CONTROL OF MAMMALIAN GENE DOSAGE USING CRISPR

Abstract

The present disclosure provides methods and compositions for precisely controlling the expression levels of mammalian genes using CRISPRi or CRISPRa and one or more modified sgRNAs. The methods and compositions are useful for, inter alia, titrating the expression of a gene of interest, identifying drug targets and mechanisms of drug resistance, and enabling the analysis of and control over metabolic and signaling pathway fluxes.

Description

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Sep. 8, 2020, is named 081906-1204127-236610WO_SL.txt and is 367,829 bytes in size.

BACKGROUND OF THE INVENTION

The complexity of biological processes arises not only from the set of expressed genes but also from quantitative differences in their expression levels. As a classic example, some genes are haploinsufficient and thus are sensitive to a 50% decrease in expression, whereas other genes are permissive to far stronger depletion (1). Enabled by tools to titrate gene expression levels such as series of promoters or hypomorphic mutants, the underlying expression-phenotype relationships have been explored systematically in yeast (2-4) and bacteria (5-8). These efforts have revealed gene- and environment-specific effects of changes in expression levels (4) and yielded insight into the opposing evolutionary forces that determine gene expression levels, including the cost of protein synthesis and the need for robustness against random fluctuations (3,6,8). The availability of equivalent tools in mammalian systems would enable similar efforts to understand these forces in more complex models as well as additional applications.

The discovery and development of artificial transcription factors, such as TALEs (10) or the CRISPR-based effectors underlying CRISPR interference (CRISPRi) and activation (CRISPRa) (11), has brought tools to precisely modify genomic sequences and systematically control gene expression in all cell types, including mammals.

There remains a need, however, for methods allowing the precise and predictable control of the expression levels of genes, including mammalian genes, to desired target levels. The present disclosure satisfies this need and provides other advantages as well.

BRIEF SUMMARY OF THE INVENTION

In one aspect, the present disclosure provides a method of generating a set of single guide RNAs (sgRNAs) capable of driving a series of discrete expression levels of a target gene in a cell population using CRISPR interference (CRISPRi) or CRISPR activation (CRISPRa), the method comprising: (i) providing a first sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the first sgRNA are 100% homologous to the target DNA sequence; (ii) providing a second sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the second sgRNA comprises one or more mismatches with the target DNA sequence such that the CRISPRi or CRISPRa activity on the gene obtained using the second sgRNA is intermediate between that obtained using the first sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene; and (iii) providing a third sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the third sgRNA comprises one or more mismatches with the target DNA sequence such that the CRISPRi or CRISPRa activity on the gene obtained using the third sgRNA is intermediate between that obtained using the second sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene; wherein the mismatches of the second and third sgRNAs are selected according to the following rules: (a) the CRISPRi or CRISPRa activity of the second sgRNA is designed to be greater than that of the third sgRNA based on the following positional relationships, wherein the positions correspond to the number of bases in the sgRNAs upstream from the sgRNA PAM: −19>−18>−17>−16−15−14>−13>−12>−11>−10>−9>−8>−4>−7−6−5−3−2−1; or (b) the CRISPRi or CRISPRa activity of the second sgRNA is designed to be greater than that of the third sgRNA based on the following base pair rankings of the mismatched nucleotides, wherein the first nucleotide in each pair corresponds to the ribonucleotide within the sgRNA and the second nucleotide corresponds to the deoxyribonucleotide within the target DNA: rG:dT>rU:dG>rG:dA rG:dG>rC:dA>rU:dT>rA:dA>rC:dT>rA:dC>rA:dG>rU:dC rC:dC.

In some embodiments, the method further comprises providing one or more additional sgRNAs, wherein the last 19 nucleotides of the targeting sequence of each of the one or more additional sgRNAs comprise at least one mismatch with the target DNA sequence, wherein each of the one or more additional sgRNAs provide CRISPRi or CRISPRa activity on the gene that is intermediate between that obtained using the third sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene, and wherein the mismatches with the template DNA of each of the one or more additional sgRNAs are selected according to rules (a) and (b) above. In some embodiments, the target gene is a mammalian gene. In some embodiments, the mammalian gene is a human gene.

In another aspect, the present disclosure provides a set of single guide RNAs (sgRNAs) for obtaining a series of discrete expression levels of a target gene using CRISPRi or CRISPRa, comprising: (i) a first sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the first sgRNA is 100% homologous to the target DNA sequence; (ii) a second sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the second sgRNA comprises one or more mismatches with the target DNA sequence such that the CRISPRi or CRISPRa activity on the gene obtained using the second sgRNA is intermediate between that obtained using the first sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene; and (iii) a third sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the third sgRNA comprises one or more mismatches with the target DNA sequence such that the CRISPRi or CRISPRa activity obtained using the third sgRNA is intermediate between that obtained using the second sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene; wherein the mismatches of the second and third sgRNAs are selected according to the following rules: (a) the CRISPRi or CRISPRa activity of the second sgRNA is designed to be greater than that of the third sgRNA based on the following positional relationships, wherein the positions correspond to the number of bases in the sgRNAs upstream from the sgRNA PAM: −19>−18 >−17>−16≈−15≈−14>−13>−12>−11>−10>−9>−8>−4>−7≈−6≈−5≈−3≈−2≈−1; or (b) the CRISPRi or CRISPRa activity of the second sgRNA is designed to be greater than that of the third sgRNA based on the following base pair rankings of the mismatched nucleotides, wherein the first nucleotide in each pair corresponds to the ribonucleotide within the sgRNA and the second nucleotide corresponds to the deoxyribonucleotide within the target DNA: rG:dT>rU:dG>rG:dA rG:dG>rC:dA>rU:dT>rA:dA>rC:dT>rA:dC>rA:dG>rU:dC≈rC:dC.

In some embodiments, the set of sgRNAs further comprises one or more additional sgRNAs, wherein the last 19 nucleotides of the targeting sequences of each of the one or more additional sgRNAs comprise at least one mismatch with the target DNA sequence, wherein each of the one or more additional sgRNAs provide CRISPRi or CRISPRa activity on the gene that is intermediate between that obtained using the third sgRNA and a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene, and wherein the CRISPRi or CRISPRa activity of each of the one or more additional sgRNAs on the gene is determined according to rules (a) and (b) above.

In some embodiments, the set comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more sgRNAs providing intermediate levels of CRISPRi or CRISPRa activity on the gene between that obtained using the first sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene.

In another aspect, the present disclosure provides a method of obtaining a series of discrete expression levels of a target gene in a plurality of cells, the method comprising: contacting the plurality of cells with the set of any of the herein-disclosed sgRNAs; and contacting the plurality of cells with a nuclease-deficient sgRNA-mediated nuclease (dCas9), wherein the dCas9 comprises a dCas9 domain fused to a transcriptional modulator; thereby generating a plurality of test cells, wherein each test cell comprises an sgRNA and the dCas9, wherein the sgRNA present in a given test cell guides the dCas9 in the test cell to the target gene and modulates its expression level as a function of the absence or presence of one or more mismatches with the target DNA sequence according to rules (a) and (b) above.

In some embodiments, the transcriptional modulator is a transcriptional repressor. In some embodiments, the transcriptional repressor is KRAB. In some embodiments, the transcriptional modulator is a transcriptional activator. In some embodiments, the transcriptional activator is VP64. In some embodiments, the cells are mammalian cells. In some embodiments, the cells are human cells. In some embodiments, each sgRNA is encoded by an expression cassette comprising a polynucleotide encoding the sgRNA, operably linked to a promoter. In some embodiments, the dCas9 is encoded by an expression cassette comprising a polynucleotide encoding the dCas9, operably linked to a promoter.

In some embodiments, the method further comprises determining the relationship between the expression level of the target gene and a phenotype, comprising: (i) determining the identity of the sgRNA present in a given test cell; (ii) assessing the phenotype of the test cell; and (iii) correlating the expression level of the gene targeted by the sgRNA identified in step (i) and the phenotype assessed in step (ii).

In some embodiments, assessing the phenotype of the cells comprises fluorescence activated cell sorting, affinity purification of the cells, measuring the transcriptomes of the cells, or measuring the growth, proliferation, and/or survival of the cells. In some embodiments, the transcriptomes of the cells are measured by perturb-seq.

In another aspect, the present disclosure provides a method of determining a therapeutic window for the inhibition of a gene, the method comprising determining the relationship between the expression level of the gene and the phenotype according to any of the herein-described methods for a plurality of sgRNAs targeting the gene, wherein the transcriptional modulator is a transcriptional repressor, and wherein the phenotype of the cells is assessed by measuring cell growth or survival; and further comprising: (iv) determining the minimum level of expression of the gene that is compatible with cell growth or survival, thereby determining the lower boundary of the therapeutic window for the inhibition of the gene.

In another aspect, the present disclosure provides a library of single guide RNAs (sgRNAs) for obtaining a series of discrete expression levels of a plurality of genes in a cell population, comprising any of the herein-described sets of sgRNAs according for each of the plurality of genes.

In some embodiments, the plurality of genes comprises 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10,000, or more genes. In some embodiments, the library contains at least 1000, 5000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, or 100,000 structurally distinct sgRNAs.

In another aspect, the present disclosure provides a method of obtaining a series of expression levels of a plurality of genes in a cell population, the method comprising: contacting the cell population with any one of the herein-disclosed sgRNA libraries; and contacting the cell population with a nuclease-deficient sgRNA-mediated nuclease (dCas9), wherein the dCas9 comprises a dCas9 domain fused to a transcriptional modulator; thereby generating a population of test cells, wherein each test cell within the population comprises an sgRNA targeting one of the plurality of genes and the dCas9; and wherein the sgRNA present in a given test cell guides the dCas9 in the test cell to the target gene of the sgRNA and modulates its expression level as a function of the absence or presence of one or more mismatches with the target DNA sequence according to rules (a) and (b) above.

In some embodiments, the transcriptional modulator is a transcriptional repressor. In some embodiments, the transcriptional repressor is KRAB. In some embodiments, the transcriptional modulator is a transcriptional activator. In some embodiments, the transcriptional activator is VP64. In some embodiments, each sgRNA within the library is encoded by an expression cassette comprising a polynucleotide encoding the sgRNA, operably linked to a promoter. In some embodiments, the dCas9 is encoded by an expression cassette comprising a polynucleotide encoding the dCas9, operably linked to a promoter.

In some embodiments, the method further comprises determining the relationship between the expression level of any one of the plurality of genes and a phenotype, comprising: (i) determining the identity of the sgRNA expressed in a given test cell within the population; (ii) assessing the phenotype of the test cell; and (iii) correlating the expression level of the target gene associated with the identified sgRNA and the assessed phenotype of the test cell.

In some embodiments, assessing the phenotype of the cells comprises fluorescence activated cell sorting, affinity purification of the cells, measuring the transcriptomes of the cells, or measuring the growth, proliferation, and/or survival of the cells. In some embodiments, the transcriptomes of the cells are measured by perturb-seq.

In another aspect, the present disclosure provides a method of identifying a gene target of a cytotoxic agent or a drug candidate, the method comprising: (i) generating a population of test cells according to any one of the herein-disclosed methods; (ii) contacting the population of test cells with a sub-lethal or sub-therapeutic amount of the cytotoxic agent or drug candidate; (iii) identifying test cells within the population that display a phenotype in the presence of the sub-lethal or sub-therapeutic amount of the cytotoxic agent or drug candidate that is not displayed by cells in the presence of the sub-lethal or sub-therapeutic amount of the cytotoxic agent or drug candidate but in the absence of the dCas9 or of an sgRNA; (iv) determining the identity of the sgRNAs present within the test cells displaying the phenotype; and (v) identifying genes that are targeted by one or more distinct sgRNAs identified in step (iv); wherein a gene that displays the phenotype at one or more levels of expression resulting from the presence of one or more distinct sgRNAs represents a candidate gene target of the cytotoxic agent or drug candidate.

In some embodiments, at least one of the sgRNAs targeting the candidate gene target comprises a mismatch with the target DNA in the last 19 nucleotides of its targeting sequence. In some embodiments, the at least one sgRNA provides a level of CRISPRi or CRISPRa activity on the gene that is less than 50% of the level obtained using an sgRNA comprising 100% homology in the last 19 nucleotides of its targeting sequence to the target DNA sequence.

BRIEF DESCRIPTION OF THE DRAWINGS

The present application includes the following figures. The figures are intended to illustrate certain embodiments and/or features of the compositions and methods, and to supplement any description(s) of the compositions and methods. The figure does not limit the scope of the compositions and methods, unless the written description expressly indicates that such is the case.

FIGS. 1A-1C. Mismatched sgRNAs titrate GFP expression at the single-cell level. (FIG. 1A) Experimental design to test knockdown conferred by all mismatched variants of a GFP-targeting sgRNA. (FIG. 1B) Distributions of GFP levels in cells with perfectly matched sgRNA (top), mismatched sgRNAs (middle), and non-targeting control sgRNA (bottom). Sequences of sgRNAs are indicated on the right (without the PAM). Figure discloses SEQ ID NOS 1196-1212, respectively, in order of appearance. (FIG. 1C) Relative activities of all mismatched sgRNAs, defined as the ratio of fold-knockdown conferred by a mismatched sgRNA to fold-knockdown conferred by the perfectly matched sgRNA. Relative activities are displayed as the mean of two biological replicates. Figure discloses SEQ ID NO: 1213.

FIGS. 2A-2B. Details of the GFP mismatch experiment. (FIG. 2A) Comparison of relative activities measured in two biological replicates. Relative activity was defined as the fold-knockdown of each mismatched variant (GFPsgRNA[non-targeting]/GFPsgRNA[variant]) divided by the fold-knockdown of the perfectly-matched sgRNA. The background fluorescence of a GFP-strain was subtracted from all GFP values prior to other calculations. (FIG. 2B) KDE plots of GFP distributions 10 days after transducing K562 GFP+ cells with the perfectly-matched sgRNA, a non-targeting sgRNA, and each of the 57 singly-mismatched variants. Fluorescence of a GFP− K562 strain is shown in gray. Although most GFP distributions are unimodal, some are broadened compared to those with the perfectly matched sgRNA or the negative control sgRNA. This heterogeneity could be a consequence of the random integration of the GFP locus, cell-to-cell differences in expression of the dCas9-KRAB effector in our polyclonal cell line, the amplification of gene expression bursts by long GFP half-lives, or a combination of these factors.

FIGS. 3A-3G. A large-scale CRISPRi screen identifies factors governing mismatched sgRNA activity. (FIG. 3A) Design of large-scale mismatched sgRNA library. (FIG. 3B) Schematic of pooled CRISPRi screen to determine activities of mismatched-sgRNAs. (FIG. 3C) Growth phenotypes (γ) in K562 and Jurkat cells for four sgRNA series, with the perfectly matched sgRNAs shown in darker colors and mismatched sgRNAs shown in corresponding lighter colors. Phenotypes represent the mean of two biological replicates. Differences in absolute phenotypes likely reflect cell type-specific essentiality. (FIG. 3D) Comparison of mismatched sgRNA relative activities in K562 and Jurkat cells. Marginal histograms depict distributions of relative activities along the corresponding axes. (FIG. 3E) Distribution of mismatched sgRNA relative activities stratified by position of the mismatch. Position −1 is closest to the PAM. (FIG. 3F) Distribution of mismatched sgRNA relative activities stratified by type of mismatch, grouped by mismatches located in positions −19 to −13 (PAM-distal region), positions −12 to −9 (intermediate region), and positions −8 to −1 (PAM-proximal/seed region). Division into these regions was based on previous work (12,13) and the patterns in FIG. 3E. (FIG. 3G) Comparison of mean apparent on-rates measured in vitro for mismatched variants of a single sgRNA (22) and mean relative activities from large-scale screen. Values are compared for identical combinations of mismatch type and mismatch position; mean relative activities were calculated by averaging relative activities for all mismatched sgRNAs with a given combination.

FIGS. 4A-4H. Additional analysis of large-scale mismatched sgRNA screen. (FIGS. 4A-4B) Comparison of growth phenotypes (γ) of all sgRNAs derived from biological replicates of the (FIG. 4A) K562 and (FIG. 4B) Jurkat screens. (FIG. 4C) Comparison of growth phenotypes (γ) of perfectly matched sgRNAs from the K562 screen in this work and a previously published K562 screen (19) (average of two biological replicates). (FIG. 4D) Comparison of growth phenotypes (γ) of perfectly matched sgRNAs in K562 and Jurkat cells reveals substantial differences, likely reflecting cell-type specific gene essentiality (average of two biological replicates). (FIG. 4E) Distribution of mismatched sgRNA relative activities for sgRNAs with 1 mismatch (left) or 2 mismatches (right). (FIG. 4F) Distribution of mismatched sgRNA relative activities stratified by sgRNA GC content, grouped by mismatches located in positions −19 to −13 (PAM-distal region), positions −12 to −9 (intermediate region), and positions −8 to −1 (PAM-proximal/seed region). (FIG. 4G) Distribution of mismatched sgRNA relative activities stratified by the identity of the 2 bases flanking the mismatch, grouped by mismatches located in positions −19 to −13 (PAM-distal region), positions −12 to −9 (intermediate region), and positions −8 to −1 (PAM-proximal/seed region). (FIG. 4H) Distribution of sgRNA series by number of sgRNAs with intermediate activity (0.1<relative activity <0.9), using only sgRNAs with a single mismatch (top) or all mismatched sgRNAs (bottom).

FIGS. 5A-5G. Identification and characterization of intermediate-activity constant regions. (FIG. 5A) Design of constant region variant library. (FIG. 5B) Mean relative activities of constant region variants, calculated by averaging relative activities for all targeting sequences. Gray margins denote 95% confidence interval. Inset: Focus on 6 constant region variants with higher activity than the original constant region. Black diamonds denote mean relative activity, gray dots relative activities with individual targeting sequences. (FIG. 5C) Mapping of constant region variant relative activities onto constant region structure. Each constant region base is colored by the average relative activity of the three single constant region variants carrying a single mutation at that position. Positions mutated in 6 highly active constant regions (inset in FIG. 5B) are indicated by colored dots. The BlpI site (gray) is used for cloning and was not mutated. Figure discloses SEQ ID NO: 1214. (FIG. 5D) Constant region activities by targeting sequence, plotted against ranked mean constant region activity. For each gene, the activities with the strongest targeting sequence are shown as rolling means with a window size of 50. (FIGS. 5E-5G) Constant region activities by targeting sequence for all three targeting sequences against the indicated genes. Growth phenotypes (γ) of each targeting sequence paired with the unmodified constant region are indicated in the legend.

FIGS. 6A-6E. Additional analysis of modified constant regions. (FIG. 6A) Comparison of growth phenotypes measured in each biological replicate after 4, 6, or 8 days of growth from t0. Data from Day 4 was used for all subsequent analyses. (FIG. 6B) Comparison of relative % knockdown (quantified via RT-qPCR) and mean relative growth phenotype for 10 intermediate-activity constant region variants paired with two targeting sequences against DPH2. (FIG. 6C) Relative activities of constant regions paired with all 30 targeting sequences, ranked by the average strength of each constant region and displayed as rolling means with a window size of 50. (FIG. 6D) Distribution of all pairwise correlations of constant region relative activities within and between gene targets. N.S.; no significant difference according to two-tailed Student's t-test (p>0.05). (FIG. 6E) Relative activity of each indicated target sequence:constant region pair vs. the mean relative activity of the respective constant region for all targets. Growth phenotypes (γ) with the unmodified constant region are indicated in the figure legends. Lines represent rolling means of individual data points.

FIGS. 7A-7D. Neural network predictions of sgRNA activity. (FIG. 7A) Schematic of a singly-mismatched sgRNA feature array (Xi) and the convolutional neural network architecture trained on pairs of such arrays and their corresponding relative activities (yi). Black squares in Xi represent the value 1 (the presence of a base at the indicated position); white represents 0. The mean prediction from 20 independently trained models was used to assign a final prediction (ŷ) to each sgRNA in the hold-out validation set. (FIG. 7B) Comparison of measured relative growth phenotypes from the large-scale screen and predicted activities assigned by the neural network. Marginal histograms show distributions of relative activities along the corresponding axes. (FIG. 7C) Distribution of Pearson r values (predicted vs. measured relative activity) for each sgRNA series in the validation set. (FIG. 7D) Comparison of measured relative activity (i.e., relative knockdown) in the GFP experiment and predicted relative sgRNA activity. Two outliers with lower-than-predicted activity are annotated with their respective mismatch position and type. Predictions are shown as mean±S.D. from the 20-model ensemble.

FIGS. 8A-8I. Additional details for the neural network. (FIG. 8A) Graph of the CNN model architecture. (FIG. 8B) Model loss, measured as root mean squared error, for training and validation data over 25 training epochs. Each line represents one of 20 models trained. The final models used for our predictions were only trained for 8 epochs, as additional cycles only reduced training loss without significant improvement in validation loss (i.e., the model becomes over-fit). (FIG. 8C) Explained variance (r2) of validation sgRNA relative activities for each individual model (black), and for the mean prediction of all 20 models (red). (FIG. 8D) Validation error stratified by mismatch position. (FIG. 8E) Validation error stratified by mismatch type. (FIG. 8F) Partitioning of sgRNAs into bins based on relative activity in the large-scale K562 screen. (FIG. 8G) Confusion matrix showing the fraction of sgRNAs in each actual (measured) activity bin that were assigned to each predicted bin by the CNN model. Each row sums to 1. (FIG. 8H) Statistics indicating the requisite number of randomly sampled sgRNAs from each activity bin to have a given probability of selecting at least one sgRNA with true activity in that bin. Simulations are based on the probabilities outlined in the confusion matrix (FIG. 8E). (FIG. 8I) Similar to FIG. 8H, with random sampling from any of the intermediate activity bins (1-3) to yield at least one sgRNA with intermediate activity (0.1-0.9).

FIGS. 9A-9F. Additional details for the linear model. (FIG. 9A) Comparison of measured relative growth phenotypes from the large-scale screen and predicted activities assigned by the elastic net linear model. Marginal histograms show distributions of relative activities along the corresponding axes. (FIG. 9B) Comparison of measured relative activity (relative knockdown) in the GFP experiment and predicted relative sgRNA activity. (FIG. 9C) Comparison of predicted relative activities from the linear model and the neural network, based on the validation set of singly-mismatched sgRNAs. (FIG. 9D) Regression coefficients assigned to each feature in the linear model. 228 features (gray, blue) describe the position and type of mismatch; 42 features (gold) carry other information about the sgRNA and genomic context surrounding the protospacer. These features are detailed in subsequent panels. (FIG. 9E) Linear coefficients for features of the sgRNA and targeted locus. TSS; transcription start site. (FIG. 9F) Linear coefficients for features covering positions in the distal, intermediate, and seed regions of the targeting sequence (highlighted blue in FIG. 9D).

FIGS. 10A-10C. Compact mismatched sgRNA library targeting essential genes. (FIG. 10A) Design of library. For activity bins lacking a previously measured sgRNA, novel mismatched sgRNAs were included according to predicted activity. (FIG. 10B) Distribution of relative activities from the large-scale library (gray) and the compact library (purple) in K562 cells. (FIG. 10C) Comparison of relative activities of mismatched sgRNAs in HeLa and K562 cells. Marginal histograms show the distributions of relative activities along the corresponding axes.

FIGS. 11A-11K. Additional analysis of the compact allelic series screen. (FIG. 11A) Composition of the compact library, in terms of previously measured relative activities in the large-scale screen (dark purple), or predicted relative activities assigned by the CNN model ensemble (light purple). Perfectly matched sgRNAs, which by definition have relative activities of 1.0, comprise 20% of the library but were not included in the histogram. (FIG. 11B) Distribution of mismatch positions and types for singly-mismatched sgRNAs in the compact library, for previously measured (dark purple) and CNN-imputed (light purple) sgRNAs. (FIG. 11C) Heatmap showing the distribution of mutated positions for doubly-mismatched sgRNAs in the compact library. (FIG. 11D) Comparison of growth phenotypes measured in each K562 biological replicate 4- and 7-days post-transduction. Data from Day 7 was used for all subsequent analyses. (FIG. 11E) Comparison of growth phenotypes measured in each HeLa biological replicate 6- and 8-days post-transduction. Data from Day 8 was used for all subsequent analyses. (FIG. 11F) Comparison of growth phenotypes in HeLa and K562 cells (γ expressed as the average of biological replicate measurements). (FIG. 11G) Measured vs. predicted relative activities of CNN-imputed sgRNAs in K562 cells (left) and HeLa cells (right). A small number of points beyond the y-axis limits were excluded to more clearly display the bulk of the distribution. n=6,147 sgRNAs; r2=squared Pearson correlation coefficient. (FIG. 11H) Comparison of sgRNA composition and model error for the large-scale and compact libraries. The CNN-imputed guides had substantially higher predicted activities than those for the large-scale validation set; higher predicted activity was generally associated with higher model error for the validation (red) and imputed (blue) sgRNA sets, consistent with the discrepancy in model performance on each set. (FIG. 11I) Distribution of the number of intermediate-activity mismatched sgRNAs targeting each gene in the compact library. The number of genes with at least 2 intermediate activity sgRNAs is indicated above each histogram; sgRNA activities were quantified for 1907 and 1442 genes in K562 and HeLa cells, respectively. Note that here activities are aggregated by gene as opposed to by series, as was done in FIG. 4I. (FIG. 11J) Comparison of phenotypes measured in each biological replicate after 12 days of growth in the drug screen. (FIG. 11K) Comparison of vehicle- (γ) and lovastatin-treatment (τ) growth phenotypes for all sgRNAs in the compact library. Knockdown of HMG-CoA reductase (HMGCR) greatly sensitizes cells to lovastatin, compared to knockdown of other genes such as tubulin (TUBB).

FIGS. 12A-12E. Summary of Perturb-seq experiment. (FIG. 12A) Schematic of Perturb-seq strategy to capture single-cell transcriptomes with matched sgRNA identities. (FIG. 12B) Summary of sequencing and perturbation assignment statistics. (FIG. 12C) Distribution of number of cells captured per perturbation. Median: 122 cells per perturbation; 5th to 95th percentile: 66-277 cells per perturbation. (FIGS. 12D-12E) Comparison of (FIG. 12D) growth phenotypes (γ) and (FIG. 12E) relative activities measured in the large-scale mismatched sgRNA screen and in the Perturb-seq experiment. Differences are likely due to the different timescales and the different vectors used.

FIGS. 13A-13B. Target gene expression in cells with indicated perturbations. (FIG. 13A) Distribution of target gene expression levels, quantified as target gene UMI count normalized to total UMI count per cell. (FIG. 13B) Mean target gene expression levels for target genes with low basal expression levels.

FIG. 14. Distributions of target gene expression in cells with indicated perturbations. Expression is quantified as raw target gene UMI count.

FIGS. 15A-15J. Rich phenotyping of cells with intermediate-activity sgRNAs by Perturb-seq. (FIG. 15A) Distributions of HSPA9 and RPL9 expression in cells with indicated perturbations. Expression is quantified as target gene UMI count normalized to total UMI count per cell. sgRNA activity is calculated using relative γ measurements from the Perturb-seq cell pool after 5 days of growth. (FIG. 15B) Distributions of total UMI counts in cells with indicated perturbations. (FIG. 15C) Comparison of median UMI count per cell and target gene expression in cells with GATA1- or POLR2H-targeting sgRNAs. (FIG. 15D) Right: Expression profiles of 100 genes in populations with HSPA9-targeting sgRNAs. Genes were selected by lowest FDR-corrected p-values in cells with the strongest sgRNA from a two-sided Kolmogorov-Smirnov test (Methods). Expression is quantified as z-score relative to population of cells with non-targeting sgRNAs. Left: Growth phenotype and knockdown for each sgRNA. (FIG. 15E) Distribution of gene expression changes in populations with indicated sgRNAs. Magnitude of gene expression change is calculated as sum of z-scores of genes differentially expressed in the series (FDR-corrected p<0.05 with any sgRNA in the series, two-sided Kolmogorov-Smirnov test, Methods), with z-scores of individual genes signed by the direction of change in cells with the perfectly matched sgRNA. Distribution for negative control sgRNAs is centered around 0 (dashed line). (FIG. 15F) Comparison of relative growth phenotype and magnitude of gene expression change for all individual sgRNAs. Growth phenotype and magnitude of gene expression change are normalized in each series to those of the sgRNA with the strongest knockdown. Individual series highlighted as indicated. (FIG. 15G) Comparison of magnitude of gene expression and target gene knockdown, as in FIG. 15F. (FIG. 15H) UMAP projection of all single cells with assigned sgRNA identity in the experiment, colored by targeted gene. Clusters clearly assignable to a genetic perturbation are labeled. Cluster labeled * contains a small number of cells with residual stress response activation and could represent apoptotic cells. Note that ˜5% cells appear to have confidently but wrongly assigned sgRNA identities, as evident within the cluster of cells with HSPAS knockdown (Methods). Given the strong trends in the other results, we concluded that such misassignment did not substantially affect our ability to identify trends within cell populations and in the future may be avoided by approaches to directly capture the expressed sgRNA34. (FIG. 15I) UMAP projection, as in FIG. 15H, with selected series colored by sgRNA activity. (FIG. 15J) Comparison of extent of ISR activation to ATP5E UMI count in cells with knockdown of ATP5E or control cells.

FIGS. 16A-16I. Phenotypes resulting from target gene titration. (FIG. 16A) Distributions of total UMI counts in cells with the perfectly matched sgRNA against the indicated genes. (FIG. 16B) Left: Comparison of median UMI count per cell and relative growth phenotype in cells with sgRNAs targeting BCR, GATA1, or POLR2H or control cells. Right: Comparison of median UMI count per cell and target gene expression. (FIG. 16C) Cell cycle scores (Methods) for populations of cells with individual sgRNAs. (FIG. 16D) Magnitudes of gene expression change of populations with perfectly matched sgRNAs targeting indicated genes. Magnitude of gene expression change is calculated as sum of z-scores of genes differentially expressed in the series (FDR-corrected p<0.05 with any sgRNA in the series, two-sided Kolmogorov-Smirnov test, Methods), with z-scores of each gene in individual cells signed by the average direction of change in the population. (FIG. 16E) Comparison of magnitude of gene expression change to growth phenotype (γ) for all perfectly matched sgRNAs in the experiment. (FIG. 16F) Comparison of relative growth phenotype and magnitude of gene expression change for all individual sgRNAs, as in FIG. 15F but without increased transparency for individual series. (FIG. 16G) Comparison of magnitude of gene expression and target gene knockdown, as in FIG. 15G but without increased transparency for individual series. (FIG. 16H) Comparison of relative growth phenotype and target gene expression, as in FIG. 15F. (FIG. 16I) Comparison of measured growth phenotype (γ, not normalized to strongest sgRNA) and target gene expression, as in FIG. 15F.

FIGS. 17A-17B. Diverse phenotypes resulting from essential gene depletion. (FIG. 17A) Clustered correlation heatmap of perturbations. Gene expression profiles for genes with mean UMI count >0.25 in the entire population were z-normalized to expression values in cells with negative control sgRNAs and then averaged for populations with the same sgRNA. Crosswise Pearson correlations of all averaged transcriptomes were clustered by the Ward variance minimization algorithm implemented in scipy. (FIG. 17A B) UMAP projection, distribution of cells with indicated sgRNAs, target gene expression (rolling mean over 50 cells), and magnitudes of transcriptional changes for all differentially expressed genes and selected ISR regulon genes (rolling mean over 50 cells) for cells with knockdown of ATP5E or control cells.

DETAILED DESCRIPTION OF THE INVENTION

1. Introduction

The present disclosure provides compositions and methods to precisely and predictably control the expression levels of mammalian genes to desired target levels. Methods and compositions are provided to systematically control the activity, e.g., by modulating the residence time, of a fusion protein of a transcriptional modulator, e.g., a transcription factor and nuclease-dead Cas9 (dCas9) at a gene of interest, thereby downregulating or upregulating the expression of the gene depending, e.g., on the residence time. Using the present methods and compositions, it is possible to regulate the expression of endogenous genes by varying degrees to levels between, e.g., 1% and 5000% of the normal expression level. These methods, inter alia, enable the titration of the expression of a gene of interest, allow for systematic mapping of gene dose-response curves, facilitate identification of drug targets and mechanisms of drug resistance, and enable analysis of and afford control over metabolic and signaling pathway fluxes.

The present methods extend previously developed CRISPR-based transcriptional repression (CRISPR interference, or CRISPRi) and overexpression (CRISPR activation, or CRISPRa), in which dCas9 is fused to a transcriptional repressor or activator, respectively, and is targeted to endogenous genes via a single guide RNA (sgRNA). The dCas9-sgRNA complex binds to DNA loci via basepairing between the sgRNA and DNA, i.e., the targeting sequence of the sgRNA and the target DNA sequence on the template DNA, and the fused transcriptional repressor or activator leads to downregulation or upregulation of the gene, respectively. The present disclosure provides methods to control the activity of dCas9 at a given DNA locus, e.g., by introducing mismatches into the sgRNA (e.g., within the targeting sequence of the sgRNA) or by introducing mutations into the sgRNA constant region. Without being bound by the following theory, it is believed that these modifications reduce the extent of transcriptional downregulation or upregulation by CRISPRi or CRISPRa, respectively, by reducing the residence time of dCas9 on the target DNA. The extent of transcriptional downregulation or upregulation can be varied systematically, thus affording precise control over expression levels of the target gene.

The present disclosure also provides sets of sgRNAs targeting individual genes, or targeting individual DNA sites within genes, allowing the generation of series of discrete expression levels of the genes, as well as libraries comprising a plurality of sgRNA sets and thereby allowing the generation of series of discrete expression levels for each of a multitude of genes, including libraries targeting up to all or virtually all of the genes in a genome. In such embodiments, each sgRNA within the set or library is selected to generate a discrete amount of transcriptional repression or activation of the targeted gene or genes by CRISPRi or CRISPRa, respectively.

The present disclosure also provides rules, factors, and parameters to determine how a given mismatch in an sgRNA targeting sequence affects the extent of transcriptional repression or activation of a target gene by CRISPRi or CRISPRa, allowing the design of sets of mismatched sgRNAs against the gene to allow its downregulation or upregulation to varying extents. In some embodiments, the information on the expression level of the target gene is encoded in the sgRNA sequence or in the vector encoding the sgRNA, and can therefore be read out by, e.g., deep sequencing and matched to a resulting phenotype. In such embodiments, experiments involving systematically mismatched sgRNAs can be conducted in a single pooled experiment, reducing experimental variation and enhancing reproducibility. It will be appreciated that any of the herein-described methods and compositions can be applied to both gene downregulation (using CRISPRi) and overexpression (using CRISPRa), as well as to other dCas9-mediated applications such as dCas9-based epigenetic modifiers.

In another aspect, the present disclosure provides specific mutations in the sgRNA constant region that lower or increase the extent of transcriptional repression or activation of a target gene by CRISPRi or CRISPRa. Using the present methods and compositions, it is therefore possible to control the expression of a number of different genes by designing multiple sgRNAs comprising different modifications in the sgRNA constant region that each give rise to a discrete level of expression of the targeted gene. Similar to the herein-disclosed methods involving mismatches in the targeting sequence of sgRNAs, methods are also provided to introduce specific mutations in the sgRNA constant region, and specific rules and parameters are provided for the design of sgRNAs comprising such mutations. In addition, a table is provided (Table 6) disclosing close to 1000 different constant region mutations and providing a ranking of their relative effects on CRISPRi or CRISPRa activity. Any one or more of these mutations can be used to modulate the expression level of any gene of interest according to the present methods.

The two different types of sgRNA modifications provided herein, i.e., comprising mismatches in the sgRNA targeting sequence and comprising mutations in the sgRNA constant region, can be combined in any way. For example, a single sgRNA can comprise both types of modification, and/or sets or libraries of sgRNAs can be used in which certain sgRNAs comprise targeting sequence mismatches and certain sgRNAs comprise constant region modifications.

This invention affords precise control over the expression level of any mammalian gene, and as such can be used in any of a large number of potential applications. For example, the methods and compositions can be used to profile the phenotypes resulting from varying degrees of downregulation or upregulation for every gene, providing information on the relationship between expression level and phenotype. The methods and compositions are also applicable to determining the cellular target and mechanism of action of, e.g., drugs with unknown mechanisms of action, of drug candidates, or of cytotoxic agents, such as drugs, drug candidates, or cytotoxic agents arising from high-throughput chemical screening efforts.

In such embodiments, this invention could be used immediately after the chemical screen to, e.g., identify the mechanism of action of compounds of interest to guide further development and characterization. In particular, profiling drug sensitivity at varying levels of knockdown and overexpression can identify genes for which small changes in expression levels cause hypersensitivity to a drug or compound of interest.

The present methods and compositions also allow for determination of gene-gene interactions for identification of synthetic lethal interactions. Additionally, the methods and compositions can be used to control the flux through a metabolic pathway or a signaling pathway of interest and to identify bottlenecks of such pathways. In some such embodiments, the methods and compositions could be used to guide metabolic engineering and synthetic biology approaches. In addition, the methods and compositions can be used to systematically analyze phenotypes associated with partial loss-of-function of essential genes. For example, the methods and compositions can be used to assign phenotypes at different expression levels of a gene. This ability can, e.g., facilitate the study of essential genes, which cannot be studied easily as their complete loss leads to cell death, and allow for the study of partial loss-of-function phenotypes.

More generally, the present methods and compositions can be used to control the activity of any CRISPR system that relies on sgRNA-DNA base pairing. The methods and compositions can also be used to comprehensively define the propensity for off-target effects during CRISPR-mediated gene editing and develop gene editing products that are tuned to minimize off-target effects.

The present methods and compositions improve on existing technology with the ability to control activity of CRISPR systems with high precision. In particular, they modulate their activity using systematic mismatches in the sgRNA or using engineered constant region variants, which obviates the need to engineer Cas9 variants with different activities or stabilities. Applications enabled by this invention can be carried out in a single genetic background and in a single experimental vessel, thereby improving data quality. The present methods and compositions also improve on previously developed technology for drug target identification, by enabling the identification of targets with higher precision.

2. Definitions

As used herein, the following terms have the meanings ascribed to them unless specified otherwise.

The terms “a,” “an,” or “the” as used herein not only include aspects with one member, but also include aspects with more than one member. For instance, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a cell” includes a plurality of such cells, and so forth.

The terms “about” and “approximately” as used herein shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Typically, exemplary degrees of error are within 20 percent (%), preferably within 10%, and more preferably within 5% of a given value or range of values. Any reference to “about X” specifically indicates at least the values X, 0.8X, 0.81X, 0.82X, 0.83X, 0.84X, 0.85X, 0.86X, 0.87X, 0.88X, 0.89X, 0.9X, 0.91X, 0.92X, 0.93X, 0.94X, 0.95X, 0.96X, 0.97X, 0.98X, 0.99X, 1.01X, 1.02X, 1.03X, 1.04X, 1.05X, 1.06X, 1.07X, 1.08X, 1.09X, 1.1X, 1.11X, 1.12X, 1.13X, 1.14X, 1.15X, 1.16X, 1.17X, 1.18X, 1.19X, and 1.2X. Thus, “about X” is intended to teach and provide written description support for a claim limitation of, e.g., “0.98X.”

The term “nucleic acid” or “polynucleotide” refers to deoxyribonucleic acids (DNA) or ribonucleic acids (RNA) and polymers thereof in either single- or double-stranded form. Unless specifically limited, the term encompasses nucleic acids containing known analogs of natural nucleotides that have similar binding properties as the reference nucleic acid and are metabolized in a manner similar to naturally occurring nucleotides. Unless otherwise indicated, a particular nucleic acid sequence also implicitly encompasses conservatively modified variants thereof (e.g., degenerate codon substitutions), alleles, orthologs, SNPs, and complementary sequences as well as the sequence explicitly indicated. Specifically, degenerate codon substitutions may be achieved by generating sequences in which the third position of one or more selected (or all) codons is substituted with mixed-base and/or deoxyinosine residues (Batzer et al., Nucleic Acid Res. 19:5081 (1991); Ohtsuka et al., J. Biol. Chem. 260:2605-2608 (1985); and Rossolini et al., Mol. Cell. Probes 8:91-98 (1994)).

The term “gene” means the segment of DNA involved in producing a polypeptide chain. It may include regions preceding and following the coding region (leader and trailer) as well as intervening sequences (introns) between individual coding segments (exons).

A “promoter” is defined as an array of nucleic acid control sequences that direct transcription of a nucleic acid. As used herein, a promoter includes necessary nucleic acid sequences near the start site of transcription, such as, in the case of a polymerase II type promoter, a TATA element. A promoter also optionally includes distal enhancer or repressor elements, which can be located as much as several thousand base pairs from the start site of transcription. The promoter can be a heterologous promoter.

An “expression cassette” is a nucleic acid construct, generated recombinantly or synthetically, with a series of specified nucleic acid elements that permit transcription of a particular polynucleotide sequence in a host cell. An expression cassette may be part of a plasmid, viral genome, or nucleic acid fragment. Typically, an expression cassette includes a polynucleotide to be transcribed, operably linked to a promoter. The promoter can be a heterologous promoter. In the context of promoters operably linked to a polynucleotide, a “heterologous promoter” refers to a promoter that would not be so operably linked to the same polynucleotide as found in a product of nature (e.g., in a wild-type organism).

As used herein, a first polynucleotide or polypeptide is “heterologous” to an organism or a second polynucleotide or polypeptide sequence if the first polynucleotide or polypeptide originates from a foreign species compared to the organism or second polynucleotide or polypeptide, or, if from the same species, is modified from its original form. For example, when a promoter is said to be operably linked to a heterologous coding sequence, it means that the coding sequence is derived from one species whereas the promoter sequence is derived from another, different species; or, if both are derived from the same species, the coding sequence is not naturally associated with the promoter (e.g., is a genetically engineered coding sequence).

“Polypeptide,” “peptide,” and “protein” are used interchangeably herein to refer to a polymer of amino acid residues. All three terms apply to amino acid polymers in which one or more amino acid residue is an artificial chemical mimetic of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers and non-naturally occurring amino acid polymers. As used herein, the terms encompass amino acid chains of any length, including full-length proteins, wherein the amino acid residues are linked by covalent peptide bonds.

The terms “expression” and “expressed” refer to the production of a transcriptional and/or translational product, e.g., of an sgRNA, dCas9, or target gene and/or a nucleic acid sequence encoding a protein (e.g., a protein encoded by a target gene). In some embodiments, the term refers to the production of a transcriptional and/or translational product encoded by a gene or a portion thereof. The level of expression of a DNA molecule in a cell may be assessed, e.g., on the basis of either the amount of corresponding mRNA that is present within the cell or the amount of protein encoded by that DNA produced by the cell.

“Conservatively modified variants” applies to both amino acid and nucleic acid sequences. With respect to particular nucleic acid sequences, “conservatively modified variants” refers to those nucleic acids that encode identical or essentially identical amino acid sequences, or where the nucleic acid does not encode an amino acid sequence, to essentially identical sequences. Because of the degeneracy of the genetic code, a large number of functionally identical nucleic acids encode any given protein. For instance, the codons GCA, GCC, GCG and GCU all encode the amino acid alanine. Thus, at every position where an alanine is specified by a codon, the codon can be altered to any of the corresponding codons described without altering the encoded polypeptide. Such nucleic acid variations are “silent variations,” which are one species of conservatively modified variations. Every nucleic acid sequence herein that encodes a polypeptide also describes every possible silent variation of the nucleic acid. One of skill will recognize that each codon in a nucleic acid (except AUG, which is ordinarily the only codon for methionine, and TGG, which is ordinarily the only codon for tryptophan) can be modified to yield a functionally identical molecule. Accordingly, each silent variation of a nucleic acid that encodes a polypeptide is implicit in each described sequence.

As to amino acid sequences, one of skill will recognize that individual substitutions, deletions or additions to a nucleic acid, peptide, polypeptide, or protein sequence which alters, adds or deletes a single amino acid or a small percentage of amino acids in the encoded sequence is a “conservatively modified variant” where the alteration results in the substitution of an amino acid with a chemically similar amino acid. Conservative substitution tables providing functionally similar amino acids are well known in the art. Such conservatively modified variants are in addition to and do not exclude polymorphic variants, interspecies homologs, and alleles.

As used in herein, the terms “identical” or percent “identity,” in the context of describing two or more polynucleotide or amino acid sequences, refer to two or more sequences or specified subsequences that are the same. Two sequences that are “substantially identical” have at least 60% identity, preferably 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identity, when compared and aligned for maximum correspondence over a comparison window, or designated region as measured using a sequence comparison algorithm or by manual alignment and visual inspection where a specific region is not designated. With regard to polynucleotide sequences, this definition also refers to the complement of a test sequence. With regard to amino acid sequences, in some cases, the identity exists over a region that is at least about 50 amino acids or nucleotides in length, or more preferably over a region that is 75-100 amino acids or nucleotides in length.

For sequence comparison, typically one sequence acts as a reference sequence, to which test sequences are compared. When using a sequence comparison algorithm, test and reference sequences are entered into a computer, subsequence coordinates are designated, if necessary, and sequence algorithm program parameters are designated. Default program parameters can be used, or alternative parameters can be designated. The sequence comparison algorithm then calculates the percent sequence identities for the test sequences relative to the reference sequence, based on the program parameters. For sequence comparison of nucleic acids and proteins, the BLAST 2.0 algorithm and the default parameters discussed below are used.

A “comparison window,” as used herein, includes reference to a segment of any one of the number of contiguous positions selected from the group consisting of from 20 to 600, usually about 50 to about 200, more usually about 100 to about 150 in which a sequence may be compared to a reference sequence of the same number of contiguous positions after the two sequences are optimally aligned.

An algorithm for determining percent sequence identity and sequence similarity is the BLAST 2.0 algorithm, which is described in Altschul et al., (1990) J. Mol. Biol. 215: 403-410. Software for performing BLAST analyses is publicly available at the National Center for Biotechnology Information website, ncbi.nlm.nih.gov. The algorithm involves first identifying high scoring sequence pairs (HSPs) by identifying short words of length W in the query sequence, which either match or satisfy some positive-valued threshold score T when aligned with a word of the same length in a database sequence. T is referred to as the neighborhood word score threshold (Altschul et al., supra). These initial neighborhood word hits acts as seeds for initiating searches to find longer HSPs containing them. The word hits are then extended in both directions along each sequence for as far as the cumulative alignment score can be increased. Cumulative scores are calculated using, for nucleotide sequences, the parameters M (reward score for a pair of matching residues; always >0) and N (penalty score for mismatching residues; always <0). For amino acid sequences, a scoring matrix is used to calculate the cumulative score. Extension of the word hits in each direction are halted when: the cumulative alignment score falls off by the quantity X from its maximum achieved value; the cumulative score goes to zero or below, due to the accumulation of one or more negative-scoring residue alignments; or the end of either sequence is reached. The BLAST algorithm parameters W, T, and X determine the sensitivity and speed of the alignment. The BLASTN program (for nucleotide sequences) uses as defaults a word size (W) of 28, an expectation (E) of 10, M=1, N=−2, and a comparison of both strands. For amino acid sequences, the BLASTP program uses as defaults a word size (W) of 3, an expectation (E) of 10, and the BLOSUM62 scoring matrix (see Henikoff & Henikoff, Proc. Natl. Acad. Sci. USA 89:10915 (1989)).

The BLAST algorithm also performs a statistical analysis of the similarity between two sequences (see, e.g., Karlin & Altschul, Proc. Nat'l. Acad. Sci. USA 90:5873-5787 (1993)). One measure of similarity provided by the BLAST algorithm is the smallest sum probability (P(N)), which provides an indication of the probability by which a match between two nucleotide or amino acid sequences would occur by chance. For example, a nucleic acid is considered similar to a reference sequence if the smallest sum probability in a comparison of the test nucleic acid to the reference nucleic acid is less than about 0.2, more preferably less than about 0.01, and most preferably less than about 0.001.

The “CRISPR-Cas” system refers to a class of bacterial systems for defense against foreign nucleic acids. CRISPR-Cas systems are found in a wide range of bacterial and archaeal organisms. CRISPR-Cas systems fall into two classes with six types, I, II, III, IV, V, and VI as well as many sub-types, with Class 1 including types I and III CRISPR systems, and Class 2 including types II, IV, V, and VI. See, e.g., Fonfara et al., Nature 532, 7600 (2016); Zetsche et al., Cell 163, 759-771 (2015); Adli (2018) Nat Commun. 2018 May 15; 9(1):1911. Endogenous CRISPR-Cas systems include a CRISPR locus containing repeat clusters separated by non-repeating spacer sequences that correspond to sequences from viruses and other mobile genetic elements, and Cas proteins that carry out multiple functions including spacer acquisition, RNA processing from the CRISPR locus, target identification, and cleavage. In class 1 systems these activities are effected by multiple Cas proteins, with Cas3 providing the endonuclease activity, whereas in class 2 systems they are all carried out by a single Cas, Cas9.

The Cas9 used in the present methods can be from any source, so long that it is capable of binding to an sgRNA of the invention and being guided to the specific DNA sequence targeted by the targeting sequence of the sgRNA. In some embodiments, Cas9 is from Streptococcus pyogenes. The Cas9 can be catalytically active, but in particular embodiments the Cas9 used in the present methods is nuclease deficient, i.e., dCas9, used either alone or as a fusion protein with another functional element such as a transcriptional modulator. In particular embodiments, the Cas9 is a dCas9 protein fused to a transcriptional repressor such as KRAB (i.e., for use in CRISPRi-based methods) or is a dCas9 protein fused to a transcriptional activator such as VP64 (i.e., for use in CRISPRa-based methods).

The sgRNAs, or single guide RNAs, used herein can be any sgRNA that can function with an endogenous or exogenous CRISPR-Cas9 system, e.g., to direct the induction of deletions or gene repression in cells, or more generally to bind to the Cas9 protein and direct it to a specific target DNA sequence determined by the targeting sequence in the sgRNA. Specifically, an sgRNA interacts with a site-directed nuclease such as Cas9 or dCas9 and specifically binds to or hybridizes to a target nucleic acid within the genome of a cell, such that the sgRNA and the site-directed nuclease co-localize to the target nucleic acid in the genome of the cell. Typically, the sgRNAs as used herein comprise a targeting sequence comprising homology (or complementarity) to a target DNA sequence in the genome, and a constant region that mediates binding to Cas9 or another site-directed nuclease. In particular embodiments, the targeting sequence may comprise one or more mismatches with the target DNA sequence, and/or the constant region may contain one or more mutations, as described in more detail elsewhere herein.

3. Detailed Description of the Embodiments

The following description recites various aspects and embodiments of the present compositions and methods. No particular embodiment is intended to define the scope of the compositions and methods. Rather, the embodiments merely provide non-limiting examples of various compositions and methods that are at least included within the scope of the disclosed compositions and methods. The description is to be read from the perspective of one of ordinary skill in the art; therefore, information well known to the skilled artisan is not necessarily included.

Provided herein are compositions and methods for generating discrete, intermediate expression levels of any gene of interest when using CRISPRi or CRISPRa. In particular, the present compositions and methods involve the introduction of one or more mismatches or mutations into the targeting sequence or constant region of sgRNAs so as to achieve a level of CRISPRi or CRISPRa activity that is, e.g., intermediate between that obtained with an sgRNA sharing 100% homology with a target DNA sequence and/or an unmodified constant region and that obtained with a scrambled sgRNA and/or sgRNA comprising a modified constant region providing no CRISPRi or CRISPRa activity on the gene in question. Further, rules are provided by which the specific effects of a given mismatch or mutation on CRISPRi or CRISPRa activity can be determined, allowing the design of sets of sgRNAs targeting a given gene and providing a series of discrete levels of expression of the gene. As described herein, such sets can be combined to form libraries targeting multiple genes, including large libraries targeting thousands of genes in the genome.

sgRNAs

The sgRNAs of the invention comprise two or more regions, including a “targeting sequence” that is complementary to, and thus targets, a target DNA sequence in the template DNA, e.g., the promoter region or region surrounding the transcription start site, and thereby modulate its expression using CRISPRi or CRISPRa. The sgRNAs also comprise a “constant region” that mediates its interaction with an sgRNA-guided nuclease such as Cas9 (e.g., dCas9).

The sgRNAs used in the present methods can also comprise additional functional or structural elements, such as barcodes to provide a specific distinct sequence for each sgRNA in a set or a library, restriction sites, primer sites, and the like.

The targeting sequence of the sgRNAs may be, e.g., 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 nucleotides in length, or 15-25, 18-22, or 19-21 nucleotides in length, and shares homology with a targeted genomic sequence, in particular at a position adjacent to a CRISPR PAM sequence. The sgDNA targeting sequence is designed to be homologous to the target DNA, i.e., to share the same sequence with the non-bound strand of the DNA template or to be complementary to the strand of the template DNA that is bound by the sgRNA. The homology or complementarity of the targeting sequence can be perfect (i.e., sharing 100% homology or 100% complementarity to the target DNA sequence) or the targeting sequence can be substantially homologous (i.e., having less than 100% homology or complementarity, e.g., with 1-4 mismatches with the target DNA sequence). In particular embodiments, the region of the sgRNA that is considered with respect to homology or complementarity for the purposes of the present methods is the last 19 nucleotides in the sgRNA that lead up to the PAM sequence in the target DNA. Accordingly, in some embodiments these 19 nucleotides are 100% homologous or complementary to the template DNA, and in some embodiments this 19-nucleotide region includes one or more mismatches with the target DNA sequence. In some embodiments, the region of the sgRNA that is considered with respect to homology or complementarity for the purposes of the present methods is the region from the second nucleotide of the sgRNA up to the PAM sequence in the target DNA, regardless of the length of the region. Accordingly, in some embodiments the sequence starting at the second nucleotide of the sgRNA and leading up to the PAM is 100% complementary to the target DNA sequence. In some embodiments the sequence starting at the second nucleotide of the sgRNA and leading up to the PAM comprises one or more mismatches with the target DNA sequence.

In some cases, G-C content of the sgRNA is preferably between about 40% and about 60% (e.g., 40%, 45%, 50%, 55%, 60%). In some cases, the targeting sequence can be selected to begin with a sequence that facilitates efficient transcription of the sgRNA. For example, the targeting sequence can begin at the 5′ end with a G nucleotide. In some cases, the binding region or the overall sgRNA can contain modified nucleotides such as, without limitation, methylated or phosphorylated nucleotides. In some embodiments, the sgRNAs selected for use in the present methods are filtered by identifying and eliminating potential targeting sequences that are likely to or could potentially give rise to significant off-target effects (i.e., if the targeting sequence is substantially homologous or complementary to one or more sequences within the genome other than the target DNA sequence). In some embodiments, sgRNAs comprising internal restriction sites recognized by restriction enzymes that may be used in one or more cloning steps of the methods may be excluded as well.

As used herein, the term “complementary” or “complementarity” refers to base pairing between nucleotides or nucleic acids, for example, and not to be limiting, base pairing between a sgRNA and a target nucleic acid. Complementary nucleotides are, generally, A and T (or A and U), and G and C. The guide RNAs described herein can comprise sequences, for example, DNA targeting sequence that are perfectly complementary or substantially complementary (e.g., having 1-4 mismatches) to a genomic sequence.

In some embodiments, the sgRNAs are targeted to specific regions at or near a gene, e.g., to a region at or near the 0-1000 bp region 5′ (upstream) of the transcription start site of a gene, or to a region at or near the 0-1000 bp region 3′ (downstream) of the transcription start site of a gene.

In some embodiments, the sgRNAs are targeted to a region at or near the transcription start site (TSS) based on an automated or manually annotated database. For example, transcripts annotated by Ensembl/GENCODE or the APPRIS pipeline (Rodriguez et al., Nucleic Acids Res. 2013 January; 41(Database issue):D110-7 can be used to identify the TSS and target genetic elements 0-750 bp or 0-1000 bp downstream of the TSS.

In some embodiments, the sgRNAs are targeted to a genomic region that is predicted to be relatively free of nucleosomes. The locations and occupancies of nucleosomes can be assayed, e.g., through the use of enzymatic digestion with micrococcal nuclease (MNase). MNase is an endo-exo nuclease that preferentially digests naked DNA and the DNA in linkers between nucleosomes, thus enriching for nucleosome-associated DNA. To determine nucleosome organization genome-wide, DNA remaining from MNase digestion is sequenced using high-throughput sequencing technologies (MNase-seq). Thus, regions having a high MNase-seq signal are predicted to be relatively occupied by nucleosomes, and regions having a low MNase-seq signal are predicted to be relatively unoccupied by nucleosomes. Thus, in some examples, the sgRNAs are targeted to a genomic region that has a low MNase-Seq signal.

In some embodiments, the sgRNAs are targeted to a region predicted to be highly transcriptionally active. For example, the sgRNAs can be targeted to a region predicted to have a relatively high occupancy for RNA polymerase II (PolII). Such regions can be identified by PolII chromatin immunoprecipitation sequencing (ChIP-seq), which includes affinity purifying regions of DNA bound to PolII using an anti-PolII antibody and identifying the purified regions by sequencing. Therefore, regions having a high PolII Chip-seq signal are predicted to be highly transcriptionally active. Thus, in some cases, sgRNAs are targeted to regions having a high PolII ChIP-seq signal as disclosed in the ENCODE-published PolII ChIP-seq database (Landt, et al., Genome Research, 2012 September; 22(9):1813-31).

In some such embodiments, the sgRNAs can be targeted to a region predicted to be highly transcriptionally active as identified by run-on sequencing or global run-on sequencing (GRO-seq). GRO-seq involves incubating cells or nuclei with a labeled nucleotide and an agent that inhibits binding of new RNA polymerase to transcription start sites (e.g., sarkosyl). Thus, only genes with an engaged RNA polymerase produce labeled transcripts. After a sufficient period of time to allow global transcription to proceed, labeled RNA is extracted and corresponding transcribed genes are identified by sequencing. Therefore, regions having a high GRO-seq signal are predicted to be highly transcriptionally active. Thus, in some cases, sgRNAs are targeted to regions having a high GRO-seq signal as disclosed in a published GRO-seq data (e.g., Core et al., Science. 2008 Dec. 19; 322(5909):1845-8; and Hah et al., Genome Res. 2013 August; 23(8):1210-23).

Each sgRNA also includes a constant region that interacts with or binds to the site-directed nuclease, e.g., Cas9 or dCas9. In the nucleic acid constructs provided herein, the constant region of an sgRNA can be from about 75 to 250 nucleotides in length, or about 75-100 nucleotides in length, or about 85-90 nucleotides in length, or 75, 76, 77, 7, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100 or more nucleotides in length. In some examples, as described in more detail elsewhere herein, the constant region is modified, e.g., comprises one or more nucleotide substitutions in the first stem loop, the second stem loop, a hairpin, a region in between the hairpins, and/or the nexus of a constant region, so as to generate intermediate levels of CRISPRi or CRISPRa activity between the levels obtained using an sgRNA with a non-modified constant region and those obtained using an sgRNA with a modified constant region that provides no CRISPRi or CRISPRa activity, e.g., by virtue of being incapable of functionally interacting with Cas9. In some embodiments, mutations in the constant region can confer CRISPRi or CRISPRa activity that is greater than that obtained using an sgRNA with an unmodified constant region.

A non-limiting example of an unmodified constant region that can be used in the constructs set forth herein is shown as cr995 in Table 6. Other constant regions that can be used are described in Gilbert et al. (2014) Cell, 159(3): 647-661, the entire disclosure of which is herein incorporated by reference. In addition, a non-limiting list of modified constant regions that include one or more mutations in the constant region, is provided herein in Table 6. Any of the constant regions or mutations shown in Table 6 can be used in the present methods.

Mismatches in the Targeting Sequence

In some embodiments, sgRNAs are provided with one or more mismatches in the targeting sequence of the sgRNA in order to generate intermediate levels of CRISPRi or CRISPRa activity. In particular embodiments, the mismatches introduced into the targeting sequence are in the last 19 nucleotides of the targeting region, i.e., the 19 nucleotides leading up to the PAM sequence in the target DNA. In some embodiments, the mismatches introduced into the targeting sequence are in the region from the second nucleotide of the sgRNA leading up to the PAM sequence in the target DNA. In some embodiments, sets of sgRNAs are provided with different mismatches so as to obtain a series of different expression levels of a target gene. A set typically includes at least one sgRNA in which this 19 nucleotide region, or in which the region from the second nucleotide of the sgRNA to the PAM, is 100% homologous to the template DNA, as well as one or more sgRNAs that comprise one or more mismatches within the 19 nucleotide region or within the region from the second nucleotide to the PAM. Mismatches in the targeting sequence selected according to the present methods reduce the CRISPRi or CRISPRa activity to an intermediate level between that of an sgRNA with 100% homology to the target DNA (e.g., providing 100% CRISPRi or CRISPRa activity) and that of a scrambled sgRNA that does not target the target DNA (i.e., with a targeting sequence comprising insufficient homology to the target DNA sequence to promote Cas9 binding and consequent CRISPRi or CRISPRa activity). It will be appreciated that a given gene can be targeted using a single set of sgRNAs that recognize a single target sequence within the gene, or with multiple sets that each target a different DNA sequence within the target gene.

In some embodiments, an sgRNA comprising one or more mismatches in the targeting sequence provides about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, or 95% CRISPRi or CRISPRa activity, wherein 100% CRISPRi or CRISPRa activity corresponds to the activity in the presence of an sgRNA targeting the same DNA sequence and comprising 100% homology to the target sequence, and wherein 0% CRISPRi or CRISPRa activity corresponds to the activity in the presence of a scrambled sgRNA with no, or only insignificant amounts of, homology to the target sequence.

Any of a number of parameters can be used to select a mismatch in the targeting sequence of the sgRNA, i.e., in the last 19 nucleotides of the targeting sequence leading up to the PAM, or in the region from the second nucleotide of the sgRNA leading up to the PAM, in order to obtain a predictable, intermediate level of CRISPRi or CRISPRa activity. For example, in some embodiments, the mismatch is selected on the basis of its distance from the PAM sequence. More precisely, the mismatch is selected on the basis of the following positional relationships, with the position indicated (e.g., −19) counted as the number of bases upstream from the sgRNA PAM, and the positions ordered by how much CRISPRi or CRISPRa activity the sgRNAs provide:

−19>−18>−17>−16≈−15≈−14>−13>−12>−11>−10>−9>−8>−4>−7≈−6≈−5≈−3≈−2≈−1

For example, an sgRNA with a mismatch in position −19 will on average have higher activity (that is, mediate stronger knockdown/overexpression by CRISPRi or CRISPRa, respectively) than an sgRNA with a mismatch in position −11.

Another parameter that can be used to select a mismatch in the targeted sequence is the identity of the nucleotides involved in pairing in the mismatched position:

rG:dT>rU:dG>rG:dA≈rG:dG>rC:dA>rU:dT>rA:dA>rC:dT>rA:dC>rA:dG>rU:dC≈rC:dCwith the identity of the mismatch indicated as rX:dY for “base X in the sgRNA opposite base Y in the DNA” (i.e., the 4 non-mismatched pairs would be rG:dC, rC:dG, rA:dT, rU:dA). As with the relative activities determined by the position of the mismatch relative to the PAM, the pairings indicated here are ordered by how much CRISPRi or CRISPRa activity the sgRNAs on average retain relative to an sgRNA with 100% homology to the target DNA (e.g., a mismatched sgRNA with a rG:dT pairing will have higher CRISPRi or CRISPRa activity than a mismatched sgRNA with a rC:dT pairing, all else being equal).

In some embodiments, the mismatches introduced into sgRNA targeting sequences are selected by taking into account both the position and the identity of the nucleotides involved in the basepairing, in particular according to the following ranking that groups together different mismatch positions:

If the mismatch is between position −19 and −13 (both inclusive):

rG:dT>rC:dA>rU:dG≈rG:dA≈rU:dT≈rC:dT≈rA:dA>rG:dG>rA:dC≈rA:dG>rU:dC≈rC:dC

If the mismatch between position −12 and −9 (both inclusive):

rG:dT>rU:dG≈rG:dA≈rG:dG>rU:dT>rC:dA≈rC:dT>rA:dA>rA:dC≈rA:dG>rU:dC≈rC:dC

If the mismatch between position −8 and −1 (both inclusive):

rG:dT>rG:dA≈rC:dA>rU:dG≈rG:dG>rA:dA≈rU:dT≈rA:dC>rC:dT>rA:dG≈rU:dC≈rC:dC

In some such embodiments, a set of sgRNAs is designed and/or prepared in which at least one sgRNA has a mismatch between positions −19 and −13, at least one has a mismatch between positions −12 and −9, and at least one has a mismatch between positions −8 and −1.

In some embodiments, the mismatches introduced into the sgRNA targeting sequences are selected by taking into account the identity of the nucleotides immediately surrounding the mismatch. For example, the activity of mismatched sgRNAs is generally higher if there is a G (in the sgRNA sequence) either immediately upstream or 1, 2, or 3 nucleotides downstream of the mismatch, and particularly so if there is a G both before and after the mismatch. Further, the activity of mismatched sgRNAs is generally lower if lower if there is a U either immediately upstream or 1, 2, or 3 nucleotides downstream of the mismatch, and particularly so if there is a U both before and after the mismatch.

In some embodiments, the mismatches introduced into the sgRNA targeting sequences are selected based on the general rule that the higher the GC content that a mismatched sgRNA has, the greater is its CRISPRi or CRISPRa activity.

Any of these rules and parameters can be used alone or in any possible combination to prepare an sgRNA with a desired level of CRISPRi or CRISPRa activity, and to prepare sets of sgRNAs targeting a single gene (i.e., a single set targeting a single DNA sequence within the gene, or multiple sets each targeting a different DNA sequence within the gene), wherein the set or sets comprise multiple sgRNAs that give rise to a series of different levels of expression of the targeted gene (e.g. with reduced expression levels using CRISPRi or increased expression levels using CRISPRa).

It will be appreciated that the specific expression of the target gene using a given sgRNA will depend to some extent upon, e.g., the gene that is being targeted, the specific DNA sequence within the target gene that is being targeted, the nature of the mismatches in the targeting sequence vis-a-vis the target DNA, and whether the sgRNA is used with CRISPRi or CRISPRa. Using the herein-described methods, however, it is possible to generate a set of sgRNAs that predictably cover any desired range of expression levels of a gene using CRISPRi or CRISPRa, e.g., cover any range of expression levels between 1% and 5000% of the normal expression level of the gene.

Assessment of Off-Target Effects

Introducing mismatches into the sgRNA targeting sequence may increase the potential for binding at non-intended genomic sites, or off-target effects. Such off-target potential can be assessed using two different strategies. In a first strategy, a FASTQ entry is created for the 23 bases of each genomic target in the genome including the PAM, with the accompanying empirical Phred score indicating an estimate of the anticipated importance of a mismatch in that base position. By aligning each sgRNA sequence back to the genome, parameterized so that sgRNAs are considered to mutually align if and only if: a) no more than 3 mismatches existed in the PAM-proximal 12 bases and the PAM, b) the summed Phred score of all mismatched positions across the 23 bases was less than a threshold, for example using Bowtie or similar software, it can be determined if a given sgRNA has no other binding sites in the genome at a given threshold. By performing this alignment iteratively with decreasing thresholds, an off-target specificity can be assigned to each sgRNA.

In a second strategy, empirical measurements of activities of sgRNAs comprising mismatches can be used to calculate the off-target potential. In a first step, all potential off-target sites up to 3 mismatches away for each sgRNA are determined, for example using Cas-OFFinder or a related method. These off-target sites can then be aggregated into a specificity score for each sgRNA:

$\mathrm{specificity}\mathrm{score}=\frac{1}{{\Sigma }_{i=1}^{n}R{A}_i·q_i}$

Where n represents the number of sites with up to 3 mismatches, RA the empirically measured relative CRISPRi activity of each sgRNA at this target site given the positions and types of mismatches, and q the number of times the ith site occurs in the genome. In particular, RA can be calculated as follows:

RA=Π _j=1 ^m RA _j

Where m represents the number of mismatches between the sgRNA and the target site and RA_j represents the mean relative activity of sgRNAs with mismatch j (given mismatch type at given sgRNA position). Because many sgRNAs by definition contain mismatches to the intended on-target site, the RA of the intended on-target site is assigned a value of 1 to keep the specificity scores on a scale of 0 to 1. A specificity score of 1 indicates that there are no off-target sites with up to 3 mismatches in the genome, whereas a specificity score of 0.001 indicates nearly complete lack of specificity.

By appropriately calculating off-target potential for sgRNAs comprising mismatches, off-target effects can be minimized.

Modifications in the Constant Sequence

In some embodiments, sgRNAs are provided with one or more nucleotide changes into the sgRNA constant region (i.e., in the region outside of the targeting sequence that is required for binding to Cas9) so as to obtain intermediate levels of CRISPRi or CRISPRa activity, or in some cases levels that exceed those obtained with an unmodified constant region. In some embodiments, sets of sgRNAs are provided comprising individual sgRNAs with different mutations so as to obtain a series of different expression levels of a target gene. In such embodiments, an sgRNA will typically be used in which the constant region is not modified, e.g., is 100% identical to the sequence shown as constant region cr995 in Table 6, and one or more additional sgRNAs will also be used that comprise one or more nucleotide or base-pair substitutions within the constant region.

A list of sgRNAs comprising 995 constant region variants, comprising all possible single nucleotide substitutions, base pair substitutions, and combinations of these changes is provided herein and shown in Table 6, along with their ranking and with the mean CRISPRi or CRISPRa activities that they confer. Any of these modified sgRNA sequences can be used in the present methods. In particular embodiments, a set of sgRNAs generating a series of discrete expression levels by CRISPRi or CRISPRa is produced using a plurality of such variants, e.g., by selecting a plurality of variants according to their ranking in Table 6. As indicated in Table 6, in some embodiments a constant region mutation will generate CRISPRi or CRISPRa activity levels that are greater than those obtained with an unmodified constant region. As such, using such modifications it is possible to generate sets of sgRNAs that cover expression levels that are both intermediate between those obtained with an unmodified constant region and those obtained with a modified region that provides no CRISPRi or CRISPRa activity, as well as expression levels that exceed those obtained with an unmodified constant region.

In some embodiments, sgRNA variants with modifications in their constant regions are selected based on one or more rules or parameters, e.g., rules or parameters deduced from the rankings shown in Table 6. For example, the mutation of bases known to mediate contacts with Cas9 (e.g., in the first stem-loop or the nexus) gives rise to greater CRISPRi or CRISPRa activity than mutations in regions not contacted by Cas9 (e.g., in the hairpin region of stem-loop 2). In some embodiments, sets are provided by selecting a plurality of sequences or mutations listed in Table 6 according to the ranking provided and/or the mean relative activities indicated, so as to obtain a plurality of gene expression levels by CRISPRi or CRISPRa.

It will be appreciated that the specific expression of the target gene using a given sgRNA will depend to some extent upon, e.g., the gene that is being targeted, the specific DNA sequence within the target gene that is being targeted, the nature of the mutation in the constant region, and whether the sgRNA is used with CRISPRi or CRISPRa. Using the herein-described methods, however, it is possible to generate a set of sgRNAs that predictably cover any desired range of expression levels of a gene using CRISPRi or CRISPRa, e.g., cover any range of expression levels between 1% and 5000% of the normal expression level of the gene.

sgRNA Sets and Libraries

In particular embodiments, the present disclosure provides sets and libraries of sgRNAs generated using the herein-described methods, i.e., introducing mismatches into the sgRNA targeting sequence and/or introducing modifications into the sgRNA constant region. For example, a set of sgRNAs can be designed and prepared to target a single gene and, when introduced into a plurality of cells, generate a series of discrete expression levels of the gene by CRISPRi or CRISPRa. The sets of sgRNAs will typically include a “wild-type” sgRNA, i.e., an sgRNA with 100% homology to the target DNA sequence in the 19 nucleotides leading up to the PAM and/or an sgRNA with no modifications in the constant region, as well as one or more additional sgRNAs with one or more mismatches in the targeting sequence and/or modifications in the constant region. The sets also optionally include a negative control sgRNA providing no CRISPRi or CRISPRa activity, e.g., an sgRNA with a scrambled targeting sequence or with sufficient modifications in the constant region to abolish Cas9 binding and therefore CRISPRi or CRISPRa activity.

Accordingly, in some embodiments, a set of sgRNAs is provided comprising 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 or more structurally distinct sgRNAs targeting a single gene, or targeting a single target sequence within a single gene. In some embodiments, the different sgRNAs of the set provide a series of discrete expression levels of the targeted gene. For example, an individual mismatched or modified sgRNA in the set may provide about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 105%, or 110% CRISPRi or CRISPRa activity, or any percentage value from 1% to 110%, as compared to a non-mismatched or unmodified sgRNA. In some embodiments, a set is generated in which at least one sgRNA is provided that generates a level of CRISPRi or CRISPRa activity within each of multiple windows of activity. For example, a set can contain one or more sgRNAs that provide from about 1%-50% activity and one or more sgRNAs that provide from about 51%-99% activity; or a set can comprise one or more sgRNAs that provide about 1%-33% activity, one or more sgRNAs that provide about 34%-66% activity, and one or more sgRNAs that provide about 67-99% activity; or a set can comprise one or more sgRNAs that provide about 1%-25% activity, one or more sgRNAs that provide about 26%-50% activity, one or more sgRNAs that provide about 51%-75% activity, and one or more sgRNAs that provide about 76%-99% activity; or a set can comprise one or more sgRNAs that provide about 1%-10% activity, one or more sgRNAs that provide about 11%-20% activity, one or more sgRNAs that provide about 21-30% activity, one or more sgRNAs that provide about 31%-40% activity, one or more sgRNAs that provide about 41-50% activity, one or more sgRNAs that provide about 51%-60% activity, one or more sgRNAs that provide about 61-70% activity, one or more sgRNAs that provide about 71%-80% activity, one or more sgRNAs that provide about 81-90% activity, and one or more sgRNAs that provide about 91%-99% activity. In some embodiments, one or more sgRNAs provide about 10%-30% activity, one or more sgRNAs provide about 30-50% activity, one or more sgRNAs provide about 50%-70% activity, and one or more sgRNAs provide about 70-90% activity.

In some embodiments, in particular with certain constant region mutations, a set will further include one or more sgRNAs that provide greater than 100% activity, e.g., 101%, 102%, 103%, 104%, 105%, 106%, 107%, 108%, 109%, 110%, or higher.

In some embodiments, the present disclosure provides libraries of sgRNAs comprising multiple sets of sgRNAs, with each set of sgRNAs targeting an individual gene or a specific target DNA within a gene. Accordingly, in some embodiments, a library of sgRNAs is provided comprising about 1000, 5000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000, 6,000,000, 7,000,000, 8,000,000, 9,000,000, 10,000,000 or more structurally distinct sgRNAs, or a library of sgRNAs is provided comprising 2 or more sets of sgRNAs targeting about 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, 11,000, 12,000, 13,000, 14,000, 15,000, 16,000, 17,000, 18,000, 19,000, 20,000 or more individual gene targets. In some embodiments, the library of sgRNAs targets a group of genes involved in a common pathway, process, or biological or physiological activity, or targets a group of genes known to produce a common phenotype. In some embodiments, all of the genes in the genome, or substantially all of the genes in the genome, are targeted.

For preparing a set of sgRNAs or a library of sgRNAs, once the target gene and the target DNA sequence or sequences within the genes have been selected, and the desired range of expression levels has been determined, a plurality of sgRNAs is designed using the herein-described rules, factors, parameters, and rankings of Table 6 for selecting mismatches in the sgRNA targeting sequence and/or mutations within the sgRNA constant region so as to obtain a set of sgRNAs that provide the desired expression levels of the targeted genes using CRISPRi or CRISPRa. In some embodiments, e.g., for sets comprising sgRNAs with mismatches in the targeting sequence, a set will comprise sgRNAs that each have mismatches in each of different regions of the targeting sequence. For example, in some embodiments, a set contains one or more sgRNAs with mismatches within 7 nucleotides of the PAM, the set contains one or more sgRNAs with mismatches located 8-12 nucleotides upstream of the PAM, and the set contains one or more sgRNAs with mismatches located 13-19 nucleotides upstream of the PAM.

In some embodiments, additional steps are included to exclude certain potential sgRNAs from a set or library. For example, a step can be included in which mismatched sgRNAs are assessed for potential off-target binding, and sgRNAs that are predicted to have or that have a potential for significant off-target binding are not used. In such embodiments, for example, for a given target in the genome, a FASTQ entry is created for the 23 bases of the target including the PAM, and the accompanying empirical Phred score is used to indicate an estimate of the anticipated importance of a mismatch at each position. Bowtie (bowtie-bio.sourceforge.net), e.g., is then used to align each designed sgRNA back to the genome, parameterized so that sgRNAs are considered to mutually align if and only if: a) no more than 3 mismatches exist in the PAM-proximal 12 bases and the PAM, and b) the summed Phred score of all mismatched positions across the 23 bases is below a threshold value. This alignment can be done iteratively with decreasing thresholds, and any sgRNAs that align successfully to no other site in the genome at a given threshold are deemed to have specificity at the threshold.

Other steps to filter potential sgRNAs can also be included, for example to exclude sgRNAs comprising one or more restriction sites that may be used for subsequent cloning or sequencing library preparation, such as BstXI, BlpI, and/or SbfI.

Applications

This invention affords precise control over the expression level of any mammalian gene, and as such can be used in any of a large number of potential applications. For example, in some embodiments a method is provided to profile the phenotypes resulting from varying degrees of downregulation or upregulation for every gene, providing information on the relationship between expression level and phenotype. Further, in some embodiments a method is provided to determine the cellular target and mechanism of action of, e.g., drugs with unknown mechanisms of action, of drug candidates, or of cytotoxic agents, such as drugs, drug candidates, or cytotoxic agents arising from high-throughput chemical screening efforts. In such embodiments, the present methods can be used immediately after the chemical screen to, e.g., identify the mechanism of action of compounds of interest to guide further development and characterization. In particular, the methods can be used to profile drug sensitivity at varying levels of knockdown and overexpression in order to identify genes for which small changes in expression levels cause hypersensitivity to a drug or compound of interest.

In some embodiments, a method is provided to determine gene-gene interactions for identification of synthetic lethal interactions. Additionally, a method is provided to control the flux through a metabolic pathway or a signaling pathway of interest and to identify bottlenecks of such pathways. In some such embodiments, the methods and compositions are used to guide metabolic engineering and synthetic biology approaches. In some embodiments, a method is provided to systematically analyze phenotypes associated with partial loss-of-function of essential genes. In some embodiments, a method is provided to assign phenotypes at different expression levels of a gene. In some such embodiments, the method is used to study an essential gene, which cannot be studied easily as its complete loss would lead to cell death, and to study partial loss-of-function phenotypes of the gene.

Also provided are methods to control the activity of any CRISPR system that relies on sgRNA-DNA base pairing. For example, the methods can also be used to comprehensively define the propensity for off-target effects during CRISPR-mediated gene editing and develop gene editing products that are tuned to minimize off-target effects.

In some embodiments, methods are provided to identify the functionally sufficient levels of gene products, which can serve as targets for rescue by gene therapy or chemical treatment when genes are affected by disease-causing loss-of-function mutations or as targets of inhibition for anti-cancer drugs, such that proliferating cancer cells experience toxicity while healthy cells are spared. In some embodiments, methods are provided to titrate the expression of individual genes in mammalian systems.

In some embodiments, a method is provided to identify the therapeutic window for restoration of a gene, e.g., a disease-associated gene whose loss-of-function leads to a disease-associated phenotype. In some such embodiments, a cell model is used that has normal levels of the disease-associated gene, but where deletion of the gene (or otherwise eliminating gene function) results in a measurable, e.g., disease-relevant, phenotype. In some such embodiments, the present methods are used with, e.g., CRISPRi to titrate the gene, i.e., produce multiple, decreased expression levels of the gene, and define the expression level at which the disease phenotype is alleviated to a relevant extent. In other such embodiments, a cell model is used that has a loss-of-function mutation in the disease-associated gene and a measurable phenotype, and the disease-associated gene is reintroduced, the resulting absence of the phenotype verified, and the expression of the reintroduced gene titrated using the present methods to define the expression level of the gene at which the disease phenotype is alleviated. It will be appreciated that such methods can be used to define the particular expression level required to alleviate or alter any measurable phenotype in any cell type, not only those associated with a disease.

In other embodiments, a method is provided of determining a therapeutic window for the inhibition of a gene, for example to lower the expression of a gene for therapeutic purposes but where elimination of the expression of the gene would be lethal or otherwise deleterious. Such methods can be used, e.g., to identify the lowest possible level of the gene that provides a therapeutic benefit but which is still compatible with survival or with otherwise avoiding the deleterious effects associated with complete loss of the gene. In some such embodiments, the relationship between decreased expression levels of the gene and the survival or growth of the cells is determined according to the herein-described methods for a plurality of sgRNAs targeting the gene using CRISPRi, and wherein the minimum level of expression of the gene that is compatible with cell growth or survival is determined, thereby determining the lower boundary of the therapeutic window for the inhibition of the gene.

In other embodiments, methods are provided of identifying a gene target of a cytotoxic agent or a drug candidate. In some such methods, a population of test cells is generated according to the present methods, where each test cell within the population expresses dCas9, e.g., dCas9 fused to a transcriptional repressor, as well as one or more sgRNAs of the invention, and the population of test cells is contacted with a sub-lethal or sub-therapeutic amount of the cytotoxic agent or drug candidate. The test cells within the population are then examined to identified test cells that display a phenotype in the presence of the sub-lethal or sub-therapeutic amount of the cytotoxic agent or drug candidate that is not displayed by cells in the absence of the dCas9 or of an sgRNA, and then the identity of the sgRNAs, and of the genes targeted by the sgRNAs, present within those phenotype-displaying test cells is determined. Genes that are targeted by one or more distinct sgRNAs in cells displaying a phenotype are candidate gene targets of the cytotoxic agent or drug candidate.

Preparation of sgRNAs, sgRNA Sets and Libraries

The sgRNAs provided herein can be synthesized using standard methods. For example, two complementary oligonucleotides (e.g., as synthesized using standard methods or obtained from a commercial supplier, e.g., Integrated DNA Technologies) containing the targeting region as well as overhangs matching those left by restriction digestion (e.g., by BstXI and/or BlpI) of an appropriate expression vector, can be annealed and ligated into an sgRNA expression vector digested using the same restriction enzymes. The ligated product is then transformed into competent cells (e.g., E. coli, e.g. as obtained from Takara Bio) and the plasmid prepared using standard protocols. Methods of synthesizing and preparing sgRNAs of the invention are disclosed, e.g., in Gilbert et al. Cell (2014) 159:647-661, the disclosure of which is herein incorporated in its entirety by reference.

In some embodiments, sgRNAs are ligated into sgRNA expression vectors such as pU6 vectors (i.e., vectors comprising CRISPR-Cas9 elements), e.g., a pU6-sgCXCR4-2 vector which also comprises a puromycin resistance cassette and mCherry. Such vectors can be obtained, e.g., from commercial suppliers (e.g., Addgene). sgRNA vectors can then be introduced into mammalian cells, e.g., by packaging the vectors in, e.g., lentivirus and transduced using standard methods into cells, e.g., K562 or Jurkat cells, which can then be grown and analyzed (e.g., by FACS, to record and/or gate on the basis of, e.g., GFP or mCherry expression).

Pooled sgRNA libraries can be cloned, e.g., as described in Gilbert et al., Cell (2014) 159:647-661; Kampmann et al., (2013) PNAS 110:E2317-E2326; Bassik et al. (2009) Nat. Methods 6:443-445, the disclosures of which are herein incorporated by reference in their entireties, or, e.g., by obtaining oligonucleotide pools containing the desired elements and, e.g., flanking restriction sites and PCR adaptors (e.g., from Agilent Technologies). The oligonucleotide pools are then amplified by PCR, digested with appropriate restriction enzymes, and ligated into vectors such as pCRISPRia-v2 that have been digested with the same enzymes. The ligation product is then purified and transformed into competent cells, e.g., electrocompetent cells such as MegaX DH10B cells (Thermo Fisher Scientific) by, e.g., electroporation using a system such as Gene Pulser Xcell system (Bio-Rad). Following growth and appropriate selection of the cells, the pooled sgRNA plasmid library is extracted, e.g., by GigaPrep (Qiagen or Zymo Research).

Site-Directed Nucleases

The present methods involve the expression of sgRNAs in cells along with a site-directed nuclease such as Cas9, e.g., dCas9, e.g., dCas9 fused to a transcriptional modulator. See, for example, Abudayyeh et al., Science 2016 Aug. 5; 353(6299): aaf5573; and Fonfara et al. Nature 532: 517-521 (2016). As used throughout, the term “Cas9 polypeptide” means a Cas9 protein or a fragment thereof present in any bacterial species that encodes a Type II CRISPR/Cas9 system. See, for example, Makarova et al. Nature Reviews, Microbiology, 9: 467-477 (2011), including supplemental information, hereby incorporated by reference in its entirety. For example, the Cas9 protein or a fragment thereof can be from Streptococcus pyogenes. Full-length Cas9 is an endonuclease comprising a recognition domain and two nuclease domains (HNH and RuvC, respectively) that creates double-stranded breaks in DNA sequences. In the amino acid sequence of Cas9, HNH is linearly continuous, whereas RuvC is separated into three regions, one left of the recognition domain, and the other two right of the recognition domain flanking the HNH domain. Cas9 from Streptococcus pyogenes is targeted to a genomic site in a cell by interacting with a guide RNA that hybridizes to a 20-nucleotide DNA sequence that immediately precedes an NGG motif recognized by Cas9. This results in a double-strand break in the genomic DNA of the cell. In some embodiments, a Cas9 nuclease that requires an NGG protospacer adjacent motif (PAM) immediately 3′ of the region targeted by the guide RNA I sused. As another example, Cas9 proteins with orthogonal PAM motif requirements can be utilized to target sequences that do not have an adjacent NGG PAM sequence. Exemplary Cas9 proteins with orthogonal PAM sequence specificities include, but are not limited to those described in Esvelt et al., Nature Methods 10: 1116-1121 (2013).

In particular embodiments, the site-directed nuclease is a deactivated site-directed nuclease, for example, a dCas9 polypeptide. As used throughout, a dCas9 polypeptide is a deactivated or nuclease-dead Cas9 (dCas9) that has been modified to inactivate Cas9 nuclease activity. Modifications include, but are not limited to, altering one or more amino acids to inactivate the nuclease activity or the nuclease domain. For example, and not to be limiting, D10A and H840A mutations can be made in Cas9 from Streptococcus pyogenes to inactivate Cas9 nuclease activity. Other modifications include removing all or a portion of the nuclease domain of Cas9, such that the sequences exhibiting nuclease activity are absent from Cas9. Accordingly, a dCas9 may include polypeptide sequences modified to inactivate nuclease activity or removal of a polypeptide sequence or sequences to inactivate nuclease activity. The dCas9 retains the ability to bind to DNA even though the nuclease activity has been inactivated. Accordingly, dCas9 includes the polypeptide sequence or sequences required for DNA binding but includes modified nuclease sequences or lacks nuclease sequences responsible for nuclease activity.

In some examples, the dCas9 protein is a full-length Cas9 sequence from S. pyogenes lacking the polypeptide sequence of the RuvC nuclease domain and/or the HNH nuclease domain and retaining the DNA binding function. In other examples, the dCas9 protein sequences have at least 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 98% or 99% identity to Cas9 polypeptide sequences lacking the RuvC nuclease domain and/or the HNH nuclease domain and retain DNA binding function.

In some examples, the deactivated site-directed nuclease, for example, a deactivated Cas9, is linked to an effector protein. Optionally, the site-directed nuclease is linked to the effector protein via a peptide linker. The linker can be between about 2 and about 25 amino acids in length. The effector protein can be a transcriptional regulatory protein or an active fragment thereof. The transcriptional regulatory protein can be a transcriptional activator or a transcriptional repressor protein or a protein domain of the activator protein or the inhibitor protein. Examples of transcriptional activators include, but are not limited to VP16, VP48, VP64, VP192, MyoD, E2A, CREB, KMT2A, NF-KB (p65AD), NFAT, TET1, p300Core and p53. Examples of transcriptional inhibitors include, but are not limited to KRAB, MXI1, SID4X, LSD1, and DNMT3A/B. The effector protein can also be an epigenome editor, such as, for example, histone acetyltransferase, histone demethylase, DNA methylase etc.

The effector protein or an active fragment thereof can be operatively linked, in series, to the amino-terminus or the carboxy-terminus of the site-directed nuclease, for example, to dCas9. Optionally, two or more activating effector proteins or active domains thereof can be operatively linked to the amino-terminus or the carboxy-terminus of dCas9. Optionally, two or more repressor effector proteins or active domains thereof can be operatively linked, in series, to the amino-terminus or the carboxy-terminus of dCas9. Optionally, the effector protein can be associated, joined or otherwise connected with the nuclease, without necessarily being covalently linked to dCas9.

Polynucleotides and Cells

In some embodiments, the compositions of the invention are introduced into cells using nucleic acid constructs. Nucleic acid constructs of the invention, e.g., polynucleotides encoding expression cassettes encoding sgRNAs or encoding dCas9, can be in any of a number of forms, e.g., in a vector, such as a plasmid, a viral vector, a lentiviral vector, etc. In some examples, the nucleic acid construct is in a host cell. The nucleic acid construct can be episomal or integrated in the host cell. The compositions provided herein can be used to modulate expression of target nucleic acid sequences in eukaryotic cells, animal cells, plant cells, fungal cells, and the like. Optionally, the cell is a mammalian cell, for example, a human cell. The cell can be in vitro or ex vivo. The cell can also be a primary cell, a germ cell, a stem cell or a precursor cell. The precursor cell can be, for example, a pluripotent stem cell or a hematopoietic stem cell. Introduction of the composition into cells can be cell cycle dependent or cell cycle independent. Methods of synchronizing cells to increase a proportion of cells in a particular phase are known in the art. Depending on the type of cell to be modified, one of skill in the art can readily determine if cell cycle synchronization is necessary.

The compositions described herein can be introduced into the cell via microinjection, lipofection, nucleofection, electroporation, nanoparticle bombardment, and the like. The compositions can also be packaged into viral particles for infection into cells.

Also provided are cells including the compositions described herein and cells modified by the compositions described herein. Cells or populations of cells comprising one or more nucleic acid constructs described herein are also provided. For example, a cell is provided comprising a nucleic acid construct comprising an expression cassette encoding an sgRNA of the invention, operably linked to a promoter, and/or a nucleic acid construct comprising an expression cassette encoding dCas9, operably linked to a promoter. Populations of cells are also provided, for example with each cell among the population comprising an expression cassette encoding a dCas9 protein, operably linked to a promoter, and comprising an expression cassette encoding one of the sgRNAs of the invention, operably linked to a promoter. In some embodiments, the sgRNA comprises a mismatch in the targeting sequence. In some embodiments, the sgRNA comprises a mutation in the constant region. In some embodiments, the sgRNA is present within a nucleic acid construct that also comprises an expression cassette encoding a unique guide barcode, e.g., as described in Adamson et al. (2016) Cell 167:1867-1882.e21, the entire disclosure of which is herein incorporated by reference). In some embodiments, the dCas9 is a fusion protein fused to a transcriptional activator or repressor such as VP64 or KRAB, respectively.

As set forth above, each nucleic acid construct can comprise one or more expression cassettes encoding a reporter gene. Thus, a different reporter gene can be used for each construct, to individually track each nucleic acid construct in a cell or a population of cells. Cells include, but are not limited to, eukaryotic cells, animal cells, plant cells, fungal cells, and the like. Optionally, the cells are in a cell culture. Optionally, the cell is a mammalian cell, for example, a human cell. The cell can be in vitro or ex vivo. The cell can also be a primary cell, a germ cell, a stem cell or a precursor cell. The precursor cell can be, for example, a pluripotent stem cell or a hematopoietic stem cell. Introduction of the composition into cells can be cell cycle dependent or cell cycle independent. Methods of synchronizing cells to increase a proportion of cells in a particular phase are known in the art. Depending on the type of cell to be modified, one of skill in the art can readily determine if cell cycle synchronization is necessary.

The method can be performed by contacting a plurality of mammalian cells with a plurality of vectors to form a plurality of vector-infected cells. In some examples, the vectors are lentiviral vectors that are packaged into viral particles for infection of cells. The multiplicity of infection (MOI) can be controlled to ensure that the majority of the cells comprise no more than a single vector or a single integration event per cell.

In some examples, the plurality of cells is a heterogeneous population of cells (i.e., a mixture of different cells types) or a homogeneous population of cells. In some examples, the plurality contains at least two different cell types. In some examples, the cells in the plurality include healthy and/or diseased cells from a thymus, white blood cells, red blood cells, liver cells, spleen cells, lung cells, heart cells, brain cells, skin cells, pancreas cells, stomach cells, cells from the oral cavity, cells from the nasal cavity, colon cells, small intestine cells, kidney cells, cells from a gland, brain cells, neural cells, glial cells, eye cells, reproductive organ cells, bladder cells, gamete cells, human cells, fetal cells, amniotic cells, or any combination thereof.

In typical embodiments of the present methods, a site-directed nuclease is expressed in the mammalian cells. In some examples, the mammalian cells stably express a site-directed nuclease. In some examples, the site-directed nuclease is constitutively expressed. In some examples, the site-directed nuclease is under the control of an inducible promoter. In some examples, the mammalian cells are infected with a vector comprising a polynucleotide sequence encoding the site-directed nuclease prior to or subsequent to infecting the cells with the plurality of vectors. In any of the methods, the site-directed nuclease can be transiently or stably expressed in the mammalian cells. In some examples, the site-directed nuclease is encoded by an expression cassette in the cell, the expression cassette comprising a promoter operably linked to a polynucleotide encoding the site-directed nuclease. In some examples, the promoter operably linked to the polynucleotide encoding the site-directed nuclease is a constitutive promoter. In other examples, the promoter operably linked to the polynucleotide encoding the site-directed nuclease is inducible. For example, and not to be limiting, the site-directed nuclease can be under the control of a tetracycline inducible promoter, a tissue-specific promoter, or an IPTG-inducible promoter.

Once the cells have been infected, the cells are cultured for a sufficient amount of time to allow sgRNA: site-directed nuclease complex formation and transcriptional modulation, such that a pool of cells expressing a detectable phenotype can be selected from or detected among the plurality of infected cells, and/or such that the individual expression levels of target genes within cells expressing distinct sgRNAs comprising one or more mismatches and/or one or more constant region mutations can be assessed.

For example, in some embodiments, large-scale libraries can be transduced into cells, e.g., K562 CRISPRi or Jurkat CRISPRi cells, e.g., at MOI of <1 using standard methods. Following growth and appropriate selection for transduced cells, cells can be harvested, e.g., by centrifugation. In some embodiments, the genomic DNA is isolated and the sgRNA-encoding region enriched, amplified, and processed for sequencing (e.g., as disclosed in Horlbeck et al. (2016), eLife 5:e19760, the entire disclosure of which is herein incorporated by reference). The region is excised, purified, quantified, and amplified by PCR, prior to sequencing using standard methods and as described in the Examples. Phenotypes such as growth can be analyzed using known methods, e.g., by calculating the log 2 change in enrichment of an sgRNA at a given time point, subtracting the equivalent median values for all non-targeting sgRNAs, and dividing by the number of doublings in the population. The relative activities of mismatched sgRNAs, for example, can be calculated by dividing the phenotypes of the mismatched sgRNAs by those for the corresponding perfectly matched sgRNAs, e.g., as described in the Examples.

Sequencing and Analysis

Any of a number of methods can be used to evaluate the effects of an sgRNA of the invention, e.g., to evaluate the precise expression level of a gene in the presence of the sgRNA and CRISPRi or CRISPRa, and/or to evaluate one or more phenotypes generated by the sgRNA in the presence of the CRISPRi or CRISPRa. In some embodiments, sets or libraries of sgRNAs and their effects on the transcriptome and/or other phenotypes are evaluated using Perturb-seq. In such methods, the sgRNAs are cloned into a vector such as a CROP-seq vector (as described, e.g., in Datlinger et al. (2017) Nat. Methods 14:297-301; Replogle et al. (2018) bioRxiv 503367, doi:10.1101/503367, the entire disclosures of which are herein incorporated by reference), packed into lentivirus, and transduced into cells, e.g., K562 CRISPRi cells. Following growth and appropriate selection of cells, cells are loaded onto a chip, e.g., a Chromium Single Cell 3′ V2 chip (10× Genomics) according to standard methods. The CROP-seq sgRNA barcode is then amplified by, e.g., PCR using a primer specific to the sgRNA expression cassette and a standard (e.g., P5) primer, pooled with the single cell RNA-seq libraries, and then sequenced, e.g., on a HiSeq 4000 (10× Genomics). Read counts, growth phenotypes, and relative sgRNA activities are determined using standard methods and as described in the Examples, as is Perturb-seq data analysis.

The phenotype can be, for example, cell growth, survival, or proliferation. In some embodiments, the phenotype is cell growth, survival, or proliferation in the presence of an agent, such as a cytotoxic agent, an oncogene, a tumor suppressor, a transcription factor, a kinase (e.g., a receptor tyrosine kinase), a gene (e.g., an exogenous gene) under the control of a promoter (e.g., a heterologous promoter), a checkpoint gene or cell cycle regulator, a growth factor, a hormone, a DNA damaging agent, a drug, or a chemotherapeutic. The phenotype can also be protein expression, RNA expression, protein activity, or cell motility, migration, or invasiveness. In some embodiments, the selecting of the cells on the basis of the phenotype comprises fluorescence activated cell sorting, affinity purification of cells, or selection based on cell motility.

In some embodiments, after selection of a pool of cells expressing a detectable phenotype, genomic DNA comprising the nucleic acid encoding the sgRNA is amplified by polymerase chain reaction (PCR) with a pair of primers that bracket the genomic segment comprising the nucleic acid encoding the sgRNA in each cell. In some embodiments, at least one of the PCR primers includes a sample barcode sequence that is added to the amplified DNA during amplification. The sample barcode sequence allows identification of all sequencing reads from the same sample, for example, when multiplexing multiple samples into single sequencing chip or lane.

In some embodiments, individual cells from the pool or population of cells expressing a detectable phenotype are placed into individual compartments. These compartments can be, but are not limited to, wells of a tissue culture plate (e.g., microwells) or microfluidic droplets. As used herein the term “droplet” can also refer to a fluid compartment such as a slug, an area on an array surface, or a reaction chamber in a microfluidic device, such as for example, a microfluidic device fabricated using multilayer soft lithography (e.g., integrated fluidic circuits). Exemplary microfluidic devices also include the microfluidic devices available from 10× Genomics (Pleasanton, Calif.).

In some embodiments, the cells are encapsulated in droplets. Relatively small droplets can be used in the methods provided herein. In some examples, the average diameter of the droplets may be less than about 5 mm, less than about 4 mm, less than about 3 mm, less than about 1 mm, less than about 500 micrometers, or less than about 100 micrometers. The “average diameter” of a population of droplets is the arithmetic average of the diameters of each of the droplets. In the methods provided herein, the droplets may be of the same shape and/or size, or of different shapes and/or sizes, depending on the particular application. In some examples, the individual droplets have a volume of about 1 picoliter to about 100 nanoliters.

A droplet generally includes an amount of a first sample fluid in a second carrier fluid. Any technique known in the art for forming droplets may be used. An exemplary method involves flowing a stream of the sample fluid containing the target material (e.g., cells expressing a detectable phenotype) such that the stream of sample fluid intersects two opposing streams of flowing carrier fluid. The carrier fluid is immiscible with the sample fluid. Intersection of the sample fluid with the two opposing streams of flowing carrier fluid results in partitioning of the sample fluid into individual sample droplets containing the target material. The carrier fluid may be any fluid that is immiscible with the sample fluid. An exemplary carrier fluid is oil. Optionally, the carrier fluid includes a surfactant or is a fluorous liquid. Optionally, the droplets contain an oil and water emulsion. Oil-phase and/or water-in-oil emulsions allow for the compartmentalization of reaction mixtures within aqueous droplets. The emulsions can comprise aqueous droplets within a continuous oil phase. The emulsions provided herein can be oil-in-water emulsions, wherein the droplets are oil droplets within a continuous aqueous phase.

In some embodiments, a microfluidic device is used to generate single cell droplets, for example, a single cell emulsion droplet. The microfluidic device ejects single cells in aqueous reaction buffer into a hydrophobic oil mixture. The device can create thousands of droplets per minute. In some cases, a relatively large number of droplets can be generated, for example, at least about 10, at least about 30, at least about 50, at least about 100, at least about 300, at least about 500, at least about 1,000, at least about 3,000, at least about 5,000, at least about 10,000, at least about 30,000, at least about 50,000, or at least about 100,000 droplets. In some cases, some or all of the droplets may be distinguishable, for example, on the basis of an oligonucleotide present in at least some of the droplets (e.g., which may include one or more unique sequences or barcodes). In some cases, at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, at least about 95%, at least about 97%, at least about 98%, or at least about 99% of the droplets may be distinguishable.

In some cases, after the droplets are created, the device ejects the mixture of droplets into a trough. The mixture can be pipetted or collected into a standard reaction tube for thermocycling and PCR amplification. Single cell droplets in the mixture can also be distributed into individual wells, for example, into a multiwell plate for thermocycling and PCR amplification in a thermal cycler. After amplification, the droplets can be analyzed, for example, by sequencing, to identify sgRNAs and their corresponding unique barcodes in each single cell. In some cases, the cells are lysed inside the droplet before or after amplification. In other cases, the droplets can be distributed onto a chip for amplification. Numerous methods of generating droplets and amplifying nucleic acids therein are known in the art. See, for example, Abate et al., “DNA sequence analysis with droplet-based microfluidic,” Lab Chip 13: 4864-4869 (2013); and Kaler et al. “Droplet microfluidics for Chip-Based Diagnostics,” Sensors 14(12): 23283-23306 (2014)), both of which are incorporated herein in their entireties by this reference.

Droplets containing cells may optionally be sorted according to a sorting operation prior to merging with one or more reagents (e.g., as a second set of droplets). In some embodiments, a cell can be encapsulated together with one or more reagents in the same droplet, for example, biological or chemical reagents, thus eliminating the need to contact a droplet containing a cell with a second droplet containing one or more reagents. Additional reagents may include DNA polymerase enzymes, reverse transcriptase enzymes, including enzymes with terminal transferase activity, primers, and oligonucleotides. In some embodiments, the droplet that encapsulates the cell already contains one or more reagents prior to encapsulating the cell in the droplet. In yet other embodiments, the reagents are injected into the droplet after encapsulation of the cell in the droplet. In some embodiments, the one or more reagents may contain reagents or enzymes such as a detergent that facilitates the breaking open of the cell and release of the cellular material therein. Once the reagents are added to the droplets containing the cells, the DNA comprising the nucleic acid encoding the sgRNA can be amplified in the droplet, for example, by polymerase chain reaction (PCR).

In some embodiments, after thermocycling and PCR, the amplified products can be recovered from the droplet using numerous techniques known in the art. For example, ether can be used to break the droplet and create an aqueous/ether layer which can be evaporated to recover the amplification products. Other methods include adding a surfactant to the droplet, flash-freezing with liquid nitrogen and centrifugation. Once the amplification products are recovered, the products can be further amplified and/or sequenced.

The methods provided herein comprise sequencing the amplified DNA. Sequencing methods include, but are not limited to, shotgun sequencing, bridge PCR, Sanger sequencing (including microfluidic Sanger sequencing), pyrosequencing, massively parallel signature sequencing, nanopore DNA sequencing, single molecule real-time sequencing (SMRT) (Pacific Biosciences, Menlo Park, Calif.), ion semiconductor sequencing, ligation sequencing, sequencing by synthesis (Illumina, San Diego, Ca), Polony sequencing, 454 sequencing, solid phase sequencing, DNA nanoball sequencing, heliscope single molecule sequencing, mass spectroscopy sequencing, pyrosequencing, Supported Oligo Ligation Detection (SOLiD) sequencing, DNA microarray sequencing, RNAP sequencing, tunneling currents DNA sequencing, and any other DNA sequencing method identified in the future. One or more of the sequencing methods described herein can be used in high throughput sequencing methods. As used herein, the term “high throughput sequencing” refers to all methods related to sequencing nucleic acids where more than one nucleic acid sequence is sequenced at a given time.

Any of the methods provided herein can optionally comprise deep sequencing of the amplified DNA. As used herein, “deep sequencing” refers to highly redundant sequencing of a nucleic acid. The redundancy (i.e., depth) of the sequencing is determined by the length of the sequence to be determined (X), the number of sequencing reads (N), and the average read length (L). The redundancy is then N×L/X. In the case of sgRNAs, the length of the sequence can be the length of the targeting sequence, the full length of the sgRNA, or the length of a portion of the sgRNA that contains the targeting sequence. The sequencing depth can be, or be at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 70, 80, 90, 100, 110, 120, 130, 150, 200, 300, 500, 500, 700, 1000, 2000, 3000, 4000, 5000 or more. Deep sequencing can provide an accurate number of the relative frequency of the sgRNAs. Deep sequencing can also provide a high confidence that even sgRNAs that are rarely present in a population of cells (e.g., a population of selected test cells) can be identified.

Once DNA is amplified from each cell, the nucleic acid encoding the sgRNA is sequenced from the amplified DNA. The barcode sequence provides a unique sequence for the sgRNA present in each cell. Once the cells and sgRNAs have been identified, the DNA targets of the sgRNAs can be further analyzed to determine their precise expression levels and/or how and/or to what extent the modulated expression of the DNA targets affect the phenotype.

Disclosed are materials, compositions, kits, and components that can be used for, can be used in conjunction with, can be used in preparation for, or are products of the disclosed embodiments. These and other materials are disclosed herein, and it is understood that when combinations, subsets, interactions, groups, etc. of these materials are disclosed that while specific reference of each various individual and collective combinations and permutations of these compositions may not be explicitly disclosed, each is specifically contemplated and described herein. For example, if a method is disclosed and discussed and a number of modifications that can be made to a number of molecules included in the method are discussed, each and every combination and permutation of the method, and the modifications that are possible are specifically contemplated unless specifically indicated to the contrary. Likewise, any subset or combination of these is also specifically contemplated and disclosed. This concept applies to all aspects of this disclosure including, but not limited to, steps in methods using the disclosed compositions. Thus, if there are a variety of additional steps that can be performed, it is understood that each of these additional steps can be performed with any specific method steps or combination of method steps of the disclosed methods, and that each such combination or subset of combinations is specifically contemplated and should be considered disclosed.

4. Examples

The present invention will be described in greater detail by way of specific examples. The following examples are offered for illustrative purposes only, and are not intended to limit the invention in any manner. Those of skill in the art will readily recognize a variety of noncritical parameters which can be changed or modified to yield essentially the same results.

Example 1. Titrating Gene Expression with Series of Systematically Compromised CRISPR Guide RNAs

Abstract

Biological phenotypes arise from the degrees to which genes are expressed, but the lack of tools to precisely control gene expression limits our ability to evaluate the underlying expression-phenotype relationships. Here, we describe a readily implementable approach to titrate expression of human genes using series of systematically compromised sgRNAs and CRISPR interference. We empirically characterize the activities of compromised sgRNAs using large-scale measurements across multiple cell models and derive the rules governing sgRNA activity using deep learning, enabling construction of a compact sgRNA library to titrate expression of 2,400 genes involved in central cell biology and a genome-wide in silico library. Staging cells along a continuum of gene expression levels combined with rich single-cell RNA-seq readout reveals gene-specific expression-phenotype relationships with expression level-specific responses. Our work provides a general tool to control gene expression, with applications ranging from tuning biochemical pathways to identifying suppressors for diseases of dysregulated gene expression.

Results

Mismatched sgRNAs Mediate Diverse Intermediate Phenotypes

To comprehensively characterize the activities of mismatched sgRNAs in CRISPRi-mediated knockdown, we introduced all 57 singly mismatched variants of a GFP-targeting sgRNA (18) into GFP+ K562 CRISPRi cells and measured GFP levels by flow cytometry (FIG. 1A). Cells harboring mismatched sgRNAs experienced knockdown levels between those of cells with the perfectly matched sgRNA (94%) and cells with a non-targeting control sgRNA (FIGS. 1B, 2A-2B, Table 1). As expected, sgRNAs with mismatches in the PAM-proximal seed region (12,13) had strongly compromised activity. By contrast, sgRNAs with mismatches in the PAM-distal region mediated GFP knockdown to an extent similar to that of the unmodified sgRNA, albeit with substantial variability depending on the type of mismatch (FIGS. 1B-1C). The distributions of GFP levels with mismatched sgRNAs were largely unimodal, although the distributions were typically broader than with the perfectly matched sgRNA or the control sgRNA (FIGS. 1B, 2B). These results suggest that series of mismatched sgRNAs can be used to titrate gene expression at the single-cell level, but that mismatched sgRNA activity is modulated by complex factors.

Rules of Mismatched sgRNA Activity Derived from a Large-Scale Screen

We reasoned that we could empirically derive the factors governing the influence of mismatches on sgRNA activity by measuring growth phenotypes imparted by a large number mismatched sgRNAs in a pooled screen. For this purpose, we generated a ˜120,000-element library comprising series of variants for 4,898 sgRNAs targeting 2,499 genes with growth phenotypes in K562 cells (19). Each individual series, herein referred to as an allelic series, contains the original, perfectly matched sgRNA and 22-23 variants with one or two mismatches (FIG. 3A). We then measured CRISPRi growth phenotypes (γ, for which a more negative value indicates a stronger growth defect) for each sgRNA in this library in both K562 (chronic myelogenous leukemia) and Jurkat (acute T-cell lymphocytic leukemia) cells using pooled screens (15,20) (FIGS. 3B, 4A-4D, Methods). Growth phenotypes of targeting sgRNAs were well-correlated in biological replicates (FIGS. 4A-4B, Pearson r2 [K562]=0.82; Pearson r2 [Jurkat]=0.82) and recapitulated previously reported phenotypes (19) (FIG. 4C).

Mismatched sgRNAs mediated a range of phenotypes, spanning from that of the corresponding perfectly matched sgRNA to those of negative control sgRNAs (FIG. 3C). To account for differences in absolute growth phenotypes, we normalized the phenotype of each mismatched sgRNA to that of its corresponding perfectly matched sgRNA (relative activity, FIG. 3B) and filtered for series in which the perfectly matched sgRNA had a strong growth phenotype (Methods). Relative activities measured in K562 and Jurkat cells were well-correlated (FIG. 3D, Pearson r2=0.71), regardless of differences in absolute phenotype of the perfectly matched sgRNAs (Pearson r2=0.74 for |γ[K562]−γ[Jurkat]|>0.2; Pearson r2=0.70 for |γ[K562]−γ[Jurkat]|<0.2). We therefore averaged relative activities from both cell lines for further analysis (Methods). Although the majority of mismatched sgRNAs were inactive (FIG. 3E), particularly if they contained two mismatches (FIG. 4E), a substantial fraction exhibited intermediate activity (19,596 sgRNAs with 0.1<relative activity <0.9, 25.5% of sgRNAs in series passing filter).

To understand the rules governing the impacts of mismatches on sgRNA activity, we stratified the relative activities of singly-mismatched sgRNAs by properties of the mismatch. As expected, mismatch position was a strong determinant of activity, with mismatches closer to the PAM leading to lower relative activity (FIG. 3E). In agreement with patterns of Cas9 off-target activity 21, sgRNAs with rG:dT mismatches (A to G mutations in the sgRNA) retained substantial activity even for mismatches close to the PAM (FIG. 3F). Other factors were of lower magnitude and more context-dependent, such as the associations of higher GC content with higher activity for mismatches located 9 or more bases upstream of the PAM (positions −9 to −19), and of mismatch-surrounding G nucleotides with marginally higher activity for mismatches in the intermediate region (FIGS. 4F-4G). The activities of mismatched sgRNAs thus appear to be determined by general biophysical rules; a premise further supported by the high correlation of relative activities obtained in two different cell lines (FIG. 3D) and the high correlation of mismatched sgRNA activities with previous in vitro measurements of dCas9 binding on-rates in the presence of mismatches (22) (FIG. 3G).

Finally, we evaluated the proportion of sgRNA series that provide access to a range of intermediate CRISPRi growth phenotypes for the targeted gene (relative activity between 0.1 and 0.9). When considering only singly-mismatched sgRNAs, 76.1% of series contain at least 2 sgRNAs with intermediate phenotypes, and that number rises to 86.7% when also including double mismatches (FIG. 4H). As we explored only ˜20% of possible single mismatches and <1% of possible double mismatches, it is likely that intermediate-activity sgRNAs also exist for the remaining series. Altogether, these results suggest that systematically mismatched sgRNAs provide a general method to titrate CRISPRi activity and, consequently, target gene expression.

Controlling sgRNA Activity with Modified Constant Regions

We also explored the orthogonal approach of generating intermediate-activity sgRNAs through modifications to the sgRNA constant region, which is required for binding to Cas9. Although previous work has established that such modifications can lead to increases or decreases in Cas9 activity or have no measurable impact (16, 23-27), the mutational landscape of the constant region has only been sparsely explored, and largely with the goal of preserving sgRNA activity.

To comprehensively assess the activities of modified sgRNA constant regions, we designed a library of 995 constant region variants comprising all possible single nucleotide substitutions, base pair substitutions, and combinations of these changes (Methods, Table 6) and determined the growth phenotypes for each variant paired with 30 different targeting sequences against 10 essential genes in a pooled screen in K562 cells (FIGS. 5A, 6A; Table 6, which shows the ranking of each constant region variant in terms of its relative CRISPRi and CRISPRa activity). We calculated relative activities for each targeting sequence:constant region pair by normalizing its phenotype to that of the targeting sequence paired with the unmodified constant region, identifying 409 constant region variants that on average conferred intermediate activity (0.1-0.9, FIG. 5B). Ten variants selected for individual evaluation also mediated intermediate levels of mRNA knockdown (FIG. 6B). Mapping the activities of constant region variants with single base substitutions onto the structure recapitulated known relationships between constant region structure and function (FIG. 5C). For example, mutation of bases known to mediate contacts (16) with Cas9 (e.g. the first stem loop or the nexus) generally reduced activity, whereas mutations in regions not contacted by Cas9 (e.g. the hairpin region of stem loop 2) were well-tolerated (FIG. 5C). Notably, several variants carrying mutations in stem loop 2 had consistently increased activities and thus could be useful tools for future applications (FIGS. 5B-5C).

Evaluating the relative activities of constant region variants across different targeting sequences revealed consistent rank ordering but substantial variation in the actual values (FIGS. 5D, 6C). For example, a targeting sequence against TUBB retained high activity with ˜100 constant region variants that otherwise abolished activity for other targeting sequences, whereas a targeting sequence against SNRPD2 lost activity with ˜50 variants that otherwise conferred intermediate activity (FIG. 5D). In some but not all (FIG. 5E) cases, this heterogeneity extended to different targeting sequences against the same gene, both at the level of growth phenotype (FIGS. 5F-5G, 6D-6E) and mRNA knockdown (FIG. 6B). These differences between targeting sequences could be a consequence of specific targeting sequence:constant region structural interactions or of differences in basal sgRNA expression levels such that lowly expressed sgRNAs are more susceptible to constant region modifications. Thus, although modified constant regions can be used to titrate gene expression, the activity of a given constant region variant for a given targeting sequence is difficult to predict. We therefore focused on sgRNAs with mismatches in the targeting region for the remainder of our work, given that the activities of these sgRNAs were governed by biophysical principles, which should be more predictable.

A Neural Network Predicts Mismatched sgRNA Activities with High Accuracy

We next sought to leverage our large-scale data set of mismatched sgRNA activities to learn the underlying rules in a principled manner and to enable predictions of intermediate-activity sgRNAs against other genes. We reasoned that a convolutional neural network (CNN) would be well-suited to uncovering these rules due to the ability of CNNs to learn complex global and local dependencies on spatially-ordered features such as nucleotide sequences (28), including factors governing guide RNA activity in orthogonal CRISPR systems (29,30).

To develop a CNN model capable of predicting mismatched sgRNA activities, we constructed a model consisting of two convolution steps, a pooling step, and a 3-layer fully connected neural network (FIGS. 7A, 8A). As inputs, the model received sgRNA relative activities paired with nucleotide sequences represented by binarized 3D arrays denoting the genomic sequence of the target and the associated sgRNA mismatch (FIG. 7A). After optimizing hyperparameters using a randomized grid search, we trained 20 independent, equivalently initialized models on the same set of randomly selected sgRNAs (80% of all series) for 8 epochs, which minimized loss without extensive over-fitting (FIG. 8B). Predicted and measured sgRNA relative activities for the validation sgRNA set (i.e., the remaining 20% of series that were not used to train the model) were well-correlated (Pearson r2=0.65), with mean predictions of the 20-model ensemble outperforming all individual models (FIGS. 7B, 8C). The distribution of correlation coefficients for individual sgRNA series was unimodal with Pearson r values in the 25th-75th percentile ranging from 0.77 to 0.93, indicating that the model performed comparably well for most series (FIG. 7C). Model accuracy varied by mismatch position and type, with the highest accuracies corresponding to mismatches in the PAM-proximal seed region (FIGS. 8D-8E). Despite the fact that the model was trained on relative growth phenotypes, it also accurately predicted relative fluorescence values measured in the GFP experiment (FIG. 7D), further supporting the hypothesis that relative growth phenotypes report on biophysical attributes of specific sgRNA:DNA interactions.

To derive intermediate-activity sgRNAs for all human genes, we used the CNN ensemble to predict relative activities for all 57 singly-mismatched sgRNAs for the top 5 sgRNAs against each gene in the hCRISPRi-v2.1 library (19). Based on the accuracy of predictions for the validation set, we estimate that for any given gene, sampling 5 sgRNAs with predicted intermediate relative activity (0.1-0.9) will yield at least one sgRNA in that activity range over 90% of the time (FIGS. 8F-8I). This resource should therefore enable titrating the expression of any gene(s) of interest.

Finally, we sought to further understand the features of mismatched sgRNAs that contribute most to their activity. As the contributions of individual features in a deep learning model are difficult to assess directly, we also trained an elastic net linear regression model on the same data using a curated set of features. This linear model explained less variance in relative activities than the CNN model (r2=0.52, FIGS. 9A-9B), implying that our feature set was incomplete and/or sgRNA activity is partly determined by non-linear combinations of features; nonetheless, the relative activities predicted by the different models were well-correlated (r2=0.74, FIG. 9C). Consistent with our earlier observations, mismatch position and type were assigned the largest absolute weights in the model, although other features such as GC content in the sgRNA and the identities of flanking bases up to 3 nucleotides away from the mismatch were heavily weighted as well (FIGS. 9D-9E). For any given position, the type of mismatch contributed differentially to the prediction, with the largest variation between types occurring in the intermediate region of the targeting sequence (FIG. 9F). Taken together, these data demonstrate that the activities of mismatch-containing sgRNAs are determined by multiple factors which can be captured using supervised machine learning approaches.

A Compact Mismatched sgRNA Library Conferring Intermediate Growth Phenotypes

We next set out to design a more compact version of our large-scale library to titrate essential genes with a limited number of sgRNAs. We selected 2,405 genes which we had found to be essential for robust growth in K562 cells in our large-scale screen, divided the relative activity space into six bins, and attempted to select mismatched variants from each of the center four bins (relative activities 0.1-0.9) for two sgRNA series targeting each gene. If a bin did not contain a previously measured sgRNA, we selected one from the CNN model ensemble predictions (FIG. 10A), filtered to exclude sgRNAs with off-target binding potential. For each gene, 2 perfectly matched and 8 mismatched sgRNAs were selected, with approximately 32% of mismatched sgRNAs imputed from the CNN model (FIGS. 11A-11C).

We evaluated the relative activities of sgRNAs in the compact library using pooled CRISPRi growth screens in K562 and HeLa (cervical carcinoma) cells. Growth phenotypes were well-correlated in biological replicates from samples harvested at different time points after t0 in both cell lines (FIGS. 11D-11F). The CNN model predicted imputed sgRNA activities with lower accuracy than the large-scale validation (FIG. 11G), although we note that imputed sgRNAs were highly enriched in PAM-distal mutations which are associated with higher model errors (FIGS. 11B, 8E). Whereas the majority of mismatched sgRNAs in the large-scale screen had little to no activity, relative activities in the compact library were evenly distributed, ranging from inactive to full activity (FIG. 10B). Relative sgRNA activities were also well-correlated between K562 and HeLa cells (r2=0.58, FIG. 10C), suggesting that our library provides access to intermediate phenotypes for this core set of genes in multiple cell types.

To explore the utility of our compact library for chemical-genetic screens, we carried out a screen in K562 cells for sensitivity to lovastatin, a potent HMG-CoA reductase inhibitor (FIG. 11J). We hypothesized that even moderate knockdown of the direct target might significantly sensitize cells to the drug, which would lead to a unique signature when comparing growth phenotypes in drug-treated and untreated cells (τ and γ, respectively). Indeed, sgRNAs targeting HMGCR strongly reduced growth in the presence of lovastatin, and a linear regression of the HMGCR series on a τ vs. γ plot yielded one of the largest slopes of all series (FIG. 11K), demonstrating the potential to identify drug-gene interactions using this approach.

Exploring Expression Phenotype Relationships with sgRNA Series

Finally, we sought to use intermediate-activity sgRNAs to explore relationships between expression levels of various genes and the resulting cellular phenotypes. To simultaneously measure gene expression levels and obtain rich phenotypes for a variety of sgRNA series, we used Perturb-seq, an experimental strategy that enables matched capture of the transcriptome and the identity of an expressed sgRNA for each individual cell in pools of cells (27, 31-33) (FIG. 12A). We chose 25 essential genes involved in diverse cell biological processes (Table 2), targeting each with a perfectly matched sgRNA and 4-5 variants with intermediate growth phenotypes (138 sgRNAs total including 10 non-targeting controls, Table 1). We then subjected pooled K562 CRISPRi cells expressing these sgRNAs from a modified CROP-seq vector 33,34 to single-cell RNA-seq (scRNA-seq), using the co-expressed sgRNA barcodes to assign unique sgRNA identities to 19,600 cells (median 122 cells per sgRNA, FIGS. 12B-12C). In addition to the single-cell transcriptomes, we measured bulk growth phenotypes conferred by sgRNAs in these cells. These growth phenotypes were well-correlated with those from the large-scale screen and were used to assign sgRNA relative activities for further analysis (Methods, FIGS. 12D-12E, Tables 3, 4).

We first used the scRNA-seq data to assess the expression of the gene targeted by each sgRNA series. To account for cell-to-cell variability in transcript capture efficiency, we quantified target gene UMIs as a fraction of total UMIs in a given cell (FIG. 13), although analyzing raw UMI counts yielded similar results (FIG. 14). Approximately half of the genes we targeted were highly expressed (median >10 UMIs per cell), allowing us to directly measure target gene expression levels on the single-cell level (FIGS. 15A, 13). These distributions are largely unimodal, with medians shifting downwards with increasing sgRNA activity (FIG. 15A). For some of these genes, however, two populations with different knockdown levels are apparent (FIGS. 15A, 13A). These populations are present both with intermediate-activity sgRNAs and the perfectly matched sgRNAs, suggesting that they are not a consequence of limited knockdown penetrance for intermediate-activity sgRNAs. Owing to the limited capture efficiency of scRNA-seq, for genes with intermediate to low expression such as CAD and COX11 we typically observed 0-4 UMIs per cell, rendering the quantification of single-cell expression levels more difficult. We nonetheless observe a shift of the distribution to lower UMI numbers with increasing sgRNA activity (FIGS. 13A, 14) as well as a decrease in mean expression levels when averaging expression across all cells with the same sgRNA (FIG. 13B).

Titration is also apparent at the level of the transcriptional responses, which provides a robust single-cell measurement of the phenotype induced by depletion of the targeted gene. In the simplest cases, knockdown led to substantial reductions in cellular UMI counts, consistent with large-scale inhibition of mRNA transcription (FIGS. 15B, 16A). Examples include GATA1, a central myeloid lineage transcription factor, POLR2H, a core subunit of RNA polymerase II (as well as RNA polymerases I and III), or to a lesser extent BCR, which is fused to the driver oncogene ABL1 in K562 cells (35,36). Notably, this effect correlates linearly with growth phenotype for intermediate activity sgRNAs (FIGS. 15B, 16B) but exhibits non-linear relationships with target gene knockdown at least in the cases of GATA1 and POLR2H (FIGS. 15C, 16B, BCR levels are difficult to quantify accurately). Both relationships appear to be sigmoidal but with different thresholds: whereas cellular UMI counts drop rapidly once GATA1 mRNA levels are reduced by 50%, a larger reduction of POLR2H mRNA levels is required to achieve a similarly sized effect. Knockdown of most other targeted genes did not perturb total UMI counts to the same extent (FIG. 16A) but resulted in other transcriptional responses. Knockdown of CAD, for example, triggered cell cycle stalling during S-phase, as had been observed previously (27), with a higher frequency of stalling with increasing sgRNA activity (FIG. 16C). By contrast, knockdown of HSPA9, the mitochondrial Hsp70 isoform, induced the expected transcriptional signature corresponding to activation of the integrated stress response (ISR) including upregulation of DDIT3 (CHOP), DDIT4, ATF5, and ASNS (27,37). The magnitude of this transcriptional signature increased with increasing sgRNA activity on both the bulk population (FIG. 15D) and single-cell levels (FIG. 15E), although populations with intermediate-activity sgRNAs had larger cell-to-cell variation in the magnitudes of transcriptional responses. Similarly, the transcriptional responses to knockdown of other genes (FIG. 16D) scaled with sgRNA activity and exhibited larger variance for intermediate-activity sgRNAs (FIG. 15E).

We next explored expression-phenotype relationships in these data. Within each series, two major metrics of phenotype, bulk population growth phenotype and transcriptional response, appear to be well-correlated, despite substantial differences in the absolute magnitudes of the transcriptional responses with different series (FIGS. 15F, 16D-16F). By contrast, the relationships between either metric of phenotype and target gene expression are strongly gene-specific (FIGS. 15G, 16G-16I). For HSPAS and GATA1, for example, a comparably small reduction in mRNA levels (˜50%) was sufficient to induce a near-maximal transcriptional response and growth defect, whereas for most other genes a larger reduction was required. These results prompt the hypothesis that K562 cells are intolerant to moderate decreases in expression of GATA1 and HSPAS, with sharp transitions from growth to death once expression levels drop below a threshold. More broadly, these results highlight the utility of titrating gene expression to systematically map expression-phenotype relationships and quantitatively define gene expression sufficiency.

Following Single-Cell Trajectories Along a Continuum of Gene Expression Levels

To gain further insight into the diversity of transcriptional responses induced by depletion of essential genes, we compared the transcriptional profiles of all perturbations. Clustering perturbations according to the similarity (Pearson correlation) of their bulk transcriptomes revealed multiple groups segregated by biological function, including a cluster of ribosomal proteins and POLR1D, a subunit of the rRNA-transcribing RNA polymerase I (and of RNA polymerase III), and a cluster of perturbations that activate the integrated stress response (HSPA9, HSPE1, and EIF2S1/eIF2α) (FIG. 17A). To further visualize the space of transcriptional states, we performed dimensionality reduction on the single-cell transcriptomes using UMAP (38). The resulting projection recapitulates the clustering, as indicated for example by the close proximity of cells with perturbations of HSPA9, HSPE1, and EIF2S1 (FIG. 15H). Within individual series, cells project further outward in UMAP space with increasing sgRNA activity, further highlighting that target gene expression levels are titrated on the single cell level (FIG. 15I).

Closer examination of the UMAP projection revealed more granular structure, including the grouping of a subset of cells with knockdown of ATP5E, a subunit of ATP synthase, with cells with ISR-activating perturbations (FIG. 15H). This subset of cells indeed exhibited classical features of ISR activation (FIG. 17B). The frequency of ISR activation increased with lower ATP5E mRNA levels, but even at the lowest levels some cells did not exhibit ISR activation (FIGS. 15J, 17B). These results suggest that depletion of ATP synthase under these conditions predisposes cells to activate the ISR, perhaps by exacerbating transient phases of mitochondrial stress, in a manner that is proportional to ATP synthase levels. More broadly, these results highlight the utility of titrating gene expression in probing cell biological phenotypes, especially in combination with rich phenotyping methods such as scRNA-seq.

Discussion

Here we describe the development of allelic series of compromised sgRNAs, with each series enabling the titration of the expression of a given gene in human cells. These series, either individually or as a pool, have a broad range of applications across basic and biomedical research. We highlight the utility of the approach in extracting rich phenotypes by single-cell RNA-seq along a continuum of gene expression levels, which enabled mapping of expression levels to various phenotypes and identification of expression level-dependent cell fates.

Our approach builds on in vitro work describing the biophysical principles by which modifications to the sgRNA modulate (d)Cas9 binding on-rates and activity (13,22,39-41). In cells, modifications to the sgRNA constant region were affected by specific interactions with targeting sequences, rendering sgRNA activities difficult to predict. By contrast, the effects of mismatches on sgRNA activity followed more readily discernable biophysical principles, enabling us to apply machine learning approaches to derive the underlying rules and predict series for arbitrary sgRNAs. The resulting genome-wide in silico library enables titration of any expressed gene of interest. We also describe a compact (25,000-element) library that enables titration of 2,400 essential genes, with potential applications for example in focused screens for sensitization to chemical or genetic perturbations. Given that target gene expression levels are largely unimodally distributed in cell populations harboring sgRNA series, these sgRNAs can be combined with both single-cell or bulk population readouts. Thus, complex phenotypes as a function of gene expression levels can be recorded by a variety of techniques tailored to the particular question, such as Perturb-seq or related techniques, microscopy, bulk metabolomics or proteomics, or targeted cell biological assays, providing substantial experimental flexibility.

These sgRNA series now enable mapping expression-to-phenotype curves directly in mammalian systems, with implications for example for evolutionary biology and biomedical research. Indeed, using sgRNA series to titrate essential gene expression, we found gene-specific expression-phenotype relationships: although all genes had a threshold expression level below which cell viability dropped rapidly, the relative locations of these thresholds varied across genes, with K562 cells being particularly sensitive to depletion of GATA1 and HSPAS. This variability in threshold location suggests different buffering capacities for different genes, in line with previous findings in yeast (4), but the logic by which these buffering capacities are determined in mammalian systems remains unclear. More comprehensive efforts to generate such dose-response curves and determine the extents to which gene expression is buffered across cell models would allow for identification of patterns for different gene sets and biological processes and thereby begin to reveal the underlying principles that have shaped gene expression levels. Analogous efforts to map such dose-response curves in cancer cell types could identify specific vulnerabilities as targets for therapeutics and, vice versa, mapping these curves for cancer driver genes or genes underlying specific diseases could enable defining the corresponding therapeutic windows, i.e., the required extents of inhibition or restoration, as goals for drug development.

Our intermediate-activity sgRNAs also provide access to a diversity of cell states including loss-of-function phenotypes that otherwise may be obscured by cell death or neomorphic behavior. Thus, our approach enables positioning cells at states of interest, for example to record chemical-gene or gene-gene interactions, or near phenotypic transitions to characterize the transcriptional trajectories. These sgRNA series will also facilitate recapitulating gene expression levels of disease-relevant states such as haploinsufficiency or partial loss-of-function diseases, enabling systematic efforts to identify suppressors or modifiers as potential therapeutic targets, or modeling quantitative trait loci associated with multigenic traits in conjunction with rich phenotyping to systematically identify the mechanisms by which they interact and contribute to such traits. Finally, sgRNA allelic series can be equivalently used to titrate dCas9 occupancy and activity in other applications such as CRISPRa or dCas9-based epigenetic modifiers.

More generally, our allelic series approach now provides a tool to systematically titrate gene expression and evaluate dose-response relationships in mammalian systems. This resource should be equally enabling to systematic large-scale efforts and detailed single-gene investigations in basic cell biology, drug development, and functional genomics.

Methods

Reagents and Cell Lines

K562 and Jurkat cells were grown in RPMI 1640 medium (Gibco) with 25 mM HEPES, 2 mM L-glutamine, 2 g/L NaHCO₃ supplemented with 10% (v/v) standard fetal bovine serum (FBS, HyClone or VWR), 100 units/mL penicillin, 100 μg/mL streptomycin, and 2 mM L-glutamine (Gibco). HEK293T and HeLa cells were grown in Dulbecco's modified eagle medium (DMEM, Gibco) with 25 mM D-glucose, 3.7 g/L NaHCO₃, 4 mM L-glutamine and supplemented with 10% (v/v) FBS, 100 units/mL penicillin, 100 μg/mL streptomycin, and 2 mM L-glutamine. K562 and HeLa cells are derived from female patients. Jurkat cells are derived from a male patient. HEK293T are derived from a female fetus. K562 and HeLa CRISPRi cell lines were previously published (15,18). Jurkat CRISPRi cells (Clone NH7) were obtained from the Berkeley Cell Culture Facility. All cell lines were grown at 37° C. in the presence of 5% CO2. All cell lines were periodically tested for Mycoplasma contamination using the MycoAlert Plus Mycoplasma detection kit (Lonza).

DNA Transfections and Virus Production

Lentivirus was generated by transfecting HEK239T cells with four packaging plasmids (for expression of VSV-G, Gag/Pol, Rev, and Tat, respectively) as well as the transfer plasmid using TransIT®-LT1 Transfection Reagent (Mirus Bio). Viral supernatant was harvested two days after transfection and filtered through 0.44 μm PVDF filters and/or frozen prior to transduction.

Cloning of Individual sgRNAs

Individual perfectly matched or mismatched sgRNAs were cloned essentially as described previously (15). Briefly, two complementary oligonucleotides (Integrated DNA Technologies), containing the targeting region as well as overhangs matching those left by restriction digest of the backbone with BstXI and BlpI, were annealed and ligated into an sgRNA expression vector digested with BstXI (NEB or Thermo Fisher Scientific) and BlpI (NEB) or Bpu1102I (Thermo Fisher Scientific). The ligation product was transformed into Stellar™ chemically competent E. coli cells (Takara Bio) and plasmid was prepared following standard protocols.

Individual Evaluation of sgRNA Phenotypes for GFP Knockdown

For individual evaluation of GFP knockdown phenotypes, sgRNAs were individually cloned as described above, ligated into a version of pU6-sgCXCR4-2 (marked with a puromycin resistance cassette and mCherry, Addgene #46917) (18), modified to include a BlpI site. Sequences used for individual evaluation are listed in Table 1. The sgRNA expression vectors were individually packaged into lentivirus and transduced into GFP+K562 CRISPRi cells (18) at MOI<1 (15-40% infected cells) by centrifugation at 1000×g and 33° C. for 0.5-2 h. GFP levels were recorded 10 d after transduction by flow cytometry using a FACSCelesta flow cytometer (BD Biosciences), gating for sgRNA-expressing cells (mCherry+). Experiments were performed in duplicate from the transduction step. Relative activities were defined as the fold-knockdown of each mismatched variant (GFPsgRNA[non-targeting]/GFPsgRNA[variant]) divided by the fold-knockdown of the perfectly-matched sgRNA. The background fluorescence of a GFP− strain was subtracted from all GFP values prior to other calculations. The distributions of GFP values in FIG. 1B were plotted following the example in seaborn.pydata.org/examples/kde_ridgeplot.

Design of Large-Scale Mismatched sgRNA Library

To generate the list of targeting sgRNAs for the large-scale mismatched sgRNA library, hit genes from a growth screen performed in K562 cells with the CRISPRi v2 library (19) were selected by calculating a discriminant score (phenotype z-score×−log 10(Mann-Whitney P)). Discriminant scores for negative control genes (randomly sampled groups of 10 non-targeting sgRNAs) were calculated as well, and hit genes were selected above a threshold such that 5% of the hits would be negative control genes (i.e., an estimated empirical 5% FDR). This procedure resulted in the selection of 2477 genes. Of these genes, 28 genes for which the second strongest sgRNA by absolute value had a positive growth phenotype were filtered out as these were likely to be scored as hits solely due to a single sgRNA. For the remaining 2,449 genes, the two sgRNAs with the strongest growth phenotype were selected, for a total of 4,898 perfectly matched sgRNAs.

For each of these sgRNAs, a set of 23 variant sgRNAs with mismatches was designed: 5 with a single randomly chosen mismatch within 7 bases of the PAM, 5 with a single randomly chosen mismatch 8-12 bases from the PAM, and 3 with a single randomly chosen mismatch 13-19 bases from the PAM (the first base of the targeting region was never selected for this purpose as it is an invariant G in all sgRNAs to enable transcription from the U6 promoter). The remaining 10 variants had 2 randomly chosen mismatches selected from positions −1 to −19.

To assess the off-target potential of mismatched sgRNAs, we extended our previous strategy to estimate sgRNA off-target effects (15,19). Briefly, for each target in the genome, a FASTQ entry was created for the 23 bases of the target including the PAM, with the accompanying empirical Phred score indicating an estimate of the anticipated importance of a mismatch in that base position. Bowtie (bowtie-bio.sourceforge.net) (42) was then used to align each designed sgRNA back to the genome, parameterized so that sgRNAs were considered to mutually align if and only if: a) no more than 3 mismatches existed in the PAM-proximal 12 bases and the PAM, b) the summed Phred score of all mismatched positions across the 23 bases was less than a threshold. This alignment was done iteratively with decreasing thresholds, and any sgRNAs which aligned successfully to no other site in the genome at a particular threshold were then deemed to have a specificity at said threshold. The compiled sgRNA sequences were then filtered for sgRNAs containing BstXI, BlpI, and SbfI sites, which are used during library cloning and sequencing library preparation, and 2,500 negative controls (randomly generated to match the base composition of our hCRISPRi-v2 library) were added.

Pooled Cloning of Mismatched sgRNA Libraries

Pooled sgRNA libraries were cloned largely as described previously (15,20,43). Briefly, oligonucleotide pools containing the desired elements with flanking restriction sites and PCR adapters were obtained from Agilent Technologies. The oligonucleotide pools were amplified by 15 cycles of PCR using Phusion polymerase (NEB). The PCR product was digested with BstXI (Thermo Fisher Scientific) and Bpu1102I (Thermo Fisher Scientific), purified, and ligated into BstXI/Bpu1102I-digested pCRISPRia-v2 at 16° C. for 16 h. The ligation product was purified by isopropanol precipitation and then transformed into MegaX DH10B electrocompetent cells (Thermo Fisher Scientific) by electroporation using the Gene Pulser Xcell system (Bio-Rad), transforming ˜100 ng purified ligation product per 100 μL cells. The cells were allowed to recover in 3-6 mL SOC medium for 2 h. At that point, a small 1-5 μL aliquot was removed and plated in three serial dilutions on LB plates with selective antibiotic (carbenicillin). The remainder of the culture was inoculated into 0.5 to 1 L LB supplemented with 100 μg/mL carbenicillin, grown at 37° C. with shaking at 220 rpm for 16 h and harvested by centrifugation. Colonies on the plates were counted to confirm a transformation efficiency greater than 100-fold over the number of elements (>100× coverage). The pooled sgRNA plasmid library was extracted from the cells by GigaPrep (Qiagen or Zymo Research). Even coverage of library elements was confirmed by sequencing a small aliquot on a HiSeq 4000 (Illumina).

Large-Scale Mismatched sgRNA Screen and Sequencing Library Preparation

Large-scale screens were conducted similarly to previously described screens (15,19,20). The large-scale library was transduced in duplicate into K562 CRISPRi and Jurkat CRISPRi cells at MOI<1 (percentage of transduced cells 2 days after transduction: 20-40%) by centrifugation at 1000×g and 33° C. for 2 h. Replicates were maintained separately in 0.5 L to 1 L of RPMI-1640 in 1 L spinner flasks for the course of the screen. 2 days after transduction, the cells were selected with puromycin for 2 days (K562: 2 days of 1 μg/mL; Jurkat: 1 day of 1 μg/mL and 1 day of 0.5 μg/mL), at which point transduced cells accounted for 80-95% of the population, as measured by flow cytometry using an LSR-II flow cytometer (BD Biosciences). Cells were allowed to recover for 1 day in the absence of puromycin. At this point, t0 samples with a 3000× library coverage (400×10⁶ cells) were harvested and the remaining cells were cultured further. The cells were maintained in spinner flasks by daily dilution to 0.5×10⁶ cells mL⁻¹ at an average coverage of greater than 2000 cells per sgRNA with daily measurements of cell numbers and viability on an Accuri bench-top flow cytometer (BD BioSciences) for 11 days, at which point endpoint samples were harvested by centrifugation with 3000× library coverage.

Genomic DNA was isolated from frozen cell samples and the sgRNA-encoding region was enriched, amplified, and processed for sequencing essentially as described previously (19). Briefly, genomic DNA was isolated using a NucleoSpin Blood XL kit (Macherey-Nagel), using 1 column per 100×10⁶ cells. The isolated genomic DNA was digested with 400 U SbfI-HF (NEB) per mg DNA at 37° C. for 16 h. To isolate the ˜500 bp fragment containing the sgRNA expression cassette liberated by this digest, size separation was performed using large-scale gel electrophoresis with 0.8% agarose gels. The region containing DNA between 200 and 800 bp of size was excised and DNA was purified using the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel). The isolated DNA was quantified using a QuBit Fluorometer (Thermo Fisher Scientific) and then amplified by 23 cycles of PCR using Phusion polymerase (NEB) and appending Illumina adapter and unique sample indices in the process. Each DNA sample was divided into 5-50 individual 100 μL reactions, each with 500 ng DNA as input. To ensure base diversity during sequencing, the samples were divided into two sets, with all samples for a given replicate always being assigned to the same set. The two sets had the Illumina adapters appended in opposite orientations, such that samples in set A were sequenced from the 5′ end of the sgRNA sequence in the first 20 cycles of sequencing and samples in set B were sequenced from the 3′ end of the sgRNA sequence in the next 20 cycles of sequencing. With updates to Illumina chemistry and software, this strategy is no longer required to ensure high sequencing quality, and all samples are amplified in the same orientation. Following the PCR, all reactions for a given DNA sample were combined and a small aliquot (100-300 μL) was purified using AMPure XP beads (Beckman-Coulter) with a two-sided selection (0.65× followed by 1×). Sequencing libraries from all samples were combined and sequencing was performed on a HiSeq 4000 (Illumina) using single-read 50 runs and with two custom sequencing primers (oCRISPRi_seq_V5 and oCRISPRi_seq_V4_3′, Table 5). For samples that were amplified in the same orientation, only a single custom sequencing primer was added (oCRISPRi_seq_V5), and the samples were supplemented with a 5% PhiX spike-in.

Sequencing reads were aligned to the library sequences, counted, and quantified using the Python-based ScreenProcessing pipeline (github.com/mhorlbeck/ScreenProcessing). Calculation of phenotypes was performed as described previously (15,19,20). Untreated growth phenotypes (γ) were derived by calculating the log 2 change in enrichment of an sgRNA in the endpoint and t0 samples, subtracting the equivalent median value for all non-targeting sgRNAs, and dividing by the number of doublings of the population (15,20). To calculate relative activities, phenotypes of mismatched sgRNAs were divided by those for the corresponding perfectly matched sgRNA. Relative activities were filtered for series in which the perfectly matched sgRNA had a growth phenotype greater than 5 z-scores outside the distribution of negative control sgRNAs for all further analysis (3,147 and 2,029 sgRNA series for K562 and Jurkat cells, respectively). Relative activities from both cell lines were averaged if the series passed the z-score filter in both. All analyses were performed in Python 2.7 using a combination of Numpy (v1.14.0), Pandas (v0.23.4), and Scipy (v1.1.0).

Design and Pooled Cloning of Constant Region Variants Library

The sequences in the library of modified constant regions were derived from the sgRNA (F+E) optimized sequence (23) modified to include a BlpI site (15). Each modified constant region was paired with 36 sgRNA targeting sequences (3 sgRNAs targeting each of 10 essential genes and six non-targeting negative control sgRNAs). The cloning strategy (described below) allowed the mutation of most positions in the sgRNA constant region. A variety of modifications were made, including substitutions of all single bases not in the BlpI restriction site (which is used for cloning), double substitutions including all substitutions at base-paired position pairs not before or in the BlpI site, and a variety of triple, quadruple, and sextuple substitutions, including base-pair-preserving substitutions at adjacent base-pairs.

The library was ordered and cloned in two parts. One part consisted of ˜100 modifications to the eight bases upstream of the BlpI restriction site. Constant region variants with mutations in this section were paired with each of the 36 targeting sequences, ordered as a pooled oligonucleotide library (Twist Biosciences), and cloned into pCRISPRia-v2 as described above. The second part consisted of ˜900 modifications to the 71 bases downstream of the BlpI restriction site. This part was cloned in two steps. First, all 36 targeting sequences were individually cloned into pCRISPRia-v2 as described above. The vectors were then pooled at an equimolar ratio and digested with BlpI (NEB) and XhoI (NEB). The modified constant region variants were ordered as a pooled oligonucleotide library (Twist Biosciences), PCR amplified with Phusion polymerase (NEB), digested with BlpI (NEB) and XhoI (NEB), and ligated into the digested vector pool, in a manner identical to previously published protocols and as described above, except for the different restriction enzymes.

Compact Mismatched sgRNA Library and Constant Region Library Screens

Screens with the compact mismatched sgRNA library and the constant region library were conducted largely as described above, with smaller modifications during the screening procedure and an updated sequencing library preparation protocol. Briefly, the libraries were transduced in duplicate into K562 CRISPRi (both libraries) or HeLa CRISPRi cells (compact mismatched sgRNA library) as described above. K562 replicates were maintained separately in 0.15 to 0.3 L of RPMI-1640 in 0.3 L spinner flasks for the course of the screen. HeLa replicates were maintained in sets of ten 15-cm plates. Cells were selected with puromycin as described above (K562: 1 day of 0.75 μg/mL and 1 day of 0.85 μg/mL; HeLa: 2 days of 0.8 μg/mL and 1 day of 1 μg/mL). The remainder of the screen was carried out at >1000× library coverage (K562 compact mismatched sgRNA library: >2000×; HeLa compact mismatched sgRNA library: >1000×; K562 constant region library: >2000×). Multiple samples were harvested after 4 to 8 days of growth. For the drug screen, 10 μM lovastatin (ApexBio) or an equivalent volume of DMSO (vehicle) was added to flasks at t=0, and 3 days later cells were pelleted and re-suspended in fresh medium. Lovastatin (12 μM) or DMSO was again added after 5 and 9 days of growth, with media exchanges 3 days after drug supplementation. Multiple samples were harvested after 4 to 8 days for the K562 and HeLa growth screens. Both drug-treated and vehicle-treated samples were harvested after 12 days for the drug screen, which allowed for a difference of 3.5 to 4.1 cell population doublings between drug- and vehicle-treated groups.

Genomic DNA was isolated from frozen cell samples as described above. The subsequent sequencing library preparation was simplified to omit the enrichment step by gel extraction. In particular, following the genomic DNA extraction, DNA was quantified by absorbance at 260 nm using a NanoDrop One spectrophotometer (Thermo Fisher Scientific) and then directly amplified by 22-23 cycles of PCR using NEBNext Ultra II Q5 PCR MasterMix (NEB), appending Illumina adapter and unique sample indices in the process. Each DNA sample was divided into 50-200 individual 100 μL reactions, each with 10 μg DNA as input. All samples were amplified using the same strategy and in the same orientation. The PCR products were purified as described above and sequencing libraries from all samples were combined. For the compact mismatched library screens, sequencing was performed on a HiSeq 4000 (Illumina) using single-read 50 runs with a 5% PhiX spike-in and a custom sequencing primer (oCRISPRi_seq_V5, Table 5). For the constant region screens, the PCR primers were adapted to allow for amplification of the entire constant region and to append a standard Illumina read 2 primer binding site (Table 5). Sequencing was then performed in the same manner including the custom sequencing primer (oCRISPRi_seq_v5) and a 5% PhiX spike-in, but using paired-read 150 runs.

Sequencing reads were processed as described above. Sequences and rankings for individual sgRNAs are available in Table 6 for the constant region screen.

Generation and Evaluation of Individual Constant Region Variants by RT-qPCR

Constant region variants were evaluated in the background of a constant region with an additional base pair substitution in the first stem loop (fourth base pair changed from AT to GC25). Ten constant region variants with average relative activities between 0.2 and 0.8 from the screen and carrying substitutions after the BlpI site were selected (Table 5). Cloning of individual constant regions was performed essentially as the cloning of sgRNA targeting regions, described above, except that the BlpI and XhoI restriction sites were used for cloning (the XhoI site is immediately downstream of the constant region) and that cloning was performed with a variant of pCRISPRia-v2 (marked with a puromycin resistance cassette and BFP, Addgene #84832)19. For each of the ten constant region variants as well as the constant region carrying only the stem loop substitution, two different targeting regions against DPH2 were then cloned as described above (Table 1). These 22 vectors as well as a vector with a non-targeting negative control sgRNA (Table 1) were individually packaged into lentivirus and transduced into K562 CRISPRi cells at MOI<1 (10-50% infected cells) by centrifugation at 1000×g and 33° C. for 2 h. Cells were allowed to recover for 2 days and then selected to purity with puromycin (1.5-3 μg/mL), as assessed by measuring the fraction of BFP-positive cells by flow cytometry on an LSR-II (BD Biosciences), allowed to recover for 1 day, and harvested in aliquots of 0.5-2×10⁶ cells for RNA extraction. RNA was extracted using the RNeasy Mini kit (Qiagen) with on-column DNase digestion (Qiagen) and reverse-transcribed using SuperScript II Reverse Transcriptase (Thermo Fisher Scientific) with oligo(dT) primers in the presence of RNaseOUT Recombinant Ribonuclease Inhibitor (Thermo Fisher Scientific). Quantitative PCR (qPCR) reactions were performed in 22 μL reactions by adding 20 μL master mix containing 1.1× Colorless GoTaq Reaction Buffer (Promega), 0.7 mM MgCl2, dNTPs (0.2 mM each), primers (0.75 μM each), and 0.1×SYBR Green with GoTaq DNA polymerase (Promega) to 2 μL cDNA or water. Reactions were run on a LightCycler 480 Instrument (Roche). For each cDNA sample, reactions were set up with qPCR primers against DPH2 and ACTB (sequences listed in Table 5). Experiments were performed in technical triplicates.

Machine Learning

In order to establish a subset of highly active sgRNAs with which to train a machine learning model, we filtered for perfectly matched sgRNAs with a growth phenotype greater than 10 z-scores outside the distribution of negative control sgRNAs in the K562 and/or Jurkat pooled screens (K562 γ<−0.21; Jurkat γ<−0.35). All singly mismatched variants derived from sgRNAs passing the filter were then included, and relative activities were calculated as described previously, averaging the replicate measurements for each sgRNA. In cases where a perfectly matched sgRNA passed the filter in the K562 and Jurkat screen, the average relative activity across both cell types was calculated for each mismatched variant; otherwise the relative activities for only one cell type were considered. This filtering scheme resulted in 26,248 mismatched sgRNAs comprising 2,034 series targeting 1,292 genes, with approximately 40% of relative activity values averaged from K562 and Jurkat cells.

For each sgRNA, a set of features was defined based on the sequences of the genomic target and the mismatched sgRNA. First, the genomic sequence extending from 22 bases 5′ of the beginning of the PAM to 1 base 3′ of the end of the PAM (26 bases in all) is binarized into a 2D array of shape (4, 26), with 0s and 1s indicating the absence or presence of a particular nucleotide at each position, respectively. Next, a similar array is constructed representing the mismatch imparted by the sgRNA, with an additional potential mismatch at the 5′ terminus of the sgRNA (position −20), which invariably begins with G in our libraries due to the mU6 promoter. Thus, the mismatched sequence array is identical to the genomic sequence array except for 1 or 2 positions. Finally, the arrays are stacked into a 3D volume of shape (4, 26, 2), which serves as the feature set for a particular sgRNA.

The training set of sgRNAs was established by randomly selecting 80% of sgRNA series, with the remaining 20% set aside for model validation. A convolutional neural network (CNN) regression model was then designed using Keras (keras.io/) with a TensorFlow backend engine, consisting of two sequential convolution layers, a max pooling layer, a flattening layer, and finally a three-layer fully connected network terminating in a single neuron. Additional regularization was achieved by adding dropout layers after the pooling step and between each fully connected layer. To penalize the model for ignoring under-represented sgRNA classes (e.g., those with intermediate relative activity), training sgRNAs were binned according to relative activity, and sample weights inversely proportional to the population in each bin were assigned. Hyperparameters were optimized using a randomized grid search with 3-fold cross-validation with the training set as input. Parameters included the size, shape, stride, and number of convolution filters, the pooling strategy, the number of neurons and layers in the dense network, the extent of dropout applied at each regularization step, the activation functions in each layer, the loss function, and the model optimizer. Ultimately, 20 CNN models with identical starting parameters were individually trained for 8 epochs in batches of 32 sgRNAs. Performance was assessed by computing the average prediction of the 20-model ensemble for each validation sgRNA and comparing it to the measured value.

A linear regression model was trained on the same set of sgRNAs, albeit with modified features more suited for this approach. These features include the identities of bases in and around the PAM, whether the invariant G at the 5′ end of the sgRNA is base paired, the GC content of the sgRNA, the change in GC content due to the point mutation, the location of the protospacer relative to the annotated transcription start site, the identities of the 3 RNA bases on either side of the mismatch, and the location and type of each mismatch. All features were binarized except for GC and delta GC content. In total, each sgRNA was represented by a vector of 270 features, 228 of which describe the mismatch position and type (19 possible positions by 12 possible types). Prior to training, feature vectors were z-normalized to set the mean to 0 and variance to 1. Finally, an elastic net linear regression model was created using the scikit-learn Python package (scikit-learn.org), and key hyperparameters (alpha and L1 ratio) were optimized using a grid search with 3-fold cross validation during training.

Design of Compact Library

Genes targeted by the compact allelic series library were required to have at least one perfectly matched sgRNA with a growth phenotype greater than 2 z-scores outside the distribution of negative control sgRNAs (γ<−0.04) in a single replicate of a K562 pooled screen (this work or Horlbeck et al. (19)). By this metric, 4,722 unique sgRNAs targeting 2,405 essential genes were included. Next, for each perfectly matched sgRNA, variants containing all 57 single mismatches in the targeting sequence (positions −19 to −1) were generated in silico, and sequences with off-target binding potential in the human genome were filtered out as described for the large-scale library. Remaining variant sgRNAs were whitelisted for potential selection in subsequent steps.

For each gene being targeted, if both of the perfectly matched sgRNAs imparted growth phenotypes greater than 3 z-scores outside the distribution of negative controls (γ<−0.06) in this work's large-scale K562 screen, then one series of 4 variant sgRNAs was generated from each. Otherwise, one series of 8 variants was generated from the sgRNA with the stronger phenotype. Both perfectly matched sgRNAs were included regardless of their growth phenotype, for a total of 2 perfectly matched and 8 mismatched sgRNAs per gene.

In order to select mismatched sgRNAs, we first divided the relative activity space into 6 bins with edges at 0.1, 0.3, 0.5, 0.7, and 0.9. For each series, we attempted to select sgRNAs from each of the middle 4 bins (centers at 0.2, 0.4, 0.6, and 0.8 relative activity) as measured in this work's K562 screen. If multiple sgRNAs were available in a particular bin, they were prioritized based on distance to the center of the bin and variance between replicate measurements. If no previously measured sgRNA was available in a given bin, then the CNN model was run on all whitelisted (novel) mismatched sgRNAs belonging to that series, and sgRNAs were selected based on predicted activity as needed. In total, the compact library was composed of 4,722 unique perfectly matched sgRNAs, 19,210 unique mismatched sgRNAs, and 1,202 non-targeting control sgRNAs. Approximately 68% of mismatched sgRNAs were evaluated in previous screens (72% single mismatches, 28% double mismatches), with the remaining 32% imputed from the CNN model (all single mismatches).

Perturb-Seq

The Perturb-seq experiment targeted 25 genes involved in a diverse range of essential functions (Table 2). For each target gene, the original sgRNAs and 4-5 mismatched sgRNAs covering the range from full relative activity to low relative activity were chosen from the large-scale screen. These 128 targeting sgRNAs as well as 10 non-targeting negative control sgRNAs (Table 1) were individually cloned into a modified variant of the CROP-seq vector (33,34) as described above, except into the different vector. Lentivirus was individually packaged for each of the 138 sgRNAs and was harvested and frozen in array. To determine viral titers, each virus was individually transduced into K562 CRISPRi cells by centrifugation at 1000×g and 33° C. for 2 h, and the fraction of transduced cells was quantified as BFP+ cells using an LSR-II flow cytometer (BD Biosciences) 48 h after transduction.

To generate transduced cells for single-cell RNA-seq analysis, virus for all 138 sgRNAs was pooled immediately before transduction and then transduced into K562 CRISPRi cells by centrifugation at 1000×g and 33° C. for 2 h. To achieve even representation at the intended time of single-cell analysis, the virus pooling was adjusted both for titer and expected growth-rate defects. 3 d after transduction, transduced (BFP+) cells were selected using FACS on a FACSAria2 (BD Biosciences) and then resuspended in conditioned media (RPMI formulated as described above except supplemented with 20% FBS and 20% supernatant of an exponentially growing K562 culture). 2 d after sorting, the cells were loaded onto three lanes of a Chromium Single Cell 3′ V2 chip (10× Genomics) at 1000 cells/μL and processed according to the manufacturer's instructions.

The CROP-seq sgRNA barcode was PCR amplified from the final single cell RNA-seq libraries with a primer specific to the sgRNA expression cassette (oBA503, Table 5) and a standard P5 primer (Table 5), purified on a Blue Pippin 1.5% agarose cassette (Sage Science) with size selection range 436-534 bp, and pooled with the single cell RNA-seq libraries at a ratio of 1:100. The libraries were sequenced on a HiSeq 4000 according to the manufacturer's instructions (10× Genomics).

To measure the growth rate defects conferred by each sgRNA for comparison with the transcriptional phenotypes, samples of 500,000 transduced cells were taken from the same transduced cell population used in the Perturb-seq experiment on days two, seven, and twelve after transduction. Genomic DNA was extracted using the Nucleospin Blood kit (Macherey-Nagel) and sgRNA amplicons were prepared as described previously and above (19), albeit with no genomic DNA digestion or gel purification, and sequenced on HiSeq 4000 as described above for the other screens. Growth phenotypes were calculated by comparing normalized sgRNA abundances at day seven and twelve to those at day two, as described above. Read counts and growth phenotypes (γ and relative activity) for individual sgRNAs are available in Table 3 and Table 4, respectively. Relative sgRNA activities measured at day seven (five days of growth) were used to assign sgRNA activities in further analysis.

Perturb-Seq Data Analysis

Raw and processed Perturb-seq data are available at GEO under accession code GSE132080.

Cell Barcode and UMI Calling, Assignment of Perturbations

UMI count tables with UMI counts for all genes in each individual cell were calculated from the raw sequencing data using CellRanger 2.1.1 (10× Genomics) with default settings. Perturbation calling was performed as described previously (27). Briefly, reads from the specifically amplified sgRNA barcode libraries were aligned to a list of expected sgRNA barcode sequences using bowtie (flags: -v3 -q -ml). Reads with common UMI and barcode identity were then collapsed to counts for each cell barcode, producing a list of possible perturbation identities contained by that cell. A proposed perturbation identity was identified as “confident” if it met thresholds derived by examining the distributions of reads and UMIs across all cells and candidate identities: (1) reads >50, (2) UMIs>3, and (3) coverage (reads/UMI) in the upper mode of the observed distribution across all candidate identities. As described previously (44), perturbation identities were called for any cell barcode with greater than 2,000 UMIs to enable capture of cells with strong growth defects. Any cell barcode containing two or more confident identities was deemed a “multiplet”, and may arise from either multiple infection or simultaneous encapsulation of more than one cell in a droplet during single-cell RNA sequencing. Cell barcodes passing the 2,000 UMI threshold and bearing a single, unambiguous perturbation barcode were included in all subsequent analyses.

Expression Normalization

Some portions of analysis use normalized expression data. We used a relative normalization procedure based on comparison to the gene expression observed in control cells bearing non-targeting sgRNAs, as described previously (27).

Total UMI counts for each cell barcode are normalized to have the median number of UMIs observed in control cells.

For each gene x, expression across all cell barcodes is z-normalized with respect to the mean (μ_x) and standard deviation (σ_x) observed in control cells:

x_“normalized”=(x−μ_x)/σ_x

Following this normalization, control cells have average expression 0 (and standard deviation 1) for all genes. Negative/positive values therefore represent under/overexpression relative to control.

Target Gene Quantification

Expression levels of genes targeted by a given sgRNA were quantified by normalizing UMI counts of the targeted gene to the total UMI count for each individual cell (FIG. 13). Considering raw UMI counts of the targeted gene (FIG. 14) or z-normalized target gene expression as described above yielded similar results. Note that the sgRNA targeting BCR is toxic due to knockdown of the BCR-ABL1 fusion present in K562 cells. Knockdown was apparent both in BCR and ABL1 expression, but we used BCR expression for further analysis as there are likely additional copies of ABL1 that are not fused to BCR (and thus would not be affected by the BCR-targeting sgRNA) contributing to ABL1 expression.

Cell Cycle Analysis

Calling of cell cycle stages was performed using a similar approach to Macosko et al. (45) and largely as described in Adamson and Norman et al. (27). Briefly, lists of marker genes showing specific expression in different cell cycle stages from the literature were first adapted to K562 cells by restricting to those that showed highly correlated expression within our experiment. The total (log 2-normalized) expression of each set of marker genes was used to create scores for each cell cycle stage within each cell, and these scores were then z-normalized across all cells. Each cell was assigned to the cell cycle stage with the highest score.

Differential Gene Expression Analysis

We took two approaches to differential expression, as described previously (44). For both approaches, we only considered genes with expression greater than 0.25 UMIs per cell on average across all cells. First, for a given gene, we could assess the changes in the expression distribution of that gene induced by a given genetic perturbation by comparing to the expression distribution observed in control cells bearing non-targeting sgRNAs. We performed this comparison using a two-sample Kolmogorov-Smirnov test and corrected for multiple hypothesis testing at an FDR of 0.001 using the Benjamini-Yekutieli procedure.

We also exploited a machine learning approach that potentially allows correlated expression patterns to be detected and that scales beyond two sample comparisons. Perturbed cells and control cells bearing non-targeting sgRNAs were each used as training data for a random forest classifier that was trained to predict which sgRNA a cell contained from its transcriptional state. As part of the training process the classifier ranks which genes have the most prognostic power in predicting sgRNA identity, which by construction will tend to vary across condition. For most further analysis, the top 100-300 genes by prognostic power were then considered.

Constructing Mean Expression Profiles

For some analyses, expression profiles were averaged across all cells with the same perturbation. In general, this was done simply by calculating the mean z-normalized expression of all genes with mean expression level of 0.25 UMI or higher across all cells in the experiment or within the specific considered subpopulation (usually all cells with sgRNAs targeting a given gene as well as all control cells with non-targeting sgRNAs).

UMAP Dimensionality Reduction

For UMAP dimensionality reduction 38 of all cells, the 300 genes with the highest prognostic power in distinguishing cells by targeted gene as ranked by a random forest classifier were selected. Dimensionality reduction was then performed on the z-normalized single-cell expression profiles of these 300 genes using the following parameters: n_neighbors=40, min_dist=0.1, metric=‘euclidean’, spread=1.0. UMAP dimensionality reduction of subpopulations containing only cells with perturbation of a given gene or control cells was performed analogously but using the expression profiles of the 100 genes with the highest prognostic power and using n_neighbors=15.

From the UMAP projection, we concluded that ˜5% cells had misassigned sgRNA identities, as evident for example by the presence of cells with negative control sgRNAs within the cluster of cells with HSPAS knockdown. These cells had confidently assigned single perturbations and only expressed the corresponding barcode transcript, suggesting that they did not evade our doublet detection algorithm. We speculate that these cells expressed two different sgRNAs but silenced expression of one of the reporter transcripts. Given the strong trends in the results above, we concluded that this rate of misassignment did not substantially affect our ability to identify trends within cell populations.

ISR Scores

Magnitude of ISR activation in individual cells was quantified as activation of the PERK (EIF2AK3) regulon from the gene set and activation coefficients determined previously (27).

REFERENCES

• 1. Huang, N., Lee, I., Marcotte, E. M. & Hurles, M. E. Characterising and Predicting Haploinsufficiency in the Human Genome. PLOS Genet. 6, e1001154 (2010).
• 2. Rest, J. S. et al. Nonlinear Fitness Consequences of Variation in Expression Level of a Eukaryotic Gene. Mol. Biol. Evol. 30, 448-456 (2013).
• 3. Bauer, C. R., Li, S. & Siegal, M. L. Essential gene disruptions reveal complex relationships between phenotypic robustness, pleiotropy, and fitness. Mol. Syst. Biol. 11, 773-773 (2015).
• 4. Keren, L. et al. Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness. Cell 166, 1282-1294.e18 (2016).
• 5. Dykhuizen, D. E., Dean, A. M. & Hard, D. L. Metabolic Flux and Fitness. Genetics 115, 25-31 (1987).
• 6. Dekel, E. & Alon, U. Optimality and evolutionary tuning of the expression level of a protein. Nature 436, 588-592 (2005).
• 7. Alper, H., Fischer, C., Nevoigt, E. & Stephanopoulos, G. Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. 102, 12678-12683 (2005).
• 8. Perfeito, L., Ghozzi, S., Berg, J., Schnetz, K. & Lassig, M. Nonlinear Fitness Landscape of a Molecular Pathway. PLOS Genet. 7, e1002160 (2011).
• 9. Michaels, Y. S. et al. Precise tuning of gene expression levels in mammalian cells. Nat. Commun. 10, 818 (2019).
• 10. Moore, R., Chandrahas, A. & Bleris, L. Transcription Activator-like Effectors: A Toolkit for Synthetic Biology. ACS Synth. Biol. 3, 708-716 (2014).
• 11. Dominguez, A. A., Lim, W. A. & Qi, L. S. Beyond editing: repurposing CRISPR-Cas9 for precision genome regulation and interrogation. Nat. Rev. Mol. Cell Biol. 17, 5-15 (2016).
• 12. Jinek, M. et al. A Programmable Dual-RNA-Guided DNA Endonuclease in Adaptive Bacterial Immunity. Science 337, 816-821 (2012).
• 13. Sternberg, S. H., Redding, S., Jinek, M., Greene, E. C. & Doudna, J. A. DNA interrogation by the CRISPR RNA-guided endonuclease Cas9. Nature 507, 62-67 (2014).
• 14. Szczelkun, M. D. et al. Direct observation of R-loop formation by single RNA-guided Cas9 and Cascade effector complexes. Proc. Natl. Acad. Sci. 111, 9798-9803 (2014).
• 15. Gilbert, L. A. et al. Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation. Cell 159, 647-661 (2014).
• 16. Nishimasu, H. et al. Crystal Structure of Cas9 in Complex with Guide RNA and Target DNA. Cell 156, 935-949 (2014).
• 17. Kocak, D. D. et al. Increasing the specificity of CRISPR systems with engineered RNA secondary structures. Nat. Biotechnol. 37, 657 (2019).
• 18. Gilbert, L. A. et al. CRISPR-Mediated Modular RNA-Guided Regulation of Transcription in Eukaryotes. Cell 154, 442-451 (2013).
• 19. Horlbeck, M. A. et al. Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation. eLife 5, e19760 (2016).
• 20. Kampmann, M., Bassik, M. C. & Weissman, J. S. Integrated platform for genome-wide screening and construction of high-density genetic interaction maps in mammalian cells. Proc. Natl. Acad. Sci. 110, E2317-E2326 (2013).
• 21. Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184-191 (2016).
• 22. Boyle, E. A. et al. High-throughput biochemical profiling reveals sequence determinants of dCas9 off-target binding and unbinding. Proc. Natl. Acad. Sci. 114, 5461-5466 (2017).
• 23. Chen, B. et al. Dynamic Imaging of Genomic Loci in Living Human Cells by an Optimized CRISPR/Cas System. Cell 155, 1479-1491 (2013).
• 24. Dang, Y. et al. Optimizing sgRNA structure to improve CRISPR-Cas9 knockout efficiency. Genome Biol. 16, 280 (2015).
• 25. Grevet, J. D. et al. Domain-focused CRISPR screen identifies HRI as a fetal hemoglobin regulator in human erythroid cells. Science 361, 285-290 (2018).
• 26. Briner, A. E. et al. Guide RNA Functional Modules Direct Cas9 Activity and Orthogonality. Mol. Cell 56, 333-339 (2014).
• 27. Adamson, B. et al. A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response. Cell 167, 1867-1882.e21 (2016).
• 28. Eraslan, G., Avsec, 2., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 1 (2019). doi:10.1038/s41576-019-0122-6
• 29. Kim, H. K. et al. Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nat. Biotechnol. 36, 239-241 (2018).
• 30. Luo, J., Chen, W., Xue, L. & Tang, B. Prediction of activity and specificity of CRISPR-Cpf1 using convolutional deep learning neural networks. BMC Bioinformatics 20, 332 (2019).
• 31. Dixit, A. et al. Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. Cell 167, 1853-1866.e17 (2016).
• 32. Jaitin, D. A. et al. Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq. Cell 167, 1883-1896.e15 (2016).
• 33. Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297-301 (2017).
• 34. Replogle, J. M. et al. Direct capture of CRISPR guides enables scalable, multiplexed, and multi-omic Perturb-seq. bioRxiv 503367 (2018). doi:10.1101/503367
• 35. Grosveld, G. et al. The chronic myelocytic cell line K562 contains a breakpoint in bcr and produces a chimeric bcr/c-abl transcript. Mol. Cell. Biol. 6, 607-616 (1986).
• 36. Shtivelman, E., Lifshitz, B., Gale, R. P. & Canaani, E. Fused transcript of abl and bcr genes in chronic myelogenous leukaemia. Nature 315, 550 (1985).
• 37. Harding, H. P. et al. An Integrated Stress Response Regulates Amino Acid Metabolism and Resistance to Oxidative Stress. Mol. Cell 11, 619-633 (2003).
• 38. McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. ArXiv180203426 Cs Stat (2018).
• 39. Semenova, E. et al. Interference by clustered regularly interspaced short palindromic repeat (CRISPR) RNA is governed by a seed sequence. Proc. Natl. Acad. Sci. 108, 10098-10103 (2011).
• 40. Wiedenheft, B. et al. RNA-guided complex from a bacterial immune system enhances target recognition through seed sequence interactions. Proc. Natl. Acad. Sci. 108, 10092-10097 (2011).
• 41. Hsu, P. D. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827-832 (2013).
• 42. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
• 43. Bassik, M. C. et al. Rapid creation and quantitative monitoring of high coverage shRNA libraries. Nat. Methods 6, 443-445 (2009).
• 44. Norman, T. M. et al. Exploring genetic interaction manifolds constructed from rich phenotypes. bioRxiv 601096 (2019). doi:10.1101/601096
• 45. Macosko, E. Z. et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell 161, 1202-1214 (2015).

TABLE 1
sgRNA sequences used in this study.
			SEQ
			ID	Target_
Experiment	Name	Sequence	NO:	gene
GFP single	EGFP-NT2	GACCAGGATGGGCACCACCC	1	EGFP
mismatches
constant region RT-	DPH2_ + _44435896.24-all	GAGTAAGCAGTCCTGGCACCC	2	DPH2
qPCR
constant region RT-	DPH2_ − _44435877.23-all	GATGTTTAGCAGCCCTGCCG	3	DPH2
qPCR
constant region RT-	non-targeting_00564	GCCGATGGTCTTGTACTACA	4	neg_ctrl
qPCR
constant region	RPL9_ + _39460483.23-P1P2	GGATGTTTCTGTGCTCGTGG	5	RPL9
screen
constant region	RPL9_ + _39460504.23-P1P2	GCTGCGTCTACTGCGAGGTA	6	RPL9
screen
constant region	RPL9_ + _39460476.23-P1P2	GCTGTGCTCGTGGGGGTACT	7	RPL9
screen
constant region	HSPE1_ − _198365117.23-P1P2	GCGGACTGCGAGTCTCTTTG	8	HSPE1
screen
constant region	HSPE1_ + _198365089.23-P1P2	GGAGACTCGCAGTCCGGCCC	9	HSPE1
screen
constant region	HSPE1_ − _198365304.23-P1P2	GGCCCGATGGCACCTTGGAG	10	HSPE1
screen
constant region	POLR1D_ + _28196016.23-P1	GGGAAGCAAGGACCGACCGA	11	POLR1D
screen
constant region	POLR1D_ + _28196036.23-P1	GCGAGGCGCGGAGGCGAAGC	12	POLR1D
screen
constant region	POLR1D_ + _28196012.23-P1	GGCAAGGACCGACCGACGGA	13	POLR1D
screen
constant region	SNRPD2_ + _46195119.23-P1P2	GAGGCCGGGCTAGGCTTAGG	14	SNRPD2
screen
constant region	SNRPD2_ + _46195138.23-P1P2	GGCGTAGTGACCATCATGTG	15	SNRPD2
screen
constant region	SNRPD2_ − _46195150.23-P1P2	GCTAGCCCGGCCTCACATGA	16	SNRPD2
screen
constant region	CDC23_ + _137548970.23-P1P2	GAGTACCTCCATGGTCCCGG	17	CDC23
screen
constant region	CDC23_ − _137548987.23-P1P2	GACAGCCACCGGGACCATGG	18	CDC23
screen
constant region	CDC23_ − _137548622.23-P1P2	GCCAGTGACAGGGCACTCAG	19	CDC23
screen
constant region	CAD_ + _27440280.23-P1P2	GGCTGGAGAGAAGCCGGGCG	20	CAD
screen
constant region	CAD_ + _27440373.23-P1P2	GCGAGTACGGAGAAGCGGGA	21	CAD
screen
constant region	CAD_ + _27440253.23-P1P2	GTAGGAGCCTCGGGCGCGCT	22	CAD
screen
constant region	TUBB_ + _30688126.23-P1	GCGGCAGGAAGGTTCTGAGA	23	TUBB
screen
constant region	TUBB_ + _30688173.23-P1	GAGGTTGGAATGCGCCCCAG	24	TUBB
screen
constant region	TUBB_ + _30688145.23-P1	GCAGCGAGGTGCAAACGCGA	25	TUBB
screen
constant region	POLR2H_ − _184081237.23-P1P2	GGTGCACGTACTCCCAACTG	26	POLR2H
screen
constant region	POLR2H_ + _184081227.23-P1P2	GTGAGAGCGCGACCACAGTT	27	POLR2H
screen
constant region	POLR2H_ + _184081251.23-P1P2	GGGGCCACGAGAGCAGCAGA	28	POLR2H
screen
constant region	DUT_ + _48624414.23-P1P2	GAGGCGAGCGAGGAGACCAC	29	DUT
screen
constant region	DUT_ − _48624041.23-P1P2	GCGTCTGGAAGGAATCCACG	30	DUT
screen
constant region	DUT_ − _48623651.23-P1P2	GCAGGACGGGCGCGTCTTCA	31	DUT
screen
constant region	DNAJC19_ + _180707414.23-	GGGATGAGCCGTGCTCCCGG	32	DNAJC19
screen	P1P2
constant region	DNAJC19_ + _180707118.23-	GCTTGCCTGGAACTCCTGTA	33	DNAJC19
screen	P1P2
constant region	DNAJC19_ + _180707491.23-	GGGCGCCTGTGCTTGAGGTT	34	DNAJC19
screen	P1P2
constant region	non-targeting_03786	GTGGCCGTTCATGGGACCGG	35	neg_ctrl
screen
constant region	non-targeting_03636	GACAATATCTGGATCGCCAA	36	neg_ctrl
screen
constant region	non-targeting_03478	GGATGGGCTCGCCTGGCCAG	37	neg_ctrl
screen
constant region	non-targeting_03229	GGTCCCACGGCGAAGCGACT	38	neg_ctrl
screen
constant region	non-targeting_00564	GCCGATGGTCTTGTACTACA	4	neg_ctrl
screen
constant region	non-targeting_00763	GGCGCGGGCCCCATAAAAAC	39	neg_ctrl
screen
perturb-seq	RPS18_ + _33239917.23-P1P2_00	GCTGCGATGCCGCTGGATCA	40	RPS18
perturb-seq	RPS18_ + _33239917.23-P1P2_01	GCTGCAATGCCGCTGGATCA	41	RPS18
perturb-seq	RPS18_ + _33239917.23-P1P2_02	GCTGGGATGCCGCTGGATCA	42	RPS18
perturb-seq	RPS18_ + _33239917.23-P1P2_08	GCTGCGATTCCGCTGGATCA	43	RPS18
perturb-seq	RPS18_ + _33239917.23-P1P2_04	GCTGCGATCCCGCTGGATCA	44	RPS18
perturb-seq	RPS14_ + _149829238.23-	GAGGCCCGGGCGCGACAATC	45	RPS14
	P1P2_00
perturb-seq	RPS14_ + _149829238.23-	GAGACCCGGGCGCGACAATC	46	RPS14
	P1P2_01
perturb-seq	RPS14_ + _149829238.23-	GAGGCCCTGGCGCGACAATC	47	RPS14
	P1P2_02
perturb-seq	RPS14_ + _149829238.23-	GAGGCCCGCGCGCGACAATC	48	RPS14
	P1P2_04
perturb-seq	RPS14_ + _149829238.23-	GAGGCCCGGGCGCGACAGTC	49	RPS14
	P1P2_13
perturb-seq	RPS14_ + _149829238.23-	GAGGCCCGGGCTCGACAATC	50	RPS14
	P1P2_08
perturb-seq	RPL9_ + _39460483.23-P1P2_00	GGATGTTTCTGTGCTCGTGG	5	RPL9
perturb-seq	RPL9_ + _39460483.23-P1P2_01	GGATGATTCTGTGCTCGTGG	51	RPL9
perturb-seq	RPL9_ + _39460483.23-P1P2_05	GGATGTTTCGGTGCTCGTGG	52	RPL9
perturb-seq	RPL9_ + _39460483.23-P1P2_04	GGATGTTTCAGTGCTCGTGG	53	RPL9
perturb-seq	RPL9_ + _39460483.23-P1P2_07	GGATGTTTCTGCGCTCGTGG	54	RPL9
perturb-seq	GNB2L1_ + _180670873.23-	GTGCAAGGCGGCGGCAGGAG	55	GNB2L1
	P1P2_00
perturb-seq	GNB2L1_ + _180670873.23-	GTGCAAGGTGGCGGCAGGAG	56	GNB2L1
	P1P2_08
perturb-seq	GNB2L1_ + _180670873.23-	GTGCAAGGCGGCGGCGGGAG	57	GNB2L1
	P1P2_13
perturb-seq	GNB2L1_ + _180670873.23-	GTGCAAGGCGGGGGCAGGAG	58	GNB2L1
	P1P2_07
perturb-seq	GNB2L1_ + _180670873.23-	GTGCAAGACGGCGGCAGGAG	59	GNB2L1
	P1P2_02
perturb-seq	RPS15_ − _1438413.23-P1P2_00	GACCAAAGCGATCTCTTCTG	60	RPS15
		C63
perturb-seq	RPS15_ − _1438413.23-P1P2_07	GACCAAAGCGGTCTCTTCTG	61	RPS15
perturb-seq	RPS15_ − _1438413.23-P1P2_02	GACCAAGGCGATCTCTTCTG	62	RPS15
perturb-seq	RPS15_ − _1438413.23-P1P2_12	GACCAAAGCGATCTCTTGTG	63	RPS15
perturb-seq	RPS15_ − _1438413.23-P1P2_01	GACCAAACCGATCTCTTCTG	64	RPS15
perturb-seq	HSPE1_ + _198365089.23-	GGAGACTCGCAGTCCGGCCC	9	HSPE1
	P1P2_00
perturb-seq	HSPE1_ + _198365089.23-	GGAGACACGCAGTCCGGCCC	65	HSPE1
	P1P2_01
perturb-seq	HSPE1_ + _198365089.23-	GGTGACTCGCAGTCCGGCCC	66	HSPE1
	P1P2_03
perturb-seq	HSPE1_ + _198365089.23-	GGAGACTGGCAGTCCGGCCC	67	HSPE1
	P1P2_02
perturb-seq	HSPE1_ + _198365089.23-	GGAGACTCGCAGTCCTGCCC	68	HSPE1
	P1P2_14
perturb-seq	RAN_ + _131356438.23-P1P2_00	GGCGGTCGCTGCGCTTAGGG	69	RAN
perturb-seq	RAN_ + _131356438.23-P1P2_02	GGCGGCCGCTGCGCTTAGGG	70	RAN
perturb-seq	RAN_ + _131356438.23-P1P2_03	GGGGGTCGCTGCGCTTAGGG	71	RAN
perturb-seq	RAN_ + _131356438.23-P1P2_04	GGCGGTCGCGGCGCTTAGGG	72	RAN
perturb-seq	RAN_ + _131356438.23-P1P2_12	GGCGGTCGCTGCGCTTAGGT	73	RAN
perturb-seq	POLR1D_ + _28196016.23-P1_00	GGGAAGCAAGGACCGACCGA	11	POLR1D
perturb-seq	POLR1D_ + _28196016.23-P1_08	GGGAAGCAGGGACCGACCGA	74	POLR1D
perturb-seq	POLR1D_ + _28196016.23-P1_03	GGTAAGCAAGGACCGACCGA	75	POLR1D
perturb-seq	POLR1D_ + _28196016.23-P1_01	GGGAAGCCAGGACCGACCGA	76	POLR1D
perturb-seq	POLR1D_ + _28196016.23-P1_07	GGGAAGCAAGGAGCGACCGA	77	POLR1D
perturb-seq	DBR1_ + _137893744.23-	GTTTGCAGGAGTCTACACCC	78	DBR1
	P1P2_00
perturb-seq	DBR1_ + _137893744.23-	GATTGCAGGAGTCTACACCC	79	DBR1
	P1P2_01
perturb-seq	DBR1_ + _137893744.23-	GTTTGCAGGGGTCTACACCC	80	DBR1
	P1P2_07
perturb-seq	DBR1_ + _137893744.23-	GTTTGCAGGAGTGTACACCC	81	DBR1
	P1P2_05
perturb-seq	DBR1_ + _137893744.23-	GTTTGCAGTAGTCTACACCC	82	DBR1
	P1P2_08
perturb-seq	SEC61A1_ − _127771295.23-	GGCACTGACGTGTCTCTCGG	83	SEC61A1
	P1_00
perturb-seq	SEC61A1_ − _127771295.23-	GGCGCTGACGTGTCTCTCGG	84	SEC61A1
	P1_02
perturb-seq	SEC61A1_ − _127771295.23-	GGCACTGTCGTGTCTCTCGG	85	SEC61A1
	P1_01
perturb-seq	SEC61A1_ − _127771295.23-	GGTACTGACGTGTCTCTCGG	86	SEC61A1
	P1_03
perturb-seq	SEC61A1_ − _127771295.23-	GGCACTGAAGTGTCTCTCGG	87	SEC61A1
	P1_04
perturb-seq	HSPA5_ + _128003624.23-	GAGCCGAGTAGGCGACGGTG	88	HSPA5
	P1P2_00
perturb-seq	HSPA5_ + _128003624.23-	GAGCCGAGAAGGCGACGGTG	89	HSPA5
	P1P2_04
perturb-seq	HSPA5_ + _128003624.23-	GAGCCGAGTGGGCGACGGTG	90	HSPA5
	P1P2_08
perturb-seq	HSPA5_ + _128003624.23-	GAACCGAGTAGGCGACGGTG	91	HSPA5
	P1P2_01
perturb-seq	HSPA5_ + _128003624.23-	GAGCCGAGTAGACGACGGTG	92	HSPA5
	P1P2_06
perturb-seq	GINS1_ − _25388381.23-P1P2_00	GGACTAGAACGAAAGGAGTG	93	GINS1
perturb-seq	GINS1_ − _25388381.23-P1P2_08	GGACTAGAGCGAAAGGAGTG	94	GINS1
perturb-seq	GINS1_ − _25388381.23-P1P2_06	GGACTAGAACGGAAGGAGTG	95	GINS1
perturb-seq	GINS1_ − _25388381.23-P1P2_03	GGACTATAACGAAAGGAGTG	96	GINS1
perturb-seq	GINS1_ − _25388381.23-P1P2_14	GGACTAGAACGAAAGGAGCG	97	GINS1
perturb-seq	CDC23_ − _137548987.23-	GACAGCCACCGGGACCATGG	18	CDC23
	P1P2_00
perturb-seq	CDC23_ − _137548987.23-	GACAGCTACCGGGACCATGG	98	CDC23
	P1P2_02
perturb-seq	CDC23_ − _137548987.23-	GACAGCCATCGGGACCATGG	99	CDC23
	P1P2_08
perturb-seq	CDC23_ − _137548987.23-	GACAGCCAACGGGACCATGG	100	CDC23
	P1P2_04
perturb-seq	CDC23_ − _137548987.23-	GACAGCCACCGGGACCACGG	101	CDC23
	P1P2_11
perturb-seq	CAD_ + _27440280.23-P1P2_00	GGCTGGAGAGAAGCCGGGCG	20	CAD
perturb-seq	CAD_ + _27440280.23-P1P2_03	GGCTGGTGAGAAGCCGGGCG	102	CAD
perturb-seq	CAD_ + _27440280.23-P1P2_07	GGCTGGAGCGAAGCCGGGCG	103	CAD
perturb-seq	CAD_ + _27440280.23-P1P2_06	GGCTGGAGAGTAGCCGGGCG	104	CAD
perturb-seq	CAD_ + _27440280.23-P1P2_13	GGCTGGAGAGAAGCCTGGCG	105	CAD
perturb-seq	TUBB_ + _30688126.23-P1_00	GCGGCAGGAAGGTTCTGAGA	23	TUBB
perturb-seq	TUBB_ + _30688126.23-P1_01	GCAGCAGGAAGGTTCTGAGA	106	TUBB
perturb-seq	TUBB_ + _30688126.23-P1_06	GCGGCAGGACGGTTCTGAGA	107	TUBB
perturb-seq	TUBB_ + _30688126.23-P1_03	GCGGCAGCAAGGTTCTGAGA	108	TUBB
perturb-seq	TUBB_ + _30688126.23-P1_10	GCGGCAGGAAGGTTCAGAGA	109	TUBB
perturb-seq	DUT_ + _48624411.23-P1P2_00	GCGAGCGAGGAGACCACCGG	110	DUT
perturb-seq	DUT_ + _48624411.23-P1P2_01	GCCAGCGAGGAGACCACCGG	111	DUT
perturb-seq	DUT_ + _48624411.23-P1P2_08	GCGAGCGAGGAGGCCACCGG	112	DUT
perturb-seq	DUT_ + _48624411.23-P1P2_07	GCGAGCGAGGAGCCCACCGG	113	DUT
perturb-seq	DUT_ + _48624411.23-P1P2_10	GCGAGCGAGGAGACCAACGG	114	DUT
perturb-seq	POLR2H_ + _184081251.23-	GGGGCCACGAGAGCAGCAGA	28	POLR2H
	P1P2_00
perturb-seq	POLR2H_ + _184081251.23-	GGGGCCACGAGAGCAGCGGA	115	POLR2H
	P1P2_11
perturb-seq	POLR2H_ + _184081251.23-	GGGGCCACGCGAGCAGCAGA	116	POLR2H
	P1P2_08
perturb-seq	POLR2H_ + _184081251.23-	GGGGCCACGAGAGCAGGAGA	117	POLR2H
	P1P2_12
perturb-seq	POLR2H_ + _184081251.23-	GGGGCCACGAGTGCAGCAGA	118	POLR2H
	P1P2_07
perturb-seq	GATA1_ − _48645022.23-	GTGAGCTTGCCACATCCCCA	119	GATA1
	P1P2_00
perturb-seq	GATA1_ − _48645022.23-	GTGCGCTTGCCACATCCCCA	120	GATA1
	P1P2_03
perturb-seq	GATA1_ − _48645022.23-	GTGAGCTTACCACATCCCCA	121	GATA1
	P1P2_04
perturb-seq	GATA1_ − _48645022.23-	GTGAGCTTTCCACATCCCCA	122	GATA1
	P1P2_08
perturb-seq	GATA1_ − _48645022.23-	GTGAGCTTGCGACATCCCCA	123	GATA1
	P1P2_06
perturb-seq	GATA1_ − _48645022.23-	GTGAGCTTGCCACATCCGCA	124	GATA1
	P1P2_12
perturb-seq	BCR_ + _23523092.23-P1P2_00	GCGCGCGGGGCCCGTCTCAG	125	BCR
perturb-seq	BCR_ + _23523092.23-P1P2_07	GCGCGCGGGGCTCGTCTCAG	126	BCR
perturb-seq	BCR_ + _23523092.23-P1P2_04	GCGCGCGGAGCCCGTCTCAG	127	BCR
perturb-seq	BCR_ + _23523092.23-P1P2_05	GCGCGCGGCGCCCGTCTCAG	128	BCR
perturb-seq	BCR_ + _23523092.23-P1P2_15	GCGCGCGGGGCCCGTCGCAG	129	BCR
perturb-seq	BCR_ + _23523092.23-P1P2_13	GCGCGCGGGGCCCATCTCAG	130	BCR
perturb-seq	HSPA9_ − _137911079.23-	GGAGCTGCGCGATGCGGTGG	131	HSPA9
	P1P2_00
perturb-seq	HSPA9_ − _137911079.23-	GGAGCTGCGGGATGCGGTGG	132	HSPA9
	P1P2_07
perturb-seq	HSPA9_ − _137911079.23-	GGAGTTGCGCGATGCGGTGG	133	HSPA9
	P1P2_02
perturb-seq	HSPA9_ − _137911079.23-	GGAGCTGCTCGATGCGGTGG	134	HSPA9
	P1P2_08
perturb-seq	HSPA9_ − _137911079.23-	GGAGCTGCGCAATGCGGTGG	135	HSPA9
	P1P2_04
perturb-seq	EIF2S1_ − _67827080.23-P1P2_00	GAGCGAAGCGCACGCTGAGG	136	EIF2S1
perturb-seq	EIF2S1_ − _67827080.23-P1P2_06	GAGCGAAGCGCGCGCTGAGG	137	EIF2S1
perturb-seq	EIF2S1_ − _67827080.23-P1P2_02	GAGCGCAGCGCACGCTGAGG	138	EIF2S1
perturb-seq	EIF2S1_ − _67827080.23-P1P2_01	GAGCGAAACGCACGCTGAGG	139	EIF2S1
perturb-seq	EIF2S1_ − _67827080.23-P1P2_07	GAGCGAAGCGCTCGCTGAGG	140	EIF2S1
perturb-seq	COX11_ + _53045977.23-	GGCTCTGGCGTCCTGGATGG	141	COX11
	P1P2_00
perturb-seq	COX11- + _53045977.23-	GGCTCTGTCGTCCTGGATGG	142	COX11
	P1P2_03
perturb-seq	COX11_ + _53045977.23-	GGCTCTGGCGCCCTGGATGG	143	COX11
	P1P2_04
perturb-seq	COX11_ + _53045977.23-	GGCTCTGGCGTCTTGGATGG	144	COX11
	P1P2_05
perturb-seq	COX11_ + _53045977.23-	GGCTCTGGCGTCCCGGATGG	145	COX11
	P1P2_10
perturb-seq	MTOR_ + _11322547.23-P1P2_00	GGGCAGGGGGCCTGAAGCGG	146	MTOR
perturb-seq	MTOR_+  _11322547.23-P1P2_07	GGGCAGGGGGTCTGAAGCGG	147	MTOR
perturb-seq	MTOR_ + _11322547.23-P1P2_05	GGGCAGGGGGCTTGAAGCGG	148	MTOR
perturb-seq	MTOR_ + _11322547.23-P1P2_06	GGGCAGGGGGGCTGAAGCGG	149	MTOR
perturb-seq	MTOR_ + _11322547.23-P1P2_10	GGGCAGGGGGCCTGAAGCAG	150	MTOR
perturb-seq	ATP5E_−  _57607036.23-P1P2_00	GGTGTCCAGGGGCACTCTGT	151	ATP5E
perturb-seq	ATP5E_ − _57607036.23-P1P2_01	GGTGTCCTGGGGCACTCTGT	152	ATP5E
perturb-seq	ATP5E_ − _57607036.23-P1P2_16	GGTGTCCAGGGGCGCTCTGT	153	ATP5E
perturb-seq	ATP5E_ − _57607036.23-P1P2_04	GGTGTCCAGGAGCACTCTGT	154	ATP5E
perturb-seq	ATP5E_ − _57607036.23-P1P2_14	GGTGTCCAGGGGCACTGTGT	155	ATP5E
perturb-seq	ALDOA_ + _30077139.23-	GGTCACCAGGACCCCTTCTG	156	ALDOA
	P1P2_00
perturb-seq	ALDOA_ + _30077139.23-	GGTCACCAGGATCCCTTCTG	157	ALDOA
	P1P2_06
perturb-seq	ALDOA_ + _30077139.23-	GGTCACCAGGCCCCCTTCTG	158	ALDOA
	P1P2_07
perturb-seq	ALDOA_ + _30077139.23-	GGTCACCAGGACCGCTTCTG	159	ALDOA
	P1P2_14
perturb-seq	ALDOA_ + _30077139.23-	GGTCACCAGGACCCCTTTTG	160	ALDOA
	P1P2_13
perturb-seq	non-targeting_00001	GTGCACCCGGCTAGGACCGG	161	neg_ctrl
perturb-seq	non-targeting_00028	GGTGGCCTTTGCAATTGGCG	162	neg_ctrl
perturb-seq	non-targeting_00054	GGGCCTGGACGAGCCTAAAA	163	neg_ctrl
perturb-seq	non-targeting_00089	GGGGTGAGGGTCCAATTCGG	164	neg_ctrl
perturb-seq	non-targeting_00217	GTGAACTCAAAAATCCCGAC	165	neg_ctrl
perturb-seq	non-targeting_00283	GGGCCGACGGATAGGAGGGA	166	neg_ctrl
perturb-seq	non-targeting_00406	GGCGCCGGACTGGACCTCGA	167	neg_ctrl
perturb-seq	non-targeting_00527	GTGGGAGCAGATCAAGACTC	168	neg_ctrl
perturb-seq	non-targeting_00802	GCACGACGCTCCGGCACGCG	169	neg_ctrl
perturb-seq	non-targeting_01040	GTACGGCATGGCGCACTGCG	170	neg_ctrl

TABLE 2
Perturb-seq gene descriptions.
ALDOA   Aldolase A; glycolytic enzyme
ATP5E   ATP synthase subunit
BCR-ABL Fusion gene; drives CML-derived K562 cells
CAD     Pyrimidine nucleotide biosynthesis enzyme; catalyzes
        multiple pathway steps
CDC23   Anaphase promoting complex/cyclosome component
COX11   Mitochondrial respiratory chain; cytochrome c oxidase
        assembly factor
DBR1    Lariat debranching enzyme; required for lariat intron
        degradation after splicing
DUT     dUTP pyrophosphatase; involved in thymidine biosynthesis
EIF2S1  eIF2α; Translation initiation factor; translational control factor
GATA1   Erythroid-lineage transcription factor
GINS1   DNA replication initiation factor
GNB2L1  RACK1; 40s ribosomal protein; associated with numerous
        signalling processes
HSPA5   BiP; ER chaperone involved in protein import and folding
HSPA9   Mortalin; Mitochondrial chaperone and import factor
HSPE1   Mitochondrial chaperone
MTOR    Kinase; regulates growth, metabolism, and autophagy
POLR1D  RNA polymerase I and III subunit
POLR2H  RNA polymerase I, II, and III subunit
RAN     G-protein that controls protein and RNA transport through the
        nuclear pore
RPL9    Ribosomal protein L9
RPS14   Ribosomal protein S14
RPS15   Ribosomal protein S15
RPS18   Ribosomal protein S18
SEC61A1 ER translocon component
TUBB    beta-tubulin; structural component of microtubules

TABLE 3
Perturb-seq pooled growth sgRNA counts.
	T0	d10	d5
ALDOA_+_30077139.23-P1P2_00	5280	2781	4056
ALDOA_+_30077139.23-P1P2_06	6015	3500	4831
ALDOA_+_30077139.23-P1P2_07	4830	3028	4284
ALDOA_+_30077139.23-P1P2_13	6699	26890	16944
ALDOA_+_30077139.23-P1P2_14	3603	6076	5347
ATP5E_−_57607036.23-P1P2_00	8197	9475	12109
ATP5E_−_57607036.23-P1P2_01	7774	8806	10487
ATP5E_−_57607036.23-P1P2_04	7209	14860	13256
ATP5E_−_57607036.23-P1P2_14	4611	15257	10750
ATP5E_−_57607036.23-P1P2_16	6210	9964	9571
BCR_+_23523092.23-P1P2_00	9644	2333	2250
BCR_+_23523092.23-P1P2_04	5355	2119	1660
BCR_+_23523092.23-P1P2_05	13439	15537	12165
BCR_+_23523092.23-P1P2_07	8081	2183	1744
BCR_+_23523092.23-P1P2_13	4304	7063	5668
BCR_+_23523092.23-P1P2_15	5377	8085	6829
CAD_+_27440280.23-P1P2_00	8671	785	2464
CAD_+_27440280.23-P1P2_03	7290	907	2087
CAD_+_27440280.23-P1P2_06	6199	4365	4967
CAD_+_27440280.23-P1P2_07	13241	4019	6008
CAD_+_27440280.23-P1P2_13	11874	19130	17097
CDC23_−_137548987.23-P1P2_00	8182	854	757
CDC23_−_137548987.23-P1P2_02	7014	1192	832
CDC23_−_137548987.23-P1P2_04	8019	1646	1646
CDC23_−_137548987.23-P1P2_08	8986	1710	1531
CDC23_−_137548987.23-P1P2_11	12707	16682	14320
COX11_+_53045977.23-P1P2_00	8084	6198	11785
COX11_+_53045977.23-P1P2_03	11251	9184	16852
COX11_+_53045977.23-P1P2_04	5234	5047	8343
COX11_+_53045977.23-P1P2_05	5205	11496	10766
COX11_+_53045977.23-P1P2_10	5206	11271	8887
DBR1_+_137893744.23-P1P2_00	13446	3583	9171
DBR1_+_137893744.23-P1P2_01	9446	1824	5512
DBR1_+_137893744.23-P1P2_05	6569	4748	6705
DBR1_+_137893744.23-P1P2_07	8500	2550	4894
DBR1_+_137893744.23-P1P2_08	5326	15989	11651
DUT_+_48624411.23-P1P2_00	14025	1570	3755
DUT_+_48624411.23-P1P2_01	25227	3576	6764
DUT_+_48624411.23-P1P2_07	4601	1157	1509
DUT_+_48624411.23-P1P2_08	15356	2392	4351
DUT_+_48624411.23-P1P2_10	6538	4466	5403
EIF2S1_−_67827080.23-P1P2_00	5718	1318	1123
EIF2S1_−_67827080.23-P1P2_01	5433	4065	3799
EIF2S1_−_67827080.23-P1P2_02	8035	2582	2570
EIF2S1_−_67827080.23-P1P2_06	4549	2436	1718
EIF2S1_−_67827080.23-P1P2_07	6931	22309	13281
GATA1_−_48645022.23-P1P2_00	5712	757	955
GATA1_−_48645022.23-P1P2_03	12276	1534	1927
GATA1_−_48645022.23-P1P2_04	4714	668	860
GATA1_−_48645022.23-P1P2_06	9440	5489	5580
GATA1_−_48645022.23-P1P2_08	7028	1873	2043
GATA1_−_48645022.23-P1P2_12	4081	11548	7289
GINS1_−_25388381.23-P1P2_00	3621	280	547
GINS1_−_25388381.23-P1P2_03	9799	2755	2982
GINS1_−_25388381.23-P1P2_06	11452	1219	1828
GINS1_−_25388381.23-P1P2_08	18173	1756	2461
GINS1_−_25388381.23-P1P2_14	6443	6093	5833
GNB2L1_+_180670873.23-P1P2_00	2280	1685	2456
GNB2L1_+_180670873.23-P1P2_02	3839	7618	6216
GNB2L1_+_180670873.23-P1P2_07	9894	8738	8322
GNB2L1_+_180670873.23-P1P2_08	24451	17083	25247
GNB2L1_+_180670873.23-P1P2_13	4708	5991	6350
HSPA5_+_128003624.23-P1P2_00	5785	2176	1756
HSPA5_+_128003624.23-P1P2_01	7580	3812	3124
HSPA5_+_128003624.23-P1P2_04	11091	4282	3304
HSPA5_+_128003624.23-P1P2_06	10180	23714	17649
HSPA5_+_128003624.23-P1P2_08	10148	3487	3005
HSPA9_−_137911079.23-P1P2_00	5450	835	944
HSPA9_−_137911079.23-P1P2_02	4345	1872	1727
HSPA9_−_137911079.23-P1P2_04	6754	10829	9346
HSPA9_−_137911079.23-P1P2_07	5941	1463	1513
HSPA9_−_137911079.23-P1P2_08	3137	2726	2803
HSPE1_+_198365089.23-P1P2_00	6813	1179	2348
HSPE1_+_198365089.23-P1P2_01	9669	2663	4228
HSPE1_+_198365089.23-P1P2_02	7969	4437	5731
HSPE1_+_198365089.23-P1P2_03	7473	2279	3034
HSPE1_+_198365089.23-P1P2_14	4808	6498	6501
MTOR_+_11322547.23-P1P2_00	17632	3144	6328
MTOR_+_11322547.23-P1P2_05	5595	3324	4083
MTOR_+_11322547.23-P1P2_06	4142	3174	3358
MTOR_+_11322547.23-P1P2_07	6761	1899	3183
MTOR_+_11322547.23-P1P2_10	7076	7827	7332
POLR1D_+_28196016.23-P1_00	11671	1496	3429
POLR1D_+_28196016.23-P1_01	12679	2528	4460
POLR1D_+_28196016.23-P1_03	10266	933	2365
POLR1D_+_28196016.23-P1_07	15589	16285	16283
POLR1D_+_28196016.23-P1_08	16414	1986	4205
POLR2H_+_184081251.23-P1P2_00	9498	1103	947
POLR2H_+_184081251.23-P1P2_07	4472	8153	6381
POLR2H_+_184081251.23-P1P2_08	6134	3869	3492
POLR2H_+_184081251.23-P1P2_11	5900	1144	898
POLR2H_+_184081251.23-P1P2_12	5334	5996	4854
RAN_+_131356438.23-P1P2_00	5444	8936	7598
RAN_+_131356438.23-P1P2_02	11853	15358	15046
RAN_+_131356438.23-P1P2_03	5056	6816	6698
RAN_+_131356438.23-P1P2_04	6001	14870	11409
RAN_+_131356438.23-P1P2_12	7627	25349	16172
RPL9_+_39460483.23-P1P2_00	10355	1014	1141
RPL9_+_39460483.23-P1P2_01	4886	1238	1108
RPL9_+_39460483.23-P1P2_04	5237	4118	3975
RPL9_+_39460483.23-P1P2_05	4950	2355	2217
RPL9_+_39460483.23-P1P2_07	7336	9339	7867
RPS14_+_149829238.23-P1P2_00	11846	2984	3190
RPS14_+_149829238.23-P1P2_01	4954	1385	1474
RPS14_+_149829238.23-P1P2_02	11519	5538	5497
RPS14_+_149829238.23-P1P2_04	9244	12547	9641
RPS14_+_149829238.23-P1P2_08	4488	17976	11681
RPS14_+_149829238.23-P1P2_13	7137	12082	9567
RPS15_−_1438413.23-P1P2_00	6757	3376	2912
RPS15_−_1438413.23-P1P2_01	9713	39345	23866
RPS15_−_1438413.23-P1P2_02	5051	3548	3113
RPS15_−_1438413.23-P1P2_07	6337	4631	3595
RPS15_−_1438413.23-P1P2_12	4661	19991	12257
RPS18_+_33239917.23-P1P2_00	6212	1535	1556
RPS18_+_33239917.23-P1P2_01	5202	2571	2658
RPS18_+_33239917.23-P1P2_02	5486	3757	3404
RPS18_+_33239917.23-P1P2_04	5132	13186	9728
RPS18_+_33239917.23-P1P2_08	8535	13839	11101
SEC61A1_−_127771295.23-P1_00	11429	2025	2151
SEC61A1_−_127771295.23-P1_01	5308	4229	4006
SEC61A1_−_127771295.23-P1_02	9991	4238	4030
SEC61A1_−_127771295.23-P1_03	5904	3563	3530
SEC61A1_−_127771295.23-P1_04	5081	10772	7999
TUBB_+_30688126.23-P1_00	13570	1125	2722
TUBB_+_30688126.23-P1_01	7125	962	1319
TUBB_+_30688126.23-P1_03	4751	1221	1680
TUBB_+_30688126.23-P1_06	6235	1158	1983
TUBB_+_30688126.23-P1_10	7085	12737	9877
non-targeting_00001	10415	31944	18946
non-targeting_00028	8871	35652	20289
non-targeting_00054	12360	49855	29818
non-targeting_00089	10841	44919	27748
non-targeting_00217	10286	42962	25185
non-targeting_00283	8188	27936	18547
non-targeting_00406	9974	39839	24099
non-targeting_00527	6840	27634	16865
non-targeting_00802	7096	27842	16759
non-targeting_01040	0	0	0

TABLE 4
Perturb-seq sgRNA sequences and pooled growth phenotypes (γ and relative
activity).
						relative
		SEQ				_act-	relative_
		ID				ivity_	activity
	Sequence	NO:	Gene	gamma_day5	gamma_day10	day5	_day10
ALDOA_ + _30077	GGTCACCA	156	ALDOA	−0.412746257	−0.366468568	1	1
139.23-P1P2_00	GGACCCCT
	TCTG
ALDOA_ + _30077	GGTCACCA	157	ALDOA	−0.396686909	−0.348503022	0.96109	0.95097657
139.23-P1P2_06	GGATCCCT					1475
	TCTG
ALDOA_ + _30077	GGTCACCA	158	ALDOA	−0.360892365	−0.335059043	0.87436	0.91429135
139.23-P1P2_07	GGCCCCCT					8595	3
	TCTG
ALDOA_ + _30077	GGTCACCA	160	ALDOA	0.017063022	−0.000220283	−0.04134	0.00060109
139.23-P1P2_13	GGACCCCT					0221	6
	TTTG
ALDOA_ + _30077	GGTCACCA	159	ALDOA	−0.175243431	−0.156611393	0.42457	0.42735286
139.23-P1P2_14	GGACCGCT					9093	6
	TCTG
ATP5E_ −	GGTGTCCA	151	ATP5E	−0.176898232	−0.224723052	1	1
_57607036.23-	GGGGCACT
P1P2_00	CTGT
ATP5E_ −	GGTGTCCT	152	ATP5E	−0.209657934	−0.228373078	1.18518	1.01624233
_57607036.23-	GGGGCACT					9542	1
P1P2_01	CTGT
ATP5E_ −	GGTGTCCA	154	ATP5E	−0.097932574	−0.120406413	0.55360	0.53579911
_57607036.23-	GGAGCACT					9686	6
P1P2_04	CTGT
ATP5E_ −	GGTGTCCA	155	ATP5E	−0.012329915	−0.035061828	0.06970	0.15602239
_57607036.23-	GGGGCACT					0615
P1P2_14	GTGT
ATP5E_ −	GGTGTCCA	153	ATP5E	−0.161607088	−0.165585326	0.91355	0.73684174
_57607036.23-	GGGGCGCT					9656	6
P1P2_16	CTGT
BCR_ + _2352309	GCGCGCGG	125	BCR	−0.84255285	−0.506782463	1	1
2.23-P1P2_00	GGCCCGTC
	TCAG
BCR_ + _2352309	GCGCGCGG	127	BCR	−0.740052021	−0.418039669	0.87834	0.82488976
2.23-P1P2_04	AGCCCGTC					4926	8
	TCAG
BCR_ + _2352309	GCGCGCGG	128	BCR	−0.353548555	−0.224691524	0.41961	0.44336878
2.23-P1P2_05	CGCCCGTC					588	2
	TCAG
BCR_ + _2352309	GCGCGCGG	126	BCR	−0.870659636	−0.486879508	1.03335	0.96072682
2.23-P1P2_07	GGCTCGTC					9078	8
	TCAG
BCR_ + _2352309	GCGCGCGG	130	BCR	−0.218335768	−0.161526418	0.25913	0.31872929
2.23-P1P2_13	GGCCCATC					5991	6
	TCAG
BCR_ + _2352309	GCGCGCGG	129	BCR	−0.231407972	−0.177296007	0.27465	0.34984637
2.23-P1P2_15	GGCCCGTC					0987	5
	GCAG
CAD_ + _2744028	GGCTGGAG	20	CAD	−0.77142522	−0.684031023	1	1
0.23-P1P2_00	AGAAGCC
	GGGCG
CAD_ + _2744028	GGCTGGTG	102	CAD	−0.768748241	−0.62669484	0.99652	0.91617897
0.23-P1P2_03	AGAAGCC					9827	2
	GGGCG
CAD_ + _2744028	GGCTGGAG	104	CAD	−0.397541377	−0.314108526	0.51533	0.45920216
0.23-P1P2_06	AGTAGCC					3654	4
	GGGCG
CAD_ + _2744028	GGCTGGAG	103	CAD	−0.602640029	−0.465864745	0.78120	0.68105791
0.23-P1P2_07	CGAAGCC					3432	9
	GGGCG
CAD_ + _2744028	GGCTGGAG	105	CAD	−0.186141893	−0.164847938	0.24129	0.24099482
0.23-P1P2_13	AGAAGCC					6094	7
	TGGCG
CDC23_ −	GACAGCCA	18	CDC23	−1.176148271	−0.65836999	1	1
_137548987.23-	CCGGGACC
P1P2_00	ATGG
CDC23_ −	GACAGCTA	98	CDC23	−1.086521687	−0.570458445	0.92379	0.86647091
_137548987.23-	CCGGGACC					6526
P1P2_02	ATGG
CDC23_ −	GACAGCCA	100	CDC23	−0.888740688	−0.536409046	0.75563	0.81475318
_137548987.23-	ACGGGACC					6607	3
P1P2_04	ATGG
CDC23_ −	GACAGCCA	99	CDC23	−0.955927382	−0.550062137	0.81276	0.83549090
_137548987.23-	TCGGGACC					0947	2
P1P2_08	ATGG
CDC23_ −	GACAGCCA	101	CDC23	−0.274524181	−0.201768195	0.23340	0.30646627
_137548987.23-	CCGGGACC					9501
P1P2_11	ACGG
COX11_ + _53045	GGCTCTGG	141	COX11	−0.181673555	−0.298760116	1	1
977.23-P1P2_00	CGTCCTGG
	ATGG
COX11_ + _53045	GGCTCTGT	142	COX11	−0.171909541	−0.287459131	0.94625	0.96217371
977.23-P1P2_03	CGTCCTGG					5175	7
	ATGG
COX11_ + _53045	GGCTCTGG	143	COX11	−0.149463107	−0.257412775	0.82270	0.86160354
977.23-P1P2_04	CGCCCTGG					1508	4
	ATGG
COX11_ + _53045	GGCTCTGG	144	COX11	−0.055498122	−0.107956558	0.30548	0.36134862
977.23-P1P2_05	CGTCTTGG					2668	8
	ATGG
COX11_ + _53045	GGCTCTGG	145	COX11	−0.124745894	−0.111555757	0.68664	0.37339574
977.23-P1P2_10	CGTCCCGG					8609	8
	ATGG
DBR1_ + _137893	GTTTGCAG	78	DBR1	−0.455632712	−0.489343933	1	1
744.23-P1P2_00	GAGTCTAC
	ACCC
DBR1_ + _137893	GATTGCAG	79	DBR1	−0.5119081	−0.547426521	1.12351	1.11869481
744.23-P1P2_01	GAGTCTAC					042	5
	ACCC
DBR1_ + _137893	GTTTGCAG	81	DBR1	−0.310235296	−0.309396024	0.68088	0.63226700
744.23-P1P2_05	GAGTGTAC					8988	7
	ACCC
DBR1_ + _137893	GTTTGCAG	80	DBR1	−0.516738373	−0.467972494	1.13411	0.95632634
744.23-P1P2_07	GGGTCTAC					1663	3
	ACCC
DBR1_ + _137893	GTTTGCAG	82	DBR1	−0.035293825	−0.052607373	0.07746	0.10750592
744.23-P1P2_08	TAGTCTAC					113	7
	ACCC
DUT_ + _4862441	GCGAGCGA	110	DUT	−0.792905177	−0.645747334	1	1
1.23-P1P2_00	GGAGACC
	ACCGG
DUT_ + _4862441	GCCAGCGA	111	DUT	−0.792381209	−0.603170546	0.99933	0.93406587
1.23-P1P2_01	GGAGACC					918	2
	ACCGG
DUT_ + _4862441	GCGAGCGA	113	DUT	−0.71971485	−0.499796619	0.90769	0.7739817
1.23-P1P2_07	GGAGCCC					3468
	ACCGG
DUT_ + _4862441	GCGAGCGA	112	DUT	−0.772472074	−0.586165942	0.97423	0.90773265
1.23-P1P2_08	GGAGGCC					0079	5
	ACCGG
DUT_ + _4862441	GCGAGCGA	114	DUT	−0.386398362	−0.319585061	0.48731	0.49490728
1.23-P1P2_10	GGAGACC					9761	7
	AACGG
EIF2S1_ −	GAGCGAAG	136	EIF2S1	−0.904664361	−0.515496826	1	1
_67827080.23-	CGCACGCT
P1P2_00	GAGG
EIF2S1_ −	GAGCGAAA	139	EIF2S1	−0.446658521	−0.303163507	0.49372	0.58809965
_67827080.23-	CGCACGCT					8438	7
P1P2_01	GAGG
EIF2S1_ −	GAGCGCAG	138	EIF2S1	−0.728758604	−0.455577923	0.80555	0.88376474
_67827080.23-	CGCACGCT					6884	8
P1P2_02	GAGG
EIF2S1_ −	GAGCGAAG	137	EIF2S1	−0.668831037	−0.363481208	0.73931	0.70510852
_67827080.23-	CGCGCGC					4011	7
P1P2_06	TGAGG
EIF2S1_ −	GAGCGAAG	140	EIF2S1	−0.083069099	−0.040040492	0.09182	0.07767359
_67827080.23-	CGCTCGCT					3114	6
P1P2_07	GAGG
GATA1_ −	GTGAGCTT	119	GATA1	−0.962732023	−0.615305642	1	1
_48645022.23-	GCCACATC
P1P2_00	CCCA
GATA1_ −	GTGCGCTT	120	GATA1	−0.985479206	−0.625910566	1.02362	1.01723521
_48645022.23-	GCCACATC					7741	3
P1P2_03	CCCA
GATA1_ −	GTGAGCTT	121	GATA1	−0.931261986	−0.603230764	0.96731	0.98037580
_48645022.23-	ACCACATC					1738	5
P1P2_04	CCCA
GATA1_ −	GTGAGCTT	123	GATA1	−0.507256622	−0.348632235	0.52689	0.56660009
_48645022.23-	GCGACATC					2853	5
P1P2_06	CCCA
GATA1_ −	GTGAGCTT	122	GATA1	−0.763232435	−0.489322207	0.79277	0.79525064
_48645022.23-	TCCACATC					7654	2
P1P2_08	CCCA
GATA1 −	GTGAGCTT	124	GATA1	−0.10842664	−0.063270746	0.11262	0.10282815
_48645022.23-	GCCACATC					3905	8
P1P2_12	CGCA
GINS1_ −	GGACTAGA	93	GINS1	−0.999320047	−0.712462976	1	1
_25388381.23-	ACGAAAG
P1P2_00	GAGTG
GINS1_ −	GGACTATA	96	GINS1	−0.746714755	−0.479674571	0.74722	0.67326245
_25388381.23-	ACGAAAGG					2831	4
P1P2_03	AGTG
GINS1_ −	GGACTAGA	95	GINS1	−0.979441588	−0.654830488	0.98010	0.91910809
_25388381.23-	ACGGAAG					8015	5
P1P2_06	GAGTG
GINS1_ −	GGACTAGA	94	GINS1	−1.038746197	−0.672280776	1.03945	0.943601
_25388381.23-	GCGAAAG					2976
P1P2_08	GAGTG
GINS1_ −	GGACTAGA	97	GINS1	−0.353499818	−0.260924277	0.35374	0.36622854
_25388381.23-	ACGAAAG					0345	2
P1P2_14	GAGCG
GNB2L1_ + _1806	GTGCAAGG	55	GNB2L1	−0.290807004	−0.305387449	1	1
70873.23-	CGGCGGC
P1P2_00	AGGAG
GNB2L1_ + _1806	GTGCAAGA	59	GNB2L1	−0.143812202	−0.127266579	0.49452	0.41673808
70873.23-	CGGCGGC					7986
P1P2_02	AGGAG
GNB2L1_ + _1806	GTGCAAGG	58	GNB2L1	−0.380032091	−0.273258144	1.30681	0.89479166
70873.23-	CGGGGGC					8906	6
P1P2_07	AGGAG
GNB2L1_ + _1806	GTGCAAGG	56	GNB2L1	−0.306071563	−0.31551831	1.05249	1.03317379
70873.23-	TGGCGGC					0343	4
P1P2_08	AGGAG
GNB2L1_ + _1806	GTGCAAGG	57	GNB2L1	−0.20971562	−0.207391481	0.72115	0.67910937
70873.23-	CGGCGGC					0513	9
P1P2_13	GGGAG
HSPA5_ + _12800	GAGCCGAG	88	HSPA5	−0.747632216	−0.427181596	1	1
3624.23-	TAGGCGA
P1P2_00	CGGTG
HSPA5_ + _12800	GAACCGAG	91	HSPA5	−0.637327036	−0.374808011	0.85246	0.87739737
3624.23-	TAGGCGAC					0638	7
P1P2_01	GGTG
HSPA5_ + _12800	GAGCCGAG	89	HSPA5	−0.754402152	−0.422480889	1.00905	0.98899599
3624.23-	AAGGCGA					5169	9
P1P2_04	CGGTG
HSPA5_ + _12800	GAGCCGAG	92	HSPA5	−0.119163968	−0.098351611	0.15938	0.23023372
3624.23-	TAGACGAC					8487	8
P1P2_06	GGTG
HSPA5_ + _12800	GAGCCGAG	90	HSPA5	−0.75656582	−0.44349394	1.01194	1.03818597
3624.23-	TGGGCGA					9195	1
P1P2_08	CGGTG
HSPA9_ −	GGAGCTGC	131	HSPA9	−0.949975554	−0.589152811	1	1
_137911079.23-	GCGATGC
P1P2_00	GGTGG
HSPA9_ −	GGAGTTGC	133	HSPA9	−0.650398211	−0.402698763	0.68464	0.68352175
_137911079.23-	GCGATGCG					7314	4
P1P2_02	GTGG
HSPA9_ −	GGAGCTGC	135	HSPA9	−0.200474473	−0.165716053	0.21103	0.28127855
_137911079.23-	GCAATGCG					1192	7
P1P2_04	GTGG
HSPA9_ −	GGAGCTGC	132	HSPA9	−0.810949638	−0.503573798	0.85365	0.85474224
_137911079.23-	GGGATGC					3165	7
P1P2_07	GGTGG
HSPA9_ −	GGAGCTGC	134	HSPA9	−0.358229634	−0.276176791	0.37709	0.46876936
_137911079.23-	TCGATGCG					3529	8
P1P2_08	GTGG
HSPE1_ + _19836	GGAGACTC	9	HSPE1	−0.701840637	−0.567192606	1	1
5089.23-	GCAGTCCG
P1P2_00	GCCC
HSPE1_ + _19836	GGAGACAC	65	HSPE1	−0.615974016	−0.483391078	0.87765	0.85225207
5089.23-	GCAGTCCG					5102	9
P1P2_01	GCCC
HSPE1_ + _19836	GGAGACTG	67	HSPE1	−0.436529138	−0.356453563	0.62197	0.62845241
5089.23-	GCAGTCCG					7575	5
P1P2_02	GCCC
HSPE1_ + _19836	GGTGACTC	66	HSPE1	−0.642742797	−0.46501262	0.91579	0.81984958
5089.23-	GCAGTCCG					5927
P1P2_03	GCCC
HSPE1_ + _19836	GGAGACTC	68	HSPE1	−0.208819998	−0.196531939	0.29753	0.34649947
5089.23-	GCAGTCCT					1928	3
P1P2_14	GCCC
MTOR_ + _11322	GGGCAGGG	146	MTOR	−0.687219844	−0.561792171	1	1
547.23-P1P2_00	GGCCTGA
	AGCGG
MTOR_ + _11322	GGGCAGGG	148	MTOR	−0.431253329	−0.344754014	0.62753	0.61366824
547.23-P1P2_05	GGCTTGA					3289	2
	AGCGG
MTOR_ + _11322	GGGCAGGG	149	MTOR	−0.393307519	−0.298854973	0.57231	0.53196713
547.23-P1P2_06	GGGCTGA					6882	8
	AGCGG
MTOR_ + _11322	GGGCAGGG	147	MTOR	−0.58933856	−0.479851388	0.85756	0.85414395
547.23-P1P2_07	GGTCTGA					9183	7
	AGCGG
MTOR_ + _11322	GGGCAGGG	150	MTOR	−0.304808003	−0.232661121	0.44353	0.41414090
547.23-P1P2_10	GGCCTGA					7837	9
	AGCAG
POLR1D_ + _2819	GGGAAGCA	11	POLR1D	−0.75939328	−0.621320058	1	1
6016.23-P1_00	AGGACCG
	ACCGA
POLR1D_ + _2819	GGGAAGCC	76	POLR1D	−0.694457525	−0.54164837	0.91448	0.87177029
6016.23-P1_01	AGGACCG					9952	4
	ACCGA
POLR1D_ + _2819	GGTAAGCA	75	POLR1D	−0.847116707	−0.683333455	1.11551	1.09980910
6016.23-P1_03	AGGACCG					7781	2
	ACCGA
POLR1D_ + _2819	GGGAAGCA	77	POLR1D	−0.301916652	−0.242974878	0.39757	0.39106234
6016.23-P1_07	AGGAGCG					6144	4
	ACCGA
POLR1D_ + _2819	GGGAAGCA	74	POLR1D	−0.808813476	−0.631725462	1.06507	1.01674725
6016.23-P1_08	GGGACCG					8526	2
	ACCGA
POLR2H_ + _1840	GGGGCCAC	28	POLR2H	−1.149173044	−0.639125666	1	1
81251.23-	GAGAGCA
P1P2_00	GCAGA
POLR2H_ + _1840	GGGGCCAC	118	POLR2H	−0.189410601	−0.142550442	0.16482	0.22303977
81251.23-	GAGTGCA					3394	1
P1P2_07	GCAGA
POLR2H_ + _1840	GGGGCCAC	116	POLR2H	−0.52081984	−0.333960225	0.45321	0.5225267
81251.23-	GCGAGCA					2719
P1P2_08	GCAGA
POLR2H_ + _1840	GGGGCCAC	115	POLR2H	−0.996608089	−0.546680283	0.86723	0.85535648
81251.23-	GAGAGCA					9354	5
P1P2_11	GCGGA
POLR2H_ + _1840	GGGGCCAC	117	POLR2H	−0.351637117	−0.229753975	0.30599	0.35948169
81251.23-	GAGAGCA					1442	1
P1P2_12	GGAGA
RAN_ + _1313564	GGCGGTCG	69	RAN	−0.197388026	−0.16148153	1	1
38.23-P1P2_00	CTGCGCTT
	AGGG
RAN_ + _1313564	GGCGGCCG	70	RAN	−0.231594252	−0.204134533	1.17329	1.26413548
38.23-P1P2_02	CTGCGCTT					4328	5
	AGGG
RAN_ + _1313564	GGGGGTCG	71	RAN	−0.21619271	−0.196985686	1.09526	1.21986511
38.23-P1P2_03	CTGCGCTT					7598	9
	AGGG
RAN_ + _1313564	GGCGGTCG	72	RAN	−0.08590181	−0.087210569	0.43519	0.54006528
38.23-P1P2_04	CGGCGCTT					2609	5
	AGGG
RAN_ + _1313564	GGCGGTCG	73	RAN	−0.046548562	−0.034259141	0.23582	0.21215516
38.23-P1P2_12	CTGCGCTT					2621	9
	AGGT
RPL9_ + _394604	GGATGTTT	5	RPL9	−1.113115402	−0.669876545	1	1
83.23-P1P2_00	CTGTGCTC
	GTGG
RPL9_ + _394604	GGATGATT	51	RPL9	−0.852800183	−0.498432114	0.76613	0.74406563
83.23-P1P2_01	CTGTGCTC					8158	1
	GTGG
RPL9_ + _394604	GGATGTTT	53	RPL9	−0.417072624	−0.294201392	0.37468	0.43918748
83.23-P1P2_04	CAGTGCTC					9473	1
	GTGG
RPL9_ + _394604	GGATGTTT	52	RPL9	−0.607331126	−0.384814478	0.54561	0.57445581
83.23-P1P2_05	CGGTGCTC					3802	8
	GTGG
RPL9_ + _394604	GGATGTTT	54	RPL9	−0.292421202	−0.20731749	0.26270	0.30948611
83.23-P1P2_07	CTGCGCTC					5198	7
	GTGG
RPS14_ + _14982	GAGGCCCG	45	RPS14	−0.790819103	−0.499486864	1	1
9238.23-	GGCGCGA
P1P2_00	CAATC
RPS14_ + _14982	GAGACCCG	46	RPS14	−0.754840524	−0.480690282	0.95450	0.96236821
9238.23-	GGCGCGA					4666	5
P1P2_01	CAATC
RPS14_ + _14982	GAGGCCCT	47	RPS14	−0.584450961	−0.38292411	0.73904	0.76663499
9238.23-	GGCGCGAC					5072	6
P1P2_02	AATC
RPS14_ + _14982	GAGGCCCG	48	RPS14	−0.302459804	−0.195757634	0.38246	0.39191748
9238.23-	CGCGCGA					3957	2
P1P2_04	CAATC
RPS14_ + _14982	GAGGCCCG	50	RPS14	0.027378614	−0.000610864	−0.03462	0.00122298
9238.23-	GGCTCGAC					0577	3
P1P2_08	AATC
RPS14_ + _14982	GAGGCCCG	49	RPS14	−0.211938981	−0.155918093	0.26799	0.31215654
9238.23-	GGCGCGA					9319	4
P1P2_13	CAGTC
RPS15_ −	GACCAAAG	60	RPS15	−0.621219313	−0.375985289	1	1
_1438413.23-	CGATCTCT
P1P2_00	TCTG
RPS15_ −	GACCAAAC	64	RPS15	0.006615792	0.001422135	−0.01064	−0.00378242
_1438413.23-	CGATCTCT					9689	2
P1P2_01	TCTG
RPS15_ −	GACCAAGG	62	RPS15	−0.492192054	−0.314547174	0.79229	0.83659436
_1438413.23-	CGATCTCT					9988	5
P1P2_02	TCTG
RPS15_ −	GACCAAAG	61	RPS15	−0.522078249	−0.307411328	0.84040	0.81761530
_1438413.23-	CGGTCTCT					8916	7
P1P2_07	TCTG
RPS15_ −	GACCAAAG	63	RPS15	0.031097436	0.011728108	0.05005	0.03119299
_1438413.23-	CGATCTCT					8707	6
P1P2_12	TGTG
RPS18_ + _33239	GCTGCGAT	40	RPS18	−0.81693013	−0.502954192	1	1
917.23-P1P2_00	GCCGCTGG
	ATCA
RPS18_ + _33239	GCTGCAAT	41	RPS18	−0.559807511	−0.377943894	0.68525	0.75144794
917.23-P1P2_01	GCCGCTGG					7516	5
	ATCA
RPS18_ + _33239	GCTGGGAT	42	RPS18	−0.489757084	−0.319123483	0.59950	0.63449810
917.23-P1P2_02	GCCGCTGG					9145	9
	ATCA
RPS18_ + _33239	GCTGCGAT	44	RPS18	−0.086970673	−0.080675056	0.10646	0.16040239
917.23-P1P2_04	CCCGCTGG					0357	4
	ATCA
RPS18_ + _33239	GCTGCGAT	43	RPS18	−0.222819542	−0.163692215	0.27275	0.32546147
917.23-P1P2_08	TCCGCTGG					2263	9
	ATCA
SEC61A1_ −	GGCACTGA	83	SEC61A1	−0.920031125	−0.562939966	1	1
_127771295.23-	CGTGTCTC
P1_00	TCGG
SEC61A1_ −	GGCACTGT	85	SEC61A1	−0.419127675	−0.291833272	0.45555	0.51840922
_127771295.23-	CGTGTCTC					8148	5
P1_01	TCGG
SEC61A1_ −	GGCGCTGA	84	SEC61A1	−0.645088499	−0.405507482	0.70115	0.72033877
_127771295.23-	CGTGTCTC					943
P1_02	TCGG
SEC61A1_ −	GGTACTGA	86	SEC61A1	−0.503132322	−0.341926825	0.54686	0.60739483
_127771295.23-	CGTGTCTC					4458
P1_03	TCGG
SEC61A1_ −	GGCACTGA	87	SEC61A1	−0.153949391	−0.115339075	0.16733	0.20488698
_127771295.23-	AGTGTCTC					0633	9
P1_04	TCGG
TUBB_ + _306881	GCGGCAGG	23	TUBB	−0.897046625	−0.699904772	1	1
26.23-P1_00	AAGGTTCT
	GAGA
TUBB_ + _306881	GCAGCAGG	106	TUBB	−0.92598755	−0.611949447	1.03226	0.87433244
26.23-P1_01	AAGGTTCT					2454
	GAGA
TUBB_ + _306881	GCGGCAGC	108	TUBB	−0.692568681	−0.495872796	0.77205	0.70848609
26.23-P1_03	AAGGTTCT					4274	1
	GAGA
TUBB_ + _306881	GCGGCAGG	107	TUBB	−0.730802408	−0.554446084	0.81467	0.79217360
26.23-P1_06	ACGGTTCT					6058	1
	GAGA
TUBB_ + _306881	GCGGCAGG	109	TUBB	−0.197799924	−0.145078576	0.22050	0.20728330
26.23-P1_10	AAGGTTC					1274	7
	AGAGA

TABLE 5
Oligonucleotide sequences used in this study.
			SEQ
			ID
Experiment	Oligo ID	Sequence	NO:	Notes
Constant region	constant_	TAAGCTGGAAACAGCATAGCAAGCTCAAATAAGACTAGTTCG	171
variants	region_1_fw	TTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTTC
Constant region	constant_	TCGAGAAAAAAAGCACCGACTCGGTGCCACTTTTTCAAGTTG	172
variants	region_1_rv	ATAACGAACTAGTCTTATTTGAGCTTGCTATGCTGTTTCCAGC
Constant region	constant_	TAAGCTGGAAACAGCATAGCAAGTTCAAATAAGGCTAGTCCG	173
variants	region_2_fw	TTATGTACTTCAAAAAGTGGCACCGAGTCGGTGCTTTTTTTC
Constant region	constant_	TCGAGAAAAAAAGCACCGACTCGGTGCCACTTTTTGAAGTAC	174
variants	region_2_rv	ATAACGGACTAGCCTTATTTGAACTTGCTATGCTGTTTCCAGC
Constant region	constant_	TAAGCTGGAAACAGCATAGCGAGTTCAAATAAGGCTCGTCCG	175
variants	region_3_fw	TTATCCACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTTC
Constant region	constant_	TCGAGAAAAAAAGCACCGACTCGGTGCCACTTTTTCAAGTGG	176
variants	region_3_rv	ATAACGGACGAGCCTTATTTGAACTCGCTATGCTGTTTCCAGC
Constant region	constant_	TAAGCTGGAAACAGCATAGCAAGTTCAAATAAAGTTAATCTG	177
variants	region_4_fw	TTATCAACTCGAAAGAGIGGCACCGAGTCGGIGCTTTTTTTC
Constant region	constant_	TCGAGAAAAAAAGCACCGACTCGGTGCCACTCTTTCGAGTTG	178
variants	region_4_rv	ATAACAGATTAACTTTATTTGAACTTGCTATGCTGTTTCCAGC
Constant region	constant_	TAAGCTGGAAACAGCATAGCAAGTTCAAATAAGGCTAGCCCG	179
variants	region_5_fw	TTATGAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTTC
Constant region	constant_	TCGAGAAAAAAAGCACCGACTCGGTGCCACTTTTTCAAGTTC	180
variants	region_5_rv	ATAACGGGCTAGCCTTATTTGAACTTGCTATGCTGTTTCCAGC
Constant region	constant_	TAAGCTGGAAACAGCATAGCAAGTTCAAATAAGGCTAGTCCG	181
variants	region_6_fw	TTATCAACTTGAAAAAGTGGCACCGGGGCGGTGCTTTTTTTC
Constant region	constant_	TCGAGAAAAAAAGCACCGCCCCGGTGCCACTTTTTCAAGTTG	182
variants	region_6_rv	ATAACGGACTAGCCTTATTTGAACTTGCTATGCTGTTTCCAGC
Constant region	constant_	TAAGCTGGAAACAGCATAGCAAGTTCAAATATGGCTAGTCCG	183
variants	region_7_fw	TTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCTTTTTTTC
Constant region	constant_	TCGAGAAAAAAAGCACCGACTCGGTGCCACTTTTTCAAGTTG	184
variants	region_7_rv	ATAACGGACTAGCCATATTTGAACTTGCTATGCTGTTTCCAGC
Constant region	constant_	TAAGCTGGAAACAGCATAGCAAGTTCAAATAAGGATATTCCG	185
variants	region_8_fw	TTATCAAGTTGAAAAACTGGCACCGAGTCGGTGCTTTTTTTC
Constant region	constant_	TCGAGAAAAAAAGCACCGACTCGGTGCCAGTTTTTCAACTTG	186
variants	region_8_rv	ATAACGGAATATCCTTATTTGAACTTGCTATGCTGTTTCCAGC
Constant region	constant_	TAAGCTGGAAACAGCATAGCAAGTTCAAATAAGGCTAGTCCG	187
variants	region_9_fw	TTATCAACTTGAGAAAGTGGCACCGGGTCGGTGCTTTTTTTC
Constant region	constant_	TCGAGAAAAAAAGCACCGACCCGGTGCCACTTTCTCAAGTTG	188
variants	region_9_rv	ATAACGGACTAGCCTTATTTGAACTTGCTATGCTGTTTCCAGC
Constant region	constant_	TAAGCTGGAAACAGCATAGCAAGTTCAAATAAGGCTAGTCCG	189
variants	region_10_fw	TTATCAACTTGAAAAAGTGGCACCGCGTCGGTGCTTTTTTTC
Constant region	constant_	TCGAGAAAAAAAGCACCGACGCGGTGCCACTTTTTCAAGTTG	190
variants	region_10_rv	ATAACGGACTAGCCTTATTTGAACTTGCTATGCTGTTTCCAGC
DPH2 knockdown	DPH2_qPCR_fw	ACCTGGACGGAGTGTACGAG	191
(CR variants)
DPH2 knockdown	DPH2_qPCR_rv	TCTCCCAATAGCTGGTCAGG	192
(CR variants)
DPH2 knockdown	ACTB_qPCR_fw	GCTACGAGCTGCCTGACG	193
(CR variants)
DPH2 knockdown	ACTB_qPCR_rv	GGCTGGAAGAGTGCCTCA	194
(CR variants)
Illumina	oCRISPRi_seq_	GTGTGTTTTGAGACTATAAGTATCCCTTGGAGAACCACCTTGT	195
sequencing	V5	TG
primer
Illumina	oCRISPRi_seq_	CCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTAAACTTG	196
sequencing	V4_3′	CTATGCTGT
primer
Constant region	oCRISPRi_PE_	AATGATACGGCGACCACCGAGATCTACACGCACAAAAGGAA	197
sequencing	constant_	ACTCACCCT
library	region_
preparation	common_
	primer
Constant region	oCRISPRi_PE_	CAAGCAGAAGACGGCATACGAGATNNNNNNGTCTCGTGGG	198	NN
sequencing	constant_	CTCGGAGATGTGTATAAGAGACAGGCCGCCTAATGGATCCTA		NN
library	region	G		NN
preparation	_indexing_			denotes
	primer			6-
				base
				pair
				Tru
				Seq
				index
Perturb-seq	oBA503	CAAGCAGAAGACGGCATACGAGATCAGCCTCGGTCTCGTGG	199
sequencing		GCTCGGAGATGTGTATAAGAGACAGGTGTTTTGAGACTATAA
library		GTATCCCTTGGAGAACCACCTTGTTG
preparation
Perturb-seq	PCR_perturb-	AATGATACGGCGACCACCGAGATCTACAC	200
sequencing	seq_P5
library
preparation

TABLE 6
Ranking of sgRNA constant region mutations. The constant region “cr995”
corresponds to the original, un-modified sequence. Each sequence begins
with the nucleotide immediately following the targeting sequence.
		SEQ		Mean
		ID	Muta-	relative
	Sequence	NO:	tion(s)	activity
cr748	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	201	U61C,	1.14554678
	CCGTTATCAACTCGAGAGAGTGGCACCGAGTCGGTGCT		A64G,	7
			A66G
cr289	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	202	U61G,	1.10155915
	CCGTTATCAACTGGAAACAGTGGCACCGAGTCGGTGCT		A66C	3
cr622	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	203	A58G,	1.09945059
	CCGTTATCAGCTGGAAACAGCGGCACCGAGTCGGTGCT		U61G,	1
			A66C,
			U69C
cr772	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	204	U61C,	1.09851461
	CCGTTATCAACTCGAAAGAGTGGCACCGAGTCGGTGCT		A66G
cr532	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	205	U60G,	1.09257901
	CCGTTATCAACGTGAAAACGTGACTCCGAGTCGGAGTT		A67C,	7
			G71A,
			A73U,
			U83A,
			C85U
cr961	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	206	U61C,	1.08755170
	CCGTTATCAACTCGAAAGAGTGCAACCGAGTCGGTTGT		A66G,	1
			G71C,
			C72A,
			G84U,
			C85G
cr942	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	207	U55C,	1.08746952
	CCGTTACCAACTTGAACAAGTGGCACCGAGTCGGTGCT		A65C	3
cr565	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	208	U60G,	1.08457776
	CCGTTATCAACGCGAAAGCGTGGCACCGAGTCGGTGCT		U61C,	5
			A66G,
			A67C
cr925	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	209	A58U,	1.08416233
	CCGTTATCATCGAGAAATCGAGGCACCGAGTCGGTGCT		U60G,	9
			U61A,
			A66U,
			A67C,
			U69A
cr234	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	210	U61C	1.07543830
	CCGTTATCAACTCGAAAAAGTGGCACCGAGTCGGTGCT			9
cr820	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	211	U61G,	1.07186330
	CCGTTATCAACTGGAGACAGTGGCACCGAGTCGGTGCT		A64G,	1
			A66C
cr936	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	212	G71U,	1.07182960
	CCGTTATCAACTTGAAAAAGTGTCACCGAGTCGGTGAT		C85A	8
cr333	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	213	C59G,	1.07174594
	CCGTTATCAAGGTGAAAACCTGGCACCGAGTCGGTGCT		U60G,	6
			A67C,
			G68C
cr156	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	214	U61A,	1.07174467
	CCGTTATCAACTAGAAATAGTGGCACCGAGTCGGTGCT		A66U
cr363	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	215	C59U,	1.06984825
	CCGTTATCAATTCGAAAGAATGGCACCGAGTCGGTGCT		U61C,	4
			A66G,
			G68A
cr534	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	216	C44U,	1.06923287
	CCGTTATCAACTTGAAAAAGTGACACCGAGTCGGTGTT		G47A,	5
			G71A,
			C85U
cr563	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	217	C59G,	1.06815963
	CCGTTATCAAGCTGAAAAGCTGGCACCGAGTCGGTGCT		U60C,	5
			A67G,
			G68C
cr176	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	218	A58G,	1.06722614
	CCGTTATCAGCTTGAAAAAGCGGCACCGAGTCGGTGCT		U69C	8
cr327	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	219	A63C	1.06626839
	CCGTTATCAACTTGCAAAAGTGGCACCGAGTCGGTGCT			6
cr360	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	220	C74U,	1.0659663
	CCGTTATCAACTTGAAAAAGTGGCATCGAGTCGATGCT		G82A
cr944	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	221	U61C,	1.06526905
	CCGTTATCAACTCGAAAGAGTGGTACCAAGTTGGTACT		A66G,	3
			C72U,
			G76A,
			C80U,
			G84A
cr612	GTTTAAGAGCTAAGCTGGATACAGCATAGCAAGTTTAAATAAGGCTAGT	222	A19U	1.06437074
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr116	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	223	U60G,	1.06400045
	CCGTTATCAACGTGAAAACGTGGCACCGAGTCGGTGCT		A67C	9
cr450	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	224	A64G,	1.06386665
	CCGTTATCAACTTGAGAAAGTGGCGCCGAGTCGGCGCT		A73G,	6
			U83C
cr567	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	225	A63C,	1.06027433
	CCGTTATCAACTTGCGAAAGTGGCACCGAGTCGGTGCT		A64G	5
cr275	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	226	U60C,	1.06001789
	CCGTTATCAACCTGAAAAGGTGGGACAGAGTCTGTCCT		A67G,	9
			C72G,
			C75A,
			G81U,
			G84C
cr488	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	227	C72G,	1.05952873
	CCGTTATCAACTTGAAAAAGTGGGACCGAGTCGGTCCT		G84C	1
cr617	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	228	C44U,	1.05837720
	CCGTTATCAGCGTGAAAACGCGGCACCGAGTCGGTGCT		G47A,	1
			A58G,
			U60G,
			A67C,
			U69C
cr022	GTTTAAGTGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	229	A7U	1.05753552
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr717	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	230	C44U,	1.05639699
	CCGTTATCAGCTTGAAAAAGCGGCACCGAGTCGGTGCT		G47A,	3
			A58G,
			U69C
cr919	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	231	A58C,	1.05607768
	CCGTTATCACCTGGAAACAGGGGCACCGAGTCGGTGCT		U61G,	1
			A66C,
			U69G
cr585	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	232	A73U,	1.05576943
	CCGTTATCAACTTGAAAAAGTGGCTCCGAGTCGGAGCT		U83A	9
cr394	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	233	A66C	1.05517587
	CCGTTATCAACTTGAAACAGTGGCACCGAGTCGGTGCT			4
cr477	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	234	U55C	1.05504247
	CCGTTACCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr380	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	235	A64G,	1.05472264
	CCGTTATCAACTTGAGAAAGTGGTACCGAGTCGGTACT		C72U,	6
			G84A
cr568	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	236	A58U,	1.05448905
	CCGTTATCATCGTGAAAACGAGGCAACGAGTCGTTGCT		U60G,	7
			A67C,
			U69A,
			C74A,
			G82U
cr723	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	237	U60C,	1.05376194
	CCGTTATCAACCTGAAAAGGTGGCAGCGAGTCGCTGCT		A67G,	5
			C74G,
			G82C
cr501	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	238	A64G,	1.05248281
	CCGTTATCAACTTGAGAAAGTGTCACCGAGTCGGTGAT		G71U,	9
			C85A
cr293	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	239	C72G,	1.05198607
	CCGTTATCAACTTGAAAAAGTGGGACCAAGTTGGTCCT		G76A,	8
			C80U,
			G84C
cr549	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	240	U60C,	1.05123166
	CCGTTATCAACCTGAAAAGGTGGCACCGAGTCGGTGCT		A67G	2
cr766	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	241	U60A,	1.05088127
	CCGTTATCAACATGAAAATGTGGCACCGAGTCGGTGCT		A67U	6
cr602	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	242	A73G,	1.04938568
	CCGTTATCAACTTGAAAAAGTGGCGCCGAGTCGGCGCT		U83C	9
cr282	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	243	G71C,	1.04869375
	CCGTTATCAACTTGAAAAAGTGCACCCGAGTCGGGTGT		C72A,	2
			A73C,
			U83G,
			G84U,
			C85G
cr531	GTTTAAGAGCTAAGCTGGTAACAGCATAGCAAGTTTAAATAAGGCTAGT	244	A18U	1.04844440
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr814	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	245	C72G,	1.04841612
	CCGTTATCAACTTGAAAAAGTGGGTCCGAGTCGGACCT		A73U,	7
			U83A,
			G84C
cr101	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	246	A73G	1.04809498
	CCGTTATCAACTTGAAAAAGTGGCGCCGAGTCGGTGCT			2
cr183	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	247	C44U,	1.04725017
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		G47A,	3
			A64G
cr240	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	248	U55A	1.04618338
	CCGTTAACAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr171	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	249	U60C,	1.04421806
	CCGTTATCAACCTGAGAAGGTGGCACCGAGTCGGTGCT		A64G,	2
			A67G
cr809	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	250	U60G,	1.04246214
	CCGTTATCAACGTGAAAACGTGGAACCGAGTCGGTTCT		A67C,	6
			C72A,
			G84U
cr356	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	251	A58C,	1.04242407
	CCGTTATCACCTTGAAAAAGGGACACCGAGTCGGTGTT		U69G,	7
			G71A,
			C85U
cr687	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	252	C75U	1.04175646
	CCGTTATCAACTTGAAAAAGTGGCACTGAGTCGGTGCT			8
cr756	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	253	C44U,	1.04120038
	CCGTTATCAACTTGAAAAAGTGGCAACGAGTCGTTGCT		G47A,	3
			C74A,
			G82U
cr623	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	254	A58C,	1.04119280
	CCGTTATCACTGTGAAAACAGGGCACCGAGTCGGTGCT		C59U,	2
			U60G,
			A67C,
			G68A,
			U69G
cr685	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	255	A58G	1.04107706
	CCGTTATCAGCTTGAAAAAGTGGCACCGAGTCGGTGCT
cr892	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	256	U60G,	1.04106180
	CCGTTATCAACGTGAAAACGTGGCGACGAGTCGTCGCT		A67C,	4
			A73G,
			C74A,
			G82U,
			U83C
cr379	GTTTAAGAGCTAAGCTGGAAACAGCCTAGCAAGTTTAAATAAGGCTAGT	257	A25C	1.04104020
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr870	GTTTAAGAGCTAAGCTGGAAACAGCAAAGCAAGTTTAAATAAGGCTAGT	258	U26A	1.04045280
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr487	GTTTAAGAGCTAAGCTGGAAACAGCATAGCACGTTTAAATAAGGCTAGT	259	A31C	1.03900164
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr832	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	260	A58G,	1.03887850
	CCGTTATCAGCTTGAAAAAGCGGTGCCGAGTCGGCACT		U69C,	1
			C72U,
			A73G,
			U83C,
			G84A
cr476	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	261	G71C,	1.03871291
	CCGTTATCAACTTGAAAAAGTGCGACCGAGTCGGTCGT		C72G,	4
			G84C,
			C85G
cr691	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	262	C72G,	1.03826758
	CCGTTATCAACTTGAAAAAGTGGGCCCGAGTCGGGCCT		A73C,	3
			U83G,
			G84C
cr821	GTTTAAGAGCTAAGCTGGAAACAGCGTAGCAAGTTTAAATAAGGCTAGT	263	A25G	1.03777650
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr727	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	264	G71U,	1.03722526
	CCGTTATCAACTTGAAAAAGTGTCGCCGAGTCGGCGAT		A73G,	3
			U83C,
			C85A
cr483	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	265	U61A,	1.03710417
	CCGTTATCAACTAGAAATAGTGGCGTCGAGTCGACGCT		A66U,	5
			A73G,
			C74U,
			G82A,
			U83C
cr776	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	266	A58U,	1.03660795
	CCGTTATCATCTAGAAATAGAGGCACCGAGTCGGTGCT		U61A,	2
			A66U,
			U69A
cr335	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	267	G71A,	1.03623132
	CCGTTATCAACTTGAAAAAGTGACGCCGAGTCGGCGTT		A73G,	9
			U83C,
			C85U
cr593	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	268	A58C,	1.03547944
	CCGTTATCACCTTGAAAAAGGGGCACCGAGTCGGTGCT		U69G	2
cr616	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	269	C59G,	1.03498308
	CCGTTATCAAGATGAAAATCTGGCACCGAGTCGGTGCT		U60A,	7
			A67U,
			G68C
cr320	GTTTAAGAGCTAAGCTGGAAACAGCATAGCATGTTTAAATAAGGCTAGT	270	A31U,	1.03491349
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		A64G	3
cr410	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	271	A58G,	1.03439489
	CCGTTATCAGCTTGAGAAAGCGGCACCGAGTCGGTGCT		A64G,	2
			U69C
cr492	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	272	A54C	1.03371058
	CCGTTCTCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr951	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	273	A64G,	1.03356538
	CCGTTATCAACTTGAGAAAGTGGCTCCGAGTCGGAGCT		A73U,	4
			U83A
cr964	GTTTAAGAGCTAAGCTGGAAACAGCATAGCTAGTTTAAATAAGGCTAGT	274	A30U,	1.03345180
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		A64G	7
cr263	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	275	A65C	1.03337417
	CCGTTATCAACTTGAACAAGTGGCACCGAGTCGGTGCT			4
cr214	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	276	A64G,	1.03321224
	CCGTTATCAACTTGAGAAAGTGGCAACGAGTCGTTGCT		C74A,	2
			G82U
cr628	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	277	C72U,	1.03313967
	CCGTTATCAACTTGAAAAAGTGGTACCAAGTTGGTACT		G76A,	6
			C80U,
			G84A
cr704	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	278	U69C	1.03273911
	CCGTTATCAACTTGAAAAAGCGGCACCGAGTCGGTGCT
cr524	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	279	A63U	1.03249928
	CCGTTATCAACTTGTAAAAGTGGCACCGAGTCGGTGCT
cr054	GTTTAACCGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	280	G6C,	1.03244356
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7C	5
cr455	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	281	C59U,	1.03107200
	CCGTTATCAATTGGAAACAATGGCACCGAGTCGGTGCT		U61G,	1
			A66C,
			G68A
cr352	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	282	A58C	1.03104846
	CCGTTATCACCTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr902	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	283	G71A,	1.0308018
	CCGTTATCAACTTGAAAAAGTGACACCGAGTCGGTGTT		C85U
cr109	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	284	U60A,	1.03065158
	CCGTTATCAACAAGAAATTGTGGCACCGAGTCGGTGCT		U61A,	4
			A66U,
			A67U
cr070	GTTTAACGGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	285	G6C,	1.02998344
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7G	2
cr271	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	286	C72U,	1.02969848
	CCGTTATCAACTTGAAAAAGTGGTCCCGAGTCGGGACT		A73C,	2
			U83G,
			G84A
cr129	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	287	U60G,	1.02876213
	CCGTTATCAACGTGAGAACGTGGCACCGAGTCGGTGCT		A64G,	2
			A67C
cr497	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	288	A64G,	1.02842968
	CCGTTATCAACTTGAGAAAGTGGAACCGAGTCGGTTCT		C72A,	3
			G84U
cr828	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAGGTTTAAATAAGGCTAGT	289	A31G	1.02830507
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr235	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	290	U60A,	1.02736287
	CCGTTATCAACATGAGAATGTGGCACCGAGTCGGTGCT		A64G,	9
			A67U
cr882	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	291	G62C	1.02729278
	CCGTTATCAACTTCAAAAAGTGGCACCGAGTCGGTGCT			6
cr515	GTTTAAGAGCTAAGCTGTAAACAGCATAGCAAGTTTAAATAAGGCTAGT	292	G17U	1.02718842
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr434	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	293	G62C,	1.02709347
	CCGTTATCAACTTCATAAAGTGGCACCGAGTCGGTGCT		A64U	9
cr797	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	294	U53G	1.02698062
	CCGTGATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			9
cr884	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	295	A58C,	1.02683106
	CCGTTATCACCTAGAAATAGGGTCACCGAGTCGGTGAT		U61A,	1
			A66U,
			U69G,
			G71U,
			C85A
cr610	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	296	C59G,	1.02674677
	CCGTTATCAAGATGAAAATCTGACACCGAGTCGGTGTT		U60A,	7
			A67U,
			G68C,
			G71A,
			C85U
cr118	GTTTAAGAGCTAAGCTGGAAACGGCATAGCAAGTTTAAATAAGGCTAGT	297	A22G	1.02533901
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr412	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	298	C59G,	1.02512486
	CCGTTATCAAGTTGAAAAACTGGCACCGAGTCGGTGCT		G68C
cr929	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	299	G71U	1.02508935
	CCGTTATCAACTTGAAAAAGTGTCACCGAGTCGGTGCT			5
cr858	GTTTAAGAGCTAAGCTGGAGACAGCATAGCAAGTTTAAATAAGGCTAGT	300	A19G	1.02503584
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr896	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	301	C72A,	1.02474392
	CCGTTATCAACTTGAAAAAGTGGACCCGAGTCGGGTCT		A73C,	2
			U83G,
			G84U
cr334	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	302	C59G,	1.02464779
	CCGTTATCAAGTGGAAACACTGGCACCAAGTTGGTGCT		U61G,	9
			A66C,
			G68C,
			G76A,
			C80U
cr934	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTTGT	303	A46U,	1.02413232
	CCGTTACCAACTTGAAAAAGTGGCACCGATTCGGTGCT		U55C,	6
			G78U
cr444	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	304	A64C	1.02269899
	CCGTTATCAACTTGACAAAGTGGCACCGAGTCGGTGCT			7
cr140	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	305	C59U,	1.02259341
	CCGTTATCAATCTGAAAAGATGGCACCGAGTCGGTGCT		U60C,	3
			A67G,
			G68A
cr600	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	306	C59G,	1.02124331
	CCGTTATCAAGTTGAGAAACTGGCACCGAGTCGGTGCT		A64G,	6
			G68C
cr710	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	307	C74G,	1.02097870
	CCGTTATCAACTTGAAAAAGTGGCAGCGAGTCGCTGCT		G82C	3
cr345	GTTTAAGAGCTAAGCTGGAACCAGCATAGCAAGTTTAAATAAGGCTAGT	308	A20C	1.02071344
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr978	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	309	C72A,	1.02058245
	CCGTTATCAACTTGAAAAAGTGGAAACGAGTCGTTTCT		C74A,	8
			G82U,
			G84U
cr561	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTTGT	310	A46U	1.02057043
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr801	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	311	A58G,	1.02042822
	CCGTTATCAGCTTGAAAAAGCGGAAGCGAGTCGCTTCT		U69C,	2
			C72A,
			C74G,
			G82C,
			G84U
cr948	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	312	A66G	1.02039913
	CCGTTATCAACTTGAAAGAGTGGCACCGAGTCGGTGCT
cr888	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	313	G62U,	1.02030698
	CCGTTATCAACTTTACAAAGTGGCACCGAGTCGGTGCT		A64C	5
cr020	GTTTAATAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	314	G6U	1.02026526
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr323	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAGGTTTAAATAAGGCTAGT	315	A31G,	1.02021061
	CCGTTATCAACTTGACAAAGTGGCACCGAGTCGGTGCT		A64C	3
cr019	GTTTAACAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	316	G6C	1.01913954
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr408	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	317	A64G,	1.0182334
	CCGTTATCAACTTGAGAAAGTGACACCGAGTCGGTGTT		G71A,
			C85U
cr730	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	318	C72G,	1.01798666
	CCGTTATCAACTTGAAAAAGTGGGGGCGAGTCGCCCCT		A73G,	1
			C74G,
			G82C,
			U83C,
			G84C
cr603	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	319	A64G,	1.01766316
	CCGTTATCAACTTGAGCAAGTGGCACCGAGTCGGTGCT		A65C	5
cr557	GTTTAAGAGCTAAGCTGGCAACAGCATAGCAAGTTTAAATAAGGCTAGT	320	A18C	1.01765734
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr283	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	321	G71A	1 .01760152
	CCGTTATCAACTTGAAAAAGTGACACCGAGTCGGTGCT			6
cr464	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	322	A58C,	1.01749147
	CCGTTATCACCTTGAAAAAGGGGCACCAAGTTGGTGCT		U69G,	7
			G76A,
			C80U
cr592	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	323	C44U,	1.0173258
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G47A
cr971	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	324	G62A,	1.01727269
	CCGTTATCAACTTAATAAAGTGGCACCGAGTCGGTGCT		A64U	6
cr366	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	325	A58G,	1.01699031
	CCGTTATCAGCTTGAAAAAGCGGCACTGAGTCAGTGCT		U69C,	9
			C75U,
			G81A
cr018	GTTTAAAAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	326	G6A	1.01664409
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			9
cr701	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	327	G78C	1.01640096
	CCGTTATCAACTTGAAAAAGTGGCACCGACTCGGTGCT			8
cr354	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	328	A58G,	1.01627913
	CCGTTATCAGCTTGAAAAAGCGATACCGAGTCGGTATT		U69C,	7
			G71A,
			C72U,
			G84A,
			C85U
cr494	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	329	A66U	1.01625224
	CCGTTATCAACTTGAAATAGTGGCACCGAGTCGGTGCT			6
cr302	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	330	U60G,	1.01620407
	CCGTTATCAACGTGAAAACGTGGTCCCGAGTCGGGACT		A67C,	2
			C72U,
			A73C,
			U83G,
			G84A
cr113	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	331	A54U	1.01605369
	CCGTTTTCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr941	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	332	C59A,	1.01572195
	CCGTTATCAAATGGAAACATTGACACCGAGTCGGTGTT		U61G,	9
			A66C,
			G68U,
			G71A,
			C85U
cr655	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	333	U53C	1.01557092
	CCGTCATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr619	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	334	A63G	1.01473992
	CCGTTATCAACTTGGAAAAGTGGCACCGAGTCGGTGCT			7
cr121	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	335	A58U,	1.01388795
	CCGTTATCATCATGAAAATGAGGCACCGAGTCGGTGCT		U60A,
			A67U,
			U69A
cr898	GTTTAAGAGCTAAGCTGGAAACAGCATAGCACGTTTAAATAAGGCTAGT	336	A31C,	1.01381218
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		A64G	5
cr239	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	337	U52C,	1.01366903
	CCGCTATCAACTTGTAAAAGTGGCACCGAGTCGGTGCT		A63U	5
cr980	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	338	U52C	1.01366157
	CCGCTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr428	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	339	A63C,	1.01283479
	CCGTTATCAACTTGCATAAGTGGCACCGAGTCGGTGCT		A65U	7
cr433	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	340	A65U	1.01239334
	CCGTTATCAACTTGAATAAGTGGCACCGAGTCGGTGCT			7
cr377	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	341	U55G	1.01216392
	CCGTTAGCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr423	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	342	A58G,	1.01174760
	CCGTTATCAGCTTGAAAAAGCGGTACCGAGTCGGTACT		U69C,	6
			C72U,
			G84A
cr690	GTTTAAGAGCTAAGCTGGAAACAGCAGAGCAAGTTTAAATAAGGCTAGT	343	U26G	1.01099309
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr037	GTTTAATCGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	344	G6U,	1.01037945
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7C	9
cr642	GTTTAAGAGCTAAGCTGGAAACAGCATAGCATGTTTAAATAAGGCTAGT	345	A31U	1.00836434
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr715	GTTTAAGAGCTAAGCTGGAAACAGCATAGCGAGTTTAAATAAGGCTAGT	346	A30G	1.00799007
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			9
cr632	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	347	A63C,	1.00755206
	CCGTTATCAACTTGCAGAAGTGGCACCGAGTCGGTGCT		A65G	1
cr348	GTTTAAGAGCTAAGCTGGAAGCAGCATAGCAAGTTTAAATAAGGCTAGT	348	A20G	1.00755146
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr510	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	349	A58U,	1.00746555
	CCGTTATCATGTTGAAAAACAGGCACCGAGTCGGTGCT		C59G,	1
			G68C,
			U69A
cr771	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	350	A64G,	1.00740692
	CCGTTATCAACTTGAGAAAGTGCCACCGAGTCGGTGGT		G71C,	7
			C85G
cr606	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	351	C59G,	1.00738312
	CCGTTATCAAGTTGAAAAACTGGCCTCGAGTCGAGGCT		G68C,	9
			A73C,
			C74U,
			G82A,
			U83G
cr144	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	352	A58U,	1.00728202
	CCGTTATCATGTAGAAATACAGGCACCGAGTCGGTGCT		C59G,	5
			U61A,
			A66U,
			G68C,
			U69A
cr559	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	353	C72U,	1.00721348
	CCGTTATCAACTTGAAAAAGTGGTACCGAGTCGGTACT		G84A	5
cr365	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	354	U53A	1.00681907
	CCGTAATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr232	GTTTAAGAGCTAAGCTGGAAACAGCACAGCAAGTTTAAATAAGGCTAGT	355	U26C	1.00634227
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr139	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	356	C74A,	1.00580446
	CCGTTATCAACTTGAAAAAGTGGCAACGAGTCGTTGCT		G82U	5
cr672	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	357	C74U	1.00454783
	CCGTTATCAACTTGAAAAAGTGGCATCGAGTCGGTGCT			9
cr656	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	358	A73C,	1.00440038
	CCGTTATCAACTTGAAAAAGTGGCCCCGAGTCGGGGCT		U83G	3
cr393	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	359	A64C,	1.00402537
	CCGTTATCAACTTGACAAAGTGGCACCGAATCGGTGCT		G78A	5
cr968	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	360	C72A,	1.00367609
	CCGTTATCAACTTGAAAAAGTGGAACCGAGTCGGTTCT		G84U	2
cr155	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	361	C59U,	1.00316492
	CCGTTATCAATTGGAAACAATGGCATCGAGTCGATGCT		U61G,	2
			A66C,
			G68A,
			C74U,
			G82A
cr901	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	362	A65G	1.00302100
	CCGTTATCAACTTGAAGAAGTGGCACCGAGTCGGTGCT			4
cr945	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	363	A65G,	1.00236082
	CCGTTATCAACTTGAAGAAGTGGCACCGATTCGGTGCT		G78U
cr128	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	364	A58G,	1.00194867
	CCGTTATCAGCTTGAAAAAGCGGCACAGAGTCTGTGCT		U69C,	8
			C75A,
			G81U
cr851	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	365	A58G,	1.00191002
	CCGTTATCAGCTTGAAAAAGCGGCACCAAGTTGGTGCT		U69C,
			G76A,
			C80U
cr923	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	366	G71A,	1.00154036
	CCGTTATCAACTTGAAAAAGTGACCCCGAGTCGGGGTT		A73C,	9
			U83G,
			C85U
cr722	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	367	A64G,	1.00111557
	CCGTTATCAACTTGAGAAAGTGGCCCCGAGTCGGGGCT		A73C,	9
			U83G
cr995	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	368		1
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr392	GTTTAAGAGCTAAGCTGGAAACAGCATAGCATGTTTAAATAAGGCTAGT	369	A31U,	0.99994239
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U	9
cr947	GTTTAAGAGCTAAGCTGGGAACAGCATAGCAAGTTTAAATAAGGCTAGT	370	A18G	0.99972128
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr172	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTTGT	371	A46U,	0.99962027
	CCGTTATCAACTTGTAAAAGTGGCACCGAGTCGGTGCT		A63U	6
cr489	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	372	A64G,	0.99912564
	CCGTTATCAACTTGAGAAAGTGGGACCGAGTCGGTCCT		C72G,	1
			G84C
cr195	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	373	A63C,	0.99859821
	CCGTTATCAACTTGCAAAAGTGGCACCGATTCGGTGCT		G78U	3
cr956	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCAAGT	374	U45A	0.99826701
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr269	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	375	G71C,	0.99791063
	CCGTTATCAACTTGAAAAAGTGCAACCGAGTCGGTTGT		C72A,	1
			G84U,
			C85G
cr713	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	376	U61A,	0.99789243
	CCGTTATCAACTAGAGATAGTGGCACCGAGTCGGTGCT		A64G,	3
			A66U
cr152	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	377	G71A,	0.99708807
	CCGTTATCAACTTGAAAAAGTGAAACCGAGTCGGTTTT		C72A,	7
			G84U,
			C85U
cr774	GTTTAAGAGCTAAGCTGGACACAGCATAGCAAGTTTAAATAAGGCTAGT	378	A19C	0.99676639
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr666	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	379	C44U,	0.99598525
	CCGTTATCAACTTGAAAAAGTGGAGCCGAGTCGGCTCT		G47A,	1
			C72A,
			A73G,
			U83C,
			G84U
cr698	GTTTAAGAGCTAAGCTGGAAACAGCATAACAAGTTTAAATAAGGCTAGT	380	G28A,	0.99559758
	CCGTAATCAACTTGTAAAAGTGGCACCGAGTCGGTGCT		U53A,	4
			A63U
cr789	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	381	G71C,	0.99547256
	CCGTTATCAACTTGAAAAAGTGCCACCGAGTCGGTGGT		C85G	7
cr932	GTTTAAGAGCTAAGCTGGAAACAGCATAGTAAGTTTAAATAAGGCTAGT	382	C29U	0.99466604
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr893	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	383	C85U	0.99323133
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGTT			9
cr446	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	384	C44U,	0.99264076
	CCGTTATCATCTTGAAAAAGAGGGACCGAGTCGGTCCT		G47A,	1
			A58U,
			U69A,
			C72G,
			G84C
cr145	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	385	A64U	0.99249969
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT			2
cr636	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	386	A63G,	0.99198315
	CCGTTATCAACTTGGTAAAGTGGCACCGAGTCGGTGCT		A64U	4
cr839	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	387	A64G	0.99180202
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT			3
cr023	GTTTAAGGGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	388	A7G	0.99173308
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr604	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCAAGT	389	U45A,	0.99123350
	CCGTTATCAACTTGACAAAGTGGCACCGAGTCGGTGCT		A64C	4
cr653	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	390	C75A,	0.99069367
	CCGTTATCAACTTGAAAAAGTGGCACAGAGTCTGTGCT		G81U	2
cr321	GTTTAAGAGCTAAGCTGGAAACAGCATAGCCAGTTTAAATAAGGCTAGT	391	A30C	0.99062396
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr670	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	392	U69G	0.99028041
	CCGTTATCAACTTGAAAAAGGGGCACCGAGTCGGTGCT			1
cr478	GTTTAAGAGCTAAGCTGGAAACAGCATGGCAAGTTTAAATAAGGCTAGT	393	A27G	0.98981927
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr609	GTTTAAGAGCTAAGCTGGAAACAGCATAGCATGTTTAAATAAGGCTAGT	394	A31U,	0.98947882
	CCGTTATCAACTTAAAAAAGTGGCACCGAATCGGTGCT		G62A,	6
			G78A
cr353	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	395	U61A	0.98845143
	CCGTTATCAACTAGAAAAAGTGGCACCGAGTCGGTGCT			2
cr669	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	396	G78A	0.98814561
	CCGTTATCAACTTGAAAAAGTGGCACCGAATCGGTGCT			2
cr973	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	397	G62A	0.98731444
	CCGTTATCAACTTAAAAAAGTGGCACCGAGTCGGTGCT			8
cr671	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	398	A67G	0.98643011
	CCGTTATCAACTTGAAAAGGTGGCACCGAGTCGGTGCT			9
cr258	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGA	399	U48A	0.98627815
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr340	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	400	U61G	0.98525699
	CCGTTATCAACTGGAAAAAGTGGCACCGAGTCGGTGCT			8
cr578	GTTTAAGAGCTAAGCTGGAAACAGCATAGCGAGTTTAAATAAGGCTAGT	401	A30G,	0.98520091
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U
cr855	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCCAGT	402	U45C,	0.98491712
	CCGTTCTCAACTTGACAAAGTGGCACCGAGTCGGTGCT		A54C,	2
			A64C
cr346	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	403	A58C,	0.98472556
	CCGTTATCACATTGAAAAATGGTCACCGAGTCGGTGAT		C59A,
			G68U,
			U69G,
			G71U,
			C85A
cr368	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	404	A58C,	0.98362079
	CCGTTATCACATTGAAAAATGGGCACCGAGTCGGTGCT		C59A,
			G68U,
			U69G
cr251	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	405	U53G,	0.98359884
	CCGTGATCAACTTGTAAAAGTGGCACCGACTCGGTGCT		A63U,	4
			G78C
cr270	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	406	A58U,	0.98350452
	CCGTTATCATCCTGAAAAGGAGGCACTGAGTCAGTGCT		U60C,
			A67G,
			U69A,
			C75U,
			G81A
cr970	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	407	A63U,	0.98255639
	CCGTTATCAACTTGTTAAAGTGGCACCGAGTCGGTGCT		A64U	1
cr843	GTTTAAGAGCTAAGCTGGAATCAGCATAGCAAGTTTAAATAAGGCTAGT	408	A20U	0.98234868
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr918	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	409	A58U,	0.98202823
	CCGTTATCATCTTGAAAAAGAGGCACCGAGTCGGTGCT		U69A	9
cr906	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	410	A64G,	0.98172688
	CCGTTATCAACTTGAGAAAGTGGCAGCGAGTCGCTGCT		C74G,	6
			G82C
cr782	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	411	G62U,	0.98164944
	CCGTTATCAACTTTAGAAAGTGGCACCGAGTCGGTGCT		A64G	4
cr969	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	412	A58U,	0.98146492
	CCGTTATCATCTTGAGAAAGAGGCACCGAGTCGGTGCT		A64G,	7
			U69A
cr747	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	413	A58C,	0.98120572
	CCGTTATCACCTTGAGAAAGGGGCACCGAGTCGGTGCT		A64G,	7
			U69G
cr926	GTTTAAGAGCTAAGCTGGAAACAGAATAGCAAGTTTAAATAAGGCTAGT	414	C24A	0.98110905
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr238	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	415	73U,	0.98091745
	CCGTTATCAACTTGAAAAAGTGGCTTCGAGTCGAAGCT		C74U,	9
			G82A,
			U83A
cr754	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	416	G62U	0.98036311
	CCGTTATCAACTTTAAAAAGTGGCACCGAGTCGGTGCT			9
cr811	GTTTAAGAGCTAAGCTGGAAACAGTATAGCAAGTTTAAATAAGGCTAGT	417	C24U	0.98025368
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr624	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	418	A54G	0.97985016
	CCGTTGTCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr226	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	419	A73C,	0.97946311
	CCGTTATCAACTTGAAAAAGTGGCCACGAGTCGTGGCT		C74A,	5
			G82U,
			U83G
cr021	GTTTAAGCGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	420	A7C	0.97855226
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr556	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	421	U61C,	0.97847942
	CCGTTATCAACTCGAAAGAGTGATACCGAGTCGGTATT		A66G,	6
			G71A,
			C72U,
			G84A,
			C85U
cr783	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	422	A58U,	0.97788302
	CCGTTATCATCTTGAAAAAGAGGAACCGAGTCGGTTCT		U69A,	9
			C72A,
			G84U
cr224	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	423	U60C,	0.97769106
	CCGTTATCAACCTGAAAAGGTGGTATCGAGTCGATACT		A67G,	6
			C72U,
			C74U,
			G82A,
			G84A
cr147	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	424	G62A,	0.97534761
	CCGTTATCAACTTATAAAAGTGGCACCGACTCGGTGCT		A63U,	5
			G78C
cr359	GTTTAAGAGCTAAGCTGGAAACAGCATCGCAAGTTTAAATAAGGCTAGT	425	A27C	0.97460129
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr307	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	426	G76A,	0.97457656
	CCGTTATCAACTTGAAAAAGTGGCACCAAGTTGGTGCT		C80U
cr159	GTTTAAGAGCTAAGCTGGAAACAGCATAGCTAGTTTAAATAAGGCTAGT	427	A30U	0.97393069
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr800	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	428	C44U,	0.97283385
	CCGTTATCATCTTGAAAAAGAGACACCGAGTCGGTGTT		G47A,	3
			A58U,
			U69A,
			G71A,
			C85U
cr299	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	429	A63G,	0.97175695
	CCGTTATCAACTTGGGAAAGTGGCACCGAGTCGGTGCT		A64G	4
cr644	GTTTAAGAGCTAAGCTGGAAACAGCATTGCAAGTTTAAATAAGGCTAGT	430	A27U	0.96944571
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr825	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	431	A73U,	0.96907861
	CCGTTATCAACTTGAAAAAGTGGCTGCGAGTCGCAGCT		C74G,	4
			G82C,
			U83A
cr287	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	432	A64U,	0.96860250
	CCGTTATCAACTTGATAAAGTGGCACCGACTCGGTGCT		G78C	8
cr161	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	433	A65C,	0.96712531
	CCGTTATCAACTTGAACAAGTGGCACCGACTCGGTGCT		G78C	8
cr994	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	434	G78U	0.96707633
	CCGTTATCAACTTGAAAAAGTGGCACCGATTCGGTGCT			3
cr102	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	435	C59U,	0.96508051
	CCGTTATCAATTTGAAAAAATGGAACCGAGTCGGTTCT		G68A,	1
			C72A,
			G84U
cr306	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTTGT	436	A46U,	0.96474194
	CCGTTATCAACTTGAAAAAGTGGCACCGTGTCGGTGCT		A77U	1
cr707	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	437	A77U	0.96317072
	CCGTTATCAACTTGAAAAAGTGGCACCGTGTCGGTGCT			9
cr831	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	438	C59U,	0.96199445
	CCGTTATCAATTTGAAAAAATGGCACCGAGTCGGTGCT		G68A	7
cr646	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	439	C59A,	0.96196816
	CCGTTATCAAATTGAAAAATTGGCTCCGAGTCGGAGCT		G68U,	5
			A73U,
			U83A
cr131	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	440	A64G,	0.95996389
	CCGTTATCAACTTGAGAAAGTGGCACAGAGTCTGTGCT		C75A,	7
			G81U
cr938	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTGGT	441	A46G	0.95960505
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			9
cr416	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	442	A73G,	0.95737807
	CCGTTATCAACTTGAAAAAGTGGCGCCAAGTTGGCGCT		G76A,	5
			C80U,
			U83C
cr267	GTTTAAGAGCTAAGCTGCAAACAGCATAGCAAGTTTAAATAAGGCTAGT	443	G17C	0.95520526
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr372	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCGAGT	444	U45G	0.95453841
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr167	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	445	U60C	0.95445747
	CCGTTATCAACCTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr205	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	446	C59U,	0.95153606
	CCGTTATCAATTTGAAAAAATGGGACCGAGTCGGTCCT		G68A,	9
			C72G,
			G84C
cr835	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	447	U83C	0.95151510
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGCGCT			7
cr264	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	448	A67U	0.95129294
	CCGTTATCAACTTGAAAATGTGGCACCGAGTCGGTGCT			2
cr397	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	449	C59A,	0.95077636
	CCGTTATCAAATTGAAAAATTGGCACCGAGTCGGTGCT		G68U	7
cr181	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	450	C44U,	0.95060466
	CCGTTATCAATTTGAAAAAATGGCGCCGAGTCGGCGCT		G47A,	2
			C59U,
			G68A,
			A73G,
			U83C
cr284	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	451	A64G,	0.94972690
	CCGTTATCAACTTGAGAAAGTGGCACCAAGTTGGTGCT		G76A,	6
			C80U
cr983	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	452	C72U	0.94909651
	CCGTTATCAACTTGAAAAAGTGGTACCGAGTCGGTGCT			5
cr529	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	453	C75U,	0.94877517
	CCGTTATCAACTTGAAAAAGTGGCACTGAGTCAGTGCT		G81A	3
cr231	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGA	454	U48A,	0.94853963
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		A64G	1
cr703	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	455	A64G,	0.94723877
	CCGTTATCAACTTGAGGAAGTGGCACCGAGTCGGTGCT		A65G	5
cr908	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	456	A64G,	0.94547205
	CCGTTATCAACTTGAGAAAGTGGCATCGAGTCGATGCT		C74U,	4
			G82A
cr285	GTTTAAGAGCTAAGCTGGAAAAAGCATAGCAAGTTTAAATAAGGCTAGT	457	C21A	0.94493325
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			9
cr718	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	458	A58U,	0.94468358
	CCGTTATCATCTTGAAAAAGAGGCCCCGAGTCGGGGCT		U69A,	4
			A73C,
			U83G
cr142	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	459	G76U,	0.94057837
	CCGTTATCAACTTGAAAAAGTGGCACCTAGTAGGTGCT		C80A	2
cr553	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTGGT	460	A46G,	0.93851135
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U
cr253	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	461	A58G,	0.93835701
	CCGTTATCAGCCTGAAAAGGCGGCACCTAGTAGGTGCT		U60C,	8
			A67G,
			U69C,
			G76U,
			C80A
cr719	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	462	C85A	0.93811116
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGAT			1
cr421	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	463	A67C	0.93807843
	CCGTTATCAACTTGAAAACGTGGCACCGAGTCGGTGCT			9
cr693	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	464	A73U,	0.93692498
	CCGTTATCAACTTGAAAAAGTGGCTCCAAGTTGGAGCT		G76A,
			C80U,
			U83A
cr823	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	465	U61A,	0.93598690
	CCGTTATCAACTAGAAATAGTGGCACTGAGTCAGTGCT		A66U,	9
			C75U,
			G81A
cr371	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	466	U53C,	0.93546240
	CCGTCAGCAACTTGAAAAAGTGGCACCGACTCGGTGCT		U55G,	1
			G78C
cr576	GTTTAAGAGCTAAGCCGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	467	U15C	0.93498232
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr953	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	468	A77C	0.93372576
	CCGTTATCAACTTGAAAAAGTGGCACCGCGTCGGTGCT			2
cr822	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGA	469	U48A,	0.93334098
	CCGTTATCAACTTGAACAAGTGGCACCGAGTCGGTGCT		A65C
cr546	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	470	C59A,	0.93261513
	CCGTTATCAAATTGAGAAATTGGCACCGAGTCGGTGCT		A64G,	6
			G68U
cr630	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	471	A54G,	0.93253594
	CCGTTGCCAACTTGAAAAAGTGGCACCGACTCGGTGCT		U55C,	8
			G78C
cr291	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	472	G71C,	0.93209066
	CCGTTATCAACTTGAAAAAGTGCCATCGAGTCGATGGT		C74U,	5
			G82A,
			C85G
cr243	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	473	U61C,	0.93192234
	CCGTTATCAACTCGAAAGAGTGGCCCTGAGTCAGGGCT		A66G,
			A73C,
			C75U,
			G81A,
			U83G
cr361	GTTTAAGAGCTAAGCTCGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	474	G16C	0.93124453
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr577	GTTTAAGAGCTAAGCTGGAAACAGCATGGCAAGTTTAAATAGGGCTAGT	475	A27G,	0.93088485
	CCGTTCTCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A41G,	2
			A54C
cr375	GTTTAAGAGCTAAGCTTGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	476	G16U	0.92935273
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr780	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	477	U61G,	0.92865664
	CCGTTATCAACTGGAAACAGTGGCACCTAGTAGGTGCT		A66C,	4
			G76U,
			C80A
cr304	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	478	C59U,	0.92845293
	CCGTTATCAATTTGAGAAAATGGCACCGAGTCGGTGCT		A64G,	2
			G68A
cr614	GTTTAAGAGCTAAGCTGAAAACAGCATAGCAAGTTTAAATAAGGCTAGT	479	G17A	0.92704840
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr769	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAGGTTTAAATAAGGCTAGT	480	A31G,	0.92679541
	CCGGAATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U52G,	8
			U53A
cr974	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	481	A64G,	0.92354510
	CCGTTATCAACTTGAGAAAGTGGCACCGTGTCGGTGCT		A77U	7
cr525	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	482	A58C,	0.92054471
	CCGTTATCACCTTGAAAAAGGGGCACGGAGTCCGTGCT		U69G,	6
			C75G,
			G81C
cr752	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	483	A64U,	0.91959212
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCG		U86G	3
cr132	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	484	A64G,	0.91958452
	CCGTTATCAACTTGAGAAAGTGGCACTGAGTCAGTGCT		C75U,	9
			G81A
cr364	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	485	U60A	0.91926419
	CCGTTATCAACATGAAAAAGTGGCACCGAGTCGGTGCT			4
cr388	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	486	G71U,	0.91882856
	CCGTTATCAACTTGAAAAAGTGTAACCGAGTCGGTTAT		C72A,	6
			G84U,
			C85A
cr838	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCCAGT	487	U45C	0.91873492
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr597	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	488	A58C,	0.91867601
	CCGTTATCACCTTGAAAAAGGGGGACCTAGTAGGTCCT		U69G,	3
			C72G,
			G76U,
			C80A,
			G84C
cr640	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	489	A73C,	0.91863035
	CCGTTATCAACTTGAAAAAGTGGCCCAGAGTCTGGGCT		C75A,	3
			G81U,
			U83G
cr136	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	490	C44U,	0.91716394
	CCGTTATCAACTTGAAAAAGTGGCGACGAGTCGTCGCT		G47A,	7
			A73G,
			C74A,
			G82U,
			U83C
cr910	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	491	C72A,	0.91707468
	CCGTTATCAACTTGAAAAAGTGGAACGGAGTCCGTTCT		C75G,	7
			G81C,
			G84U
cr409	GTTTAAGAGCTAAGCTGGAAAGAGCATAGCAAGTTTAAATAAGGCTAGT	492	C21G	0.91575503
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr977	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	493	A58U,	0.91270854
	CCGTTATCATCTTGAAAAAGAGGCACCAAGTTGGTGCT		U69A,	2
			G76A,
			C80U
cr387	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTCGT	494	A46C	0.90938800
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr503	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	495	U83G	0.90794052
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGGGCT
cr863	GTTTAAGAGCTAAGCTGGAAACCGCATAGCAAGTTTAAATAAGGCTAGT	496	A22C	0.90310217
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr256	GTTTAAGAGCTAAGCTGGAAACAGCATAACAAGTTTAAATAAGGCTAGT	497	G28A	0.90225105
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr777	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	498	U52G	0.90220219
	CCGGTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr141	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCCAGT	499	U45C,	0.90181817
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		A64G	1
cr626	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	500	A64C,	0.90061608
	CCGTTATCAACTTGACAAAGTGGCACCGCGTCGGTGCT		A77C	7
cr367	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGACTAGT	501	G43A,	0.89977091
	TCGTTATCAACTCGAAAGAGTGGTACCGAGTCGGTACT		C49U,
			U61C,
			A66G,
			C72U,
			G84A
cr702	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	502	G71C	0.89771066
	CCGTTATCAACTTGAAAAAGTGCCACCGAGTCGGTGCT			6
cr402	GTTTAAGAGCTAAGCTGGAAACTGCATAGCAAGTTTAAATAAGGCTAGT	503	A22U	0.89765234
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr694	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	504	U52G,	0.89629996
	CCGGTATCAACTTGTAAAAGTGGCACCGAGTCGGTGCT		A63U	8
cr206	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	505	U60C,	0.89457114
	CCGTTATCAACCTGAAAAAGTGGCACCGACTCGGTGCT		G78C	9
cr013	GTTTGAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	506	A4G	0.89069202
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr705	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	507	C75G,	0.89014285
	CCGTTATCAACTTGAAAAAGTGGCACGGAGTCCGTGCT		G81C	1
cr520	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	508	U52A,	0.88738904
	CCGATATCAACTTGCAAAAGTGGCACCGAGTCGGTGCT		A63C	9
cr123	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	509	U60G	0.88706910
	CCGTTATCAACGTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr909	GTTTAAGAGCTAAGCTGGAAACAGCATATCAAGTTTAAATAAGGCTAGT	510	G28U,	0.88641919
	CCGTGATCAACTTGTAAAAGTGGCACCGAGTCGGTGCT		U53G,	7
			A63U
cr869	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	511	U69A	0.88612497
	CCGTTATCAACTTGAAAAAGAGGCACCGAGTCGGTGCT			4
cr806	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAGGGCTAGT	512	A41G	0.88611665
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr358	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	513	C44U,	0.88603636
	CCGTTATCAAGTTGAAAAACTGGCACTGAGTCAGTGCT		G47A,
			C59G,
			G68C,
			C75U,
			G81A
cr984	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	514	C85G	0.8857801
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGGT
cr749	GTTTAAGAGCTAAGCTGGAAACAGCATAGCGAGTTTAAATAAGGCTAGT	515	A30G,	0.88566025
	CCGTCATCAACTTGAAAAAGTGGCACCGCGTCGGTGCT		U53C,	6
			A77C
cr414	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	516	A73U	0.88521836
	CCGTTATCAACTTGAAAAAGTGGCTCCGAGTCGGTGCT			1
cr286	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCGAGT	517	U45G,	0.88502424
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		A64G	9
cr759	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTCGT	518	A46C,	0.88424137
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		A64G	2
cr396	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	519	C74A,	0.88256931
	CCGTTATCAACTTGAAAAAGTGGCAACAAGTTGTTGCT		G76A,	6
			C80U,
			G82U
cr781	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	520	G84C	0.88134119
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTCCT			6
cr729	GTTTAAGAGCTAAGCTGGAAATAGCATAGCAAGTTTAAATAAGGCTAGT	521	C21U	0.87934632
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr768	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	522	U79C	0.87879335
	CCGTTATCAACTTGAAAAAGTGGCACCGAGCCGGTGCT			4
cr236	GTTTAAGAGCTAAGCTGGAAACAACATAGCAAGTTTAAATAAGGCTAGT	523	G23A	0.87448856
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr260	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	524	U52A	0.87399738
	CCGATATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr153	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	525	C59A,	0.87349549
	CCGTTATCAAAATGAAAATTTGGCACCGAGTCGGTGCT		U60A,	7
			A67U,
			G68U
cr991	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	526	U52A,	0.8694336
	CCGATATCAACTTGACAAAGTGGCACCGAGTCGGTGCT		A64C
cr105	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCCAGT	527	U45C,	0.86716970
	CCGTTATCAACTTGACAAAGTGGCACCGAGTCGGTGCT		A64C	5
cr692	GTTTAAGAGCTAAGCTAGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	528	G16A	0.86645330
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr184	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	529	U52G,	0.86534903
	CCGGTATCAACTTGGAAAAGTGGCACCGAGTCGGTGCT		A63G	2
cr216	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGACTAGT	530	G43A,	0.86518536
	TCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C49U	2
cr643	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGACTAGT	531	G43A,	0.86495771
	TCGTTATCAGCTTGAAAAAGCGGCATCGAGTCGATGCT		C49U,	7
			A58G,
			U69C,
			C74U,
			G82A
cr891	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGACTAGT	532	G43A,	0.86406316
	TCGTTATCAACTTGAAAAAGTGGTACCGAGTCGGTACT		C49U,	1
			C72U,
			G84A
cr521	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTGGT	533	A46G,	0.86375827
	CCGTTATCAACTTGAAAAAGTGGCACCGTGTCGGTGCT		A77U	6
cr865	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	534	A58U	0.86372953
	CCGTTATCATCTTGAAAAAGTGGCACCGAGTCGGTGCT			9
cr788	GTTTAAGAGCTAAGCTGGAAACAGCATATCAAGTTTAAATAAGGCTAGT	535	G28U	0.86173539
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr899	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGACTAGT	536	G43A,	0.85831302
	TCGTTATCAGCTTGAAAAAGCGGTACCGAGTCGGTACT		C49U,
			A58G,
			U69C,
			C72U,
			G84A
cr829	GTTTAAGAGCTAAGCTGGAAACAGCATACCAAGTTTAAATAAGGCTAGT	537	G28C	0.85711850
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			9
cr676	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	538	A63G,	0.85063962
	CCGTTATCAACTTGGAAAAGTTGCACCGAGTCGGTGCT		G70U	2
cr914	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGACTAGT	539	G43A,	0.84935014
	TCGTTATCAACTTGAAAAAGTGGGACCGAGTCGGTCCT		C49U,	5
			C72G,
			G84C
cr688	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	540	A64U,	0.84797361
	CCGTTATCAACTTGATAAAGTGGCACCGAGCCGGTGCT		U79C
cr878	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	541	C44U,	0.84695212
	CCGTTATCAATTTGAAAAAATGGCATCGAGTCGATGCT		G47A,	6
			C59U,
			G68A,
			C74U,
			G82A
cr943	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	542	G71A,	0.84643782
	CCGTTATCAACTTGAAAAAGTGACAACGAGTCGTTGTT		C74A,	9
			G82U,
			C85U
cr495	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	543	U79A	0.84615752
	CCGTTATCAACTTGAAAAAGTGGCACCGAGACGGTGCT			7
cr169	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	544	A64G,	0.84535985
	CCGTTATCAACTTGAGAAAGTGGCACCGAGCCGGTGCT		U79C	4
cr960	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	545	A58U,	0.84464285
	CCGTTATCATATTGAAAAATAGGCACCGAGTCGGTGCT		C59A,	3
			G68U,
			U69A
cr319	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAGGGCTAGT	546	A41G,	0.84187252
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		A64G	1
cr370	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	547	A64G,	0.84170145
	CCGTTATCAACTTGAGAAAGTGGCACGGAGTCCGTGCT		C75G,	2
			G81C
cr647	GTTTAAGAGCTAAGCTGGAAACAGGATAGCAAGTTTAAATAAGGCTAGT	548	C24G	0.84082895
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr785	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCGAGT	549	U45G,	0.83777538
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCG		U86G	6
cr590	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	550	A64C,	0.83609198
	CCGTTATCAACTTGACAAAGTGGCACCGAGCCGGTGCT		U79C	4
cr554	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAGGTTTAAATAAGGCTAGT	551	A31G,	0.83318759
	CCGTTACCAACTTGAAAAAGTAGCACCGAGTCGGTGCT		U55C,	5
			G70A
cr816	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	552	G70U	0.83153575
	CCGTTATCAACTTGAAAAAGTTGCACCGAGTCGGTGCT			4
cr871	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGCCTAGT	553	G43C,	0.83130740
	GCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C49G	4
cr435	GTTTAAGAGCTAAGCTGGAAACAGCATAGCCAGTTTAAATAAGGCTAGT	554	A30C,	0.82809025
	CCGGTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U52G	7
cr407	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGCCTAGT	555	G43C,	0.82775832
	GCGTTATCAACCTGAAAAGGTGGCATCGAGTCGATGCT		C49G,	2
			U60C,
			A67G,
			C74U,
			G82A
cr162	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	556	U83A	0.82627090
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGAGCT			9
cr543	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTCTAAATAAGGCTAGT	557	U34C	0.82305110
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr420	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	558	G71U,	0.82289830
	CCGTTATCAACTTGAAAAAGTGTGACCTAGTAGGTCAT		C72G,	8
			G76U,
			C80A,
			G84C,
			C85A
cr601	GTTTAAGAGCTAAGCTGGAAACAGCATAGAAAGTTTAAATAAGGCTAGT	559	C29A	0.82162492
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr391	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGCCTAGT	560	G43C,	0.82156418
	GCGTTATCAACTTGAAAAAGTGGCAACGAGTCGTTGCT		C49G,	5
			C74A,
			G82U
cr362	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	561	C75A	0.82137057
	CCGTTATCAACTTGAAAAAGTGGCACAGAGTCGGTGCT			4
cr916	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGACTAGT	562	G43A,	0.82065028
	TCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		C49U,	9
			A64G
cr697	GTTTAAGAGCTAAGCTGGAAACAGCATTGCAAGTTTAAATAAGGCGAGT	563	A27U,	0.81828644
	CCGTTATCAACTTGCAAAAGTGGCACCGAGTCGGTGCT		U45G,	7
			A63C
cr621	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGACTAGT	564	G43A,	0.81795299
	TCGTTATCAACGTGAAAACGTGGCACCAAGTTGGTGCT		C49U,	4
			U60G,
			A67C,
			G76A,
			C80U
cr517	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	565	G81U	0.81725880
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCTGTGCT			2
cr740	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	566	G71C,	0.81525923
	CCGTTATCAACTTGAAAAAGTGCCAGCGAGTCGCTGGT		C74G,	6
			G82C,
			C85G
cr911	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCACGT	567	U45A,	0.81010645
	CCGTTATCAACTTGAAAAAGTGGCACCGAATCGGTGCT		A46C,	3
			G78A
cr470	GTTTAAGAGCTAAGCTGGAAACATCATAGCAAGTTTAAATAAGGCTAGT	568	G23U	0.80895739
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr678	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	569	G84A	0.80811121
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTACT			2
cr506	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGCCTAGT	570	G43C,	0.80782074
	GCGTTATCAACTTGAAAAAGTGGGACCGAGTCGGTCCT		C49G,	7
			C72G,
			G84C
cr733	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	571	C74A,	0.80699500
	CCGTTATCAACTTGAAAAAGTGGCAAAGAGTCTTTGCT		C75A,	8
			G81U,
			G82U
cr104	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	572	C72A	0.80628936
	CCGTTATCAACTTGAAAAAGTGGAACCGAGTCGGTGCT			8
cr215	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	573	G70A	0.80585861
	CCGTTATCAACTTGAAAAAGTAGCACCGAGTCGGTGCT			8
cr931	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	574	A73C,	0.80442078
	CCGTTATCAACTTGAAAAAGTGGCCCTGAGTCAGGGCT		C75U,	1
			G81A,
			U83G
cr876	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	575	G81A	0.80374722
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCAGTGCT			2
cr905	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	576	G71A,	0.80191610
	CCGTTATCAACTTGAAAAAGTGATACCAAGTTGGTATT		C72U,	6
			G76A,
			C80U,
			G84A,
			C85U
cr516	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGCCTAGT	577	G43C,	0.80091127
	GCGTTATCAGCTTGAAAAAGCGGCACCGAGTCGGTGCT		C49G,	9
			A58G,
			U69C
cr879	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCGAGT	578	U45G,	0.79803408
	CCGTCAGCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U53C,	2
			U55G
cr484	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	579	G71C,	0.79778173
	CCGTTATCAACTTGAAAAAGTGCCACCAAGTTGGTGGT		G76A,	5
			C80U,
			C85G
cr818	GTTTAAGAGCTAAGCTGGAAACAGCATAGGAAGTTTAAATAAGGCTAGT	580	C29G	0.79724822
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr652	GTTTAAGAGCTAAGCTGGAAACAGCATCGCAAGTTTAAATAAGGCTAGT	581	A27C,	0.79590116
	CCGGTATCAACTTGGAAAAGTGGCACCGAGTCGGTGCT		U52G,	6
			A63G
cr199	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	582	A64G,	0.79490499
	CCGTTATCAACTTGAGAAAGTGGCACCTAGTAGGTGCT		G76U,
			C80A
cr805	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAGGGCTAGT	583	A41G,	0.79274168
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U	5
cr119	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	584	G68U	0.79015629
	CCGTTATCAACTTGAAAAATTGGCACCGAGTCGGTGCT			5
cr886	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	585	C59A,	0.77914743
	CCGTTATCAAATAGAAATATTGGCAGCGAGTCGCTGCT		U61A,	9
			A66U,
			G68U,
			C74G,
			G82C
cr219	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGCCTAGT	586	G43C,	0.77785352
	GCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		C49G,	7
			A64G
cr164	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	587	A73C	0.77755220
	CCGTTATCAACTTGAAAAAGTGGCCCCGAGTCGGTGCT			8
cr443	GTTTAAGAGCTAAGCGGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	588	U15G	0.77743236
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr712	GTTTAAGAGCTAAGCTGGAAACACCATAGCAAGTTTAAATAAGGCTAGT	589	G23C	0.77442100
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr449	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGCCTAGT	590	G43C,	0.76980003
	GCGTTATCAACTTGAAAAAGTGGCAGCGAGTCGCTGCT		C49G,	1
			C74G,
			G82C
cr558	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	591	A73U,	0.76788418
	CCGTTATCAACTTGAAAAAGTGGCTCGGAGTCCGAGCT		C75G,	5
			G81C,
			U83A
cr550	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	592	C75A,	0.76787138
	CCGTTATCAACTTGAAAAAGTGGCACAAAGTTTGTGCT		G76A,
			C80U,
			G81U
cr684	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	593	C75U,	0.76717563
	CCGTTATCAACTTGAAAAAGTGGCACTAAGTTAGTGCT		G76A,	2
			C80U,
			G81A
cr819	GTTTAAGAGCTAAGCTGGAAACAGCATCGCAAGTTTAAATAAGGCTGGT	594	A27C,	0.76666697
	CCGGTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A46G,	5
			U52G
cr508	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCCAGT	595	U45C,	0.76359189
	CCGTGATCAACTTGAAAAAGTGGCACCGAGCCGGTGCT		U53G,	1
			U79C
cr859	GTTTAAGAGCTAAGCTGGAAACAGCATACCAAGTTTAAATAAGGCTTGT	596	G28C,	0.76253874
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		A46U,	9
			A64G
cr836	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	597	C59U	0.75741139
	CCGTTATCAATTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr852	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	598	C59G,	0.75416601
	CCGTTATCAAGAAAAAATACTGGCACCGAGTCGGTGCT		U60A,	1
			U61A,
			G62A,
			A66U,
			G68C
cr890	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	599	A77C,	0.75125702
	CCGTTATCAACTTGAAAAAGTGGCACCGCGTCGGTGCC		U86C	2
cr779	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	600	C59A,	0.75124325
	CCGTTATCAAATTGAAAAATTGGTAACGAGTCGTTACT		G68U,	8
			C72U,
			C74A,
			G82U,
			G84A
cr695	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	601	U52A,	0.75099608
	CCGATGTCAACTTGTAAAAGTGGCACCGAGTCGGTGCT		A54G,	4
			A63U
cr439	GTTTAAGAGCTAAGCAGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	602	U15A	0.75055046
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr887	GTTTAAGAGCTAAGCTGGAAACAGCATAGCCAGTTTAAATAAGGCTCGT	603	A30C,	0.75035737
	CCGTTATCAACTTCAAAAAGTGGCACCGAGTCGGTGCT		A46C,	6
			G62C
cr867	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	604	C49U	0.75021527
	TCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr505	GTTTAAGAGCTAAGCTGGAAACAGCATTGCAAGTTTAAATAGGGCTAGT	605	A27U,	0.74590947
	CCGTTATCAACTTGAAAAAGTGGCACCGATTCGGTGCT		A41G,	4
			G78U
cr875	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	606	A58C,	0.74296049
	CCGTTATCACCTGGAAACAGGGGCACCCAGTGGGTGCT		U61G,	8
			A66C,
			U69G,
			G76C,
			C80G
cr594	GTTTAAGAGCTAAGCTGGAAACAGCATTGCAAGTTTAAATAGGGCTAGT	607	A27U,	0.73951937
	CCGCTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A41G,
			U52C
cr278	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGC	608	U48C,	0.73676832
	CCGTTATCAACTTGAAAAAGTGGCACCGAATCGGTGCT		G78A	8
cr341	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGTCTAGT	609	G43U,	0.72986761
	ACGTTATCAACTTGAAAAAGTGCTACCGAGTCGGTAGT		C49A,	2
			G71C,
			C72U,
			G84A,
			C85G
cr426	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAATTTAAATAAGGCTAGT	610	G32A,	0.72645204
	CCGCTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U52C
cr763	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGC	611	U48C	0.72636557
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr474	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	612	G76U	0.72544546
	CCGTTATCAACTTGAAAAAGTGGCACCTAGTCGGTGCT			3
cr457	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	613	G84U	0.72540834
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTTCT			2
cr168	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGACTAGT	614	G43A,	0.72384389
	TCGTTATCAAATTGAAAAATTGGCACCGAGTCGGTGCT		C49U,	3
			C59A,
			G68U
cr555	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGTCTAGT	615	G43U,	0.71899532
	ACGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C49A	6
cr645	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGCCTAGT	616	G43C,	0.71792415
	GCGTTATCAACGTGAAAACGTGGCACGGAGTCCGTGCT		C49G,	9
			U60G,
			A67C,
			C75G,
			G81C
cr635	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	617	A77G	0.71742960
	CCGTTATCAACTTGAAAAAGTGGCACCGGGTCGGTGCT			7
cr742	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	618	C44A,	0.71407471
	CCGTTATCAACTCGAAAGAGTGGCACCGAGTCGGTGCT		G47U,	9
			U61C,
			A66G
cr539	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGTCTAGT	619	G43U,	0.71347240
	ACGTTATCAATTGGAAACAATGGCACCGAGTCGGTGCT		C49A,	4
			C59U,
			U61G,
			A66C,
			G68A
cr725	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAATTTAAATAAGGCTAGT	620	G32A	0.70944712
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr522	GTTTAAGAGCTAAGCTGGAAACAGCATTGCAAGTTTAAATAAGGCTAGT	621	A27U,	0.70838082
	CCGTTGTCAACTTGAAAAAGTGGCACCGAGCCGGTGCT		A54G,	4
			U79C
cr015	GTTTACGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	622	A5C	0.70222589
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr117	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	623	A63G,	0.69658802
	CCGTTATCAACTTGGAAAAGTGGCACCGGGTCGGTGCT		A77G	2
cr575	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	624	C75G	0.69545400
	CCGTTATCAACTTGAAAAAGTGGCACGGAGTCGGTGCT			7
cr734	GTTTAAGAGCTAAGCTGGAAACAGCATCGCAAGTTTAAATAAGGCTAGT	625	A27C,	0.69058295
	CCGTTCTCAACTTGAAAAAGTCGCACCGAGTCGGTGCT		A54C,	8
			G70C
cr292	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	626	G70C	0.69023779
	CCGTTATCAACTTGAAAAAGTCGCACCGAGTCGGTGCT			2
cr596	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	627	C72G	0.69002218
	CCGTTATCAACTTGAAAAAGTGGGACCGAGTCGGTGCT			6
cr658	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	628	C44A,	0.68825905
	CCGTTATCAATTCGAAAGAATGGCACCGAGTCGGTGCT		G47U,	1
			C59U,
			U61C,
			A66G,
			G68A
cr552	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	629	U52A,	0.68635127
	CCGATGGCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A54G,
			U55G
cr877	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	630	U60C,	0.68597396
	CCGTTATCAACCTGAAAAGGTGCCACGGAGTCCGTGGT		A67G,	1
			G71C,
			C75G,
			G81C,
			C85G
cr437	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	631	U60G,	0.68540274
	CCGTTATCAACGTGAAAACGTGGCACACAGTGTGTGCT		A67C,	8
			C75A,
			G76C,
			C80G,
			G81U
cr607	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	632	G78A,	0.68471269
	CCGTTATCAACTTGAAAAAGTGGCACCGAACCGGTGCT		U79C	3
cr279	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	633	U79C,	0.67831028
	CCGTTATCAACTTGAAAAAGTGGCACCGAGCCGGTGCC		U86C	7
cr962	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	634	C72G,	0.67332357
	CCGTTATCAACTTGAAAAAGTGGGACCCAGTGGGTCCT		G76C,	2
			C80G,
			G84C
cr273	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGTCTAGT	635	G43U,	0.67247738
	ACGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		C49A,	5
			A64G
cr848	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	636	A64G,	0.67143035
	CCGTTATCAACTTGAGAAAGTGGCACCGGGTCGGTGCT		A77G	4
cr840	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	637	A64G,	0.66880636
	CCGTTATCAACTTGAGAAAGTAGCACCGAGTCGGTGCT		G70A	6
cr133	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCAAGC	638	U45A,	0.66636251
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U48C	5
cr699	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTACT	639	C44G,	0.66279088
	CCGTTATCAACGCGAAAGCGTGGCACCGAGTCGGTGCT		G47C,	3
			U60G,
			U61C,
			A66G,
			A67C
cr815	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	640	G82A	0.65834857
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGATGCT			4
cr667	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	641	U33C,	0.65332502
	CCGTTATCAACTCGAAAGAGTGGCACCGAGTCGGTGCT		U61C,	9
			A66G
cr917	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAAT	642	C44U,	0.65213917
	CCGTTATCAACTTGAAAAAGTGGCATCTAGTAGATGCT		G47A,	7
			C74U,
			G76U,
			C80A,
			G82A
cr885	GTTTAAGAGCTAAGCTGGAAACAGCATAGCCAGTTTAAATAAGGCTAGT	643	A30C,	0.64981212
	CCGTTATCAACTTGAAAAAGTTGCACCGAGTCGGTGCT		G70U	1
cr017	GTTTAGGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	644	A5G	0.63898791
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr844	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	645	A58G,	0.63456307
	CCGTTATCAGCTTGAAAAAGCGGCATTGAGTCAATGCT		U69C,	7
			C74U,
			C75U,
			G81A,
			G82A
cr793	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	646	G82U	0.63432551
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGTTGCT			1
cr913	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAACAAGGCTAGT	647	U39C	0.63301621
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			9
cr213	GTTTAAGAGCTAAGCTGGAAACAGCATAACAAGTTTAAATAGGGCTAGT	648	G28A,	0.63252338
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		A41G,	3
			A64G
cr861	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	649	U55G,	0.63185692
	CCGTTAGCAACTTGAAAAAGTAGCACCGAGTCGGTGCT		G70A	8
cr026	GTTTGATAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	650	A4G,	0.6314288
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6U
cr001	ATTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	651	G0A	0.63118333
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr679	GTTTAAGAGCTAAGCTGGAAACAGCATATCAAGTTTAAATAAGGCTAGT	652	G28U,	0.62919092
	CCGGTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U52G	7
cr746	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAATTTAAATAAGGCTAGT	653	G32A,	0.62904932
	CCGTTATCAACTTGACAAAGTGGCACCGAGTCGGTGCT		A64C	8
cr406	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	654	C56U	0.62852747
	CCGTTATTAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr189	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	655	G76A	0.62702816
	CCGTTATCAACTTGAAAAAGTGGCACCAAGTCGGTGCT			6
cr441	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	656	C56A	0.62198502
	CCGTTATAAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr442	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	657	U33C,	0.61690182
	CCGTTATCAACCTGAAAAGGTGGCACCGAGTCGGTGCT		U60C,	3
			A67G
cr650	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	658	C56G	0.61576590
	CCGTTATGAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr086	GTTTACTAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	659	A5C,	0.61399866
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6U	4
cr937	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTACT	660	C44G,	0.61374680
	CCGTTATCAACTCGAAAGAGTGGCACAGAGTCTGTGCT		G47C,	3
			U61C,
			A66G,
			C75A,
			G81U
cr227	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	661	U52C,	0.60622290
	CCGCTAACCACTTGAAAAAGTGGCACCGAGTCGGTGCT		U55A,	6
			A57C
cr639	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	662	C44A,	0.60569626
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G47U	4
cr451	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	663	C44A,	0.60528018
	CCGTTATCAACTTGAAAAAGTGGCTCCGAGTCGGAGCT		G47U,	6
			A73U,
			U83A
cr212	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAATTTAAATAAGGCTAGT	664	G32A,	0.60348445
	CCGTTATCAACTTGAAGAAGTGGCACCGAGTCGGTGCT		A65G	5
cr631	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	665	C44A,	0.60140486
	CCGTTATCAACATGAAAATGTGGCAACGAGTCGTTGCT		G47U,	5
			U60A,
			A67U,
			C74A,
			G82U
cr479	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	666	C59G,	0.59938023
	CCGTTATCAAGTTGAAAAACTGGGACCCAGTGGGTCCT		G68C,	4
			C72G,
			G76C,
			C80G,
			G84C
cr915	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	667	G76C,	0.59653699
	CCGTTATCAACTTGAAAAAGTGGCACCCAGTGGGTGCT		C80G
cr458	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	668	A64G,	0.59579192
	CCGTTATCAACTTGAGAAAGTCGCACCGAGTCGGTGCT		G70C	4
cr008	GTCTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	669	U2C	0.59551136
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr481	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTACT	670	C44G,	0.59364768
	CCGTTATCAACTTGAAAAAGTGACACCGAGTCGGTGTT		G47C,	2
			G71A,
			C85U
cr598	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	671	U33C,	0.59344375
	CCGTTATCAACTGGAAACAGTGGCACCGAGTCGGTGCT		U61G,	8
			A66C
cr927	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	672	A73G,	0.59205452
	CCGTTATCAACTTGAAAAAGTGGCGCCCAGTGGGCGCT		G76C,	9
			C80G,
			U83C
cr300	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	673	C44A,	0.59198894
	CCGTTATCAACTTGAAAAAGTGATACCGAGTCGGTATT		G47U,	1
			G71A,
			C72U,
			G84A,
			C85U
cr726	GTTTAAGAGCTAAGCTGGAAACAGCATAGCATGTTTAAATAAGGCTAGT	674	A31U,	0.59028186
	CCATTCTCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G51A,	5
			A54C
cr440	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	675	G68C	0.58633892
	CCGTTATCAACTTGAAAAACTGGCACCGAGTCGGTGCT			4
cr338	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTCTAAATAAGGCGAGT	676	U34C,	0.58305614
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U45G	2
cr456	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAATTTAAATAAGGCAAGT	677	G32A,	0.58211812
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U45A	3
cr827	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	678	C44A,	0.57919268
	CCGTTATCAACTTGAAAAAGTGGCTACGAGTCGTAGCT		G47U,	7
			A73U,
			C74A,
			G82U,
			U83A
cr803	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	679	C44A,	0.57804521
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		G47U,	6
			A64G
cr491	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	680	C44A,	0.57750644
	CCGTTATCAAGTTGAAAAACTGGCACCGAGTCGGTGCT		G47U,
			C59G,
			G68C
cr330	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	681	C44A,	0.57616244
	CCGTTATCAACTTGAAAAAGTGCCACCGAGTCGGTGGT		G47U,	9
			G71C,
			C85G
cr500	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	682	U79G	0.57426318
	CCGTTATCAACTTGAAAAAGTGGCACCGAGGCGGTGCT			5
cr134	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	683	C56U,	0.56922496
	CCGTTATTAACTTGAACAAGTGGCACCGAGTCGGTGCT		A65C	8
cr223	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTACT	684	C44G,	0.56669424
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G47C	5
cr651	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	685	A54U,	0.56370364
	CCGTTTGCAACTTGAAAAAGTAGCACCGAGTCGGTGCT		U55G,
			G70A
cr989	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	686	G68A	0.56234333
	CCGTTATCAACTTGAAAAAATGGCACCGAGTCGGTGCT			4
cr976	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTACT	687	C44G,	0.55937371
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		G47C,	3
			A64G
cr957	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTACT	688	C44G,	0.55763237
	CCGTTATCAACTTGAAAAAGTGCCACCGAGTCGGTGGT		G47C,	3
			G71C,
			C85G
cr533	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	689	A58G,	0.55578553
	CCGTTATCAGCTTGAAAAAGCGGCGGCGAGTCGCCGCT		U69C,	7
			A73G,
			C74G,
			G82C,
			U83C
cr842	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	690	U33C,	0.55441830
	CCGTTATCAGCTTGAAAAAGCGGCACCGAGTCGGTGCT		A58G,	1
			U69C
cr837	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	691	G76C	0.55433378
	CCGTTATCAACTTGAAAAAGTGGCACCCAGTCGGTGCT			8
cr846	GTTTAAGAGCTAAGCTGGAAACAGCATGGCAAGTTTAAATAAGGCTAGT	692	A27G,	0.55275978
	CCGTTATCAACTTAAAAAAGTGGCACCGGGTCGGTGCT		G62A,	5
			A77G
cr254	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	693	C75A,	0.55028900
	CCGTTATCAACTTGAAAAAGTGGCACACAGTGTGTGCT		G76C,	3
			C80G,
			G81U
cr342	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	694	C56U,	0.54224265
	CCGTTATTAACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U	1
cr940	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	695	U33C,	0.53990422
	CCGTTATCAACGTGAAAACGTGGCACCGAGTCGGTGCT		U60G,	9
			A67C
cr427	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	696	C56A,	0.53833838
	CCGTTATAAACTTGAAAAAGTGGCACCGAATCGGTGCT		G78A	5
cr098	GTTTACGTGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	697	A5C,	0.53404029
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7U	9
cr430	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGACTAGT	698	G43A,	0.53277657
	TCGTTATCAAATTGAAAAATTGGCACAGAGTCTGTGCT		C49U,	4
			C59A,
			G68U,
			C75A,
			G81U
cr959	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	699	C59U,	0.52511598
	CCGTTATCAATTCGAAATACTGGCACCGAGTCGGTGCT		U61C,	8
			A66U,
			G68C
cr511	GTTTAAGAGCTAAGCTGGAAACAGCATTGCCAGTTTAAATAAGGCTAGT	700	A27U,	0.52473745
	CCGTTATCAACTTGAAAAAGTGGCACCGCGTCGGTGCT		A30C,	3
			A77C
cr166	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	701	U33C,	0.52449451
	CCGTTATCAACTTGAAAAAGTGGTACCGAGTCGGTACT		C72U,	6
			G84A
cr072	GTCTAAGTGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	702	U2C,	0.52361717
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7U	9
cr150	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	703	C74G	0.52298794
	CCGTTATCAACTTGAAAAAGTGGCAGCGAGTCGGTGCT			3
cr724	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	704	A57G	0.51772190
	CCGTTATCGACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr265	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGTTAGT	705	C44U	0.51571786
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr548	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	706	C74A	0.51302693
	CCGTTATCAACTTGAAAAAGTGGCAACGAGTCGGTGCT			4
cr599	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGC	707	U48C,	0.51123773
	CCGTTATCAACGTGAAAAAGTGGCACCGAGTCGGTGCT		U60G
cr005	GCTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	708	U1C	0.51080241
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr255	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	709	U33C,	0.50965183
	CCGTTATCAACTTGAAAAAGTGGGACCGAGTCGGTCCT		C72G,	9
			G84C
cr675	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	710	U33C,	0.50843344
	CCGTTATCACCTTGAAAAAGGGGCACCGAGTCGGTGCT		A58C,	8
			U69G
cr659	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTACATAAGGCTAGT	711	A37C	0.50403513
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr190	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	712	U53A,	0.50362754
	CCGTAATTAACTTGAAAAAGTGGCACCGAGACGGTGCT		C56U,
			U79A
cr317	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATATGGCTAGT	713	A41U	0.50271410
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr120	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	714	A64G,	0.5005408
	CCGTTATCAACTTGAGAAAGTGGCACCCAGTGGGTGCT		G76C,
			C80G
cr530	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGC	715	U48C,	0.49980262
	CCGATATCAACTTGAACAAGTGGCACCGAGTCGGTGCT		U52A,	2
			A65C
cr824	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	716	A64C,	0.49899163
	CCGTTATCAACTTGACAAAGTGGCACCGAGGCGGTGCT		U79G	6
cr611	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	717	U55G,	0.49537338
	CCGTTAGAAACTTGAAAAAGTGGCACCGAATCGGTGCT		C56A,
			G78A
cr765	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	718	U61G,	0.49313142
	CCGTTATCAACTGGAAACAGTGGCACTCAGTGAGTGCT		A66C,
			C75U,
			G76C,
			C80G,
			G81A
cr175	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	719	A57C	0.49138007
	CCGTTATCCACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr016	GTTTATGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	720	A5U	0.48760843
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr620	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTACT	721	C44G,	0.48424212
	CCGTTATCAACTTGAAAAAGTGGAACTGAGTCAGTTCT		G47C,	7
			C72A,
			C75U,
			G81A,
			G84U
cr682	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGCGTACT	722	G43C,	0.47942043
	GCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44G,	2
			G47C,
			C49G
cr770	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	723	U33C,	0.47712207
	CCGTTATCAACTTGAAAAAGTGGAACCGAGTCGGTTCT		C72A,	7
			G84U
cr177	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAAGAAGGCTAGT	724	U39G	0.47708504
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr124	GTTTAAGAGCTAAGCTGGAAACAGCATAGCATGTTTAAATATGGCTAGT	725	A31U,	0.47570216
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A41U	2
cr369	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	726	C59A	0.47541190
	CCGTTATCAAATTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr071	GTTTACGGGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	727	A5C,	0.47500821
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7G	8
cr493	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGC	728	U48C,	0.47356427
	CCGTTACAAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U55C,	9
			C56A
cr399	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	729	G81C	0.47270010
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCCGTGCT			7
cr721	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	730	U52C,	0.46963681
	CCGCTATTAACTTGAAAAAGTGGCACCGAATCGGTGCT		C56U,	2
			G78A
cr904	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGTCTAGT	731	G43U,	0.46772125
	ACGTTATCAATTTGAAAAAATGGCAACGAGTCGTTGCT		C49A,	2
			C59U,
			G68A,
			C74A,
			G82U
cr163	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	732	U33C	0.46620624
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr758	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	733	U33C,	0.46135032
	CCGCTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U52C	9
cr405	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	734	G71U,	0.45819411
	CCGTTATCAACTTGAAAAAGTGTCAGCAAGTTGCTGAT		C74G,	6
			G76A,
			C80U,
			G82C,
			C85A
cr920	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	735	A54G,	0.45669801
	CCGTTGACTACTTGAAAAAGTGGCACCGAGTCGGTGCT		U55A,	2
			A57U
cr873	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	736	U33C,	0.45459228
	CCGTTATCAACTAGAAATAGTGGCACCGAGTCGGTGCT		U61A,	1
			A66U
cr955	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAAT	737	G47A	0.45412799
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr868	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTCAAATAAGGCTAGT	738	U35C	0.45170637
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr668	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	739	C59G	0.44447612
	CCGTTATCAAGTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr866	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	740	G71U,	0.44339453
	CCGTTATCAACTTGAAAAAGTGTCCCCGAGTCGGTGCT		A73C	8
cr485	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTTGT	741	A46U,	0.44190287
	CCGTTATCAACTTGAAAAAGTTGCACCGTGTCGGTGCT		G70U,	5
			A77U
cr425	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	742	A58U,	0.44091617
	CCGTTATCATCCTGAAAATGCGGCACCGAGTCGGTGCT		U60C,	9
			A67U,
			U69C
cr486	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	743	G71A,	0.44090443
	CCGTTATCAACTTGAAAAAGTGAAACTGAGTCAGTTTT		C72A,	2
			C75U,
			G81A,
			G84U,
			C85U
cr460	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTACT	744	C44G,	0.43813879
	CCGTTATCAACTTGAAAAAGTGGCTCAGAGTCTGAGCT		G47C,	8
			A73U,
			C75A,
			G81U,
			U83A
cr566	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	745	U33C,	0.42991833
	CCGTTATCAACATGAAAATUGGCACCGAGTCGGTGCT		U60A,	2
			A67U
cr148	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	746	U33C,	0.42755995
	CCGTTATCAACTTGAAAAAGTGGCGCCGAGTCGGCGCT		A73G,	3
			U83C
cr318	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATATGGCTAGT	747	A41U,	0.42691626
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U	5
cr040	ATTTACGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	748	G0A,	0.42325934
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		ASC	1
cr165	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	749	G42A,	0.42241109
	CTGTTATCAACTCGAAAGAGTGTCACCGAGTCGGTGAT		C50U,	8
			U61C,
			A66G,
			G71U,
			C85A
cr736	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGATTAAATAAGGCTAGT	750	U33A,	0.42076454
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCA		U86A	1
cr385	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	751	A73C,	0.41856506
	CCGTTATCAACTTGAAAAAGTGGCCACCAGTGGTGGCT		C74A,	6
			G76C,
			C80G,
			G82U,
			U83G
cr728	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	752	A57G,	0.41551803
	CCGTTATCGACTTGACAAAGTGGCACCGAGTCGGTGCT		A64C	2
cr794	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	753	G71C,	0.41103318
	CCGTTATCAACTTGAAAAAGTGCCACGGAGTCCGTGGT		C75G,	5
			G81C,
			C85G
cr775	GTTTAAGAGCTAAGCTGGAAACAGCATAGCGATTTTAAATAAGGCTAGT	754	A30G,	0.40516743
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G32U	1
cr126	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAATTTAAATAAGGCTCGT	755	G32A,	0.40344846
	CCGTTATCAACTTTAAAAAGTGGCACCGAGTCGGTGCT		A46C,	9
			G62U
cr799	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	756	C50U	0.40310439
	CTGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr804	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	757	U33C,	0.40252915
	CCGTTATCAACTTGAAAAAGTGACACCGAGTCGGTGTT		G71A,	6
			C85U
cr569	GTTTAAGAGCTAAGCTGGAAACAGCATGGCAAGTTTAAATAAGGCTCGT	758	A27G,	0.39919149
	CCGTTATGAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A46C,	4
			C56G
cr545	GTTTAAGAGCTAAGCTGGAAACAGCATAACAAGTTTAAATAAGGCTAGT	759	G28A,	0.39355317
	CCGTCATAAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U53C,	3
			C56A
cr432	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	760	U33C,	0.39234392
	CCGTTATCAAGTTGAAAAACTGGCACCGAGTCGGTGCT		C59G,	8
			G68C
cr296	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	761	C72U,	0.39110065
	CCGTTATCAACTTGAAAAAGTGGTACGCAGTGCGTACT		C75G,	2
			G76C,
			C80G,
			G81C,
			G84A
cr473	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	762	U52A,	0.38921036
	CCGATATAAACTTGCAAAAGTGGCACCGAGTCGGTGCT		C56A,	1
			A63C
cr249	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	763	U53G,	0.38863339
	CCGTGATTAACTTGAAAAAGTGGCACCGAGACGGTGCT		C56U,	8
			U79A
cr459	GTTTAAGAGCTAAGCTGGAAACAGCATAGCGAGTTTAAATAAGGCTAGT	764	A30G,	0.38369736
	CCGTTATCTACTTGAAAAAGTGGCACCGAGTCGGTGCT		A57U	4
cr856	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	765	C44A,	0.38256316
	CCGTTATCAAATTGAAAAATTGGCTCCGAGTCGGAGCT		G47U,	5
			C59A,
			G68U,
			A73U,
			U83A
cr755	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	766	U33C,	0.38143930
	CCGTTATCAACTTGAAAAAGTGGCTCCGAGTCGGAGCT		A73U,	7
			U83A
cr813	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	767	A57U	0.38026547
	CCGTTATCTACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr527	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTGTAAATAAGGCTAGT	768	U34G	0.38018693
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr200	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAATTTAAATAAGGCTAGT	769	G32A,	0.37991573
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCC		U86C	4
cr404	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	770	A57C,	0.37837378
	CCGTTATCCACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U	9
cr523	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	771	U33C,	0.37458916
	CCGTTATCAACTTGAAAAAGTGTCACCGAGTCGGTGAT		G71U,	2
			C85A
cr088	ATTTAGGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	772	G0A,	0.37450544
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5G
cr343	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	773	A58U,	0.37325235
	CCGTTATCATGAAGAAAATGAGGCACCGAGTCGGTGCT		C59G,	1
			U60A,
			U61A,
			A67U,
			U69A
cr201	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	774	G42A,	0.37310290
	CTGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C50U	4
cr895	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATAAGGCTAGT	775	G32U,	0.37087207
	CCGTTATCAACTTGTAAAAGTGGCACCGAGTCGGTGCT		A63U	5
cr966	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	776	C59G,	0.36991893
	CCGTTATCAAGTTGAAAAACTGGCATGGAGTCCATGCT		G68C,	9
			C74U,
			C75G,
			G81C,
			G82A
cr003	TTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	777	G0U	0.36775399
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			9
cr398	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTGAATAAGGCTAGT	778	A36G	0.36671184
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr127	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATATGGCTAGT	779	A41U,	0.36585642
	CCGTTATCAACTTGAGAAAGTGGCACCGACTCGGTGCT		A64G,	6
			G78C
cr315	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	780	G42A,	0.36416477
	CTGTTATCAACTGGAAACAGTGGCCCCGAGTCGGGGCT		C50U,	3
			U61G,
			A66C,
			A73C,
			U83G
cr924	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	781	G42A,	0.36379822
	CTGTTATCAACTTGAAAAAGTGGCGCCGAGTCGGCGCT		C50U,	5
			A73G,
			U83C
cr196	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	782	C74G,	0.36258355
	CCGTTATCAACTTGAAAAAGTGGCAGCCAGTGGCTGCT		G76C,	4
			C80G,
			G82C
cr471	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	783	G71U,	0.36247118
	CCGTTATCAACTTGAAAAAGTGTCACACAGTGTGTGAT		C75A,	5
			G76C,
			C80G,
			G81U,
			C85A
cr192	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	784	A73C,	0.36087432
	CCGTTATCAACTTGAAAAAGTGGCCCCCAGTGGGGGCT		G76C,	6
			C80G,
			U83G
cr252	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	785	G51A	0.35809579
	CCATTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr182	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATAAGGCTAGT	786	G32U	0.35589435
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr351	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTCGT	787	A46C,	0.35572605
	CCGTTATAAACTTAAAAAAGTGGCACCGAGTCGGTGCT		C56A,
			G62A
cr689	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	788	U33C,	0.35467518
	CCGTTTTCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A54U,	3
			A64U
cr634	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAGGTTTAAATAAGGCTAGT	789	A31G,	0.35083966
	CCGTTATCGACTTGAAAAAGTGGCACCGAGTCGGTGCT		A57G
cr074	GTTTATGCGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	790	A5U,	0.34104410
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7C	7
cr014	GTTTCAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	791	A4C	0.33952866
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr064	GTTTTAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	792	A4U	0.33827790
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr051	TTTTAACAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGTC	793	G0U,	0.33671106
	CGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6C	9
cr889	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	794	G42A,	0.33467887
	CTGTTATCAACTTGAAAAAGTGTCACCGAGTCGGTGAT		C50U,	4
			G71U,
			C85A
cr349	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	795	C80U	0.33263721
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTTGGTGCT			2
cr573	GTTTAAGAGCTAAGCTGGAAACAGCATAGCTAGTTTAAATAAGGCTAGT	796	A30U,	0.3296978
	CCGATATCAACTTGAAAAAGTCGCACCGAGTCGGTGCT		U52A,
			G70C
cr006	GGTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	797	U1G	0.32749112
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr242	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTACT	798	C44G,	0.32442403
	CCGTTATCAAGTTGAAAAACTGGCACCTAGTAGGTGCT		G68C,	1
			G76U,
			C80A
cr069	GCTTAAAAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	799	U1C,	0.32437081
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6A	5
cr173	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	800	G42A,	0.32435517
	CTGTTATCAACTTGAAAAAGTGGTAGCGAGTCGCTACT		C50U,	4
			C72U,
			C74G,
			G82C,
			G84A
cr897	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTATATAAGGCTAGT	801	A37U	0.32331910
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr850	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	802	A77G,	0.32254175
	CCGTTATCAACTTGAAAAAGTGGCACCGGGGCGGTGCT		U79G	8
cr108	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	803	G42A,	0.32214077
	CTGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		C50U,	5
			A64G
cr709	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	804	C56U,	0.32107991
	CCGTTATTAACTTGAAAAAGTTGCACCGAGTCGGTGCT		G70U	3
cr468	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGATTAAATAAGGCTAGT	805	U33A	0.3200489
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr002	CTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	806	G0C	0.3194695
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr466	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAAAAAGGCTAGT	807	U39A	0.31532235
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr146	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	808	C75G,	0.31411676
	CCGTTATCAACTTGAAAAAGTGGCACGCAGTGCGTGCT		G76C,	1
			C80G,
			G81C
cr210	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	809	A57U,	0.31302175
	CCGTTATCTACTTGACAAAGTGGCACCGAGTCGGTGCT		A64C	9
cr004	GATTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	810	U1A	0.31030084
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			9
cr055	GGTTAAGCGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	811	U1G,	0.30453876
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7C	7
cr322	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGC	812	U48C,	0.30330076
	CCGTTATGAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C56G	3
cr209	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATCAGGCTAGT	813	A40C	0.29592804
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr586	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	814	U33C,	0.29391167
	CCGTTATCAACTTGAAAAAGTGGCCCCGAGTCGGGGCT		A73C,	4
			U83G
cr591	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	815	A54G,	0.29329260
	CCGTTGTCTACTTGAAAAAGTGGCACCGAGTCGGTGCT		A57U	4
cr027	GTCTAAAAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	816	U2C,	0.28781518
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6A	3
cr125	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATAAGGCTAGT	817	G32U,	0.28523061
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U	3
cr237	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	818	C44A,	0.28434303
	CCGTTATCAACGTGAAAACGTGGCACCCAGTGGGTGCT		G47U,	6
			U60G,
			A67C,
			G76C,
			C80G
cr982	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	819	U33C,	0.28424175
	CCGTTATCAACTTGAAAAAGTGGCAACGAGTCGTTGCT		C74A,	5
			G82U
cr344	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	820	G51A,	0.28364044
	CCATTATCAACTTGAATAAGTGGCACCGAGTCGGTGCT		A65U
cr331	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	821	U33C,	0.28280001
	CCGTTATCAACTTGAAAAAGTGGCATCGAGTCGATGCT		C74U,	5
			G82A
cr154	GTTTAAGAGCTAAGCTGGAAACAGCATCGCAAGTTTAAATAAGGCTAGT	822	A27C,	0.28265111
	CCATTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G51A	6
cr417	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATAAGGCTAGT	823	G32U,	0.28212921
	CCGTTATCAACTTGAAGAAGTGGCACCGAGTCGGTGCT		A65G	7
cr907	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	824	G51A,	0.28205923
	CCATTATCAACTTGTAAAAGTGGCACCGATTCGGTGCT		A63U,	2
			G78U
cr217	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATAAGGCTAGT	825	G32U,	0.28018899
	CCGTTATCAACTTGACAAAGTGGCACCGAGTCGGTGCT		A64C	7
cr992	GTTTAAGAGCTAAGCTGGAAACAGCATTGCAAGTTTAAATAAGGCTAGT	826	A27U,	0.27850684
	CCATTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G51A	8
cr246	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAAAAAGGCTGGT	827	U39A,	0.27791250
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A46G	4
cr972	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGTGTACT	828	G43U,	0.27672337
	ACGTTATCAACCTGAAAAGGTGGCACCGAGTCGGTGCT		C44G,
			G47C,
			C49A,
			U60C,
			A67G
cr076	AATTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	829	G0A,	0.27411866
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U1A	7
cr277	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	830	G42A,	0.27320316
	CTGTTATCAACTAGAAATAGTGGCACAGAGTCTGTGCT		C50U,	2
			U61A,
			A66U,
			C75A,
			G81U
cr654	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGATTAAATAAGGCTAGT	831	U33A,	0.26915994
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U
cr374	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATAGT	832	C44A	0.26856496
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr073	GTTTTAGCGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	833	A4U,	0.26502082
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7C	2
cr046	GGTTAACAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	834	U1G,	0.25916315
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6C	5
cr965	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTACT	835	C44G,	0.25715684
	CCGTTATCAACTTGAAAAAGTGGCCTCGAGTCGAGGCT		G47C,	8
			A73C,
			C74U,
			G82A,
			U83G
cr696	GTTTAAGAGCTAAGCTGGAAACAGCATAGCGAGTTTAAATAAGGCTCGT	836	A30G,	0.25531685
	CCGTTATCCACTTGAAAAAGTGGCACCGAGTCGGTGCT		A46C,	6
			A57C
cr110	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	837	C44A,	0.25473053
	CCGTTATCAACTTGAAAAAGTGGCATTGAGTCAATGCT		G47U,	4
			C74U,
			C75U,
			G81A,
			G82A
cr480	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	838	C59A,	0.25432339
	CCGTTATCAAATTGAAAAATTGACACCTAGTAGGTGTT		G68U,	5
			G71A,
			G76U,
			C80A,
			C85U
cr077	GATTAAGTGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	839	U1A,	0.25379713
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7U	2
cr662	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTATAAATAAGGCTAGT	840	U34A	0.24911940
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr720	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGTTAAT	841	U33C,	0.24809349
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44U,	1
			G47A
cr792	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	842	U33C,	0.24199913
	CCGTTATCAACTTGAAAAAGTGCCACCGAGTCGGTGGT		G71C,	3
			C85G
cr158	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGTTAAT	843	G42A,	0.24028055
	CTGTTATCAACTCGAAAGAGTGGCACCGAGTCGGTGCT		C44U,	2
			G47A,
			C50U,
			U61C,
			A66G
cr025	CTTTAAAAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	844	G0C,	0.23960450
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6A	3
cr403	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	845	U33C,	0.23811666
	CCGTTATCAACTTGAAAAAGTGGCACAGAGTCTGTGCT		C75A,	3
			G81U
cr198	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATAAGGCTAGT	846	G32U,	0.23597958
	CCGTTATCAACTTGAAAAAGTGGCACCGAATCGGTGCT		G78A	8
cr262	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	847	U33C,	0.23293143
	CCGTTATCATCTTGAAAAAGAGGCACCGAGTCGGTGCT		A58U,	4
			U69A
cr308	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	848	G42A,	0.22487561
	CTGTTATCATCTTGAAAAAGAGGCTCCGAGTCGGAGCT		C50U,	5
			A58U,
			U69A,
			A73U,
			U83A
cr220	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATCAGGCTAGT	849	A40C,	0.22292893
	CCATAATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G51A,	8
			U53A
cr207	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	850	C80G	0.21537753
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTGGGTGCT			8
cr276	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	851	C44A,	0.21429568
	CCGTTATCAGCTTGAAAAAGCGGCACCCAGTGGGTGCT		G47U,	9
			A58G,
			U69C,
			G76C,
			C80G
cr853	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATAAGGCGAGT	852	G32U,	0.21213594
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U45G	2
cr194	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	853	C49A	0.21128789
	ACGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr854	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	854	C74G,	0.20988350
	CCGTTATCAACTTGAAAAAGTGGCAGGGAGTCCCTGCT		C75G,	8
			G81C,
			G82C
cr011	GTTCAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	855	U3C	0.20883537
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr180	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATCAGGCTAGT	856	A40C,	0.20797546
	CCGTTATCAACTTGAAAAAGTGGCACCGAGCCGGTGCT		U79C	7
cr218	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATAAGGCTAGT	857	G32U,	0.20699910
	CCGTTATCAACTTAAACAAGTGGCACCGAGTCGGTGCT		G62A,	3
			A65C
cr373	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	858	U33C,	0.19930318
	CCGTTATCAACTTGAAAAAGTGGCAGCGAGTCGCTGCT		C74G,	7
			G82C
cr429	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGAATATT	859	G43A,	0.19596394
	TCGTTATCAACTTGAAAAAGTGGCAACGAGTCGTTGCT		C44A,	3
			G47U,
			C49U,
			C74A,
			G82U
cr732	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAGGGCTAGT	860	A41G,	0.19257399
	CCGGTATCAACTTGAAAAAGTCGCACCGAGTCGGTGCT		U52G,	8
			G70C
cr347	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGCCTAGT	861	U33C,	0.19150948
	GCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43C,	9
			C49G
cr760	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAGGTTTAAATCAGGCTAGT	862	A31G,	0.19086803
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A40C,	4
			A64U
cr294	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGAATATT	863	G43A,	0.18828983
	TCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44A,	3
			G47U,
			C49U
cr090	GTTTTATAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	864	A4U,	0.18647663
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6U	3
cr900	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTGAAATAAGGCTAGT	865	U35G	0.18459068
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr605	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	866	C44A,	0.18385681
	CCGTTATCAACTTGAAAAAGTGGCACCCAGTGGGTGCT		G47U,	5
			G76C,
			C80G
cr452	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAATTTAAATAAGGCTAGT	867	G32A,	0.18130680
	CCGTTATAAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C56A	9
cr311	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTTGT	868	A46U,	0.17902707
	CCGTTATATACTTGAAAAAGTGGCACCGAGTCGGTGCT		C56A,	1
			A57U
cr812	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTACT	869	G47C	0.17658719
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr979	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	870	U52G,	0.17650523
	CCGGTATCTACTTGAAAAAGTGGCACCGAGTCGGTGCT		A57U	7
cr185	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	871	C80A	0.17551027
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTAGGTGCT			6
cr047	GTTTTAGTGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	872	A4U,	0.16858817
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7U	9
cr029	GTTTCAGGGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	873	A4C,	0.16628472
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7G	8
cr826	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATAAGGCTAGC	874	G32U,	0.16327899
	CCGTTTTCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U48C,	8
			A54U
cr447	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	875	U33C,	0.16159763
	CCGTTATCAACTTGAAAAAGTGGCACCAAGTTGGTGCT		G76A,	4
			C80U
cr544	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	876	G32C,	0.16051593
	CCGTTATCAACTCGAAAGAGTGGCACCGAGTCGGTGCT		U61C,	6
			A66G
cr230	GTTTAAGAGCTAAGCTGGAAACAGCATAACAAGTTTAAATAAGGCTAGT	877	G28A,	0.15978141
	CCATTATCAACTTGAACAAGTGGCACCGAGTCGGTGCT		G51A,
			A65C
cr618	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAGTAAGGCTAGT	878	A38G	0.15709594
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr683	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	879	C49U,	0.15683481
	TCGTTATGAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C56G	4
cr063	ATTTTAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	880	G0A,	0.15646058
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4U	5
cr221	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAATTTAAATAAGGCTAGT	881	G32A,	0.15527100
	CCGTTATGAACTTGAGAAAGTGGCACCGAGTCGGTGCT		C56G,	1
			A64G
cr075	GTTTCATAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	882	A4C,	0.15213573
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6U	5
cr502	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	883	G82C	0.14848903
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGCTGCT			7
cr229	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATACGGCTAGT	884	A41C	0.14780650
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr228	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTAGT	885	C44G	0.14243724
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr808	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	886	G42A,	0.14168757
	CTGTTATCAAATTGAAAAATTGTCACCGAGTCGGTGAT		C50U,	2
			C59A,
			G68U,
			G71U,
			C85A
cr395	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATATGGCTAGT	887	A41U,	0.13968530
	CCGTTATCAACTTGAAAAAGTAGCACCGAATCGGTGCT		G70A,	8
			G78A
cr761	GTTTAAGAGCTAAGCTGGAAACAGCATAGCTAGTTTAAATAAGGCTAGT	888	A30U,	0.13692485
	CCGTTGTCTACTTGAAAAAGTGGCACCGAGTCGGTGCT		A54G,	5
			A57U
cr881	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	889	G51U,	0.13665256
	CCTTTAACAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U55A	9
cr091	GTTTTACAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	890	A4U,	0.13664142
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6C	6
cr798	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGATATT	891	C44A,	0.13612444
	CCGTTATCAACTTGAAAAAGTGGCATCCAGTGGATGCT		G47U,	7
			C74U,
			G76C,
			C80G,
			G82A
cr390	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAATTTAAATAAGGCTAGT	892	G32A,	0.13544846
	CCGTTATCAACTTGAAAAAGTAGCACCGAGTCGGTGCT		G70A	3
cr560	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	893	U33C,	0.13471112
	CCGTTATCAACTTGAAAAAGTGGCACTGAGTCAGTGCT		C75U,	5
			G81A
cr872	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTATT	894	G47U	0.1317391
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr490	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	895	C59A,	0.13168726
	CCGTTATCAAATTGAAAAATTGGCGCCCAGTGGGCGCT		G68U,	6
			A73G,
			G76C,
			C80G,
			U83C
cr274	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	896	G32C,	0.13030086
	CCGTTATCAACCTGAAAAGGTGGCACCGAGTCGGTGCT		U60C,	9
			A67G
cr259	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	897	C56G,	0.12430892
	CCGTTATGTACTTCAAAAAGTGGCACCGAGTCGGTGCT		A57U,	3
			G62C
cr089	GTTTATAAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	898	A5U,	0.11827319
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6A	7
cr461	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	899	G51U,	0.11467393
	CCTTAATCAACTTGAAGAAGTGGCACCGAGTCGGTGCT		U53A,	8
			A65G
cr257	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATCAGGCTAGT	900	A40C,	0.11088059
	CCGTTATCAACTTGAAAAAGTTGCACCGAGTCGGTGCT		G70U	7
cr059	GGTTAAAAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	901	U1G,	0.11059782
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6A	2
cr538	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGACTAGT	902	U33C,	0.11036172
	TCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43A,	5
			C49U
cr411	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGACTAGT	903	G43A	0.10859452
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr580	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAGATAAGGCTAGT	904	A37G	0.10760803
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr087	CTTTGAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	905	G0C,	0.10411385
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4G	6
cr290	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	906	G51A,	0.10268076
	CCATTATCAACTTGAAAAAGTTGCACCGAGTCGGTGCT		G70U	1
cr288	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	907	G32C,	0.10159477
	CCGTTATCAACTGGAAACAGTGGCACCGAGTCGGTGCT		U61G,	8
			A66C
cr062	GTTCAAGTGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	908	U3C,	0.10033929
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7U	1
cr401	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	909	A73U,	0.09780867
	CCGTTATCAACTTGAAAAAGTGGCTCCGAGTCTGCGCT		G81U,	4
			U83C
cr796	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTAAAATAAGGCTAGT	910	U35A	0.09600498
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			9
cr453	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAACTAAGGCTAGT	911	A38C	0.09528018
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr513	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	912	G32C,	0.09470092
	CCGTTATCACCTTGAAAAAGGGGCACCGAGTCGGTGCT		A58C,	7
			U69G
cr084	GTCTGAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	913	U2C,	0.09372306
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4G	1
cr112	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGTTAAT	914	G42A,	0.09330089
	CTGTTATCAATTTGAAAAAATGGCACCGAGTCGGTGCT		C44U,	7
			G47A,
			C50U,
			C59U,
			G68A
cr714	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	915	U33C,	0.09294380
	CCGTTATCAAATTGAAAAATTGGCACCGAGTCGGTGCT		C59A,	2
			G68U
cr325	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	916	G32C,	0.09142578
	CCGTTATCAGCTTGAAAAAGCGGCACCGAGTCGGTGCT		A58G,	5
			U69C
cr066	ATTCAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	917	G0A,	0.09114989
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U3C	6
cr762	GTTTAAGAGCTAAGCTGGAAACAGCATAGCACGTTTAAATAAGGCTAGT	918	A31C,	0.08849885
	CUTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G51U	6
cr739	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	919	G32C,	0.08764186
	CCGTTATCAACGTGAAAACGTGGCACCGAGTCGGTGCT		U60G,	1
			A67C
cr791	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	920	G42A,	0.07648447
	CTGTTATCAACTTGAAAAAGTGGCACCCAGTGGGTGCT		C50U,	4
			G76C,
			C80G
cr985	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	921	G51U	0.07561542
	CUTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr588	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	922	G32C,	0.07506900
	CCGTTATCAACTTGAAAAAGTGGTACCGAGTCGGTACT		C72U,	5
			G84A
cr082	CGTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	923	G0C,	0.07409889
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U1G	7
cr038	GTTTGCGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	924	A4G,	0.07320819
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5C	7
cr582	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATACGGCTTGT	925	A41C,	0.07314933
	CCGTTATCAACTTCAAAAAGTGGCACCGAGTCGGTGCT		A46U,	4
			G62C
cr438	GTTTAAGAGCTAAGCTGGAAACAGCATAGCCAGTTTAAATACGGCTAGT	926	A30C,	0.06886058
	CCGTTGTCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A41C,	8
			A54G
cr097	GCTTACGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	927	U1C,	0.06585236
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5C	4
cr467	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	928	U33C,	0.06565261
	CCGTTATCAATTTGAAAAAATGGCACCGAGTCGGTGCT		C59U,	8
			G68A
cr197	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	929	G42U,	0.06435841
	CAGTTATCAACTGGAAACAGTGGCACCGAGTCGGTGCT		C50A,	6
			U61G,
			A66C
cr741	GTTTAAGAGCTAAGCTGGAAACAGCATGGCAAGTTTAAATACGGCTAGA	930	A27G,	0.06347491
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A41C,
			U48A
cr298	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	931	G51U,	0.06335444
	CUTTATCAACTTGAAAAAGTGGCACCGACTCGGTGCT		G78C	6
cr193	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	932	G42A,	0.06081432
	CTGTTATCAATTTGAAAAAATGCCACCGAGTCGGTGGT		C50U,	5
			C59U,
			G68A,
			G71C,
			C85G
cr514	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	933	G32C,	0.05775985
	CCGTTATCAACTTGAAAAAGTGGGACCGAGTCGGTCCT		C72G,	1
			G84C
cr061	GTTCAACAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	934	U3C,	0.05690570
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6C	1
cr419	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	935	G32C,	0.05682243
	CCGTTATCAACTTGAAAAAGTGGAACCGAGTCGGTTCT		C72A,	2
			G84U
cr928	GTTTAAGAGCTAAGCTGGAAACAGCATAGCTAGTTTAAATGAGGCTAGT	936	A30U,	0.05583216
	CCGTAATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A40G,
			U53A
cr115	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	937	G42U,	0.05506005
	CAGTTATCAACTCGAAAGAGTGGCACCGAGTCGGTGCT		C50A,	2
			U61C,
			A66G
cr422	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	938	C72U,	0.04929988
	CCGTTATCAACTTGAAAAAGTGGTACCGAGTCGTTCCT		G82U,	7
			G84C
cr465	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGATATT	939	U33C,	0.04863886
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44A,	7
			G47U
cr764	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	940	G32C	0.04862881
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr045	TTTTATGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	941	G0U,	0.04838579
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5U	9
cr050	CTTTAGGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	942	G0C,	0.04784583
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5G	1
cr677	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATTAGGCTAGT	943	A40U	0.04623113
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr188	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	944	G42U,	0.04409470
	CAGTTATCAACCTGAAAAGGTGGCACCGAGTCGGTGCT		C50A,	3
			U60C,
			A67G
cr625	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	945	U33C,	0.04331986
	CCGTTATCAACTTGAAAAAGTGGCACCTAGTAGGTGCT		G76U,	9
			C80A
cr138	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	946	U33C,	0.04306703
	CCGTTATCAACTTGAAAAAGTGGCACGGAGTCCGTGCT		C75G,	4
			G81C
cr211	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGTCTAGT	947	G43U	0.04074650
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr708	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	948	G42U,	0.03939779
	CAGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C50A	7
cr222	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	949	U33G,	0.03915274
	CCGTTATCAGCTTGAAAAAGCGGCACCGAGTCGGTGCT		A58G,	9
			U69C
cr107	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	950	G42U,	0.03837022
	CAGTTATCAACTTGAAAAAGTGGCAACGAGTCGTTGCT		C50A,	2
			C74A,
			G82U
cr767	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGATTAAATAAGGCTAGT	951	U33A,	0.03810067
	CCGTTATCAACTTGAAAAAGTGGCACCGTGTCGGTGCT		A77U	3
cr208	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	952	G51U,	0.03710978
	CCTTCATCAACTTGAAGAAGTGGCACCGAGTCGGTGCT		U53C,	1
			A65G
cr498	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAATTAAGGCTAGT	953	A38U	0.03563223
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr681	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTACT	954	G47C,	0.03559186
	CCGTTATTAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C56U
cr078	GTTTTGGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	955	A4U,	0.03506607
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5G	4
cr743	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	956	G32C,	0.03495136
	CCGTTATCAACTTGAAAAAGTGGCGCCGAGTCGGCGCT		A73G,	2
			U83C
cr202	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	957	G32C,	0.03423087
	CCGTTATCAACTTGAAAAAGTGACACCGAGTCGGTGTT		G71A,	5
			C85U
cr526	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	958	G42U,	0.03359079
	CAGTTATCAACTTGAAAAAGTGGCGCCGAGTCGGCGCT		C50A,
			A73G,
			U83C
cr738	GTTTAAGAGCTAAGCTGGAAACAGCATAGCATGTTTAAATTAGGCTAGT	959	A31U,	0.03305876
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A40U	8
cr241	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	960	G32C,	0.03304388
	CCGTTATCAACATGAAAATUGGCACCGAGTCGGTGCT		U60A,	9
			A67U
cr314	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	961	U33G,	0.03303268
	CCGTTATCAACTTGAAAAAGTGGAACCGAGTCGGTTCT		C72A,
			G84U
cr007	GTATAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	962	U2A	0.03285098
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr357	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	963	U33G,	0.03252715
	CCGTTATCAACTTGAAAAAGTGGCTCCGAGTCGGAGCT		A73U,	9
			U83A
cr574	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTCAATAAGGCTAGT	964	A36C	0.03217055
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			5
cr313	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	965	C72G,	0.03202363
	CCGTTATCAACTTGAAAAAGTGGGTGGGAGTCGCTCCT		A73U,	4
			C74G,
			C75G,
			G82C,
			G84C
cr571	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	966	U33G,	0.03194090
	CCGTTATCAACTCGAAAGAGTGGCACCGAGTCGGTGCT		U61C,	9
			A66G
cr151	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	967	G32C,	0.03186099
	CCGGTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U52G	3
cr880	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	968	U33G,	0.03182692
	CCGTTATCAACTTGAAAAAGTGGCATCGAGTCGATGCT		C74U,
			G82A
cr301	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	969	G32C,	0.03131975
	CCGTTATCAACTTGAAAAAGTGGCACCGATTCGGTGCT		G78U	2
cr988	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	970	G32C,	0.03049523
	CCGTTATCAAGTTGAAAAACTGGCACCGAGTCGGTGCT		C59G,	8
			G68C
cr716	GTTTAAGAGCTAAGCTGGAAACAGCATAGCCATTTTAAATAAGGCTAGT	971	A30C,	0.03032557
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G32U	9
cr312	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	972	U33G,	0.03032324
	CCGTTATCAACGTGAAAACGTGGCACCGAGTCGGTGCT		U60G,	7
			A67C
cr638	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGGTACT	973	U33C,	0.03011832
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44G,	1
			G47C
cr010	GTTAAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	974	U3A	0.02975655
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			2
cr095	GTATAAGTGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	975	U2A,	0.0297361
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7U
cr830	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGG	976	U48G,	0.02925330
	CCGTTATCAACTTGAAAAAGTGTCACCGAGTCGGTGCT		G71U	4
cr378	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATGAGGCTAGT	977	A40G	0.02892768
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT
cr933	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	978	U33G,	0.02860249
	CCGTTATCAACTTGAAAAAGTGTCACCGAGTCGGTGAT		G71U,	3
			C85A
cr757	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	979	G32C,	0.02850858
	CCGTTATCAACTAGAAATAGTGGCACCGAGTCGGTGCT		U61A,	4
			A66U
cr570	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	980	G32C,	0.02846076
	CCGTTATCAACTTGAAAAAGTGGCTCCGAGTCGGAGCT		A73U,	9
			U83A
cr009	GTGTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	981	U2G	0.02782799
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr012	GTTGAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	982	U3G	0.02768842
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			7
cr096	GTTTGTGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	983	A4G,	0.02763834
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5U	2
cr627	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	984	G42U,	0.02763056
	CAGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		C50A,	2
			A64G
cr048	TTTCAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	985	G0U,	0.02761154
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U3C	7
cr641	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	986	U33G	0.02687199
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr711	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	987	U33G,	0.02669595
	CCGTTATCAACTTGAAAAAGTGCCACCGAGTCGGTGGT		G71C,	1
			C85G
cr085	GTTTTTGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	988	A4U,	0.02665626
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5U	6
cr032	GATTGAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	989	U1A,	0.02645980
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4G	2
cr745	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGCTAGT	990	G42A	0.02630658
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr952	GTTTAAGAGCTAAGCTGGAAACAGCATAGCTAGTTTAAATAAGGCTAGG	991	A30U,	0.02620670
	CCGTAATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U48G,	9
			U53A
cr454	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	992	G42U,	0.02618139
	CAGTTATCACCTTGAAAAAGGGGTACCGAGTCGGTACT		C50A,	5
			A58C,
			U69G,
			C72U,
			G84A
cr986	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGG	993	U48G	0.02579911
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr737	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	994	C74G,	0.02539394
	CCGTTATCAACTTGAAAAAGTGGCAGGCTGTGGCTGCT		C75G,	1
			G76C,
			A77U,
			C80G,
			G82C
cr958	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	995	G32C,	0.02490160
	CCGTTATCAACTTGAAAAAGTGGCACTGAGTCAGTGCT		C75U,	2
			G81A
cr581	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTTAATAAGGCTAGT	996	A36U	0.02487978
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			3
cr043	GGTTTAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	997	U1G,	0.02450376
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4U
cr633	GTTTAAGAGCTAAGCTGGAAACAGCATGGCAATTTTAAATACGGCTAGT	998	A27G,	0.02444926
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G32U,	8
			A41C
cr386	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	999	U33G,	0.02427673
	CCGTTATCAACCTGAAAAGGTGGCACCGAGTCGGTGCT		U60C,	3
			A67G
cr935	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATGAGGCTAGT	1000	A40G,	0.02412571
	CCGTTATCAACTTGACAAAGTGGCACCGAGTCGGTGCT		A64C	5
cr946	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1001	G42C,	0.02369839
	CGGTTATCAACTGGAAACAGTGGCGCCGAGTCGGCGCT		C50G,	8
			U61G,
			A66C,
			A73G,
			U83C
cr922	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1002	U33G,	0.02327413
	CCGTTATCAACTTGAAAAAGTGGCAACGAGTCGTTGCT		C74A,	3
			G82U
cr080	TTTTTAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1003	G0U,	0.02322959
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4U	3
cr950	GTTTAAGAGCTAAGCTGGAAACAGCATAGCTAGGTTAAATAAGGCTAGT	1004	A30U,	0.02240303
	CCGTGATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U33G,	7
			U53G
cr547	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATGAGGCTAGT	1005	A40G,	0.02193573
	CCGTTATCAACTTGAAAAAGTGGCACCGAATCGGTGCT		G78A	2
cr542	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGATTAAATAAGGCTAGT	1006	U33A,	0.02115297
	CCGTCATCGACTTGAAAAAGTGGCACCGAGTCGGTGCT		U53C,	5
			A57G
cr700	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATTAGGCTAGT	1007	A40U,	0.02102791
	CCGTTATCAACTTGAAAAAGTGGCACCGCGTCGGTGCT		A77C	1
cr030	GCTTTAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1008	U1C,	0.02089383
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4U	7
cr680	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1009	G42C,	0.02085932
	CGGTTATCAACATGAAAATGTGGCTCCGAGTCGGAGCT		C50G,	1
			U60A,
			A67U,
			A73U,
			U83A
cr496	GTTTAAGAGCTAAGCTGGAAACAGCATAACAAGTTTAAATAAGGCTAGT	1010	G28A,	0.02078354
	CCTTTATCAACTTGAAAAAGTGGCACCGAGACGGTGCT		G51U,	8
			U79A
cr305	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1011	U33G,	0.02073443
	CCGTTATCAACTTGAAAAAGTGGCCCCGAGTCGGGGCT		A73C,	3
			U83G
cr418	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1012	U33G,	0.02069461
	CCGTTATCAACATGAAAATGTGGCACCGAGTCGGTGCT		U60A,	5
			A67U
cr186	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1013	U33G,	0.01990264
	CCGTTATCAAGTTGAAAAACTGGCACCGAGTCGGTGCT		C59G,	4
			G68C
cr507	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	1014	G42U,	0.01953843
	CAGTTATCAACTTGAAAAAGTGGCACAGAGTCTGTGCT		C50A,	8
			C75A,
			G81U
cr389	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAACTAGT	1015	G42A,	0.01943923
	TTGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43A,	1
			C49U,
			C50U
cr519	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1016	U33G,	0.01932892
	CCGTTATCAACTTGAAAAAGTGGCGCCGAGTCGGCGCT		A73G,	7
			U83C
cr834	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1017	U33G,	0.01861355
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		A64G	7
cr541	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAAGGTACT	1018	G42A,	0.01856181
	CTGTTATCAACTTGAAAAAGTGGTACCGAGTCGGTACT		C44G,	3
			G47C,
			C50U,
			C72U,
			G84A
cr987	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGG	1019	U48G,	0.01814568
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U	1
cr954	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1020	U33G,	0.01751493
	CCGTTATCAAATTGAAAAATTGGCACCGAGTCGGTGCT		C59A,	9
			G68U
cr448	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGCCTAGT	1021	G32C,	0.01746477
	GCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43C,
			C49G
cr382	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAACGCTAGT	1022	U33G,	0.01714583
	CGGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G42C,	5
			C50G
cr324	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1023	G42C,	0.01711128
	CGGTTATCAACCTGAAAAGGTGGCACCGAGTCGGTGCT		C50G,	5
			U60C,
			A67G
cr504	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1024	U33G,	0.01684638
	CCGTTATCAACTGGAAACAGTGGCACCGAGTCGGTGCT		U61G,	4
			A66C
cr864	GTTTAAGAGCTAAGCTGGAAACAGCATAACAAGTTTAAATTAGGCTAGT	1025	G28A,	0.01677620
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A40U	7
cr031	GTAAAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1026	U2A,	0.01676682
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U3A	2
cr042	GTGGAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1027	U2G,	0.01640069
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U3G	1
cr841	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1028	U33G,	0.01635699
	CCGTTATCACCTTGAAAAAGGGGCACCGAGTCGGTGCT		A58C,	1
			U69G
cr336	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGGTACT	1029	C44G,	0.01615738
	CCGTTATCAAATTGAAAAATTGGCACCCAGTGGGTGCT		G47C,	6
			C59A,
			G68U,
			G76C,
			C80G
cr963	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1030	G42C,	0.01550548
	CGGTTATCAACTTGAAAAAGTGGCGCTGAGTCAGCGCT		C50G,	6
			A73G,
			C75U,
			G81A,
			U83C
cr731	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1031	U33G,	0.01504990
	CCGTTATCAACTTGAAAAAGTGGTACCGAGTCGGTACT		C72U,	5
			G84A
cr170	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1032	U33G,	0.01500767
	CCGTTATCAACTTGAAAAAGTGACACCGAGTCGGTGTT		G71A,	1
			C85U
cr462	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAAATTAAATAAGGCTAGT	1033	G32A,	0.01488192
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U33A	9
cr261	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1034	U33G,	0.01441044
	CCGGTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U52G	1
cr384	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAATCTAGT	1035	G42A,	0.01438038
	ATGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43U,	3
			C49A,
			C50U
cr413	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1036	G32C,	0.01426502
	CCGTTATCAACTTGAAAAAGTGGCAACGAGTCGTTGCT		C74A,	4
			G82U
cr316	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	1037	G42U,	0.01421508
	CAGTTATCAACTTGAAAAAGTGGCACCAAGTTGGTGCT		C50A,	7
			G76A,
			C80U
cr041	GCTTCAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1038	U1C,	0.01415250
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4C	7
cr562	GTTTAAGAGCTAAGCTGGAAACAGCATAGCGACTTTAAATAAGGCTAGT	1039	A30G,	0.01393306
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		G32C,	6
			A64G
cr157	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1040	G32C,	0.01383452
	CCGTTATCAACTTGAAAAAGTGGCATCGAGTCGATGCT		C74U,
			G82A
cr028	GTCCAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1041	U2C,	0.01375137
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U3C	1
cr248	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1042	G42C,	0.01368982
	CGGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C50G	2
cr310	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATTAGGCTAGT	1043	A40U,	0.01362721
	CCGTTATCAACTTGAATAAGTGGCACCGAGTCGGTGCT		A65U	9
cr191	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1044	G32C,	0.01357575
	CCGTTATCAACTTGAAAAAGTGGCAGCGAGTCGCTGCT		C74G,	8
			G82C
cr773	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTTGT	1045	U33G,	0.01327276
	CCGTTATCAACTTGGAAAAGTGGCACCGAGTCGGTGCT		A46U,
			A63G
cr424	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1046	G42C,	0.01318765
	CGGTTATCAACTTGAAAAAGTGGCGTCGAGTCGACGCT		C50G,	7
			A73G,
			C74U,
			G82A,
			U83C
cr337	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1047	G42C,	0.01313069
	CGGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		C50G,	4
			A64G
cr111	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1048	G32C,	0.01299682
	CCGTTATCAACTTGAAAAAGTGGCACAGAGTCTGTGCT		C75A,	4
			G81U
cr665	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1049	G32C,	0.01293633
	CCGTTATCAACTTGAAAAAGTGCCACCGAGTCGGTGGT		G71C,	7
			C85G
cr280	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1050	G32C,	0.01272726
	CCGTTATCAACTTGAAAAAGTGTCACCGAGTCGGTGAT		G71U,	2
			C85A
cr103	GTTTAAGAGCTAAGCTGGAAACAGCATAGCATGTTTAAATAAGGCTAGT	1051	A31U,	0.01258621
	CCTTTATCAACTTGAAAAAGTGGCACCGAGGCGGTGCT		G51U,	1
			U79G
cr528	GTTTAAGAGCTAAGCTGGAAACAGCATAGCCGCTTTAAATAAGGCTAGT	1052	A30C,	0.01243089
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A31G,	8
			G32C
cr204	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGTCTAGT	1053	U33C,	0.01236156
	ACGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43U,	9
			C49A
cr079	GGTCAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1054	U1G,	0.01223007
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U3C	3
cr024	TTGTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1055	G0U,	0.01205485
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U2G	5
cr268	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1056	U33G,	0.01165832
	CCGTTATCAACTTGAAAAAGTGGCACAGAGTCTGTGCT		C75A,	3
			G81U
cr332	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATATTAAATAAGGCTAGT	1057	G32U,	0.01144860
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U33A	8
cr649	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGTTAAT	1058	G32C,	0.01138431
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44U,	8
			G47A
cr475	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATTAGGCTAGT	1059	A40U,	0.01129852
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U	9
cr613	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGGTACT	1060	G42C,	0.01125210
	CGGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44G,	5
			G47C,
			C50G
cr750	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1061	U33G,	0.01124813
	CCGTTATCAATTTGAAAAAATGGCACCGAGTCGGTGCT		C59U,	8
			G68A
cr663	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1062	U33G,	0.01081332
	CCGTTATCAACTTGAAAAAGTGGCACCTAGTAGGTGCT		G76U,	9
			C80A
cr445	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1063	C74U,	0.01078478
	CCGTTATCAACTTGAAAAAGTGGCATCCAGTTGCTGCT		G76C,	5
			C80U,
			G82C
cr509	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1064	G42C,	0.01060821
	CGGTTATCAACTTGAAAAAGTGGCAACGAGTCGTTGCT		C50G,	2
			C74A,
			G82U
cr044	GTGTAAGTGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1065	U2G,	0.01046099
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A7U	6
cr860	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAAGCTAGT	1066	U33C,	0.01022314
	CTGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G42A,	3
			C50U
cr949	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1067	U33G,	0.01022133
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A64U	3
cr250	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGA	1068	U48A,	0.01011888
	CCGTTATCAACTTGAAAAAGTCGCACCGAGGCGGTGCT		G70C,
			U79G
cr067	CTTGAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1069	G0C,	0.01011543
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U3G	6
cr795	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	1070	G42U,	0.00991111
	CAGTTATCAACTTGAAAAAGTCTCTCCGAGTCGGAGAT		C50A,	2
			G71U,
			A73U,
			U83A,
			C85A
cr587	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGCCTAGT	1071	G43C	0.00980486
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr993	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1072	G32C,	0.00968956
	CCGTTATCATCTTGAAAAAGAGGCACCGAGTCGGTGCT		A58U,	5
			U69A
cr130	GTTTAAGAGCTAAGCTGGAAACAGCATAGCATTTTTAAATAAGGCTAGT	1073	A31U,	0.00950709
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G32U	1
cr328	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1074	G32C,	0.00938366
	CCGTTATCAACTTGAAAAAGTGGCCCCGAGTCGGGGCT		A73C,
			U83G
cr187	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1075	U33G,	0.00928
	CCGTTATCAACTTGAAAAAGTGGCACCAAGTTGGTGCT		G76A,
			C80U
cr052	ATGTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1076	G0A,	0.00923802
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U2G	7
cr081	ATTAAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1077	G0A,	0.00920730
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U3A	9
cr114	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGTTAATC	1078	G42U,	0.00858876
	AGTTATCAACTTGAAAAAGTGGCCCCGAGTCGGGGCT		C44U,	5
			G47A,
			C50A,
			A73C,
			U83G
cr137	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAGGGCTAGT	1079	A41G,	0.00851592
	CCTTTATCAACTTGCAAAAGTGGCACCGAGTCGGTGCT		G51U,	2
			A63C
cr648	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGTCTAGT	1080	G32C,	0.00830519
	ACGTDVTCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43U,	3
			C49A
cr295	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1081	U33G,	0.00826811
	CCGTTATCAACTTGAAAAAGTGGGACCGAGTCGGTCCT		C72G,	1
			G84C
cr436	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1082	U33G,	0.00812877
	CCGTTATCATCTTGAAAAAGAGGCACCGAGTCGGTGCT		A58U,	9
			U69A
cr862	GTTTAAGAGCTAAGCTGGAAACAGCATATCACCTTTAAATAAGGCTAGTC	1083	G28U,	0.00788739
	CGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A31C,	2
			G32C
cr160	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATCTTAAATAAGGCTAGT	1084	G32U,	0.00787920
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U33C	4
cr807	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATTAGGCTAGT	1085	A40U,	0.00776015
	CCGTTATCAACTTGAAAAAGTGGCACCGTGTCGGTGCT		A77U	7
cr664	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACCTTAAATAAGGCTAGT	1086	G32C,	0.00743456
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U33C	4
cr512	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGCCTAGT	1087	U33G,	0.00741391
	GCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43C,	6
			C49G
cr415	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1088	G51C,	0.00728079
	CCCTTATCAACTTGAAATAGTGGCACCGAGTCGGTGCT		A66U	1
cr499	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1089	G32C,	0.00725709
	CCGATATCAACTTGAAAAAGTGGCACCGAGGCGGTGCT		U52A,	5
			U79G
cr778	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1090	C50G	0.00719960
	CGGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr339	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACCCTAGT	1091	G42C,	0.00717030
	GGGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43C,	9
			C49G,
			C50G
cr247	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATAAGGCTAGT	1092	G32U,	0.00715877
	CCGTTATCAACTTGAAAAAGTAGCACCGAGTCGGTGCT		G70A	9
cr883	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAGGGCTAGT	1093	A41G,	0.00697489
	CGGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C50G	1
cr376	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1094	G32C,	0.00696306
	CCGTTATCAACTTGAAAAAGTGGCACGGAGTCCGTGCT		C75G,	3
			G81C
cr518	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATGAGGCTAGT	1095	G32U,	0.00688861
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A40G
cr092	GTGTAGGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1096	U2G,	0.00687503
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5G	1
cr281	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1097	A73U,	0.00680234
	CCGTTATCAACTTGAAAAAGTGGCTGGCAGTCCGAGCT		C74G,	1
			C75G,
			G76C,
			G81C,
			U83A
cr463	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1098	G42C,	0.00679918
	CGGTTATCACCTTGAAAAAGGGGCACCTAGTAGGTGCT		C50G,	1
			A58C,
			U69G,
			G76U,
			C80A
cr174	GTTTAAGAGCTAAGCTGGAAACAGCATACCAAGTTTAAATAAGGCTAGG	1099	G28C,	0.00666861
	CCGTTATCAACTTGAAAAAGTGGCACCGATTCGGTGCT		U48G,	1
			G78U
cr706	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1100	U33G,	0.00659509
	CCGTTATCAACTTGAAAAAGTGGCACGGAGTCCGTGCT		C75G,
			G81C
cr967	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1101	G42C,	0.00644569
	CGGTTATCAACTTGAAAAAGTGGAACCAAGTTGGTTCT		C50G,	5
			C72A,
			G76A,
			C80U,
			G84U
cr272	GTTTAAGAGCTAAGCTGGAAACAGCATAGCCACTTTAAATAAGGCTAGT	1102	A30C,	0.00637966
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G32C	2
cr744	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGATTAAATCAGGCTAGT	1103	U33A,	0.00634918
	CCGTTATCAACTTGTAAAAGTGGCACCGAGTCGGTGCT		A40C,	4
			A63U
cr615	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1104	G71C,	0.00633273
	CCGTTATCAACTTGAAAAAGTGCGTGCGAGTCGGAGGT		C72G,	6
			A73U,
			C74G,
			U83A,
			C85G
cr100	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATGAGGCTAGC	1105	A40G,	0.00592343
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U48C	9
cr584	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATATGGCTAGT	1106	A41U,	0.00591156
	CCTTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G51U	2
cr057	GCTCAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1107	U1C,	0.00575747
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U3C
cr266	GTTTAAGAGCTAAGCTGGAAACAGCATGGCAAGGTTAAATAAGGCGAG	1108	A27G,	0.00555163
	TCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U33G,	7
			U45G
cr537	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATTAGGCTAGT	1109	U33C,	0.00527642
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A40U	2
cr060	GTGCAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1110	U2G,	0.00518162
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U3C	2
cr309	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGG	1111	U33G,	0.00504472
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U48G	8
cr472	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGTTAAT	1112	U33G,	0.00491748
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44U,	3
			G47A
cr297	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGG	1113	U48G,	0.00487154
	CCGTTATCAACTTGAAAAAGTCGCACCGAGTCGGTGCT		G70C	3
cr849	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACGTTAAATAAGGCTAGT	1114	G32C,	0.00470573
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U33G	8
cr845	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAAGCTAGT	1115	U33G,	0.00464434
	CTGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G42A,	1
			C50U
cr400	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGATATT	1116	U33G,	0.00451030
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44A,	5
			G47U
cr094	GATTTAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1117	U1A,	0.00441375
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4U	1
cr203	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1118	G32C,	0.00433309
	CCTTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G51U
cr753	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1119	G42C,	0.00430245
	CGGTTATCATCTTGAAAAAGAGGAACCGAGTCGGTTCT		C50G,
			A58U,
			U69A,
			C72A,
			G84U
cr857	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATTAGGCTAGT	1120	A40U,	0.00427408
	CCGTTATCAACTTGAAAAAGTCGCACCGAGCCGGTGCT		G70C,
			U79C
cr833	GTTTAAGAGCTAAGCTGGAAACAGCATCGCAACTTTAAATAAGGCTAGT	1121	A27C,	0.00421504
	CCGTTATCAACTTGAAAAAGTCGCACCGAGTCGGTGCT		G32C,	6
			G70C
cr660	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTAGT	1122	G42U	0.00410246
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr536	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGATATT	1123	G32C,	0.00395083
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44A,	1
			G47U
cr245	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGACTAGT	1124	U33G,	0.00372006
	TCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43A,	4
			C49U
cr303	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTTGT	1125	G32C,	0.00357317
	CCGTTATCAACTTGAAAAAGTTGCACCGAGTCGGTGCT		A46U,	9
			G70U
cr981	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1126	G42C,	0.00326636
	CGGTTATCAACTGGAAACAGTGGCACCGAGTCGGTGCT		C50G,	7
			U61G,
			A66C
cr469	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1127	G51C	0.00321140
	CCCTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			6
cr049	GTGTTAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1128	U2G,	0.00320308
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4U	7
cr381	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATTAGGCTCGT	1129	A40U,	0.00319558
	CCGTTATCAACTTGACAAAGTGGCACCGAGTCGGTGCT		A46C,	2
			A64C
cr482	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTTGT	1130	A46U,	0.00300577
	CCCTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G51C	4
cr068	GTTAGAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1131	U3A,	0.00295873
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4G	7
cr674	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1132	G42C,	0.00279049
	CGGTTATCATCTTGAAAAAGAGGCACCGAGTCGGTGCT		C50G,	7
			A58U,
			U69A
cr572	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACCGAACT	1133	G42C,	0.00278236
	CGGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43C,	4
			C44G,
			U45A,
			G47C,
			C50G
cr225	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGCTATTC	1134	G42U,	0.00265752
	CGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G47U	7
cr657	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1135	C49G	0.00260388
	GCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			8
cr735	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1136	G32C,	0.00259569
	CCGTTATCAACTTGAAAAAGTCGCACCGAGTCGGTGCT		G70C	5
cr608	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGGTACT	1137	G32C,	0.00256753
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44G,	3
			G47C
cr903	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1138	G51C,	0.00221972
	CCCTTATCAACTTGAAAAAGTGGCACCGAATCGGTGCT		G78A	6
cr975	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATATGGCTAGT	1139	G32C,	0.00215224
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A41U	7
cr874	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAATGCTAGT	1140	U33G,	0.00214364
	CAGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G42U,	1
			C50A
cr810	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1141	U33G,	0.00202570
	CCGTTATCAACTTGAAAAAGTGGCACCCAGTGGGTGCT		G76C,	2
			C80G
cr355	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1142	U33G,	0.00177064
	CCGTTATCAACTAGAAATAGTGGCACCGAGTCGGTGCT		U61A,	4
			A66U
cr786	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATTAGGCTAGT	1143	A40U,	0.00174934
	CCGTTGTGAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A54G,	8
			C56G
cr930	GTTTAAGAGCTAAGCTGGAAACAGCATAGCATGTTTAAATAAGGCTAGG	1144	A31U,	0.00172050
	CCGTTATTAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U48G,	2
			C56U
cr149	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1145	G42C,	0.00166965
	CGGTTATCAACTTGAAAAAGTGTCACCGAGTCGGTGAT		C50G,	2
			G71U,
			C85A
cr143	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGACTAGT	1146	G32C,	0.00152365
	TCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43A,	1
			C49U
cr244	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGGTACT	1147	U33G,	0.00149049
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44G,	5
			G47C
cr093	GTTTCGGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1148	A4C,	0.00145631
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5G	9
cr350	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAATGCTAGT	1149	U33C,	0.00142283
	CAGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G42U,
			C50A
cr083	GTGTGAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1150	U2G,	0.00140262
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4G
cr106	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGTCTAGT	1151	U33G,	0.00135486
	ACGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43U,	3
			C49A
cr787	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAACGCTAGT	1152	G32C,	0.00084829
	CGGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G42C,	5
			C50G
cr035	GTTGATGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1153	U3G,	0.00069273
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5U	1
cr099	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATAAGGCTAGG	1154	G32U,	0.00050524
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U48G	3
cr939	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCAAGT	1155	G32C,	4.94E−05
	CCCTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U45A,
			G51C
cr912	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATGAGGCTAGT	1156	U33C,
	CCGGTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A40G,	−1.05E−05
			U52G
cr629	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1157	G32C,
	CCGTTATCAAATTGAAAAATTGGCACCGAGTCGGTGCT		C59A,	−5.97E−5
			G68U
cr431	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1158	U33G,	−0.00027128
	CCGTTATCAACTTGAGAAAGTGGCACCGCGTCGGTGCT		A64G,
			A77C
cr579	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1159	G51C,	−0.00041798
	CCCATATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U52A	5
cr535	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1160	C50A	−0.00042808
	CAGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			4
cr751	GTTTAAGAGCTAAGCTGGAAACAGCATCGCAACTTTAAATAAGGCTAGT	1161	A27C,	−0.00055700
	CCTTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G32C,	4
			G51U
cr039	GATTATGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1162	U1A,	−0.00061806
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5U	4
cr178	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAATTTTAAATGAGGCTAGT	1163	G32U,	−0.00087438
	CCGTTATCGACTTGAAAAAGTGGCACCGAGTCGGTGCT		A40G,	9
			A57G
cr326	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAACGCTAGT	1164	U33C,	−0.00106221
	CGGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G42C,	6
			C50G
cr637	GTTTAAGAGCTAAGCTGGAAACAGCATATCAAGATTAAATAAGGCTAGT	1165	G28U,	−0.00126524
	CCGTTATCAACTTGAGAAAGTGGCACCGAGTCGGTGCT		U33A,
			A64G
cr564	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1166	U33G,	−0.00195195
	CCGTTATCAACTTGAAAAAGTGGCAGCGAGTCGCTGCT		C74G,	2
			G82C
cr921	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAAGTTAAATAAGGCTAGT	1167	G32A,	−0.00220269
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U33G	5
cr817	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAGGGCTAGT	1168	U33G,	−0.00251447
	CCGTTATCAACTTGATAAAGTGGCACCGAGTCGGTGCT		A41G,	3
			A64U
cr847	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGCTTAAATAAGGCTAGT	1169	U33C,	−0.00268195
	CCGTTATCAACTTGAAAAAGTGGCACCCAGTGGGTGCT		G76C,	9
			C80G
cr595	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1170	G42C	−0.00298431
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1
cr065	GTATACGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1171	U2A,	−0.00314793
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A5C	3
cr058	GTTGCAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1172	U3G,	−0.00315143
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4C
cr894	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1173	G32C,	−0.00340798
	CCGTTATCAATTTGAAAAAATGGCACCGAGTCGGTGCT		C59U,	4
			G68A
cr122	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1174	G32C,	−0.00340841
	CCGTTATTAACTTGATAAAGTGGCACCGAGTCGGTGCT		C56U,	6
			A64U
cr233	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1175	G32C,	−0.00351086
	CCGTTATCAACTTGAAAAAGTGGCACCCAGTGGGTGCT		G76C,	1
			C80G
cr053	GATTCAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1176	U1A,	−0.00370399
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4C	7
cr686	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1177	G32C,	−0.00401893
	CCGTTATCAACTTGAAAAAGTGGCACCGAGACGGTGCT		U79A	5
cr179	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGGTTAAATAAGGCTAGT	1178	U33G,	−0.00449278
	CCGTTATCAACTTGAAAAAGTGGCACTGAGTCAGTGCT		C75U,	7
			G81A
cr033	GTTAAAAAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1179	U3A,	−0.00465615
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G6A	9
cr673	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1180	G32C,	−0.00507235
	CCGTTATCAACTTGAAAAAGTGGCACCGTGTCGGTGCT		A77U	7
cr589	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATGATATTC	1181	G42U,	−0.00510801
	AGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		C44A,
			G47U,
			C50A
cr802	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1182	G42C,	−0.00537686
	CGGTTATCAAGTTGAAAAACTGGCAGCGAGTCGCTGCT		C50G,	5
			C59G,
			G68C,
			C74G,
			G82C
cr383	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1183	G32C,	−0.00550463
	CCGTTATCAACTTGAAAAAGTGGCACCTAGTAGGTGCT		G76U,	1
			C80A
cr036	GCTAAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1184	U1C,	−0.00567874
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U3A
cr034	GCATAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1185	U1C,	−0.00719105
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U2A	1
cr329	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAAGCTAGT	1186	G32C,	−0.00760657
	CTGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G42A,	1
			C50U
cr135	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAACGCTAGT	1187	G42C,	−0.00878040
	CGGTTATCAAATTGAAAAATTGGCGCCGAGTCGGCGCT		C50G,	6
			C59A,
			G68U,
			A73G,
			U83C
cr056	GTGTCAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAAGGCTAGT	1188	U2G,	−0.00951153
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		A4C	1
cr583	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATCAGGCTAGT	1189	A40C,	−0.01012720
	CCATTATCTACTTGAAAAAGTGGCACCGAGTCGGTGCT		G51A,	7
			A57U
cr661	GTTTAAGAGCTAAGCTGGAAACAGCATAACAAGTTTAAATAAGGCTAGT	1190	G28A,	−0.01151227
	CCCTTATCAACTTGAATAAGTGGCACCGAGTCGGTGCT		G51C,	7
			A65U
cr784	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAAGTTTAAATAATCCTAGTT	1191	G42U,	−0.01212699
	CGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G43C,	9
			C49U
cr540	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAATGCTAGTC	1192	G32C,	−0.01220390
	AGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT		G42U,	3
			C50A
cr990	GTTTAAGAGCTAAGCTGGAAACAGCATATCAAGCTTAAATAAGGCTAGT	1193	G28U,	−0.01629740
	CCGTTATGAACTTGAAAAAGTGGCACCGAGTCGGTGCT		U33C,	7
			C56G
cr790	GTTTAAGAGCTAAGCTGGAAACAGCATAGCAACTTTAAATAAGGCTAGT	1194	G32C,	−0.0169697
	CCGTTATCAACTTGAAAAAGTGGCACCAAGTTGGTGCT		G76A,
			C80U
cr551	GTTTAAGAGCTAAGCTGGAAACAGCTTAGCAAGTTTAAATAAGGCTAGT	1195	A25U	−0.10897159
	CCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGCT			1

Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, one of skill in the art will appreciate that certain changes and modifications may be practiced within the scope of the appended claims. In addition, each reference provided herein is incorporated by reference in its entirety to the same extent as if each reference was individually incorporated by reference.

Claims

1. A method of generating a set of single guide RNAs (sgRNAs) capable of driving a series of discrete expression levels of a target gene in a cell population using CRISPR interference (CRISPRi) or CRISPR activation (CRISPRa), the method comprising: (i) providing a first sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the first sgRNA are 100% homologous to the target DNA sequence;(ii) providing a second sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the second sgRNA comprises one or more mismatches with the target DNA sequence such that the CRISPRi or CRISPRa activity on the gene obtained using the second sgRNA is intermediate between that obtained using the first sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene; and(iii) providing a third sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the third sgRNA comprises one or more mismatches with the target DNA sequence such that the CRISPRi or CRISPRa activity on the gene obtained using the third sgRNA is intermediate between that obtained using the second sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene;wherein the mismatches of the second and third sgRNAs are selected according to the following rules:(a) the CRISPRi or CRISPRa activity of the second sgRNA is designed to be greater than that of the third sgRNA based on the following positional relationships, wherein the positions correspond to the number of bases in the sgRNAs upstream from the sgRNA PAM:−19>−18>−17>−16≈−15≈−14>−13>−12>−11>−10>−9>−8>−4>−7≈−6≈−5≈−3≈−2≈−1; or(b) the CRISPRi or CRISPRa activity of the second sgRNA is designed to be greater than that of the third sgRNA based on the following base pair rankings of the mismatched nucleotides, wherein the first nucleotide in each pair corresponds to the ribonucleotide within the sgRNA and the second nucleotide corresponds to the deoxyribonucleotide within the target DNA:rG:dT>rU:dG>rG:dA≈rG:dG>rC:dA>rU:dT>rA:dA>rC:dT>rA:dC>rA:dG>rU:dC≈rC:dC.

2. The method of claim 1, further comprising providing one or more additional sgRNAs, wherein the last 19 nucleotides of the targeting sequence of each of the one or more additional sgRNAs comprise at least one mismatch with the target DNA sequence, wherein each of the one or more additional sgRNAs provide CRISPRi or CRISPRa activity on the gene that is intermediate between that obtained using the third sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene, and wherein the mismatches with the template DNA of each of the one or more additional sgRNAs are selected according to rules (a) and (b) of claim 1.

3. The method of claim 1, wherein the target gene is a mammalian gene.

4. The method of claim 3, wherein the mammalian gene is a human gene.

5. A set of single guide RNAs (sgRNAs) for obtaining a series of discrete expression levels of a target gene using CRISPRi or CRISPRa, comprising (i) a first sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the first sgRNA is 100% homologous to the target DNA sequence;(ii) a second sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the second sgRNA comprises one or more mismatches with the target DNA sequence such that the CRISPRi or CRISPRa activity on the gene obtained using the second sgRNA is intermediate between that obtained using the first sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene; and(iii) a third sgRNA that targets the gene, wherein the last 19 nucleotides of the targeting sequence of the third sgRNA comprises one or more mismatches with the target DNA sequence such that the CRISPRi or CRISPRa activity obtained using the third sgRNA is intermediate between that obtained using the second sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene;wherein the mismatches of the second and third sgRNAs are selected according to the following rules:(a) the CRISPRi or CRISPRa activity of the second sgRNA is designed to be greater than that of the third sgRNA based on the following positional relationships, wherein the positions correspond to the number of bases in the sgRNAs upstream from the sgRNA PAM:−19>−18>−17>−16≈−15≈−14>−13>−12>−11>−10>−9>−8>−4>−7≈−6≈−5≈−3≈−2≈−1; or(b) the CRISPRi or CRISPRa activity of the second sgRNA is designed to be greater than that of the third sgRNA based on the following base pair rankings of the mismatched nucleotides, wherein the first nucleotide in each pair corresponds to the ribonucleotide within the sgRNA and the second nucleotide corresponds to the deoxyribonucleotide within the target DNA:rG:dT>rU:dG>rG:dA≈rG:dG>rC:dA>rU:dT>rA:dA>rC:dT>rA:dC>rA:dG>rU:dC≈rC:dC.

6. The set of sgRNAs of claim 5, further comprising one or more additional sgRNAs, wherein the last 19 nucleotides of the targeting sequences of each of the one or more additional sgRNAs comprise at least one mismatch with the target DNA sequence, wherein each of the one or more additional sgRNAs provide CRISPRi or CRISPRa activity on the gene that is intermediate between that obtained using the third sgRNA and a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene, and wherein the CRISPRi or CRISPRa activity of each of the one or more additional sgRNAs on the gene is determined according to rules (a) and (b) of claim 5.

7. The set of sgRNAs of claim 6, wherein the set comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more sgRNAs providing intermediate levels of CRISPRi or CRISPRa activity on the gene between that obtained using the first sgRNA and that obtained using a scrambled sgRNA providing no CRISPRi or CRISPRa activity on the gene.

8. A method of obtaining a series of discrete expression levels of a target gene in a plurality of cells, the method comprising: contacting the plurality of cells with the set of sgRNAs of claim 5; andcontacting the plurality of cells with a nuclease-deficient sgRNA-mediated nuclease (dCas9), wherein the dCas9 comprises a dCas9 domain fused to a transcriptional modulator;thereby generating a plurality of test cells, wherein each test cell comprises an sgRNA and the dCas9,wherein the sgRNA present in a given test cell guides the dCas9 in the test cell to the target gene and modulates its expression level as a function of the absence or presence of one or more mismatches with the target DNA sequence according to rules (a) and (b) of claim 5.

9. The method of claim 8, wherein the transcriptional modulator is a transcriptional repressor.

10. The method of claim 9, wherein the transcriptional repressor is KRAB.

11. The method of claim 8, wherein the transcriptional modulator is a transcriptional activator.

12. The method of claim 11, wherein the transcriptional activator is VP64.

13. The method of claim 8, wherein the cells are mammalian cells.

14. The method of claim 13, wherein the cells are human cells.

15. The method of claim 8 wherein each sgRNA is encoded by an expression cassette comprising a polynucleotide encoding the sgRNA, operably linked to a promoter.

16. The method of 8, wherein the dCas9 is encoded by an expression cassette comprising a polynucleotide encoding the dCas9, operably linked to a promoter.

17. The method of claim 8, further comprising determining the relationship between the expression level of the target gene and a phenotype, comprising: (i) determining the identity of the sgRNA present in a given test cell;(ii) assessing the phenotype of the test cell; and(iii) correlating the expression level of the gene targeted by the sgRNA identified in step (i) and the phenotype assessed in step (ii).

18. The method of claim 17, wherein assessing the phenotype of the cells comprises fluorescence activated cell sorting, affinity purification of the cells, measuring the transcriptomes of the cells, or measuring the growth, proliferation, and/or survival of the cells.

19. The method of claim 18, wherein the transcriptomes of the cells are measured by perturb-seq.

20. A method of determining a therapeutic window for the inhibition of a gene, the method comprising determining the relationship between the expression level of the gene and the phenotype according to the method of claim 18 for a plurality of sgRNAs targeting the gene, wherein the transcriptional modulator is a transcriptional repressor, and wherein the phenotype of the cells is assessed by measuring cell growth or survival; and further comprising: (iv) determining the minimum level of expression of the gene that is compatible with cell growth or survival, thereby determining the lower boundary of the therapeutic window for the inhibition of the gene.