

(40.90%); UTR

3921089; 3921091;

(18.18%);

3921092; 3921094;

INTRONIC

3921096; 3921097;

(40.90%);

3921098; 3921099;

3921100; 3921101;

3921102; 3921104;

3921105; 3921107;

3921109; 3921112;

3921115; 3921116;

3921118; 3921119;

Block_5358
0.046743
chr4: 102196342 . . . 102200906; −
1
PPP3CA;
INTRONIC
2
2779709; 2779739;

(100%);

Block_5575
0.046743
chr4: 106155858 . . . 106158231; +
1
TET2;
CODING
2
2738167; 2738170;

(100%);

Block_5710
0.046743
chr5: 56219003 . . . 56219619; −
1
MIER3;
CODING
2
2857736; 2857737;

(100%);

Block_1923
0.047375
chr12: 97945516 . . . 97949840; +
1
RMST;
INTRONIC
2
3427537; 3427541;

(100%);

Block_2122
0.047375
chr13: 76395328 . . . 76397948; +
1
LMO7;
CODING
2
3494214; 3494216;

(100%);

Block_2950
0.047375
chr17: 3743397 . . . 3746434; −
1
C17orf85;
CODING
2
3741707; 3741708;

(100%);

Block_2090
0.048014
chr13: 31231614 . . . 31232191; +
1
USPL1;
CODING
2
3484044; 3484045;

(100%);

Block_2246
0.048014
chr14: 76446944 . . . 76447361; −
1
TGFB3;
CODING
2
3572536; 3572538;

(50%); UTR

(50%);

Block_6421
0.048014
chr6: 64286908 . . . 64288684; +
1
PTP4A1;
INTRONIC
3
2911920; 2911921;

(100%);

2911925;

Block_6888
0.048014
chr8: 18725208 . . . 18729431; −
1
PSD3;
CODING
2
3126326; 3126328;

(100%);

Block_1477
0.048661
chr11: 66391897 . . . 66392352; +
1
RBM14;
CODING
2
3336384; 3336386;

(100%);

Block_1662
0.048661
chr12: 52485769 . . . 52486601; −
0

INTERGENIC
2
3455115; 3455117;

(100%);

Block_171
0.048661
chr1: 53363109 . . . 53370744; −
1
ECHDC2;
CODING
3
2413037; 2413040;

(100%);

2413044;

Block_6153
0.048661
chr6: 35623219 . . . 35655662; −
1
FKBP5;
INTRONIC
7
2951608; 2951610;

(100%);

2951614; 2951615;

2951616; 2951619;

2951627;

Block_7713
0.048661
chrX: 2541426 . . . 2541450; +
1
CD99P1;
ncTRANSCRIPT
2
3966810; 4028424;

(100%);

Block_875
0.048661
chr10: 33195427 . . . 33195769; −
1
ITGB1;
CODING
2
3284196; 3284197;

(50%);

INTRONIC

(50%);

Block_934
0.048661
chr10: 79593681 . . . 79603456; −
1
DLG5;
CODING
3
3296448; 3296449;

(100%);

3296455;

Block_1166
0.049314
chr10: 128816976 . . . 128817096; +
1
DOCK1;
CODING
2
3269979; 3269980;

(100%);

Block_1946
0.049314
chr12: 111655706 . . . 111701632; +
1
CUX2;
CODING
2
3431801; 3431809;

(100%);

Block_2243
0.049314
chr14: 75745675 . . . 75748413; −
1
FOS;
CODING_AS
2
3572391; 3572392;

(100%);

Block_4019
0.049314
chr2: 239162223 . . . 239164537; −
1
PER2;
CODING
2
2605759; 2605760;

(100%);

Block_4293
0.049314
chr2: 223781554 . . . 223782703; +
1
ACSL3;
ncTRANSCRIPT
2
2529572; 2529573;

(50%);

INTRONIC

(50%);

Block_6218
0.049314
chr6: 90385836 . . . 90387413; −
1
MDN1;
CODING
2
2964413; 2964414;

(100%);

Block_3693
0.049976
chr19: 54080311 . . . 54081259; +
1
ZNF331;
CODING
2
3840995; 3840999;

(50%); UTR

(50%);

Block_819
0.049976
chr1: 229242103 . . . 229242133; +
0

INTERGENIC
2
2384497; 4042435;

(100%);

TABLE 24

SEQ ID NO.:
Block ID
Comparison
Probe Set ID

293
Block_7113
BCR
3103710

297
Block_7113
BCR
3103707

300
Block_7113
BCR
3103712

303
Block_7113
BCR
3103708

309
Block_7113
BCR
3103706

311
Block_7113
BCR
3103713

312
Block_7113
BCR
3103715

316
Block_7113
BCR
3103704

481
Block_2879
BCR
3662122

482
Block_2879
BCR
3662124

483
Block_2879
BCR
3662156

484
Block_2879
BCR
3662163

485
Block_2922
GS
3670638

486
Block_2922
GS
3670639

487
Block_2922
GS
3670641

488
Block_2922
GS
3670644

489
Block_2922
GS
3670645

490
Block_2922
GS
3670650

491
Block_2922
GS
3670659

492
Block_2922
GS
3670660

493
Block_4271
GS
2528108

494
Block_4271
GS
2528110

495
Block_4271
GS
2528111

496
Block_4271
GS
2528112

497
Block_4271
GS
2528113

498
Block_4271
GS
2528115

499
Block_5000
GS
2608324

500
Block_5080
GS
2624393

501
Block_5080
GS
2624394

502
Block_5080
GS
2624395

503
Block_5080
GS
2624399

504
Block_5080
GS
2624416

505
Block_5080
GS
2624421

506
Block_5080
GS
2624427

507
Block_5080
GS
2624429

508
Block_5080
GS
2624453

509
Block_5080
GS
2624459

510
Block_5080
GS
2624460

511
Block_5080
GS
2624461

512
Block_5080
GS
2624462

513
Block_5080
GS
2624465

514
Block_5080
GS
2624466

515
Block_5080
GS
2624467

516
Block_5080
GS
2624470

517
Block_5080
GS
2624472

518
Block_5080
GS
2624473

519
Block_5080
GS
2624475

520
Block_5080
GS
2624477

521
Block_5080
GS
2624479

522
Block_5080
GS
2624480

523
Block_5080
GS
2624481

524
Block_5080
GS
2624482

525
Block_5080
GS
2624484

526
Block_5080
GS
2624485

527
Block_5080
GS
2624487

528
Block_5080
GS
2624488

529
Block_5080
GS
2624491

530
Block_5080
GS
2624494

531
Block_5080
GS
2624499

532
Block_5080
GS
2624500

533
Block_5080
GS
2624501

534
Block_5080
GS
2624502

535
Block_5080
GS
2624503

536
Block_5080
GS
2624504

537
Block_5080
GS
2624505

538
Block_5080
GS
2624507

539
Block_5080
GS
2624511

540
Block_5080
GS
2624515

541
Block_5080
GS
2624516

542
Block_5080
GS
2624518

543
Block_5080
GS
2624519

544
Block_5080
GS
2624526

545
Block_5470
BCR
2719689

546
Block_5470
BCR
2719692

547
Block_5470
BCR
2719694

548
Block_6371
BCR
2902713

549
Block_6371
BCR
2902730

550
Block_6592
BCR
3046457

551
Block_6592
BCR
3046459

552
Block_6592
BCR
3046460

553
Block_6592
BCR
3046461

554
Block_6592
BCR
3046462

555
Block_6592
BCR
3046465

556
Block_7113
BCR
3103714

557
Block_7113
BCR
3103717

558
Block_7716
GS
3970026

559
Block_7716
GS
3970034

560
Block_5470
BCR
2719696

561
Block_2922
GS
3670666

562
Block_4627
BCR
3956596

563
Block_4627
BCR
3956601

564
Block_5080
GS
2624397

565
Block_5080
GS
2624398

566
Block_5080
GS
2624400

567
Block_5080
GS
2624401

568
Block_5080
GS
2624402

569
Block_5080
GS
2624403

570
Block_5080
GS
2624404

571
Block_5080
GS
2624405

572
Block_5080
GS
2624406

573
Block_5080
GS
2624407

574
Block_5080
GS
2624408

575
Block_5080
GS
2624411

576
Block_5080
GS
2624412

577
Block_5080
GS
2624413

578
Block_5080
GS
2624415

579
Block_5080
GS
2624417

580
Block_5080
GS
2624422

581
Block_5080
GS
2624424

582
Block_5080
GS
2624426

583
Block_5080
GS
2624428

584
Block_5080
GS
2624432

585
Block_5080
GS
2624434

586
Block_5080
GS
2624435

587
Block_5080
GS
2624438

588
Block_5080
GS
2624439

589
Block_5080
GS
2624440

590
Block_5080
GS
2624441

591
Block_5080
GS
2624442

592
Block_5080
GS
2624443

593
Block_5080
GS
2624444

594
Block_5080
GS
2624446

595
Block_5080
GS
2624458

596
Block_5080
GS
2624490

597
Block_5080
GS
2624492

598
Block_5080
GS
2624493

599
Block_5080
GS
2624495

600
Block_5080
GS
2624496

601
Block_5080
GS
2624508

602
Block_5080
GS
2624512

603
Block_5080
GS
2624529

604
Block_5080
GS
2624531

605
Block_5080
GS
2624533

606
Block_5080
GS
2624537

607
Block_5155
BCR
2642733

608
Block_5155
BCR
2642735

609
Block_5155
BCR
2642740

610
Block_5155
BCR
2642741

611
Block_5155
BCR
2642744

612
Block_5155
BCR
2642745

613
Block_5155
BCR
2642746

614
Block_5155
BCR
2642747

615
Block_5155
BCR
2642748

616
Block_5155
BCR
2642750

617
Block_5155
BCR
2642753

618
Block_5000
GS
2608331

619
Block_5000
GS
2608332

620
Block_7716
GS
3970036

621
Block_7716
GS
3970039

622
Block_2879
BCR
3662175

623
Block_4627
BCR
3956603

624
Block_5080
GS
2624430

625
Block_5155
BCR
2642738

626
Block_5155
BCR
2642739

627
Block_2922
GS
3670661

628
Block_4271
GS
2528118

629
Block_5000
GS
2608321

630
Block_5000
GS
2608326

631
Block_5080
GS
2624389

632
Block_5080
GS
2624527

633
Block_5470
BCR
2719695

634
Block_6592
BCR
3046448

635
Block_6592
BCR
3046449

636
Block_6592
BCR
3046450

637
Block_7113
BCR
3103705

638
Block_7113
BCR
3103718

639
Block_7113
BCR
3103720

640
Block_7113
BCR
3103721

641
Block_7113
BCR
3103725

642
Block_7113
BCR
3103726

TABLE 25

Train
Test

Low Risk
13
12

Upgraded
16
15

TABLE 26

SEQ ID NO:
Probe Set ID
GENE SYMBOL
DESCRIPTION

442
2343088
AK5
adenylate kinase 5

443
2476697
RASGRP3
RAS guanyl releasing protein 3 (calcium and DAG-

regulated)

444
2518183
UBE2E3
ubiquitin-conjugating enzyme E2E 3 (UBC4/5

homolog, yeast)

445
2523351
BMPR2
bone morphogenetic protein receptor, type II

(serine/threonine kinase)

446
2609586
RP11-58B17.1-015

447
2791421
FAM198B
family with sequence similarity 198, member B

448
2825939
PRR16
proline rich 16

449
3018630
SLC26A4
solute carrier family 26, member 4

450
3046126
AOAH
acyloxyacyl hydrolase (neutrophil)

451
3245912
WDFY4
WDFY family member 4

452
3331849
GLYATL1
glycine-N-acyltransferase-like 1

453
3332352
MS4A6E MS4A7
membrane-spanning 4-domains, subfamily A, member

MS4A14
6E; 7; 14

454
3374811
AP000640.10
NA

455
3490910
OLFM4
olfactomedin 4

456
3490922
OLFM4
olfactomedin 4

457
4030108
USP9Y
ubiquitin specific peptidase 9, Y-linked

TABLE 27

SEQ ID NO.:
Probe Set ID
Overlapping Gene

436
3454547
METTL7A

643
2351754
RP11-165H20.1

644
2352207
WNT2B

645
2425758
COL11A1

646
2425760
COL11A1

647
2439143
CD5L

648
2443478
SELE

649
2445999
ANGPTL1

650
2497104
IL1RL1; IL18R1

651
2537182
FAM150B

652
2557961
GKN2

653
2563801
AC096579.13; AC096579.7

654
2590074
ZNF385B

655
2597353
ACADL

656
2630510
ROBO2

657
2665784
ZNF385D

658
2690307
LSAMP

659
2690547
LSAMP; RP11-384F7.2

660
2735071
SPP1

661
2745931
HHIP

662
2745967
HHIP

663
2763608
PPARGC1A

664
2773359

665
2773360
PPBP

666
2877981
DNAJC18

667
2899180
HIST1H2BD

668
2931616
AKAP12

669
2992595
IL6

670
3010526
CD36

671
3039672
SOSTDC1

672
3066159
LHFPL3

673
3090264
ADAM28

674
3094812
TACC1

675
3094826
TACC1

676
3111647
PKHD1L1

677
3125131
DLC1

678
3127576

679
3128830
ADRA1A

680
3128833
ADRA1A

681
3142382
RP11-157I4.4

682
3142383
FABP4

683
3148249
RP11-152P17.2

684
3165878
TEK

685
3214804
OGN

686
3217691
NR4A3

687
3219225
KLF4

688
3248306
CDK1

689
3256240
AGAP11

690
3290059
PCDH15

691
3324452
FIBIN

692
3388860
MMP12

693
3388865
MMP12

694
3388870
MMP12

695
3388876
MMP12

696
3388879
MMP12

697
3420066
WIF1

698
3424154

699
3443978

700
3452294
SLC38A1

701
3461802
PTPRB

702
3489790
DLEU1

703
3517284
DACH1

704
3587566
GREM1

705
3589514
THBS1

706
3598183
AC069368.3; PLEKHO2

707
3620424
PLA2G4F

708
3624798

709
3629110
CSNK1G1; KIAA0101

710
3662123
MT1A

711
3716397
BLMH

712
3720984
TOP2A

713
3751793
SLC6A4

714
3763391
TMEM100

715
3834346
CEACAM5

716
3834373
CEACAM5

717
3834374
CEACAM5

718
3847635
RFX2

719
3847641
RFX2

720
3863109
ATP5SL

721
3863235
CEACAM5

TABLE 28

SEQ ID NO.:
Probe Set ID
Overlapping Gene

722
2325656
CLIC4

723
2340120
CACHD1

724
2343484
IFI44L

725
2370193

726
2372698

727
2425831
COL11A1

728
2432674
POLR3C

729
2451729

730
2464140
AKT3; RP11-370K11.1

731
2475754
LCLAT1

732
2477458
QPCT

733
2513024

734
2525590
MAP2

735
2525606
MAP2

736
2555049
BCL11A

737
2560264
AUP1

738
2570667
BUB1

739
2580618
LYPD6

740
2585026
SCN3A

741
2585470
SCN9A

742
2619699
SNRK

743
2643586

744
2647355
TM4SF4

745
2653664
KCNMB2

746
2654937
MCCC1

747
2658328
RP11-175P19.3

748
2658606

749
2685706
EPHA6

750
2700315
CPHL1P

751
2701244
MBNL1

752
2709053
IGF2BP2

753
2722908
PCDH7

754
2726525
OCIAD1

755
2730161
CSN1S1

756
2779454
DNAJB14

757
2794412
HPGD

758
2800740
ADCY2

759
2853946

760
2884854
GABRB2

761
2909786
C6orf141

762
2915096

763
2917256

764
2921416
SLC16A10

765
2925362
LAMA2

766
2933343
SNX9

767
2934286

768
2953202
RP1-278E11.5

769
2959207
LGSN

770
2959221

771
2977998
EPM2A

772
2982935

773
2983725
PACRG

774
2991528
HDAC9

775
2993670
CBX3

776
2996608
BMPER

777
3004356
ZNF679; RP11-3N2.13; RP11-3N2.1

778
3004687
ZNF138

779
3013087
COL1A2

780
3070073
FAM3C

781
3083209
CSMD1

782
3098089
ST18

783
3100188
RAB2A

784
3100290
CHD7

785
3105938
CPNE3

786
3106163

787
3118048

788
3124338
XKR6

789
3128057

790
3147448
UBR5

791
3153550
ASAP1

792
3154681

793
3194227

794
3241027
MAP3K8

795
3246418
TIMM23B; MSMB

796
3280411
C10orf112

797
3287743
RP11-463P17.1

798
3305180
COL17A1

799
3308634
PDZD8

800
3342551
PCF11

801
3393506
RP11-728F11.6; FXYD6

TABLE 29

SEQ ID NO.:
Probe Set ID
Overlapping Gene

653
2563801
AC096579.13; AC096579.7

663
2763608
PPARGC1A

685
3214804
OGN

802
2345084
CLCA4

803
2345093
CLCA4

804
2353490
ATP1A1

805
2374204
NR5A2

806
2451596
CHI3L1

807
2456712
SLC30A10

808
2490340
REG1A

809
2513937
B3GALT1

810
2513980
AC016723.4

811
2533060
UGT1A8; UGT1A10; UGT1A9

812
2563797
AC096579.13; AC096579.7

813
2563798
AC096579.13; AC096579.7

814
2594140
SATB2

815
2633196
CPOX

816
2635219
HHLA2

817
2730869
SLC4A4

818
2767399
ATP8A1

819
2772567
ENAM

820
2772569
IGJ

821
2772570
IGJ

822
2775911
PLAC8

823
2779235
ADH1B

824
2782578
CAMK2D

825
2872078
SEMA6A

826
2923919
PKIB

827
2974957
SLC2A12

828
2985814
THBS2

829
3018675
SLC26A3

830
3023440
AHCYL2

831
3039871
AGR3; RAD17P1

832
3047577
AC005027.3; INHBA

833
3062085
PDK4

834
3062104
PDK4

835
3090313
ADAMDEC1

836
3103850
HNF4G

837
3105612
CA2

838
3105614
CA2

839
3105622
CA2

840
3105629
CA2

841
3141870
TPD52

842
3142977
CA1

843
3142991
CA1

844
3163930

845
3165029
CDKN2B-AS1

846
3165030
CDKN2B-AS1

847
3174167
MAMDC2

848
3174519
GDA

849
3175362
PCSK5

850
3175465
PCSK5

851
3246960
PRKG1

852
3258838
NOC3L

853
3332433
MS4A12

854
3348424
C11orf93

855
3364272
RP11-396O20.2

856
3385068
SYTL2

857
3392098
FAM55D

858
3392111
FAM55D

859
3392128

860
3392143

861
3392145

862
3392151

863
3392154

864
3392167

865
3392170

866
3392175

867
3392180

868
3392181

869
3392189

870
3392191

871
3392197

872
3392211

873
3392215

874
3392223

875
3407503
PDE3A

876
3407520
PDE3A

877
3449955

878
3449956

TABLE 30

SEQ ID NO.:
Probe Set ID
Overlapping Gene

879
4012531
XIST

880
4012532
XIST

881
4012534
XIST

882
4012535
XIST

883
4012537
XIST

884
4012538
XIST

885
4012540
XIST

886
4012541
XIST

887
4012542
XIST

888
4012545
XIST

889
4012546
XIST

890
4012550
XIST

891
4012570
XIST

892
4012571
XIST

893
4012573
XIST

894
4012575
XIST

895
4012577
XIST

896
4012579
XIST

897
4012585
XIST

898
4012589
XIST

899
4012595
XIST

900
4012597
XIST

901
4012599
XIST

902
4030193
DDX3Y

903
4036117
KDM5D

Number	Date	Country
61730426	Nov 2012	US
61652044	May 2012	US
61570194	Dec 2011	US

Cancer Diagnostics Using Non-Coding Transcripts

Information

Publication Number

Date Filed

Date Published

Inventors

Original Assignees

CPC

US Classifications

International Classifications

Abstract

Description

Claims

Parent Case Info

PCT Information

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