Methods for selectively suppressing non-target sequences

Information

  • Patent Grant
  • 11408024
  • Patent Number
    11,408,024
  • Date Filed
    Wednesday, September 9, 2015
    9 years ago
  • Date Issued
    Tuesday, August 9, 2022
    2 years ago
  • Inventors
  • Original Assignees
    • Molecular Loop Biosciences, Inc. (Allston, MA, US)
  • Examiners
    • Whisenant; Ethan C
    Agents
    • Brown Rudnick LLP
    • Meyers; Thomas C.
Abstract
The invention generally relates to negative selection of nucleic acids. The invention provides methods and systems that remove unwanted segments of nucleic acid in a sample so that a target gene or region of interest may be analyzed without interference from the unwanted segments. A sample is obtained that includes single-stranded nucleic acid with one or more unwanted segments. Complementary nucleic acid is added to the single-stranded nucleic acid to create a double-stranded region that includes the unwanted segment. The double-stranded region is then digested, leaving single-stranded nucleic acid that includes the target gene or region of interest. This allows paralogs, pseudogenes, repetitive elements, and other segments of the genome that may be similar to the target gene or region of interest to be removed from the sample.
Description
TECHNICAL FIELD

The invention generally relates to negative selection of nucleic acids.


BACKGROUND

The advent of high-throughput DNA sequencing has the potential to revolutionize modern biology and transform diagnostic medicine. Instruments for next-generation sequencing (NGS) continue to generate more data and become more inexpensive at a rate far outpacing Moore's Law. However, the most popular sequencers have an extremely short read length, limiting their ability to characterize any gene containing paralogous sequence or repetitive elements. As nearly two thirds of the genome is highly repetitive and over 20,000 pseudogenic regions exist, much of the genome is very difficult to characterize in a modern whole-genome sequencing experiment. Unfortunately, for many genes of clinical interest, characterizing those genes is made difficult by the presence of paralogs, pseudogenic homologs, and other segments of the genome that may be similar to the gene of interest and thus stymie attempts to detect, sequence, or isolate the gene of interest. As a result, despite the power of NGS instruments, some disease-related genes and mutations, even where known, are difficult to detect.


SUMMARY

The invention provides methods and systems that remove unwanted segments of nucleic acid in a sample so that a target gene or region of interest may be analyzed without interference from the unwanted segments. A sample is obtained that includes single-stranded nucleic acid with one or more unwanted segments. Primers that are specific or preferentially bind to the unwanted segment are hybridized to the single-stranded nucleic acid within the unwanted region or in a non-repetitive section upstream of the unwanted region and extended by a polymerase to create a double-stranded region that includes the unwanted segment. The double-stranded region is then digested, leaving single-stranded nucleic acid that includes the target gene or region of interest. This allows paralogs, pseudogenes, repetitive elements, and other segments of the genome that may be similar to the target gene or region of interest to be removed from the sample. The target gene or region of interest may thus be detected or characterized by analysis without interference from the unwanted segments. This may provide an improved ability to detect features such as disease-related genes and mutations, thus improving the clinical value of NGS technologies.


Systems and methods of the invention may be used to remove unwanted regions from genomic DNA (such as homologous genes, pseudogenes, or repetitive elements) prior to any DNA-based experimental procedure, including but not limited to microarray hybridization, quantitative or standard polymerase chain reaction, multiplex target capture, or DNA sequencing (either targeted or shotgun). Systems and methods of the invention provide for the identification of mutations in previously difficult-to-characterize genes, and therefore allow practitioners to expand the number of genes included in a targeted or whole-genome sequencing assay.


In certain aspects, the invention provides a method of removing unwanted segments of a nucleic acid from a sample. The method includes annealing a nucleic acid primer to a portion of a single-stranded nucleic acid that flanks an unwanted segment of the nucleic acid, extending the annealed primer in order to create a double-stranded region that includes the unwanted segment; and digesting the double-stranded region, thereby removing the unwanted segment from the nucleic acid.


The nucleic acid in the sample may include DNA, RNA, modified nucleic acids, or combinations thereof. The method may include obtaining a sample from a subject and denaturing double-stranded DNA in the sample. Denaturing can include the use of methods such as exposing the sample to heat, a detergent, or an acidic or basic solution.


The primer may be annealed within the unwanted segment or within an area upstream of the unwanted segment and extended. A pair or a number of primers may be used and primers that flank the unwanted segment may be used. In certain embodiments, a plurality of primers are annealed to a plurality of portions of that nucleic acid that flank an unwanted segment. The primer or primers are preferably extended using a polymerase enzyme under conditions sufficient to cause extension of the primer in a template-dependent manner. In some embodiments, a primer or oligonucleotide is hybridized to the unwanted segment to create the double-stranded region containing the unwanted segment without need for an extension step.


The double-stranded region is digested. This can include exposing the sample to an enzyme that preferentially digests double-stranded nucleic acid such as certain double-stranded endonucleases, restriction endonucleases, or nicking enzymes. After digestion, the enzyme may be de-activated (e.g., by heat, chemicals, etc.). Digestion preferably results in intact genomic DNA lacking one or more unwanted segment and that is compatible with a nucleic acid analysis assay. Nucleic acid that is not digested may be analyzed by a nucleic acid analysis assay.


Assays suitable for analysis of the remaining un-digested nucleic acid may make use of molecular inversion probe capture, hybrid capture, Haloplex, sequencing (e.g., Sanger sequencing, NGS, or both), other methodologies, or combinations thereof. Where the unwanted segment is a paralog, a pseudogene, or non-paralogous repetitive element, such elements may be removed from the sample by methods of the invention.


In certain aspects, the invention provides a method of removing nucleic acid from a sample. The method includes annealing at least one oligonucleotide to single-stranded DNA in a sample, wherein the single-stranded DNA comprises target and non-target sequence. The oligonucleotide may be annealed to the non-target sequence to create double-stranded DNA that includes the non-target sequence or the oligonucleotide may be annealed elsewhere and extended to create double-stranded DNA that includes the non-target sequence. The non-target sequence is removed from the sample by digesting the double-stranded DNA. The target sequence may be analyzed using, e.g., molecular inversion probes, microarray hybridization, multiplex ligation-dependent probe amplification (MLPA), sequencing, fingerprinting techniques such as RFLP/AFLP, chromatography, others, or combinations thereof. In some embodiments, the method includes first obtaining the sample from a subject and denaturing double-stranded subject DNA to produce the single-stranded DNA. Preferably, that single-stranded DNA consists essentially of genomic DNA from the subject prior to the annealing of the oligo. The annealing may include annealing a pair of oligonucleotides to the single-stranded DNA at sites that flank the non-target sequence (i.e., to remove both strands of the unwanted segment or non-target sequence. In certain embodiments, the target and non-target sequence are both located on at least one single strand of the single-stranded DNA, and extending the at least one oligonucleotide and digesting the double-stranded DNA results in removing the non-target sequence from the at least one single strand of the single-stranded DNA.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 diagrams a method of removing unwanted segments of a nucleic acid.



FIG. 2 illustrates methods according to certain embodiments.



FIG. 3 gives a diagram of a system according to embodiments of the invention.





DETAILED DESCRIPTION

To enable the characterization of difficult genomic regions using high-throughput short-read sequencing, the invention provides methods for the removal of unwanted genomic regions from a population of DNA molecules (e.g. genomic DNA). Most DNA-based techniques rely on the amplification of specific regions of interest or sequencing library molecules in a positive selection process (e.g. amplification utilizing primers that are unique to a single paralog). Methods of the invention instead involve a negative selection technique that removes any undesired analogous sequence, allowing application of standard high-throughput sequencing techniques or other analyses to any difficult-to-characterize gene of interest.


Applicability of methods of the invention may be illustrated by reference to two exemplary genes of interest for which direct high-throughput sequencing-based approaches are currently insufficient. One gene is “glucosidase beta acid,” or GBA, which has been implicated as causative in Gaucher disease. Currently, long-range polymerase chain reaction experiments are required to characterize this gene, as a pseudogene with nearly identical sequence exists a mere 15,000 base pairs away. By removing this pseudogenic region using the invention, GBA can be characterized with high specificity, enabling construction of a genetic screen for Gaucher disease. This gene is a suitable target for methods of the invention, as it is relatively small and contains nearby unique flanking sequence.


An additional gene of interest is “survival of motor neuron 1,” or SMN1. This gene has been implicated in spinal muscular atrophy. Currently, due to the presence of a paralogous gene known as SMN2 that is 100,000 base pairs away from SMN1, characterization of SMN1 is extremely challenging. By removing SMN2 using the invention, SMA could be screened for with a high-throughput sequencing approach that would not require a complex statistical model.


Additionally, novel causative mutations in genes such as SMN1 could also be identified. This gene is a suitable target for methods of the invention. It is of a suitable size and flanked by highly repetitive regions.



FIG. 1 diagrams a method 101 of removing unwanted segments of a nucleic acid from a sample according to embodiments of the invention. The method includes obtaining 105 a sample that includes nucleic acid. An oligonucleotide is annealed 109 to an unwanted segment of the nucleic acid or a portion of the nucleic acid that flanks an unwanted segment of the nucleic acid. In embodiments in which the oligonucleotide flanks the unwanted segment, the oligonucleotide is extended 113 to create a double-stranded region that includes the unwanted segment. In embodiments in which the oligonucleotide is annealed to the unwanted segment, a double-stranded region that includes the unwanted segment is created by virtue of the hybridization of the oligonucleotide at that segment. The double-stranded region is digested 117, thus removing the unwanted segment from the nucleic acid. This allows for a region or gene of interest to be analyzed 121.


The sample that includes nucleic acid may be obtained 105 by any suitable method. The sample may be obtained from a tissue or body fluid that is obtained in any clinically acceptable manner. Body fluids may include mucous, blood, plasma, serum, serum derivatives, bile, blood, maternal blood, phlegm, saliva, sweat, amniotic fluid, menstrual fluid, mammary fluid, follicular fluid of the ovary, fallopian tube fluid, peritoneal fluid, urine, and cerebrospinal fluid (CSF), such as lumbar or ventricular CSF. A sample may also be a fine needle aspirate or biopsied tissue. A sample also may be media containing cells or biological material. Samples may also be obtained from the environment (e.g., air, agricultural, water and soil) or may include research samples (e.g., products of a nucleic acid amplification reaction, or purified genomic DNA, RNA, proteins, etc.).


Isolation, extraction or derivation of genomic nucleic acids may be performed by methods known in the art. Isolating nucleic acid from a biological sample generally includes treating a biological sample in such a manner that genomic nucleic acids present in the sample are extracted and made available for analysis. Generally, nucleic acids are extracted using techniques such as those described in Green & Sambrook, 2012, Molecular Cloning: A Laboratory Manual 4 edition, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. (2028 pages), the contents of which are incorporated by reference herein. A kit may be used to extract DNA from tissues and bodily fluids and certain such kits are commercially available from, for example, BD Biosciences Clontech (Palo Alto, Calif.), Epicentre Technologies (Madison, Wis.), Gentra Systems, Inc. (Minneapolis, Minn.), and Qiagen Inc. (Valencia, Calif.). User guides that describe protocols are usually included in such kits.


It may be preferable to lyse cells to isolate genomic nucleic acid. Cellular extracts can be subjected to other steps to drive nucleic acid isolation toward completion by, e.g., differential precipitation, column chromatography, extraction with organic solvents, filtration, centrifugation, others, or any combination thereof. The genomic nucleic acid may be resuspended in a solution or buffer such as water, Tris buffers, or other buffers. In certain embodiments the genomic nucleic acid can be re-suspended in Qiagen DNA hydration solution, or other Tris-based buffer of a pH of around 7.5.


Any nucleic acid may be analyzed using methods of the invention. Nucleic acids suitable for use in aspects of the invention may include without limit genomic DNA, genomic RNA, synthesized nucleic acids, whole or partial genome amplification product, and high molecular weight nucleic acids, e.g. individual chromosomes. In certain embodiments, a sample is obtained that includes double-stranded DNA, such as bulk genomic DNA from a subject, and the double-stranded DNA is then denatured.


Double stranded nucleic acid may be denatured using any suitable method such as, for example, through the use of heat, detergent incubation, or an acidic or basic solution.



FIG. 2 illustrates the progress of methods according to certain embodiments. As shown in FIG. 2, methods may start with double stranded DNA (dotted shading if not otherwise hatched) that contains a gene of interest (first angled hatching pattern) and a paralog of the gene of interest (second angled hatching pattern). It will be appreciated that methods of the invention may operate starting with any suitable nucleic acid such as double- or single-stranded DNA or RNA or any combination thereof. The unwanted segment may be any sequence for which removal is desired from the starting nucleic acid. For example, the unwanted segment may include a paralog or homolog of a gene or region of interest; a pseudogene; or non-paralogous repetitive element. As used herein, homolog refers to a gene related to a second gene by descent from a common ancestral DNA sequence. Homolog describes the relationship between genes separated by the event of speciation (i.e., orthology) or to the relationship between genes separated by the event of genetic duplication (i.e., paralogy). Orthologs generally refers to genes in different species that evolved from a common ancestral gene by speciation. Normally, orthologs retain the same function in the course of evolution and paralogs are genes related by duplication within a genome. See Fitch, 1970, Distinguishing homologs from analogous proteins, Syst Biol 19(2):99-113 and Jensen, 2001, Orthologs and paralogs—we need to get it right, Genome Biol 2(8):1002-1002.3. Pseudogenes include dysfunctional relatives of genes that have lost their protein-coding ability or are otherwise no longer expressed in the cell. Methods of the invention may be used to target a pseudogene that is present as a homolog to another gene or pseudogene within a sample and methods of the invention may be used to target a pseudogene that is present even where no known homologs of the pseudogene are suspected to also be present in the sample.


As illustrated in FIG. 2, the double-stranded DNA is denatured into its two complementary strands prior to primer hybridization. Any suitable method may be used to denature nucleic acid. Heat-based denaturing is a process by which double-stranded nucleic acid unwinds and separates into single-stranded strands. Heat denaturation of a nucleic acid of an unknown sequence typically uses a temperature high enough to ensure denaturation of even nucleic acids having a very high GC content, e.g., 95° C.-98° C. in the absence of any chemical denaturant. It is well within the abilities of one of ordinary skill in the art to optimize the conditions (e.g., time, temperature, etc.) for denaturation of the nucleic acid. Temperatures significantly lower than 95° C. can also be used if the DNA contains nicks (and therefore sticky overhangs of low Tm), sequence of sufficiently low Tm, or chemical additives such as betaine.


Denaturing nucleic acids with the use of pH is also well known in the art, and such denaturation can be accomplished using any method known in the art such as introducing a nucleic acid to high or low pH, low ionic strength, and/or heat, which disrupts base-pairing causing a double-stranded helix to dissociate into single strands. For methods of pH-based denaturation see, for example, Ageno et al., 1969, The alkaline denaturation of DNA, Biophys J 9:1281-1311.


Nucleic acids can also be denatured via electro-chemical means, for example, by applying a voltage to a nucleic acid within a solution by means of an electrode. Varying methods of denaturing by applying a voltage are discussed in detail in U.S. Pat. Nos. 6,197,508 and 5,993,611. After denaturation, unwanted segments can be targeted for removal.


Methods of the invention include targeting unwanted segments of nucleic acid for removal. An unwanted segment of nucleic acid can be targeted for removal by making it into a double-stranded segment. The unwanted segment can be made double-stranded by hybridizing a complementary oligonucleotide to the unwanted segment, by hybridizing a complementary oligonucleotide to a genomic segment flanking the unwanted segment and extending the oligonucleotide, or a combination thereof (e.g., an oligonucleotide can be hybridized so that it sits partially within the unwanted segment and then extended via methods described herein).


In certain embodiments, the oligonucleotide to be hybridized is a primer that is unique to the unwanted segment. For example, methods may include using a primer that is unique to a certain paralog or other element. The invention provides methods of making a primer and primer extension reactions that are unique to a paralog or similar segment by including or using a primer with a 3′ end that terminates on a differentiating base (i.e., the 3′-most base or bases of the primer may be complementary to a base or bases that appear only in association with the segment (e.g., paralog) targeted for removal.


In some embodiments, double stranded DNA is created by hybridization alone (e.g., rather than by using oligonucleotide primer with polymerase extension). One or more long segments of nucleic acid complementary to the unwanted segments could be used. For example, long segments of synthetic DNA could be used. The segments of complementary nucleic acid could have any suitable length such as, for example, tens of bases, hundreds of bases, length of an exon, length of a gene, etc. Use of one or more long segments of nucleic acid complementary to the unwanted segments (e.g., followed by digestion of dsDNA) may provide for enrichment of, for example, target relative to non-target.


As noted above, the recognition site for the oligonucleotide, primer, or complementary nucleic acid may flank the unwanted segment, lie within the unwanted segment, or both. Additionally, methods may include using one or any suitable number of oligonucleotides or primers to target an unwanted segment or segments of nucleic acid.


In the non-limiting, illustrative embodiment shown in FIG. 2, primers (cross-hatching pattern) are annealed to unique genomic segments flanking the paralogous region. The primer may be annealed at any suitable location. For example, it may be preferable to anneal any of the one or more primers to a portion within 50 or fewer bases from the unwanted segment, although it may not be necessary to anneal the primers within 50 bases of the unwanted region. As shown in FIG. 2, primers are annealed at locations that flank the unwanted segment, i.e., each primer of a pair hybridizes to its target strand in a region that flanks the 5′ end of the unwanted segment. In this way, extension of the primers will result in most or all of the unwanted segments being present in exclusively double-stranded form, whereas the desired region(s) should remain in a primarily single-stranded state.


In certain embodiments, polymerase (drawn as an open circle in FIG. 2) is used to perform second-strand synthesis over the paralogous region. Extending the annealed primer creates a double-stranded region that includes the unwanted segment. The primer is extended using a polymerase enzyme under conditions sufficient to cause extension of the primer in a template-dependent manner. Suitable polymerase enzymes include phi29, Bst, Exo-minus E. Coli Polymerase I, Taq Polymerase, and T7 Polymerase I.


An enzymatic digestion (the digestion enzyme is represented by a darkened hexagon in FIG. 2) is then used to degrade only the double-stranded paralogous region, leaving behind the gene of interest. Any suitable digestion platform may be employed such as, for example, dsDNAse, fragmentase, a non-specific nicking enzyme such as a modified Vvn, restriction enzymes such as MspJI and FspEI, and a combination of USER plus T7 endonuclease I.


Thermo Scientific dsDNase is an engineered shrimp DNase designed for rapid and safe removal of contaminating genomic DNA from RNA samples. It is an endonuclease that cleaves phosphodiester bonds in DNA to yield oligonucleotides with 5′-phosphate and 3′-hydroxyl termini. Highly specific activity towards double-stranded DNA ensures that RNA and single-stranded DNA such as cDNA and primers are not cleaved. dsDNase is easily inactivated by moderate heat treatment (55° C.). Thermo Scientific dsDNAse is available from Thermo Fisher Scientific, Inc. (Waltham, Mass.).


Fragmentase includes the enzyme sold under the trademark NEBNEXT dsDNA fragmentase by New England Biolabs (Ipswich, Mass.). NEBNEXT dsDNA fragmentase generates dsDNA breaks in a time-dependent manner to yield 50-1,000 bp DNA fragments depending on reaction time. NEBNext dsDNA Fragmentase contains two enzymes, one randomly generates nicks on dsDNA and the other recognizes the nicked site and cuts the opposite DNA strand across from the nick, producing dsDNA breaks. The resulting DNA fragments contain short overhangs, 5′-phosphates, and 3′-hydroxyl groups. The random nicking activity of NEBNext dsDNA Fragmentase has been confirmed by preparing libraries for next-generation sequencing. A comparison of the sequencing results between genomic DNA (gDNA) prepared with NEBNext dsDNA fragmentase and with mechanical shearing demonstrates that the NEBNext dsDNA Fragmentase does not introduce any detectable bias during the sequencing library preparation and no difference in sequence coverage is observed using the two methods


The Vibrio vulnificus nuclease, Vvn, is a non-specific periplasmic nuclease capable of digesting DNA and RNA. It has been suggested that Vvn hydrolyzes DNA by a general single-metal ion mechanism. See Li, et al., 2003, DNA binding and cleavage by the periplasmic nuclease Vvn: a novel structure with a known active site, EMBO J 22(15):4014-4025.


MspJI is a modification dependent endonuclease that recognizes certain methylation patterns. The most common epigenetic modifications found in eukaryotic organisms are methylation marks at CpG or CHG sites. A subset of these modified sites are recognized and cleaved by MspJI. MspJI is available from New England Biolabs. T7 Endonuclease I recognizes and cleaves non-perfectly matched DNA, cruciform DNA structures, Holliday structures or junctions, hetero-duplex DNA and more slowly, nicked double-stranded DNA. The cleavage site is at the first, second or third phosphodiester bond that is 5′ to the mismatch. The protein is the product of T7 gene 3. Any other suitable enzyme for digesting the target unwanted segments may be used.


The added enzymes may then be deactivated using an irreversible heat or chemical treatment, leaving genomic DNA lacking an intact undesired region(s) yet still compatible with any downstream assay (e.g. molecular inversion probe capture or any other library construction methodology).


The digesting step results in intact genomic DNA lacking one or more unwanted segment and that is compatible with a nucleic acid analysis assay. This DNA can then be utilized for any downstream assay. Downstream assays may include molecular inversion capture, sequencing, others, or a combination thereof.


Methods of the invention can be used to negatively select out pseudogenic regions from the genome. Methods of the invention can be combined with a genetic test, screening, or other assay in order to screen patients for mutations in a gene (e.g., GBA, SMN1, or other genes containing paralogous regions). Some background may be found in published international patent application WO 2013/191775, to Nugen Technologies, Inc.


After removing the unwanted segment from the nucleic acid, the sample may be enriched for genes of interest using methods known in the art, such as hybrid capture. Methods suitable for use may be found discussed in U.S. Pat. Nos. 8,529,744; 7,985,716; 7,666,593; and 6,613,516. As will be described in more detail below, a preferable capture method uses molecular inversion probes.


Nucleic acids, including genomic nucleic acids, can be fragmented using any of a variety of methods, such as mechanical fragmenting, chemical fragmenting, and enzymatic fragmenting. Methods of nucleic acid fragmentation are known in the art and include, but are not limited to, DNase digestion, sonication, mechanical shearing, and the like. U.S. Pub 2005/0112590 provides a general overview of various methods of fragmenting known in the art.


Genomic nucleic acids can be fragmented into uniform fragments or randomly fragmented. In certain aspects, nucleic acids are fragmented to form fragments having a fragment length of about 5 kilobases or 100 kilobases. Desired fragment length and ranges of fragment lengths can be adjusted depending on the type of nucleic acid targets one seeks to capture and the design and type of probes such as molecular inversion probes (MIPs) that will be used. Chemical fragmentation of genomic nucleic acids can be achieved using methods such as a hydrolysis reaction or by altering temperature or pH. Nucleic acid may be fragmented by heating a nucleic acid immersed in a buffer system at a certain temperature for a certain period to time to initiate hydrolysis and thus fragment the nucleic acid. The pH of the buffer system, duration of heating, and temperature can be varied to achieve a desired fragmentation of the nucleic acid. Mechanical shearing of nucleic acids into fragments can be used e.g., by hydro-shearing, trituration through a needle, and sonication. The nucleic acid can also be sheared via nebulization, hydro-shearing, sonication, or others. See U.S. Pat. Nos. 6,719,449; 6,948,843; and 6,235,501. Nucleic acid may be fragmented enzymatically. Enzymatic fragmenting, also known as enzymatic cleavage, cuts nucleic acids into fragments using enzymes, such as endonucleases, exonucleases, ribozymes, and DNAzymes. Varying enzymatic fragmenting techniques are well-known in the art. Additionally, DNA may be denatured again as needed after the digestion and any other sample prep steps. For example, during a fragmentation step, ssDNA may anneal to form dsDNA and it may be desirable to again denature the dsDNA. In certain embodiments, the sample nucleic acid is captured or targeted using any suitable capture method or assay such as hybridization capture or capture by probes such as MIPs.


MIPs, or molecular inversion probes, can be used to detect or amplify particular nucleic acid sequences in complex mixtures. Use of molecular inversion probes has been demonstrated for detection of single nucleotide polymorphisms (Hardenbol et al., 2005, Highly multiplexed molecular inversion probe genotyping: over 10,000 targeted SNPs genotyped in a single tube assay, Genome Res 15:269-75) and for preparative amplification of large sets of exons (Porreca et al., 2007, Multiplex amplification of large sets of human exons, Nat Methods 4:931-6, Krishnakumar et al., 2008, A comprehensive assay for targeted multiplex amplification of human DNA sequences, PNAS 105:9296-301). One of the main benefits of the method is in its capacity for a high degree of multiplexing, because generally thousands of targets may be captured in a single reaction containing thousands of probes.


In certain embodiments, molecular inversion probes include a universal portion flanked by two unique targeting arms. The targeting arms are designed to hybridize immediately upstream and downstream of a specific target sequence located on a genomic nucleic acid fragment. The molecular inversion probes are introduced to nucleic acid fragments to perform capture of target sequences located on the fragments. According to the invention, fragmenting aids in capture of target nucleic acid by molecular inversion probes. As described in greater detail herein, after capture of the target sequence (e.g., locus) of interest, the captured target may further be subjected to an enzymatic gap-filling and ligation step, such that a copy of the target sequence is incorporated into a circle. Capture efficiency of the MIP to the target sequence on the nucleic acid fragment can be improved by lengthening the hybridization and gap-filing incubation periods. (See, e.g., Turner et al., 2009, Massively parallel exon capture and library-free resequencing across 16 genomes, Nature Methods 6:315-316.)


A library of molecular inversion probes may be created and used in capturing DNA of genomic regions of interests (e.g., SMN1, SMN2, control DNA). The library includes a plurality of oligonucleotide probes capable of capturing one or more genomic regions of interest (e.g., SMN1, SMN2 and control loci) within the samples to be tested.


The result of MIP capture as described above is a library of circular target probes, which then can be processed in a variety of ways. Adaptors for sequencing may be attached during common linker-mediated PCR, resulting in a library with non-random, fixed starting points for sequencing. For preparation of a shotgun library, a common linker-mediated PCR is performed on the circle target probes, and the post-capture amplicons are linearly concatenated, sheared, and attached to adaptors for sequencing. Methods for shearing the linear concatenated captured targets can include any of the methods disclosed for fragmenting nucleic acids discussed above. In certain aspects, performing a hydrolysis reaction on the captured amplicons in the presence of heat is the desired method of shearing for library production.


In some embodiments, the amount of target nucleic acid and probe used for each reaction is normalized to avoid any observed differences being caused by differences in concentrations or ratios. In some embodiments, in order to normalize genomic DNA and probe, the genomic DNA concentration is read using a standard spectrophotometer or by fluorescence (e.g., using a fluorescent intercalating dye). The probe concentration may be determined experimentally or using information specified by the probe manufacturer.


Similarly, once a locus has been captured, it may be amplified and/or sequenced in a reaction involving one or more primers. The amount of primer added for each reaction can range from 0.1 pmol to 1 nmol, 0.15 pmol to 1.5 nmol (for example around 1.5 pmol). However, other amounts (e.g., lower, higher, or intermediate amounts) may be used.


A targeting arm may be designed to hybridize (e.g., be complementary) to either strand of a genetic locus of interest if the nucleic acid being analyzed is DNA (e.g., genomic DNA). For MIP probes, whichever strand is selected for one targeting arm will be used for the other one. In the context of RNA analysis, a targeting arm should be designed to hybridize to the transcribed RNA. It also should be appreciated that MIP probes referred to herein as “capturing” a target sequence are actually capturing it by template-based synthesis rather than by capturing the actual target molecule (other than for example in the initial stage when the arms hybridize to it or in the sense that the target molecule can remain bound to the extended MIP product until it is denatured or otherwise removed).


A targeting arm may include a sequence that is complementary to one allele or mutation (e.g., a SNP or other polymorphism, a mutation, etc.) so that the probe will preferentially hybridize (and capture) target nucleic acids having that allele or mutation. Sequence tags (also referred to as barcodes) may be designed to be unique in that they do not appear at other positions within a probe or a family of probes and they also do not appear within the sequences being targeted. Uniformity and reproducibility can be increased by designing multiple probes per target, such that each base in the target is captured by more than one probe.


The length of a capture molecule on a nucleic acid fragment (e.g., a target nucleic acid or sub-region thereof) may be selected based upon multiple considerations. For example, where analysis of a target involves sequencing, e.g., with a next-generation sequencer, the target length should typically match the sequencing read-length so that shotgun library construction is not necessary. However, it should be appreciated that captured nucleic acids may be sequenced using any suitable sequencing technique as aspects of the invention are not limited in this respect.


It is also to be appreciated that some target nucleic acids on a nucleic acid fragment are too large to be captured with one probe. Consequently, it may be helpful to capture multiple sub-regions of a target nucleic acid in order to analyze the full target.


Methods of the invention also provide for combining the method of fragmenting the nucleic acid prior to capture with other MIP capture techniques that are designed to increase target uniformity, reproducibility, and specificity. Other MIP capture techniques are shown in U.S. Pub. 2012/0165202, incorporated by reference.


Multiple probes, e.g., MIPs, can be used to amplify each target nucleic acid. In some embodiments, the set of probes for a given target can be designed to ‘tile’ across the target, capturing the target as a series of shorter sub targets. In some embodiments, where a set of probes for a given target is designed to ‘tile’ across the target, some probes in the set capture flanking non-target sequence). Alternately, the set can be designed to ‘stagger’ the exact positions of the hybridization regions flanking the target, capturing the full target (and in some cases capturing flanking non-target sequence) with multiple probes having different targeting arms, obviating the need for tiling. The particular approach chosen will depend on the nature of the target set. For example, if small regions are to be captured, a staggered-end approach might be appropriate, whereas if longer regions are desired, tiling might be chosen. In all cases, the amount of bias-tolerance for probes targeting pathological loci can be adjusted by changing the number of different MIPs used to capture a given molecule.


Probes for MIP capture reactions may be synthesized on programmable microarrays because of the large number of sequences required. Because of the low synthesis yields of these methods, a subsequent amplification step is required to produce sufficient probe for the MIP amplification reaction. The combination of multiplex oligonucleotide synthesis and pooled amplification results in uneven synthesis error rates and representational biases. By synthesizing multiple probes for each target, variation from these sources may be averaged out because not all probes for a given target will have the same error rates and biases.


Using methods described herein, a single copy of a specific target nucleic acid may be amplified to a level that can be sequenced. Further, the amplified segments created by an amplification process such as PCR may be, themselves, efficient templates for subsequent PCR amplifications.


Amplification or sequencing adapters or barcodes, or a combination thereof, may be attached to the fragmented nucleic acid. Such molecules may be commercially obtained, such as from Integrated DNA Technologies (Coralville, Iowa). In certain embodiments, such sequences are attached to the template nucleic acid molecule with an enzyme such as a ligase. Suitable ligases include T4 DNA ligase and T4 RNA ligase, available commercially from New England Biolabs (Ipswich, Mass.). The ligation may be blunt ended or via use of complementary overhanging ends. In certain embodiments, following fragmentation, the ends of the fragments may be repaired, trimmed (e.g. using an exonuclease), or filled (e.g., using a polymerase and dNTPs) to form blunt ends. In some embodiments, end repair is performed to generate blunt end 5′ phosphorylated nucleic acid ends using commercial kits, such as those available from Epicentre Biotechnologies (Madison, Wis.). Upon generating blunt ends, the ends may be treated with a polymerase and dATP to form a template independent addition to the 3′-end and the 5′-end of the fragments, thus producing a single A overhanging. This single A can guide ligation of fragments with a single T overhanging from the 5′-end in a method referred to as T-A cloning. Alternatively, because the possible combination of overhangs left by the restriction enzymes are known after a restriction digestion, the ends may be left as-is, i.e., ragged ends. In certain embodiments double stranded oligonucleotides with complementary overhanging ends are used.


In certain embodiments, one or more bar code is attached to each, any, or all of the fragments. A bar code sequence generally includes certain features that make the sequence useful in sequencing reactions. The bar code sequences are designed such that each sequence is correlated to a particular portion of nucleic acid, allowing sequence reads to be correlated back to the portion from which they came. Methods of designing sets of bar code sequences is shown for example in U.S. Pat. No. 6,235,475, the contents of which are incorporated by reference herein in their entirety. In certain embodiments, the bar code sequences range from about 5 nucleotides to about 15 nucleotides. In a particular embodiment, the bar code sequences range from about 4 nucleotides to about 7 nucleotides. In certain embodiments, the bar code sequences are attached to the template nucleic acid molecule, e.g., with an enzyme. The enzyme may be a ligase or a polymerase, as discussed above. Attaching bar code sequences to nucleic acid templates is shown in U.S. Pub. 2008/0081330 and U.S. Pub. 2011/0301042, the content of each of which is incorporated by reference herein in its entirety. Methods for designing sets of bar code sequences and other methods for attaching bar code sequences are shown in U.S. Pat. Nos. 6,138,077; 6,352,828; 5,636,400; 6,172,214; 6,235,475; 7,393,665; 7,544,473; 5,846,719; 5,695,934; 5,604,097; 6,150,516; 7,537,897; 6,172,218; and 5,863,722, the content of each of which is incorporated by reference herein in its entirety. After any processing steps (e.g., obtaining, isolating, fragmenting, amplification, or barcoding), nucleic acid can be sequenced.


Sequencing may be by any method known in the art. DNA sequencing techniques include classic dideoxy sequencing reactions (Sanger method) using labeled terminators or primers and gel separation in slab or capillary, sequencing by synthesis using reversibly terminated labeled nucleotides, pyrosequencing, 454 sequencing, Illumina/Solexa sequencing, allele specific hybridization to a library of labeled oligonucleotide probes, sequencing by synthesis using allele specific hybridization to a library of labeled clones that is followed by ligation, real time monitoring of the incorporation of labeled nucleotides during a polymerization step, polony sequencing, and SOLiD sequencing. Separated molecules may be sequenced by sequential or single extension reactions using polymerases or ligases as well as by single or sequential differential hybridizations with libraries of probes.


A sequencing technique that can be used includes, for example, Illumina sequencing. Illumina sequencing is based on the amplification of DNA on a solid surface using fold-back PCR and anchored primers. Genomic DNA is fragmented, and adapters are added to the 5′ and 3′ ends of the fragments. DNA fragments that are attached to the surface of flow cell channels are extended and bridge amplified. The fragments become double stranded, and the double stranded molecules are denatured. Multiple cycles of the solid-phase amplification followed by denaturation can create several million clusters of approximately 1,000 copies of single-stranded DNA molecules of the same template in each channel of the flow cell. Primers, DNA polymerase and four fluorophore-labeled, reversibly terminating nucleotides are used to perform sequential sequencing. After nucleotide incorporation, a laser is used to excite the fluorophores, and an image is captured and the identity of the first base is recorded. The 3′ terminators and fluorophores from each incorporated base are removed and the incorporation, detection and identification steps are repeated. Sequencing according to this technology is described in U.S. Pat. Nos. 7,960,120; 7,835,871; 7,232,656; 7,598,035; 6,911,345; 6,833,246; 6,828,100; 6,306,597; 6,210,891; U.S. Pub. 2011/0009278; U.S. Pub. 2007/0114362; U.S. Pub. 2006/0292611; and U.S. Pub. 2006/0024681, each of which are incorporated by reference in their entirety.


Sequencing generates a plurality of reads. Reads generally include sequences of nucleotide data wherein read length may be associated with sequencing technology. For example, the single-molecule real-time (SMRT) sequencing technology of Pacific Bio produces reads thousands of base-pairs in length. For 454 pyrosequencing, read length may be about 700 bp in length. In some embodiments, reads are less than about 500 bases in length, or less than about 150 bases in length, or less than about 90 bases in length. In certain embodiments, reads are between about 80 and about 90 bases, e.g., about 85 bases in length. In some embodiments, these are very short reads, i.e., less than about 50 or about 30 bases in length.


The sequence reads may be analyzed to characterize the target gene or region of interest. For example, mutations can be “called” (i.e., identified and reported), a haplotypte for the sample may be reported, or other analyses may be performed. Mutation calling is described in U.S. Pub. 2013/0268474. In some embodiments, an analysis may include determining copy number states of genomic regions of interest. A set of sequence reads can be analyzed by any suitable method known in the art. For example, in some embodiments, sequence reads are analyzed by hardware or software provided as part of a sequence instrument. In some embodiments, individual sequence reads are reviewed by sight (e.g., on a computer monitor). A computer program may be written that pulls an observed genotype from individual reads. In certain embodiments, analyzing the reads includes assembling the sequence reads and then genotyping the assembled reads.


Sequence assembly can be done by methods known in the art including reference-based assemblies, de novo assemblies, assembly by alignment, or combination methods. Assembly can include methods described in U.S. Pat. No. 8,209,130 titled Sequence Assembly by Porecca and Kennedy, the contents of each of which are hereby incorporated by reference in their entirety for all purposes. In some embodiments, sequence assembly uses the low coverage sequence assembly software (LOCAS) tool described by Klein, et al., in LOCAS-A low coverage sequence assembly tool for re-sequencing projects, PLoS One 6(8) article 23455 (2011), the contents of which are hereby incorporated by reference in their entirety. Sequence assembly is described in U.S. Pat. Nos. 8,165,821; 7,809,509; 6,223,128; U.S. Pub. 2011/0257889; and U.S. Pub. 2009/0318310, the contents of each of which are hereby incorporated by reference in their entirety.


Functions described above such as sequence read analysis or assembly can be implemented using systems of the invention that include software, hardware, firmware, hardwiring, or combinations of any of these.



FIG. 3 gives a diagram of a system 301 according to embodiments of the invention. System 301 may include an analysis instrument 303 which may be, for example, a sequencing instrument (e.g., a HiSeq 2500 or a MiSeq by Illumina). Instrument 303 includes a data acquisition module 305 to obtain results data such as sequence read data. Instrument 303 may optionally include or be operably coupled to its own, e.g., dedicated, analysis computer 333 (including an input/output mechanism, one or more processor, and memory). Additionally or alternatively, instrument 303 may be operably coupled to a server 313 or computer 349 (e.g., laptop, desktop, or tablet) via a network 309.


Computer 349 includes one or more processors and memory as well as an input/output mechanism. Where methods of the invention employ a client/server architecture, steps of methods of the invention may be performed using the server 313, which includes one or more of processors and memory, capable of obtaining data, instructions, etc., or providing results via an interface module or providing results as a file. The server 313 may be engaged over the network 309 by the computer 349 or the terminal 367, or the server 313 may be directly connected to the terminal 367, which can include one or more processors and memory, as well as an input/output mechanism.


In system 301, each computer preferably includes at least one processor coupled to a memory and at least one input/output (I/O) mechanism.


A processor will generally include a chip, such as a single core or multi-core chip, to provide a central processing unit (CPU). A process may be provided by a chip from Intel or AMD.


Memory can include one or more machine-readable devices on which is stored one or more sets of instructions (e.g., software) which, when executed by the processor(s) of any one of the disclosed computers can accomplish some or all of the methodologies or functions described herein. The software may also reside, completely or at least partially, within the main memory and/or within the processor during execution thereof by the computer system. Preferably, each computer includes a non-transitory memory such as a solid state drive, flash drive, disk drive, hard drive, etc. While the machine-readable devices can in an exemplary embodiment be a single medium, the term “machine-readable device” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions and/or data. These terms shall also be taken to include any medium or media that are capable of storing, encoding, or holding a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention. These terms shall accordingly be taken to include, but not be limited to one or more solid-state memories (e.g., subscriber identity module (SIM) card, secure digital card (SD card), micro SD card, or solid-state drive (SSD)), optical and magnetic media, and/or any other tangible storage medium or media.


A computer of the invention will generally include one or more I/O device such as, for example, one or more of a video display unit (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device (e.g., a keyboard), a cursor control device (e.g., a mouse), a disk drive unit, a signal generation device (e.g., a speaker), a touchscreen, an accelerometer, a microphone, a cellular radio frequency antenna, and a network interface device, which can be, for example, a network interface card (NIC), Wi-Fi card, or cellular modem.


Any of the software can be physically located at various positions, including being distributed such that portions of the functions are implemented at different physical locations.


System 301 or components of system 301 may be used to perform methods described herein. Instructions for any method step may be stored in memory and a processor may execute those instructions. System 301 or components of system 301 may be used for the analysis of genomic sequences or sequence reads (e.g., sequence assembly or variant calling).


In certain embodiments, as part of the analysis and determination of copy number states and subsequent identification of copy number variation, the sequence read counts for genomic regions of interest are normalized based on internal controls. In particular, an intra-sample normalization is performed to control for variable sequencing depths between samples. The sequence read counts for each genomic region of interest within a sample will be normalized according to the total read count across all control references within the sample.


After normalizing read counts for both the genomic regions of interest and control references, copy number states may be determined. In one embodiment, the normalized values for each sample of interest will be compared to the normalized values for a control sample. A ratio, for example, may be generated based on the comparison, wherein the ratio is indicative of copy number and further determinative of any copy number variation. In the event that the determined copy number of a genomic region of interest of a particular sample falls within a tolerable level (as determined by ratio between test and control samples), it can be determined that genomic region of interest does not present copy number variation and thus the patient is at low risk for being a carrier of a condition or disease associated with such. In the event that the determined copy number of a genomic region of interest of a particular sample falls outside of a tolerable level, it can be determined that genomic region of interest does present copy number variation and thus the patient is at risk for being a carrier of a condition or disease associated with such.


INCORPORATION BY REFERENCE

References and citations to other documents, such as patents, patent applications, patent publications, journals, books, papers, web contents, have been made throughout this disclosure. All such documents are hereby incorporated herein by reference in their entirety for all purposes.


EQUIVALENTS

Various modifications of the invention and many further embodiments thereof, in addition to those shown and described herein, will become apparent to those skilled in the art from the full contents of this document, including references to the scientific and patent literature cited herein. The subject matter herein contains important information, exemplification and guidance that can be adapted to the practice of this invention in its various embodiments and equivalents thereof.


EXAMPLES
Example 1
Determination of Copy Number State of SMN1

Approximately 28 samples are collected to determine carrier status with respect to spinal muscular atrophy (SMA). Genomic DNA is extracted from whole human blood using a Gentra Puregene Blood Kit and following the Puregene protocol for DNA Purification from Whole Blood (Qiagen). Of the 28 samples, there is 1 water negative control and 7 control DNA samples and 20 test samples. Each of the control samples includes two or more genomic regions of interest (e.g. loci) having known (or stable) copy numbers. Control samples 1-4 each include control loci and survival motor neuron genes (SMN), including telomeric SMN (SMN1) and centromeric SMN (SMN2) genes. There are a total of 17 control loci, 5 SMN1, and 5 SMN2, all of which have a known copy number of 2. Control sample 5 includes 17 control loci, each having a known copy number of 2, and 5 SMN1, each having a known copy number of 0. Control sample 6 includes 17 control loci, each having a known copy number of 2, and 5 SMN1, each having a known copy number of 1. Control sample 7 includes 17 control loci, each having a known copy number of 2, and 5 SMN1, each having a known copy number of 3 or more.


Samples are processed via method 101 to remove copies of SMN2. The sample is treated (e.g., heated) to denature genomic dsDNA. Primers specific to SMN2 that are complementary to regions flanking the SMN2 sequence are introduced. The primers are annealed to the ssDNA in the regions flanking the unwanted SMN2 segment. The annealed primers are then extended using a polymerase in a template-dependent manner to make double-stranded any single-stranded instance of SMN2 present in any sample. A double-stranded endonuclease is introduced and allowed to digest all dsDNA, thus digesting any segments that include SMN2. This stage of processing of the sample is completed by inactivating the ds endonuclease and the remaining DNA is analyzed for SMN1 by MIP capture and sequencing.


The processed samples are then fragmented and/or denatured in preparation for hybridization with molecular inversion probes. The genomic DNA of each sample is fragmented/denatured by any known method or technique sufficient to fragment genomic DNA.


Once it is isolated, MIP capture probes are hybridized to the fragmented genomic DNA in each sample by introducing capture probe mix into each sample well. In particular, the capture probe mix will generally include a plurality of SMA molecular inversion probes that are capable of binding to one or more of the genomic regions of interest (e.g., SMN1) or the control DNA. A library of molecular inversion probes is generated. The library may include a variety of different probe configurations. For example, one or more probes are capable of hybridizing specifically to the control loci and one or more probes are capable of hybridizing only to SMN1. Of those probes specific to SMN1, some are capable of producing sequences specific to that paralog while some are not capable of producing paralog-specific sequences. The library may also include one or more probes capable of hybridizing nonspecifically to both SMN1 and SMN2. However, since SMN2 segments are removed from the sample via methods of the invention, copies of SMN2 will not interfere with analysis of SMN1.


Diluted probes are introduced to the isolated fragmented genomic DNA in each sample and the isolated whole genomic DNA is incubated in the diluted probe mix to promote hybridization. The time and temperature for incubation may be based on any known hybridization protocol, sufficient to result in hybridization of the probes to the DNA. After capture of the genomic region of interest (e.g., SMN1) the captured region is subjected to an enzymatic gap-filling and ligation step, in accordance with any known methods or techniques, including those generally described herein. The captured material may further be purified.


The purified captured DNA is then amplified by any known amplification methods or techniques. In one embodiment, the purified captured DNA is amplified using barcode-based PCR. The resulting barcodes PCRs for each sample are then combined into a master pool and quantified.


After PCR, portions of the PCR reactions for each sample are pooled and purified, then quantified. In particular, the PCR reactions for all samples are pooled in equal volumes into one master pool. The master sample pool is then purified via a PCR cleanup protocol according to manufacturer's instructions. The purified pool is then run on a microfluidics-based platform for sizing, quantification and quality control of DNA, RNA, proteins and cells. In particular, the purified pool and control samples (pre-purification) are run on an Agilent Bioanalyzer for the detection and quantification of SMN1 probe products.


Next, the sample pool is prepared for sequencing. In a preferred embodiment, IIlumina sequencing techniques are used. Prior to sequencing, the sample pool is reduced to 2 nM by diluting with 1×TE. Template DNA for cluster generation is prepared by combining 10 micro-Liter of 0.1 N NaOH with 10 micro-Liter of 2 nM DNA library (sample pool) and incubating said mixture at room temperature for 5 min. The mixture is then mixed with 980 micro-Liter of HT1 buffer (Illumina), thereby reducing the denatured library to a concentration of 20 pM. This mixture is then mixed (e.g., inversion) and pulse centrifuged. Next, 225 micro-Liter of the 20 pM library is mixed with 775 micro-Liter of HT1 buffer to reduce the library pool to a concentration of 4.5 pM. The library pool having a concentration of 4.5 pM is used for on-board clustering in the sequencing.


The sequencing is carried out on the HiSeq 2500/1500 system sold by Illumina, Inc. (San Diego, Calif.). Sequencing is carried out with the TruSeq Rapid PE Cluster Kit and TruSeq Rapid SBS 200 cycle kit (Illumina) and in accordance with manufacturer's instructions. In addition to the reagents and mixes included within the kits, additional reagents are prepared for genomic read sequencing primers and reverse barcode sequencing primers.


The library pool undergoes sequencing under paired-end, dual-index run conditions. Sequencing generates a plurality of reads. Reads generally include sequences of nucleotide data less than about 150 bases in length, or less than about 90 bases in length. After obtaining sequence reads, they are further processed as described in U.S. Pat. No. 8,209,130.


Read counts for a genomic region of interest are normalized with respect to an internal control DNA. Normalized read counts are compared to the internal control DNA, thereby obtaining a ratio. A copy number state of the genomic region of interest is determined based on the comparison, specifically the ratio.


The plurality of reads generated by the sequencing method described above are analyzed to determine copy number states, and ultimately copy number variation, in any of the genomic regions of interest (e.g., SMN1) that would necessarily indicate the presence of an autosomal recessive trait in which copy number variation is diagnostic (e.g., spinal muscular atrophy). Analysis of the read counts is carried out using Illumina's HiSeq BclConverter software. Files (e.g. qSeq files) may be generated for both the genomic and barcode reads. In particular, in accordance with one method of the present invention, genomic read data for each sample is split based upon the barcode reads, which yields separate FASTQ files for each sample.


Based on the ratios, loci copy numbers may be called as follows: a ratio of <0.1 will be called a copy number state of 0; a ratio between 0.1 and 0.8 will be called a copy number state of 1; a ratio between 0.8 and 1.25 will be called a copy number state of 2; and a ratio of >1.25 will be called a copy number state of 3+.


The determined copy numbers can then be used to determine the carrier status of an individual from which the sample was obtained (i.e. whether the patient is a carrier of the disease). In particular, if the copy number state is determined to vary from the normal copy state (e.g., CN is 0, 1 or 3+), it is indicative the condition (e.g., carrier of SMA).

Claims
  • 1. A method of removing a paralog of a gene of interest in a nucleic acid from a sample, the method comprising: obtaining a single-stranded nucleic acid that contains a gene of interest and a paralog of the gene of interest;annealing an oligonucleotide to a portion of the single-stranded nucleic acid that flanks the paralog of the gene of interest; andextending the annealed oligonucleotide to create a double-stranded region that contains the paralog of the gene of interest;removing the paralog of the gene of interest by digesting the double-stranded region, thereby leaving only intact genomic DNA including the gene of interest;performing a molecular inversion probe capture assay on the intact genomic DNA; andsequencing the gene of interest.
  • 2. The method of claim 1, wherein the extending step is conducted using a polymerase enzyme under conditions sufficient to cause extension of the primer in a template-dependent manner.
  • 3. The method of claim 1, wherein the digesting step comprising exposing the sample to an enzyme that preferentially digests double-stranded nucleic acid.
  • 4. The method of claim 3, wherein the enzyme is selected from double-stranded endonucleases, restriction endonucleases, and nicking enzymes.
  • 5. The method of claim 4, further comprising the step of deactivating the enzyme.
  • 6. The method of claim 1, wherein the sequencing is Next Generation Sequencing.
  • 7. The method of claim 1, further comprising the step of obtaining a sample from a subject and denaturing double-stranded DNA in the sample.
  • 8. The method of claim 7, wherein the denaturing step comprises exposing the sample to heat, a detergent, or a basic solution.
  • 9. The method of claim 1, wherein the gene comprises survival of motor neuron 1 (SMN1) and the paralog targeted for removal is survival of motor neuron 2 (SMN2).
  • 10. The method of claim 9, wherein the sequencing step further comprises detection of a spinal muscular atrophy (SMA) mutation.
  • 11. A method of sequencing a gene of interest, the method comprising: obtaining a single-stranded nucleic acid that contains a gene of interest and a pseudogene of the gene of interest;annealing an oligonucleotide to a portion of the single-stranded nucleic acid adjacent to the pseudogene of the gene of interest; andextending the annealed oligonucleotide to create a double-stranded region that contains the pseudogene of the gene of interest;removing the pseudogene of the gene of interest by digesting the double-stranded region, thereby leaving only intact genomic DNA including the gene of interest;performing a molecular inversion probe capture assay on the intact genomic DNA; andsequencing the gene of interest.
  • 12. The method of claim 11, wherein the gene comprises glucosidase beta acid (GBA).
  • 13. The method of claim 12, wherein the sequencing step further comprises detection of a Gaucher disease mutation.
  • 14. The method of claim 11, wherein the extending step is conducted using a polymerase enzyme under conditions sufficient to cause extension of the primer in a template-dependent manner.
  • 15. The method of claim 11, wherein the digesting step comprising exposing the sample to an enzyme that preferentially digests double-stranded nucleic acid.
  • 16. The method of claim 11, wherein the sequencing is Next Generation Sequencing.
CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to, and the benefit of, U.S. Provisional Patent Application Ser. No. 62/048,452, filed Sep. 10, 2014, the contents of which are incorporated by reference.

US Referenced Citations (364)
Number Name Date Kind
4683195 Mullis et al. Jul 1987 A
4683202 Mullis Jul 1987 A
4988617 Landegren et al. Jan 1991 A
5060980 Johnson et al. Oct 1991 A
5210015 Gelfand et al. May 1993 A
5234809 Boom et al. Aug 1993 A
5242794 Whiteley et al. Sep 1993 A
5348853 Wang et al. Sep 1994 A
5434049 Okano Jul 1995 A
5459307 Klotz, Jr. Oct 1995 A
5486686 Zdybel, Jr. et al. Jan 1996 A
5491224 Bittner Feb 1996 A
5494810 Barany et al. Feb 1996 A
5567583 Wang et al. Oct 1996 A
5583024 McElroy et al. Dec 1996 A
5604097 Brenner Feb 1997 A
5636400 Young Jun 1997 A
5674713 McElroy et al. Oct 1997 A
5695934 Brenner Dec 1997 A
5700673 McElroy et al. Dec 1997 A
5701256 Marr et al. Dec 1997 A
5830064 Bradish et al. Nov 1998 A
5846719 Brenner et al. Dec 1998 A
5863722 Brenner Jan 1999 A
5866337 Schon Feb 1999 A
5869252 Bouma et al. Feb 1999 A
5869717 Frame et al. Feb 1999 A
5871921 Landegren et al. Feb 1999 A
5888788 De Miniac Mar 1999 A
5942391 Zhang et al. Aug 1999 A
5971921 Timbel Oct 1999 A
5993611 Moroney, III et al. Nov 1999 A
5994056 Higuchi Nov 1999 A
6020127 MacKenzie Feb 2000 A
6033854 Kumit et al. Mar 2000 A
6033872 Bergsma et al. Mar 2000 A
6100099 Gordon et al. Aug 2000 A
6138077 Brenner Oct 2000 A
6150516 Brenner et al. Nov 2000 A
6171785 Higuchi Jan 2001 B1
6172214 Brenner Jan 2001 B1
6172218 Brenner Jan 2001 B1
6197508 Stanley Mar 2001 B1
6197574 Miyamoto et al. Mar 2001 B1
6210891 Nyren et al. Apr 2001 B1
6223128 Allex et al. Apr 2001 B1
6235472 Landegren et al. May 2001 B1
6235475 Brenner et al. May 2001 B1
6235501 Gautsch et al. May 2001 B1
6235502 Weissman et al. May 2001 B1
6258568 Nyren Jul 2001 B1
6274320 Rothberg et al. Aug 2001 B1
6306597 Macevicz Oct 2001 B1
6352828 Brenner Mar 2002 B1
6360235 Tilt et al. Mar 2002 B1
6361940 Van Ness et al. Mar 2002 B1
6403320 Read et al. Jun 2002 B1
6462254 Vemachio et al. Oct 2002 B1
6489105 Matlashewski et al. Dec 2002 B1
6558928 Landegren May 2003 B1
6569920 Wen et al. May 2003 B1
6582938 Su et al. Jun 2003 B1
6585938 Machida et al. Jul 2003 B1
6613516 Christians et al. Sep 2003 B1
6714874 Myers et al. Mar 2004 B1
6716580 Gold et al. Apr 2004 B2
6719449 Laugham, Jr. et al. Apr 2004 B1
6818395 Quake et al. Nov 2004 B1
6828100 Ronaghi Dec 2004 B1
6833246 Balasubramanian Dec 2004 B2
6858412 Willis et al. Feb 2005 B2
6911345 Quake et al. Jun 2005 B2
6913879 Schena Jul 2005 B1
6927024 Dodge et al. Aug 2005 B2
6941317 Chamberlin et al. Sep 2005 B1
6948843 Laugham, Jr. et al. Sep 2005 B2
7034143 Preparata et al. Apr 2006 B1
7041481 Anderson et al. May 2006 B2
7049077 Yang May 2006 B2
7057026 Barnes et al. Jun 2006 B2
7071324 Preparata et al. Jul 2006 B2
7074564 Landegren Jul 2006 B2
7074586 Cheronis et al. Jul 2006 B1
7115400 Adessi et al. Oct 2006 B1
7169560 Lapidus et al. Jan 2007 B2
7211390 Rothberg et al. May 2007 B2
7232656 Balasubramanian et al. Jun 2007 B2
7244559 Rothberg et al. Jul 2007 B2
RE39793 Brenner Aug 2007 E
7264929 Rothberg et al. Sep 2007 B2
7282337 Harris Oct 2007 B1
7297518 Quake et al. Nov 2007 B2
7320860 Landegren et al. Jan 2008 B2
7323305 Leamon et al. Jan 2008 B2
7335762 Rothberg et al. Feb 2008 B2
7351528 Landegren Apr 2008 B2
7393665 Brenner Jul 2008 B2
7510829 Faham et al. Mar 2009 B2
7523117 Zhang et al. Apr 2009 B2
7537889 Sinha et al. May 2009 B2
7537897 Brenner et al. May 2009 B2
7544473 Brenner Jun 2009 B2
7582431 Drmanac et al. Sep 2009 B2
7598035 Macevicz Oct 2009 B2
7629151 Gold et al. Dec 2009 B2
7642056 Ahn et al. Jan 2010 B2
7666593 Lapidus Feb 2010 B2
7700323 Willis et al. Apr 2010 B2
7774962 Ladd Aug 2010 B1
7776616 Heath et al. Aug 2010 B2
RE41780 Anderson et al. Sep 2010 E
7790388 Landegren et al. Sep 2010 B2
7809509 Milosavljevic Oct 2010 B2
7835871 Kain et al. Nov 2010 B2
7862999 Zheng et al. Jan 2011 B2
7865534 Genstruct Jan 2011 B2
7883849 Dahl Feb 2011 B1
7957913 Chinitz et al. Jun 2011 B2
7960120 Rigatti et al. Jun 2011 B2
7985716 Yershov et al. Jul 2011 B2
7993880 Willis et al. Aug 2011 B2
8024128 Rabinowitz et al. Sep 2011 B2
8114027 Triva Feb 2012 B2
8165821 Zhang Apr 2012 B2
8209130 Kennedy et al. Jun 2012 B1
8283116 Bhattacharyya Oct 2012 B1
8462161 Barber Jun 2013 B1
8463895 Arora et al. Jun 2013 B2
8474228 Adair et al. Jul 2013 B2
8496166 Burns et al. Jul 2013 B2
8529744 Marziali et al. Sep 2013 B2
8738300 Porreca et al. May 2014 B2
8778609 Umbarger Jul 2014 B1
8812422 Nizzari et al. Aug 2014 B2
8847799 Kennedy et al. Sep 2014 B1
8976049 Kennedy et al. Mar 2015 B2
9074244 Sparks et al. Jul 2015 B2
9115387 Umbarger Aug 2015 B2
9228233 Kennedy et al. Jan 2016 B2
9292527 Kennedy et al. Mar 2016 B2
9535920 Kennedy et al. Jan 2017 B2
9567639 Oliphant et al. Feb 2017 B2
9677124 Umbarger Jun 2017 B2
10066259 Gore et al. Sep 2018 B2
10202637 Umbarger Feb 2019 B2
10227635 Umbarger et al. Mar 2019 B2
10604799 Porreca et al. Mar 2020 B2
10683533 Umbarger et al. Jun 2020 B2
20010007742 Landergren Jul 2001 A1
20010046673 French et al. Nov 2001 A1
20020001800 Lapidus Jan 2002 A1
20020040216 Dumont et al. Apr 2002 A1
20020042052 Nilsen Apr 2002 A1
20020091666 Rice et al. Jul 2002 A1
20020164629 Quake et al. Nov 2002 A1
20020172954 Mao Nov 2002 A1
20020182609 Arcot Dec 2002 A1
20020187496 Andersson et al. Dec 2002 A1
20020190663 Rasmussen Dec 2002 A1
20030166057 Hildebrand et al. Sep 2003 A1
20030175709 Murphy Sep 2003 A1
20030177105 Xiao et al. Sep 2003 A1
20030203370 Yakhini et al. Oct 2003 A1
20030208454 Rienhoff et al. Nov 2003 A1
20030224384 Sayood et al. Dec 2003 A1
20040029264 Robbins Feb 2004 A1
20040053275 Shafer Mar 2004 A1
20040106112 Nilsson et al. Jun 2004 A1
20040121373 Friedlander et al. Jun 2004 A1
20040142325 Mintz et al. Jul 2004 A1
20040152108 Keith et al. Aug 2004 A1
20040161773 Rogan Aug 2004 A1
20040170965 Scholl et al. Sep 2004 A1
20040171051 Holloway Sep 2004 A1
20040175719 Christians Sep 2004 A1
20040197813 Hoffman et al. Oct 2004 A1
20040209299 Pinter et al. Oct 2004 A1
20050003369 Christians et al. Jan 2005 A1
20050026204 Landegren Feb 2005 A1
20050032095 Wigler et al. Feb 2005 A1
20050048505 Fredrick et al. Mar 2005 A1
20050059048 Gunderson et al. Mar 2005 A1
20050100900 Kawashima et al. May 2005 A1
20050112590 Boom et al. May 2005 A1
20050186589 Kowalik Aug 2005 A1
20050214811 Margulies et al. Sep 2005 A1
20050244879 Schumm et al. Nov 2005 A1
20050250147 Macevicz Nov 2005 A1
20050272065 Lakey et al. Dec 2005 A1
20060008824 Ronaghi et al. Jan 2006 A1
20060019304 Hardenbol et al. Jan 2006 A1
20060024681 Smith et al. Feb 2006 A1
20060030536 Yu et al. Feb 2006 A1
20060078894 Winkler et al. Apr 2006 A1
20060149047 Nanduri et al. Jul 2006 A1
20060177837 Borozan et al. Aug 2006 A1
20060183132 Fu et al. Aug 2006 A1
20060192047 Goossen Aug 2006 A1
20060195269 Yeatman et al. Aug 2006 A1
20060246500 Browne Nov 2006 A1
20060263789 Kincaid Nov 2006 A1
20060281098 Miao et al. Dec 2006 A1
20060286577 Jia Dec 2006 A1
20060292585 Nautiyal et al. Dec 2006 A1
20060292611 Berka et al. Dec 2006 A1
20070009925 Fang et al. Jan 2007 A1
20070020640 McCloskey et al. Jan 2007 A1
20070042369 Reese et al. Feb 2007 A1
20070092883 Schouten et al. Apr 2007 A1
20070114362 Feng et al. May 2007 A1
20070128624 Gormley et al. Jun 2007 A1
20070161013 Hantash Jul 2007 A1
20070162983 Hesterkamp et al. Jul 2007 A1
20070166705 Milton et al. Jul 2007 A1
20070212704 Dong et al. Sep 2007 A1
20070225487 Nilsson et al. Sep 2007 A1
20070238122 Allbritton et al. Oct 2007 A1
20070244675 Shai et al. Oct 2007 A1
20070264653 Berlin et al. Nov 2007 A1
20080003142 Link et al. Jan 2008 A1
20080014589 Link et al. Jan 2008 A1
20080076118 Tooke et al. Mar 2008 A1
20080081330 Kahvejian Apr 2008 A1
20080085836 Kearns et al. Apr 2008 A1
20080090239 Shoemaker et al. Apr 2008 A1
20080125324 Petersdorf May 2008 A1
20080176209 Muller et al. Jul 2008 A1
20080269068 Church et al. Oct 2008 A1
20080280955 McCamish Nov 2008 A1
20080293589 Shapero Nov 2008 A1
20090009904 Yasuna et al. Jan 2009 A1
20090019156 Mo et al. Jan 2009 A1
20090026082 Rothberg et al. Jan 2009 A1
20090029385 Christians et al. Jan 2009 A1
20090035777 Kokoris et al. Feb 2009 A1
20090042206 Schneider et al. Feb 2009 A1
20090098551 Landers et al. Apr 2009 A1
20090099041 Church et al. Apr 2009 A1
20090105081 Rodesch et al. Apr 2009 A1
20090119313 Pearce May 2009 A1
20090127589 Rothberg et al. May 2009 A1
20090129647 Dimitrova et al. May 2009 A1
20090156412 Boyce, IV et al. Jun 2009 A1
20090163366 Nickerson et al. Jun 2009 A1
20090181389 Li et al. Jul 2009 A1
20090191565 Lapidus et al. Jul 2009 A1
20090192047 Parr et al. Jul 2009 A1
20090202984 Cantor Aug 2009 A1
20090203014 Wu et al. Aug 2009 A1
20090220955 Verrant Sep 2009 A1
20090226975 Sabot et al. Sep 2009 A1
20090233814 Bashkirov et al. Sep 2009 A1
20090298064 Batzoglou et al. Dec 2009 A1
20090301382 Patel Dec 2009 A1
20090318310 Liu et al. Dec 2009 A1
20100035243 Muller et al. Feb 2010 A1
20100035252 Rothberg et al. Feb 2010 A1
20100063742 Hart et al. Mar 2010 A1
20100069263 Shendure et al. Mar 2010 A1
20100076185 Adey Mar 2010 A1
20100086914 Bentley et al. Apr 2010 A1
20100086926 Craig et al. Apr 2010 A1
20100105107 Hildebrand et al. Apr 2010 A1
20100137143 Rothberg et al. Jun 2010 A1
20100137163 Link et al. Jun 2010 A1
20100143908 Gillevet Jun 2010 A1
20100159440 Messier et al. Jun 2010 A1
20100188073 Rothberg et al. Jul 2010 A1
20100196911 Hoffman et al. Aug 2010 A1
20100197507 Rothberg et al. Aug 2010 A1
20100216151 Lapidus et al. Aug 2010 A1
20100216153 Lapidus et al. Aug 2010 A1
20100227329 Cuppens Sep 2010 A1
20100248984 Shaffer et al. Sep 2010 A1
20100282617 Rothberg et al. Nov 2010 A1
20100285578 Selden et al. Nov 2010 A1
20100297626 Mckernan et al. Nov 2010 A1
20100300559 Schultz et al. Dec 2010 A1
20100300895 Nobile et al. Dec 2010 A1
20100301042 Kahlert Dec 2010 A1
20100301398 Rothberg et al. Dec 2010 A1
20100304982 Hinz et al. Dec 2010 A1
20100311061 Korlach et al. Dec 2010 A1
20100330619 Willis et al. Dec 2010 A1
20110004413 Carnevali et al. Jan 2011 A1
20110009278 Kain et al. Jan 2011 A1
20110015863 Pevzner et al. Jan 2011 A1
20110021366 Chinitz et al. Jan 2011 A1
20110034342 Fox Feb 2011 A1
20110092375 Zamore et al. Apr 2011 A1
20110098193 Kingsmore et al. Apr 2011 A1
20110117544 Lexow May 2011 A1
20110159499 Hindson et al. Jun 2011 A1
20110166029 Margulies et al. Jul 2011 A1
20110224105 Kurn et al. Sep 2011 A1
20110230365 Rohlfs et al. Sep 2011 A1
20110257889 Klammer et al. Oct 2011 A1
20110288780 Rabinowitz et al. Nov 2011 A1
20110301042 Steinmann et al. Dec 2011 A1
20120015050 Abkevich et al. Jan 2012 A1
20120021930 Schoen et al. Jan 2012 A1
20120046877 Hyland et al. Feb 2012 A1
20120059594 Hatchwell et al. Mar 2012 A1
20120074925 Dliver Mar 2012 A1
20120079980 Taylor et al. Apr 2012 A1
20120115736 Bjorson et al. May 2012 A1
20120164630 Porreca et al. Jun 2012 A1
20120165202 Porreca et al. Jun 2012 A1
20120179384 Kuramitsu et al. Jul 2012 A1
20120214678 Rava et al. Aug 2012 A1
20120216151 Sarkar et al. Aug 2012 A1
20120236861 Ganeshalingam et al. Sep 2012 A1
20120245041 Brenner et al. Sep 2012 A1
20120252020 Shuber Oct 2012 A1
20120252684 Selifonov et al. Oct 2012 A1
20120258461 Weisbart Oct 2012 A1
20120270212 Rabinowitz et al. Oct 2012 A1
20120270739 Rava et al. Oct 2012 A1
20130129755 Song May 2013 A1
20130130921 Gao et al. May 2013 A1
20130178378 Hatch et al. Jul 2013 A1
20130183672 de Laat et al. Jul 2013 A1
20130222388 McDonald Aug 2013 A1
20130268206 Porreca et al. Oct 2013 A1
20130268474 Nizzari et al. Oct 2013 A1
20130274146 Umbarger et al. Oct 2013 A1
20130275103 Struble et al. Oct 2013 A1
20130288242 Stoughton et al. Oct 2013 A1
20130323730 Curry et al. Dec 2013 A1
20130332081 Reese et al. Dec 2013 A1
20130337447 Porreca et al. Dec 2013 A1
20130344096 Chiang et al. Dec 2013 A1
20140129201 Kennedy et al. May 2014 A1
20140136120 Colwell et al. May 2014 A1
20140206552 Rabinowitz et al. Jul 2014 A1
20140222349 Higgins et al. Aug 2014 A1
20140228226 Yin et al. Aug 2014 A1
20140255931 Porreca et al. Sep 2014 A1
20140274741 Hunter Sep 2014 A1
20140308667 Umbarger Oct 2014 A1
20140318274 Zimmerman et al. Oct 2014 A1
20140342354 Evans et al. Nov 2014 A1
20140361022 Finneran Dec 2014 A1
20150051085 Vogelstein et al. Feb 2015 A1
20150056613 Kural Feb 2015 A1
20150111208 Umbarger et al. Apr 2015 A1
20150178445 Cibulskis et al. Jun 2015 A1
20150258170 McCabe Sep 2015 A1
20150299767 Armour et al. Oct 2015 A1
20150310163 Kingsmore Oct 2015 A1
20150354003 Umbarger Dec 2015 A1
20160003812 Porreca et al. Jan 2016 A1
20160034638 Spence et al. Feb 2016 A1
20160068889 Gole et al. Mar 2016 A1
20160188793 Muzzey Jun 2016 A1
20160210486 Porreca et al. Jul 2016 A1
20160251719 Umbarger Sep 2016 A1
20170044610 Johnson Feb 2017 A1
20170129964 Cheung May 2017 A1
20170183731 Mann et al. Jun 2017 A1
20170275676 Umbarger Sep 2017 A1
20180371533 Gore et al. Dec 2018 A1
20190233881 Umbarger et al. Aug 2019 A1
20200181696 Porreca et al. Jun 2020 A1
Foreign Referenced Citations (46)
Number Date Country
1 321 477 Jun 2003 EP
1 564 306 Aug 2005 EP
2425240 Mar 2012 EP
2 437 191 Apr 2012 EP
2716766 Apr 2014 EP
95011995 May 1995 WO
1996019586 Jun 1996 WO
98014275 Apr 1998 WO
98044151 Oct 1998 WO
00018957 Apr 2000 WO
02093453 Nov 2002 WO
2004018497 Mar 2004 WO
2004083819 Sep 2004 WO
2005003304 Jan 2005 WO
2006084132 Aug 2006 WO
2007010251 Jan 2007 WO
2007061284 May 2007 WO
2007107717 Sep 2007 WO
2007123744 Nov 2007 WO
2007135368 Nov 2007 WO
2008067551 Jun 2008 WO
2009036525 Mar 2009 WO
2009076238 Jun 2009 WO
2010024894 Mar 2010 WO
2010115154 Oct 2010 WO
2010126614 Nov 2010 WO
2011006020 Jan 2011 WO
2011066476 Jun 2011 WO
2011067378 Jun 2011 WO
2011102998 Aug 2011 WO
2011155833 Dec 2011 WO
2012006291 Jan 2012 WO
2012040387 Mar 2012 WO
2012051208 Apr 2012 WO
2012087736 Jun 2012 WO
2012109500 Aug 2012 WO
2012134884 Oct 2012 WO
2012149171 Nov 2012 WO
2012170725 Dec 2012 WO
2013058907 Apr 2013 WO
2013148496 Oct 2013 WO
2013177086 Nov 2013 WO
2013191775 Dec 2013 WO
2014052909 Apr 2014 WO
2014074246 May 2014 WO
2015119941 Aug 2015 WO
Non-Patent Literature Citations (323)
Entry
Craig et al., Removal of repetitive sequences from FISH probes using PCR-assisted affinity chromatography. Human Genetics 100 : 472 (1997). (Year: 1997).
Shagin et al., A novel method for SNP Detection using a new duplex-specific nuclease from crab hapatopancreas. Genome Research 12 : 1935 (2002). (Year: 2002).
Zhulidov et al., Simple cDNA normalization using kamchatka crab duplex-specific nuclease. Nucleic Acids Research 32 (3) : e37 (2004). (Year: 2004).
Hiatt et al., Single molecule molecular inversion probes for targeted, high-accuracy detection of low-frequency variation. Genome Research 23 : 843 (Year: 2013).
Treangen et al., Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nature Reviews | Genetics 13 : 36 (published online Nov. 2011) (Year: 2012).
Shen et al., High-quality DNA sequence capture of 524 disease candidate genes. PNAS 108 (16) :6549-6554 (Year: 2011).
Steege et al., PCR-based DNA test to confirm clinical diagnosis of autosomal recessive spinal muscular atrophy. The Lancet 345 :985-986 (Year: 1995).
Wirth et al., Quantitative Analysis of Survival Motor Neuron Copies: Identification of Subtle SMN1 Mutations in Patients with Spinal Muscular Atrophy, Genotype-Phenotype Correlation, and Implications for Genetic Counseling. Am. J. of Human Genetics 64 : 1340-1356 (Year: 1999).
Zirmran et al.,A glucocerebrosidase fusion gene in Gaucher disease. Implications for the molecular anatomy, pathogenesis, and diagnosis of this disorder. J. of Clinical Investigations 85 : 219-222 (Year: 1990).
Brison et al.,General Method for Cloning Amplified DNA by Differential Screening with Genomic Probes. Molecular and Cellular Biology 2(5) :578-587 (Year: 1982).
Albert et al., Direct selection of human genomic loci by microarray hybridization . Nature Methods 4(11) : 903-905 (Year: 2007).
Hodgeds et al., Genome-wide in situ exon capture for selective resequencing. Nature Genetics 39(12) : 1522-1526 (Year: 2007).
Okou et al., Microarray-based genomic selection for high-throughput resequencing. Nature Methods 4(11) : 907-909 (Year: 2007).
Miyake et al., PIK3CA gene mutations and amplifications in uterine cancers, identified by methods that avoid confounding by PIK3CA pseudogene sequences. Cancer Letters 261:120-126 (Year: 2008).
Gupta et al., Expanding the genetic tool kit: ZFNs,TALENs, and CRISPR-Cas9. J. of Clinical Investigations 124(10) : 4154 (Year: 2014).
Dou et al., Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions. Biology Direct 7:17 (Year: 2012).
Chou et al., Clinical Chemistry 56(1): 62 (Year: 2010).
Fu et al., Repeat subtraction-mediated sequence capture from a complex genome the Plant Journal 62:898. (Year: 2010).
Meyer et al., Parallel tagged sequencing on the 454 platform. Nature Protocols 3(2) :267 (Year: 2008).
Sonnhammer et al., Orthology, paralogy and proposed classification for paralog subtypes. Trends in Genetics 18(12) : 619 (Year: 2002).
Archer et al., 2014, Selective and flexible depletion of problematic sequences from RNA-seq libraries at the cDNA stage, BMC Genomics 15(1):401.
Carpenter, 2013, Pulling out the 1%: whole-genome capture for the targeted enrichment of ancient DNA sequencing libraries. Am J Hum Genet 93(5):852-864.
Dolinsek, 2013, Depletion of unwanted nucleic acid templates by selection cleavage; LNAzymes, catalytically active oligonucleotides containing locked nucleic acids, open a new window for detecting rare microbial community members, App Env Microbiol 79(5):1534-1544.
Fitch, 1970, Distinguishing homologous from analogous proteins, Syst Biol 19(2):99-113.
Green & Minz, 2005, Suicide polymerase endonuclease restriction, a novel technique for enhancing PCR amplification of minor DNA template, Appl Env Microbiol 71(8):4721-4727.
Hardenbol et al., 2005, Highly multiplexed molecular inversion probe genotyping: over 10,000 targeted SNPs genotyped in a single tube assay, Genome Res 15:269-75.
Housley et al., 2009, SNP discovery and haplotype analysis in the segmentally duplicated DRD5 coding region, Ann Hum Genet 73(3):274-282.
International Search Report and Written Opinion dated Dec. 2, 2015, for International Patent Application No. PCT/US2015/049132 with International Filing Date Sep. 9, 2015 (14 pages).
Jensen, 2001, Orthologs and paralogs—we need to get it right, Genome Biol 2(8):1002-1002.3.
Krishnakumar et al., 2008, A comprehensive assay for targeted multiplex amplification of human DNA sequences, PNAS 105:9296-301.
Li, et al., 2003, DNA binding and cleavage by the periplasmic nuclease Vvn: a novel structure with a known active site, EMBO J 22(15):4014-4025.
Porreca et al., 2007, Multiplex amplification of large sets of human exons, Nat Meth 4(11):931-936.
Turner et al., 2009, Massively parallel exon capture and library-free resequencing across 16 genomes, Nature Methods 6:315-316.
Abravaya, 1995, Detection of point mutations with a modified ligase chain reaction (Gap-LCR), Nucleic Acids Research, 23(4): 675-682.
Adey, 2010, Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition, Genome Biol 11:R119.
Ageno, 1969, The alkaline denaturation of DNA, Biophys J 9(11):1281-1311.
Agrawal, 1990, Site-specific functionalization of oligodeoxynucleotides for non-radioactive labelling, Tetrahedron Let 31:1543-1546.
Akhras, 2007, Connector inversion probe technology: A powerful one-primer multiplex DNA amplification system for numerous scientific applications, PLoSOne 9:e915.
Akhras, 2007, PathogenMip Assay: a multiplex pathogen detection assay, PLOS One 2:e2230.
Alazard, 2002, Sequencing of production-scale synthetic oligonucleotides by enriching for coupling failures using matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry, Anal Biochem 301:57-64.
Alazard, 2006, Sequencing oligonucleotides by enrichment of coupling failures using matrix-assisted laser desorption/onization time-of-flight mass spectrometry, Curr Protoc Nucleic Acid Chem, Chapter 10, Unit 10:1-7.
Albert, 2007, Direct selection of human genomic loci by microarray hybridization, Nature Methods 4(11):903-5.
Aljanabi, 1997, Universal and rapid salt-extraction of high quality genomic DNA for PCR-based techniques, Nucl. Acids Res 25:4692-4693.
Antonarakis and the Nomenclature Working Group, 1998, Recommendations for a nomenclature system for human gene mutations, Human Mutation 11:1-3.
Balzer, 2013, Filtering duplicate reads from 454 pyrosequencing data, Bioinformatics 29(7):830-836.
Barany, 1991, Genetic disease detection and DNA amplification using cloned thermostable ligase, PNAS 88:189-193.
Barany, 1991, The Ligase Chain Reaction in a PCR World, Genome Research 1:5-16.
Bau, 2008, Targeted next-generation sequencing by specific capture of multiple genomic loci using low-volume microfluidic DNA arrays, Analytical and Bioanal Chem 393(1):171-5.
Beer, 1962, Determination of base sequence in nucleic acids with the electron microscope: visibility of a marker, PNAS 48(3):409-416.
Bell, 2011, Carrier testing for severe childhood recessive diseases by next-generation sequencing, Sci Trans Med 3 (65ra4).
Benner, 2001, Evolution, language and analogy in functional genomics, Trends Genet 17:414-8.
Bentzley, 1996, Oligonucleotide sequence and composition determined by matrix-assisted laser desorption/ionization, Anal Chem 68:2141-2146.
Bentzley, 1998, Base specificity of oligonucleotide digestion by calf spleen phosphodiesterase with matrix-assisted laser desorption ionization analysis, Anal Biochem 258:31-37.
Bhangale, 2006, Automating resequencing-based detection of insertion-deletion polymorphisms, Nature Genetics 38:1457-1462.
Bickle, 1993, Biology of DNA Restriction, Microbiol Rev 57(2):434-50.
Bonfield, 2013, Compression of FASTQ and SAM format sequencing data, PLoS One 8(3):e59190.
Bose, 2012, BIND—An algorithm for loss-less compression of nucleotide sequence data, J Biosci 37(4):785-789.
Boyden, 2013, High-throughput screening for SMN1 copy number loss by next-generation sequencing, American Society of Human Genetics 63rd Annual Meeting, Abstract, Oct. 22, 2013.
Boyer, 1971, DNA restriction and modification mechanisms in bacteria, Ann Rev Microbiol 25:153-76.
Braasch, 2001, Locked nucleic acid (LNA): fine-tuning the recognition of DNA and RNA, Chemistry & Biology 8(1):1-7.
Braslavsky, 2003, Sequence information can be obtained from single DNA molecules, PNAS 100:3960-4.
Brinkman, 2004, Splice Variants as Cancer Biomarkers, Clin Biochem 37:584.
Brison, 1982, General method for cloning amplified DNA by differential screening, Mol Cell Biol 2(5):578-587.
Brown, 1979, Chemical synthesis and cloning of a tyrosine tRNA gene, Methods Enzymol 68:109-51.
Browne, 2002, Metal ion-catalyzed nucleic Acid alkylation and fragmentation, J Am Chem Soc 124(27):7950-7962.
Brownstein, 2014, An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge, Genome Biol 15:R53.
Bunyan, 2004, Dosage analysis of cancer predisposition genes by multiplex ligation-dependent probe amplification, British Journal of Cancer, 91(6):1155-59.
Burrow, 1994, A block-sorting lossless data compression algorithm, Technical Report 124, Digital Equipment Corporation, CA. (24 pages).
Caruthers, 1985, Gene synthesis machines: DNA chemistry and its uses, Science 230:281-285.
Castellani, 2008, Consenses on the use of and interpretation of cystic fibrosis mutation analysis in clinical practice, J Cyst Fib 7:179-196.
Challis, 2012, An integrative variant analysis suite for whole exome next-generation sequencing data, BMC Informatics 13(8):1-12.
Chan, 2011, Natural and engineered nicking endonucleases-from cleavage mechanism to engineering of strand-specificity, Nucl Acids Res 39(1):1-18.
Chen, 2010, Identification of racehorse and sample contamination by novel 24-plex STR system, Forensic Sci Int: Genetics 4:158-167.
Chennagiri, 2013, A generalized scalable database model for storing and exploring genetic variations detected using sequencing data, American Society of Human Genetics 63rd Annual Meeting, Abstract, Oct. 22, 2013.
Chevreux, 1999, Genome sequence assembly using trace signals and additional sequence information, Proc GCB 99:45-56.
Chirgwin, 1979, Isolation of biologically active ribonucleic acid from sources enriched in ribonuclease, Biochemistry, 18:5294-99.
Choe, 2010, Novel CFTR Mutations in a Korean Infant with Cystic Fibrosis and Pancreatic Insufficiency, J Korean Med Sci 25:163-5.
Ciotti, 2004, Triplet repeat prmied PCR (TP PCR) in molecular diagnostic testing for Friedrich ataxia, J Mol Diag 6 (4):285-9.
Cock, 2010, The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res 38(6):1767-1771.
Collins, 2004, Finishing the euchromatic sequence of the human genome, Nature 431(7011):931-45.
Craig, 1997, Removal of repetitive sequences from FISH probes, Hum Genet 100:472.
Cremers, 1998, Autosomal Recessive Retinitis Pigmentosa and Cone-Rod Dystrophy Caused by Splice Site Mutations in the Stargardt's Disease Gene ABCR, Hum Mol Gen 7(3):355.
Cronin, 1996, Cystic Fibrosis Mutation Detection by Hybridization to Light-Generated DNA Probe Arrays Human Mutation 7:244.
Iqbal, 2 012, De novo assembly and genotyping of variants using colored de Bruijn graphs, Nature Genetics 44:226-232.
Isosomppi, 2009, Disease-causing mutations in the CLRN1 gene alter normal CLRN1 protien trafficking to the plasma membrane, Mol Vis 15:1806-1818.
Jaijo, 2010, Microarray-based mutation analysis of 183 Spanish families with Usher syndrome, Invest Ophthalmol Vis Sci 51(3):1311-7.
Jones, 2008, Core signaling pathways in human pancreatic cancers revealed by global genomic analyses, Science 321(5897):1801-1806.
Kambara, 1988, Optimization of Parameters in a DNA Sequenator Using Fluorescence Detection, Nature Biotechnology 6:816-821.
Kennedy, 2013, Accessing more human genetic variation with short sequencing reads, American Society of Human Genetics 63rd Annual Meeting, Abstract, Oct. 22, 2013.
Kent, 2002, BLAT—The BLAST-ike alignment tool, Genome Res 12(4): 656-664.
Kerem, 1989, Identification of the cystic fibrosis gene: genetic analysis, Science 245:1073-1080.
Kinde, 2012, FAST-SeqS: a simple an effective method for detection of aneuploidy by massively parallel sequencing, PLoS One 7(7):e41162.
Kircher, 2010, High-througput DNA sequencing—concepts and limitations, Bioassays 32:524-36.
Kirpekar, 1994, Matrix assisted laser desorption/ionization mass spectrometry of enzymatically synthesized RNA up to 150 kDa, Nucl Acids Res 22:3866-3870.
Klein, 2011, LOCAS—A low coverage sequence assembly tool for re-sequencing projects, PLoS One 6(8):e23455.
Kneen, 1998, Green fluorescent protein as a noninvasive intracellular pH indicator, Biophys J 74(3):1591-99.
Koboldt, 2009, VarScan: variant detection in massively parallel sequencing of individual and pooled samples, Bioinformatics 25:2283-85.
Krawitz, 2010, Microindel detection in short-read sequence data, Bioinformatics 26(6):722-729.
Kreindler, 2010, Cystic fibrosis: exploiting its genetic basis in the hunt for new therapies, Pharmacol Ther 125 (2):219-229.
Kumar, 2010, Comparing de novo assemblers for 454 transcriptome data, Genomics 11:571.
Kurtz, 2004, Versatile and open software for comparing large genomes, Genome Biol 5:R12.
Lam, 2008, Compressed indexing and local alignment of DNA, Bioinformatics 24(6):791-97.
Langmead, 2009, Ultrafast and memory-efficient alignment of short DNA sequences to the human genome, Genome Biol 10:R25.
Larkin, 2007, Clustal W and Clustal X version 2.0, Bioinformatics, 23(21):2947-2948.
Lecompte, 2001, Multiple alignment of complete sequences (MACS) in the post-genomic era, Gene 270(1-2):17-30.
Li, 2008, SOAP: short oligonucleotide alignment program, Bioinformatics 24(5):713-14.
Li, 2009, Fast and accurate short read alignment with Burrows-Wheeler transform, Bioinformatics, 25 (14):1754-60.
Li, 2009, SOAP2: an improved ultrafast tool for short read alignment, Bioinformatics 25(15):1966-67.
Li, 2009, The Sequence Alignment/Map format and SAMtools, Bioinformatics 25(16):2078-9.
Li, 2010, Fast and accurate long-read alignment with Burrows-Wheeler transform, Bioinformatics 26(5):589-95.
Li, 2011, Improving SNP discovery by base alignment quality, Bioinformatics 27:1157.
Li, 2011, Single nucleotide polymorphism genotyping and point mutation detection by ligation on microarrays, J Manosci Nanotechnol 11(2):994-1003.
Li, 2012, A new approach to detecting low-level mutations in next-generation sequence data, Genome Biol 13:1-15.
Li, 2014, HUGO: Hierarchical mUlti-reference Genome compression for aligned reads, JAMIA 21:363-373.
Lin, 2008, ZOOM! Zillions of Oligos Mapped, Bioinformatics, 24:2431.
Lin, 2010, A molecular inversion prove assay for detecting alternative splicing, BMC Genomics 11(712):1-14.
Lin, 2012, Development and evaluation of a reverse dot blot assay for the simultaneous detection of common alpha and beta thalassemia in Chinese, Blood Cells Molecules, and Diseases 48(2):86-90.
Lipman, 1985, Rapid and sensitive protein similarity searches, Science 227(4693):1435-41.
Liu, 2012, Comparison of next-generation sequencing systems, J Biomed Biotech 2012:251364.
Llopis, 1998, Measurement of cytosolic, mitochondrial, and Golgi pH in single living cells with green fluorescent proteins, PNAS 95(12):6803-08.
Ma, 2006, Application of real-time polymerase chain reaction (RT-PCR), J Am Soc 1-15.
MacArthur, 2014, Guidelines for investigating causality of sequence variants in human disease, Nature 508:469-76.
Maddalena, 2005, Technical standards and guidelines: molecular genetic testing for ultra-rare disorders, Genet Med 7:571-83.
Malewicz, 2010, Pregel: a system for large-scale graph processing, Proc. ACM SIGMOD Int Conf Mgmt Data 135-46.
Mamanova, 2010, Target-enrichment sliategies for next-generation sequencing, Nat Meth 7(2):111-118.
Margulies, 2005, Genome sequencing in micro-fabricated high-density picotiter reactors, Nature, 437:376-380.
Marras, 1999, Multiplex detection of single-nucleotide variations using molecular beacons, Genetic Analysis: Biomolecular Engineering 14:151.
Maxam, 1977, A new method for sequencing DNA, PNAS, 74:560-564.
May 1988, How Many Species Are There on Earth?, Science 241(4872):1441-9.
McDonnell, 2007, Antisepsis, disinfection, and sterilization: types, action, and resistance, p. 239.
McKenna, 2010, The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data, Genome Research 20:1297-1303.
Messiaen, 1999, Exon 10b of the NF1 gene represents a mutational hotspot and harbors a recurrent missense mutation Y489C associated with aberrant splicing, Genetics in Medicine, 1(6):248-253.
Meyer, 2007, Targeted high-throughput sequencing of tagged nucleic acid samples, Nucleic Acids Research 35(15):e97 (5 pages).
Dahl, 2005, Multiplex amplification enabled by selective circularization of large sets of genomic DNA fragments, Nucleic Acids Res 33(8):e71.
Danecek, 2011, The variant call format and VCFtools, Bioinformatics 27(15):12156-2158.
De la Bastide, 2007, Assembling genome DNA sequences with PHRAP, Current Protocols in Bioinformatics 17:11.4.1-11.4.15.
Delcher, 1999, Alignment of whole genomes, Nuc Acids Res 27(11):2369-2376.
Den Dunnen, 2003, Mutation Nomenclature, Curr Prat Hum Genet 7.13.1-7.13.8.
Deng et al., 2012, Supplementary Material, Nature Biotechnology, S1-1-S1-1 1, Retrieved from the Internet on Oct. 24, 2012.
Deng, 2009, targeted bisulfite sequencing reveals changes in DNA methylation, Nat Biotech 27(4):353-360.
Deorowicz, 2013, Data compression for sequencing data, Alg for Mole Bio 8:25.
Diep, 2012, Library-free methylation sequencing with bisulfite padlock probes, Nature Methods 9:270-272 (and supplemental information).
DiGuistini, 2009, De novo sequence assembly of a filamentous fungus using Sanger, 454 and Illumina sequence data, Genome Biology, 10:R94.
Dong, 2011, Mutation surveyor: An in silico tool for sequencing analysis, Methods Mol Biol 760:223-37.
Drmanac, 1992, Sequencing by hybridization: towards an automated sequencing of one million M13 clones arrayed on membranes, Elctrophoresis 13:566-573.
Dudley, 2009, A quick guide for developing effective bioinformatics programming skills, PLoS Comp Biol 5(12):e1000589.
Ericsson, 2008, A dual-tag microarray platform for high-performance nucleic acid and protein analyses, Nucl Acids Res 36:e45.
Fares, 2008, Carrier frequency of autosomal-recessive disorders in the Ashkenazi Jewish population: should the rationale for mutation choice for screening be reevaluated?, Prenatal Diagnosis 28:236-41.
Faulstich, 1997, A sequencing method for RNA oligonucleotides based on mass spectrometry, Anal Chem 69:4349-4353.
Faust, 2014, SAMBLASTER: fast duplicate marking and structural variant read extraction, Bioinformatics published online May 7, 2014.
Flaschker, 2007, Description of the mutations in 15 subjects with variant forms of maple syrup urine disease, J Inherit Metab Dis 30:903-909.
Frey, 2006, Statistics Hacks 108-115.
Friedenson, 2005, BRCA1 and BRCA2 Pathways and the Risk of Cancers Other Than Breast or Ovarian, Medscape General Medicine 7(2):60.
Furtado, 2011, Characterization of large genomic deletions in the FBN1 gene using multiplex ligation-dependent probe amplification, BMC Med Gen 12:119-125.
Garber, 2008, Fixing the front end, Nat Biotech 26(10):1101-1104.
Gemayel, 2010, Variable tandem repeats accelerate evolution of coding and regulatory sequences, Ann Rev Genet 44:445-77.
Giusti, 1993, Synthesis and Characterization of f-Fluorescent-dye-labeled Oligonucleotides, PCR Meth Appl 2:223-227.
Glover, 1995, Sequencing of oligonucleotides using high performance liquid chromatography and electrospray mass spectrometry, Rapid Com Mass Spec 9:897-901.
Gnirke, 2009, Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing, nature biotechnology 27:182-9.
Goto, 1994, A Study on Development of a Deductive Object-Oriented Database and Its Application to Genome Analysis, PhD Thesis, Kyushu University, Kyushu, Japan (106 pages).
Soto, 2010, BioRuby: bioinformatics software for the Ruby programming language, Bioinformatics 26(20):2617-2619.
Guerrero-Fernandez, 2013, FQbin: a compatible and optimize dformat for storing and managing sequence data, IWBBIO Proceedings, Granada 337-344.
Gupta, 1991, A general method for the synthesis of 3′-sulfhydryl and phosphate group containing oligonucleotides, Nucl Acids Res 19(11):3019-3025.
Gupta, 2014, Expanding the genetic toolkit: ZFNs, TALENs, and CRISPR-Cas9, J Clin Invest 124(10):4154.
Gustincich, 1991, A fast method for high-quality genomic DNA extraction from whole human blood, BioTechniques 11 (3):298-302.
Gut, 1995, A procedure for selective DNA alkylation and detection by mass spectrometry, Nucl Acids Res 23 (8):1367-1373.
Hallam, 2014, Validation for Clinical Use of, and Initial Clinical Experience with, a Novel Approach to Population-Based Carrier Screening using High-Throughput Next-Generation DNA Sequencing, J Mol Diagn 16:180-9.
Hammond, 1996, Extraction of DNA from preserved animal specimens for use in randomly amplified polymorphic DNA analysis, Anal Biochem 240:298-300.
Hardenbol, 2003, Multiplexed genotyping with sequence-tagged molecular inversion probes, Nat Biotech 21:673-8.
Harris, 2006, Defects can increase the melting temperature of DNA-nanoparticle assemblies, J Phys Chem B 110 (33):16393-6.
Harris, 2008, Helicos True Single Molecule Sequencing (tSMS) Science 320:106-109.
Harris, 2008, Single-molecule DNA sequencing of a viral genome, Science 320(5872):106-9.
Heger, 2006, Protonation of Cresol Red in Acidic Aqueous Solutions Caused by Freezing, J Phys Chem B 110 (3):1277-1287.
Heid, 1996, Real time quantitative PCR, Genome Res 6:986-994.
Hiatt, 2013, Single molecule molecular inversion probes for targeted, high-accuracy detection of low-frequency variation, Genome Res 23:843-54.
Hodges, 2007, Genome-wide in situ exon capture for selective resequencing, Nat Genet 39(12):1522-7.
Holland, 2008, BioJava: an open-source framework for bioinformatics, Bioinformatics 24(18):2096-2097.
Homer, 2008, Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays. PLoS One 4(8):e1000167.
Homer, 2009, BFAST: An alignment tool for large scale genome resequencing, PLoS ONE 4(11):e7767.
Huang, 2008, Comparative analysis of common CFTR polymorphisms poly-T, TGrepeats and M470V in a healthy Chinese population, World J Gastroenterol 14(12):1925-30.
Husemann, 2009, Phylogenetic Comparative Assembly, Algorithms in Bioinformatics: 9th International Workshop, pp. 145-156, Salzberg & Warnow, Eds. Springer-Verlag, Berlin, Heidelberg.
Illumina, 2010, De Novo assembly using Illumina reads, Technical Note (8 pages).
International Human Genome Sequencing Consortium, 2004, Finishing the euchromatic sequence of the human genome, Nature 431:931-945.
Meyer, 2008, Parallel tagged sequencing on the 454 platform, Nat Protocol 3(2):267-278.
Miesenbock, 1998, Visualizing secretion and synaptic transmission with pH-sensitive green fluorescent proteins, Nature 394(6689):192-95.
Miller, 2010, Assembly algorithms for next-generation sequencing data, Genomics 95:315-327.
Mills, 2010, Mapping copy number variation by population-scale genome sequencing, Nature 470(7332):59-65.
Miner, 2004, Molecular barcodes detect redundancy and contamination in hairpin-bisulfite PCR, Nucl Acids Res 32 (17):e135.
Minton, 2011, Mutation Surveyor: software for DNA sequence analysis, Meth Mol Biol 688:143-53.
Miyake, 2009, PIK3CA gene mutations and umplification in uterine cancers, Cane Lett 261:120-126.
Miyazaki, 2009, Characterization of deletion breakpoints in patients with dystrophinopathy carrying a deletion of exons 45-55 of the Duchenne muscular dystrophy (DMD) gene, J Hum Gen 54:127-30.
Mockler, 2005, Applications of DNA tiling arrays for whole-genome analysis, Genomics 85(1):1-15.
Mohammed, 2012, DELIMINATE—a fast and efficient methods for loss-less compression of genomice sequences, Bioinformatics 28(19):2527-2529.
Moudrianakis, 1965, Base Sequence Determination in Nucleic Acids with the Electron Microscope, III. Chemistry and Microscopy of Guanine-Labeled DNA, PNAS, 53:564-71.
Mullan, 2002, Multiple sequence alignment-the gateway to further analysis, Brief Bioinform 3(3):303-5.
Munne, 2012, Preimplantation genetic diagnosis for aneuploidy and translocations using array comparative genomic hybridization, Curr Genomics 13(6):463-470.
Nan, 2006, A novel CFTR mutation found in a Chinese patient with cystic fibrosis, Chinese Med J 119(2):103-9.
Narang, 1979, Improved phosphotriester method for the synthesis of gene fragments, Meth Enz 68:90-98.
Nelson, 1989, Bifunctional oligonucleotide probes synthesized using a novel CPG support are able to detect single base pair mutations, Nucl Acids Res 17(18):7187-7194.
Ng, 2009, Targeted capture and massively parallel sequencing of 12 human exomes, Nature 461 (7261):272-6.
Nicholas, 2002, Strategies for multiple sequence alignment, Biotechniques 32:572-91.
Nickerson, 1990, Automated DNA diagnostics using an ELISA-based oligonucleotide ligation assay, PNAS 87:8923-7.
Nielsen, 1999, Peptide Nucleic Acids, Protocols and Applications (Norfolk: Horizon Scientific Press, 1-19).
Nilsson, 2006, Analyzing genes using closing and replicating circles, Trends in Biotechnology 24:83-8.
Ning, 2001, SSAHA: a fast search method for large DNA databases, Genome Res 11(10):1725-9.
Nordhoff, 1993, Ion stability of nucleic acids in infrared matrix-assisted laser desorption/ ionization mass spectrometry, Nucl Acid Res 21(15):3347-57.
Nuttle, 2013, Rapid and accurate large-scale genotyping of duplicated genes and discovery of interlocus gene conversions, Nat Meth 10(9):903-909.
Nuttle, 2014, Resolving genomic disorder-associated breakpoints within segmental DNA duplications using massively parallel sequencing, Nat Prat 9(6):1496-1513.
O'Roak, 2012, Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders, Science 338(6114):1619-1622.
Oefner, 1996, Efficient random sub-cloning of DNA sheared in a recirculating point-sink flow system, Nucleic Acids Res 24(20):3879-3886.
Oka, 2006, Detection of loss of heterozygosity in the p53 gene in renal cell carcinoma and bladder cancer using the polymerase chain reaction, Mol Carcinogenesis 4(1):10-13.
Okoniewski, 2013, Precise breakpoint localization of large genomic deletions using PacBio and Illumina next-generation sequencers, Biotechniques 54(2):98-100.
Okou, 2007, Microarray-based genomic selection for high-throughput reseuqencing, Nat Meth 4(11):907-909.
Oliphant, 2002, BeadArray technology: enabling an accurate, cost-effective approach to high-throughput genotyping, Biotechniques Suppl:56-8, 60-1.
Ordahl, 1976, Sheared DNA fragment sizing: comparison of techniques, Nucleic Acids Res 3:2985-2999.
Ostrer, 2001, A genetic profile of contemporary Jewish populations, Nat Rev Genet 2(11):891-8.
Owens, 1998, Aspects of oligonucleotide and peptide sequencing with MALDI and electrospray mass spectrometry, Bioorg Med Chem 6:1547-1554.
Parameswaran, 2007, A pyrosequencing-tailored nucleotide barcode design unveils opportunities for large-scale sample multiplexing, Nucl Acids Rese 35:e130.
Parkinson, 2012, Preparation of high-quality next-generation sequencing libraries from picogram quantities of target DNA, Genome Res 22:125-133.
Pastor, 2010, Conceptual modeling of human genome mutations: a dichotomy between what we have and what we shoudl have, 2010 Proc BIOSTEC Bioinformatics, pp. 160-166.
Paton, 2000, Conceptual modelling of genomic information, Bioinformatics 16(6):548-57.
Pearson, 1988, Improved tools for biological sequence comparison, PNAS 85(8):2444-8.
Pertea, 2003, TIGR gene indices clustering tools (TGICL), Bioinformatics 19(5):651-52.
Pieles, 1993, Matrix-assisted laser desorption ionization time-of-flight mass spectrometry: A powerful tool for the mass and sequence analysis of natural and modified oligonucleotides, Nucleic Acids Res 21:3191-3196.
Pinho, 2013, MFCompress: a compression tool for FASTA and multi-FASTA data, Bioinformatics 30(1):117-8.
Porreca, 2013, Analytical performance of a Next-Generation DNA sequencing-based clinical workflow for genetic carrier screening, American Society of Human Genetics 63rd Annual Meeting, Abstract, Oct. 22, 2013.
Pourmand, 2006, PathgoenMIPer: a tool for the design of molecular inversion probes, BMC informatics 7:500.
Procter, 2006, Molecular diagnosis of Prader-Willi and Angelman syndromes by methylation-specific melting analysis and methylation-specific multiplex ligation-dependent probe amplification, Clin Chem 52(7):1276-83.
Qiagen, 2011, Gentra Puregene handbook, 3d Ed. (72 pages).
Quail, 2010, DNA: Mechanical Breakage, In Encyclopedia of Life Sciences, John Wiley & Sons Ltd, Chicester (5 pages).
Rambaut, 1997, Seq-Gen:an application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees, Bioinformatics 13:235-38.
Richards, 2008 ACMG recommendations for standards for interpretation and reporting of sequence variations: Revisions, Genet Med 10(4):294-300.
Richter, 2008, MetaSim—A Sequencing Simulator for Genomics and Metagenomics, PLoS ONE 3:e3373.
Roberts, 1980, Restriction and modification enzymes and their recognition sequences, Nucleic Acids Res 8(1):r63-r80.
Rodriguez, 2010, Constructions from Dots and Lines, Bull Am Soc Inf Sci Tech 36(6):35-41.
Rosendahl, 2013, CFTR, SPINK1, CTRC and PRSS1 variants in chronic pancreatitis: is the role of mutated CFTR overestimated?, Gut 62:582-592.
Rothberg, 2011, An integrated semiconductor device enabling non-optical genome sequencing, Nature 475:348-352.
Rowntree, 2003, The phenotypic consequences of CFTR mutations, Ann Hum Gen 67:471-485.
Saihan, 2009, Update on Usher syndrome, Curr Op Neurology 22(1):19-24.
Sanger, 1977, DNA Sequencing with chain-terminating inhibitors, PNAS 74(12):5463-5467.
Santa Lucia, 1998, A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics, PNAS 95(4):1460-5.
Sargent, 1987, Isolation of differentially expressed genes, Meth Enzym 152:423-432.
Sauro, 2004, How Do You Calculate a Z-Score/ Sigma Level?, https://www.measuringusability.com/zcalc.htm (online publication).
Sauro, 2004, What's a Z-score and Why Use It in Usability Testing?, https://www.measuringusability.com/z.htm (online publication).
Schadt, 2010, A window into third-generation sequencing, Human Mol Genet 19(R2):R227-40.
Schatz, 2010, Assembly of large genomes using second-generation sequencing, Genome Res., 20:1165-1173.
Schiffman, 2009, Molecular inversion probes reveal patterns of 9p21 deletion and copy No. aberrations in childhood leukemia, Cancer Genetics and Cytogenetics 193:9-18.
Schneeberger, 2011, Reference-guided assembly of four diverse Arabidopsis thaliana genomes, PNAS 108 (25):10249-10254.
Schouten, 2002, Relative Quantification of 40 Nucleic Acid Sequences by Multiplex Ligation-Dependent Probe Amplification, Nucle Acids Res 30 (12):257.
Schrijver, 2005, Diagnostic testing by CFTR gene mutation analysis in a large group of Hispanics, J Mol Diag 7 (2):289-299.
Schuette, 1995, Sequence analysis of phosphorothioate oligonucleotides via matrix-assisted laser desorption ionization time-of-flight mass spectrometry, J Pharm Biomed Anal 13:1195-1203.
Schwartz, 2009, Identification of cystic fibrosis variants by polymerase chain reaction/oligonucleotide ligation assay, J Mol Diag 11(3):211-15.
Schwartz, 2011, Clinical utility of single nucleotide polymorphism arrays, Clin Lab Med 31(4):581-94.
Sequeira, 1997, Implementing generic, object-oriented models in biology, Ecological Modeling 94.1:17-31.
Shagin, 2002, A novel method for SNP detection, Genome Res 12:1935.
Shen, 2011, High quality DNA sequence capture of 524 disease candidate genes, PNAS 108(16):6549-6554.
Shen, 2013, Multiplex capture with double-stranded DNA probes, Genome Medicine 5(50):1-8.
Sievers, 2011, Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega, Mol Syst Biol 7:539.
Simpson, 2009, ABySS: A parallel assembler for short read sequence data, Genome Res., 19(6):1117-23.
Slater, 2005, Automated generation of heuristics for biological sequence comparison, BMC Bioinformatics 6:31.
Smirnov, 1996, Sequencing oligonucleotides by exonuclease digestion and delayed extraction matrix-assisted laser desorption ionization time-of-flight mass spectrometry, Anal Biochem 238:19-25.
Smith, 1985, The synthesis of oligonucleotides containing an aliphatic amino group at the 5′ terminus: synthesis of fluorescent DNA primers for use in DNA sequence analysis, Nucl Acid Res 13:2399-2412.
Smith, 2010, Highly-multiplexed barcode sequencing: an efficient method for parallel analysis of pooled samples, Nucleic Acids Research 38(13):e142 (8 pages).
Soni, 2007, Progress toward ultrafast DNA sequencing using solid-state nanopores, Clin Chem 53(11):1996-2001.
Spanu, 2010, Genome expansion and gene loss in powdery mildew fungi reveal tradeoffs in extreme parasitism, Science 330(6010):1543-46.
Sproat, 1987, The synthesis of protected 5′-mercapto-2′,5′-dideoxyribonucleoside-3′-O-phosphoramidites; uses of 5′-mercapto-oligodeoxyribonucleotides, Nucl Acid Res 15:4837-4848.
Streit, 2003, CFTR gene: molecular analysis in patients from South Brazil, Molecular Genetics and Metabolism 78:259-264.
Strom, 2005, Mutation detection, interpretation, and applications in the clinical laboratory setting, Mutat Res 573:160-67.
Summerer, 2009, Enabling technologies of genomic-scale sequence enrichment for targeted high-throughput sequencing, Genomics 94(6):363-8.
Summerer, 2010, Targeted High Throughput Sequencing of a Cancer-Related Exome Subset by Specific Sequence Capture With a Fully Automated Microarray Platform, Genomics 95(4):241-246.
Sunnucks, 1996, Microsatellite and chromosome evolution of parthenogenetic sitobion aphids in Australia, Genetics 144:747-756.
Tan, 2014, Clinical outcome of preimplantation genetic diagnosis and screening using next generation sequencing, GigaScience 3(30):1-9.
Thauvin-Robinet, 2009, The very low penetrance of cystic fibrosis for the R117H mutation: a reappraisal for genetic counseling and newborn screening, J Med Genet 46:752-758.
Thiyagarajan, 2006, PathogenMIPer: a tool for the design of molecular inversion probes to detect multiple pathogens, BMC Bioinformatics 7:500.
Thompson, 1994, Clustal W: improving the sensitivity of progressive mulitple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice, Nuc Acids Res 22:4673-80.
Thompson, 2011, The properties and applications of single-molecule DNA sequencing, Genome Biol 12(2):217.
Thorstenson, 1998, An Automated Hydrodynamic Process for Controlled, Unbiased DNA Shearing, Genome Res 8(8):848-855.
Thorvaldsdottir, 2012, Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 24(2):178-92.
Tkachuk, 1990, Detection of bcr-abl Fusion in Chronic Myelogeneous Leukemia by in Situ Hybridization, Science 250:559.
Tobler, 2005, The SNPlex Genotyping System: A Flexible and Scalable Platform for SNP Genotyping, J Biomol Tech 16(4):398.
Tokino, 1996, Characterization of the human p57 KIP2 gene: alternative splicing, insertion/deletion polymorphisms in VNTR sequences in the coding region, and mutational analysis, Human Genetics 96:625-31.
Treangen, 2011, Repetitive DNA and next-generation sequencing: computational challenges and solutions, Nat Rev Gen 13(1):36-46.
Turner, 2009, Methods for genomic partitioning, Ann Rev Hum Gen 10:263-284.
Umbarger, 2013, Detecting contamination in Next Generation DNA sequencing libraries, American Society of Human Genetics 63rd Annual Meeting, Abstract, Oct. 22, 2013.
Umbarger, 2014, Next-generation carrier screening, Gen Med 16(2):132-140.
Veeneman, 2012, Oculus: faster sequence alignment by streaming read compression, BMC Bioinformatics 13:297.
Wahl, 1979, Efficient transfer of large DNA fragments from agarose gels to diazobenzyloxymethyl-paper and rapid hybridization by using dextran sulfate, PNAS 76:3683-3687.
Wallace, 1979, Hybridization of synthetic oligodeoxyribonucteotides to dp x 174DNA:the effect of single base pair mismatch, Nucl Acids Res 6:3543-3557.
Wallace, 1987, Oligonucleotide probes for the screening of recombinant DNA libraries, Meth Enz 152:432-442.
Wang, 2005, Allele quantification using molecular inversion probes (MIP), Nucleic Acids Res 33(21):e183.
Warner, 1996, A general method for the detection of large CAG repeat expansions by fluorescent PCR, J Med Genet 33(12):1022-6.
Warren, 2007, Assembling millions of short DNA sequences using SSAKE, Bioinformatics, 23:500-501.
Waszak, 2010, Systematic inference of copy-number genotypes from personal genome sequencing data reveals extensive olfactory gene content diversity, PLoS Comp Biol 6(11):e1000988.
Watson, 2004, Cystic fibrosis population carrier screening: 2004 revision of American College of Medical Genetics mutation panel, Genetics in Medicine 6(5):387-391.
Williams, 2003, Restriction endonucleases classification, properties, and applications, Mol Biotechnol 23(3):225-43.
Wirth, 1999, Quantitative analysis of survival motor neuron copies, Am J Hum Genet 64:1340-1356.
Wittung, 1997, Extended DNA-Recognition Repertoire of Peptide Nucleic Acid (PNA): PNA-dsDNA Triplex Formed with Cytosine-Rich Homopyrimidine PNA, Biochemistry 36:7973.
Wu, 1998, Sequencing regular and labeled oligonucleotides using enzymatic digestion and ionspray mass spectrometry, Anal Biochem 263:129-138.
Wu, 2001, Improved oligonucleotide sequencing by alkaline phosphatase and exonuclease digestions with mass spectrometry, Anal Biochem 290:347-352.
Xu, 2012, FastUniq: A fast de novo duplicates removal tool for paired short reads, PLoS One 7(12):e52249.
Yau, 1996, Accurate diagnosis of carriers of deletions and duplications in Duchenne/Becker muscular dystrophy by fluorescent dosage analysis, J Med Gen 33(7):550-8.
Ye, 2009, Pindel: a pattern growth approach to detect break points of large deletions and medium size insertions from paired-end short reads, Bioinformatics 25(21):2865-2871.
Yershov, 1996, DNA analysis and diagnostics on oligonucleotide microchips, PNAS 93:4913-4918.
Yoo, 2009, Applications of DNA microarray in disease diagnostics, J Microbiol Biotech19(7):635-46.
Yoon, 2014, MicroDuMIP: target-enrichment technique for microarray-based duplex molecular inversion probes, Nucl Ac Res 43(5):e28.
Yoshida, 2004, Role of BRCA1 and BRCA2 as regulators of DNA repair, transcription, and cell cycle in response to DNA damage, Cancer Science 95(11)866-71.
Yu, 2007, A novel set of DNA methylation markers in urine sediments for sensitive/specific detection of bladder cancer, Clin Cancer Res 13(24):7296-7304.
Yuan, 1981, Structure and mechanism of multifunctional restriction endonucleases, Ann Rev Biochem 50:285-319.
Zerbino, 2008, Velvet: Algorithms for de novo short read assembly using de Bruijn graphs, Genome Research 18 (5):821-829.
Zhang, 2011, Is Mitochondrial tRNAphe Variant m.593T.Ca Synergistically Pathogenic Mutation in Chinese LHON Families with m.11778G.A? PLoS ONE 6(10):e26511.
Zhao, 2009, PGA4genomics for comparative genome assembly based on genetic algorithm optimization, Genomics 94 (4):284-6.
Zheng, 2011, iAssembler: a package for de novo assembly of Roche-454/Sanger transcriptome sequences, BMC Bioinformatics 12:453.
Zhou, 2014, Bias from removing read duplication in ultra-deep sequencing experiments, Bioinformatics 30 (8):1073-1080.
Zhulidov, 2004, Simple cDNA normalization using kamchatka crab duplex-specific nuclease, Nucl Acids Res 32(3):e37.
Zimmerman, 2010, A novel custom resequencing array for dilated cardiomyopathy, Gen Med 12(5):268-78.
Zimran, 1990, A glucocerebrosidase fusion gene in Gaucher disease, J Clin Invest 85:219-222.
Zuckerman, 1987, Efficient methods for attachment of thiol specific probes to the 3′-ends of synthetic oligodeoxyribonucleotides, Nucl Acid Res 15(13):5305-5321.
Ball, 2009, Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells, Nat Biotech 27:361-8.
Blasczyk, 1996, Sequence analysis of the 2nd intron revealed common sequence motifs providing the means for a unique sequencing based typing protocol of the HLA-A locus, Tissue Antigens, 47:102-110.
Daly, 2007, Multiplex Assay for Comprehensive Genotyping of Genes Involved in Drug Metabolism, Excretion, and Transport, Clinical Chemistry, 53:7:1222-1230.
Schiffman, 2007, Adapting molecular inversion probe (MIP) technology for allele quantification in childhood leukemia, Journal of Clinical Oncology, 25, p. 530, 5 pages.
Tarhini, 2018, Predictive and on-treatment monitoring biomarkers in advanced melanoma: Moving toward personalized medicine, Cancer Treatment Reviews, 71:8-18.
Wang, 2007, Analysis of molecular inversion probe performance for allele copy number determination, Genome Biology, 8(11):R246.1-R246.14.
Related Publications (1)
Number Date Country
20160068889 A1 Mar 2016 US
Provisional Applications (1)
Number Date Country
62048452 Sep 2014 US