Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

Min Zhao, Qingguo Wang, Quan Wang, Peilin Jia, Zhongming Zhao, Min Zhao, Qingguo Wang, Quan Wang, Peilin Jia, Zhongming Zhao

Abstract

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.

Figures

Figure 1
Figure 1
Five approaches to detect CNVs from NGS short reads. A. Paired-end mapping (PEM) strategy detects CNVs through discordantly mapped reads. A discordant mapping is produced if the distance between two ends of a read pair is significantly different from the average insert size. B. Split read (SR)-based methods use incompletely mapped read from each read pair to identify small CNVs. C. Read depth (RD)-based approach detects CNV by counting the number of reads mapped to each genomic region. In the figure, reads are mapped to three exome regions. D. Assembly (AS)-based approach detects CNVs by mapping contigs to the reference genome. E. Combinatorial approach combines RD and PEM information to detect CNVs.

References

    1. Alkan C, Coe BP, Eichler EE. Genome structural variation discovery and genotyping. Nat Rev Genet. 2011;12:363–376. doi: 10.1038/nrg2958.
    1. Freeman JL, Perry GH, Feuk L, Redon R, McCarroll SA, Altshuler DM, Aburatani H, Jones KW, Tyler-Smith C, Hurles ME. et al.Copy number variation: new insights in genome diversity. Genome Res. 2006;16:949–961. doi: 10.1101/gr.3677206.
    1. Sebat J, Lakshmi B, Troge J, Alexander J, Young J, Lundin P, Maner S, Massa H, Walker M, Chi M. et al.Large-scale copy number polymorphism in the human genome. Science. 2004;305:525–528. doi: 10.1126/science.1098918.
    1. Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y, Scherer SW, Lee C. Detection of large-scale variation in the human genome. Nat Genet. 2004;36:949–951. doi: 10.1038/ng1416.
    1. Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, Fiegler H, Shapero MH, Carson AR, Chen W. et al.Global variation in copy number in the human genome. Nature. 2006;444:444–454. doi: 10.1038/nature05329.
    1. Fiegler H, Redon R, Andrews D, Scott C, Andrews R, Carder C, Clark R, Dovey O, Ellis P, Feuk L. et al.Accurate and reliable high-throughput detection of copy number variation in the human genome. Genome Res. 2006;16:1566–1574. doi: 10.1101/gr.5630906.
    1. Beckmann JS, Estivill X, Antonarakis SE. Copy number variants and genetic traits: closer to the resolution of phenotypic to genotypic variability. Nat Rev Genet. 2007;8:639–646.
    1. Beroukhim R, Mermel CH, Porter D, Wei G, Raychaudhuri S, Donovan J, Barretina J, Boehm JS, Dobson J, Urashima M. et al.The landscape of somatic copy-number alteration across human cancers. Nature. 2010;463:899–905. doi: 10.1038/nature08822.
    1. Buysse K, Delle Chiaie B, Van Coster R, Loeys B, De Paepe A, Mortier G, Speleman F, Menten B. Challenges for CNV interpretation in clinical molecular karyotyping: lessons learned from a 1001 sample experience. Eur J Med Genet. 2009;52:398–403. doi: 10.1016/j.ejmg.2009.09.002.
    1. Carter NP. Methods and strategies for analyzing copy number variation using DNA microarrays. Nat Genet. 2007;39:S16–S21. doi: 10.1038/ng2028.
    1. Snijders AM, Nowak N, Segraves R, Blackwood S, Brown N, Conroy J, Hamilton G, Hindle AK, Huey B, Kimura K. et al.Assembly of microarrays for genome-wide measurement of DNA copy number. Nat Genet. 2001;29:263–264. doi: 10.1038/ng754.
    1. Shendure J, Ji H. Next-generation DNA sequencing. Nat Biotechnol. 2008;26:1135–1145. doi: 10.1038/nbt1486.
    1. Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet. 2010;11:31–46. doi: 10.1038/nrg2626.
    1. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 2010;11:685–696. doi: 10.1038/nrg2841.
    1. Korbel JO, Urban AE, Affourtit JP, Godwin B, Grubert F, Simons JF, Kim PM, Palejev D, Carriero NJ, Du L. et al.Paired-end mapping reveals extensive structural variation in the human genome. Science. 2007;318:420–426. doi: 10.1126/science.1149504.
    1. Medvedev P, Stanciu M, Brudno M. Computational methods for discovering structural variation with next-generation sequencing. Nat Methods. 2009;6:S13–20. doi: 10.1038/nmeth.1374.
    1. Chen K, Wallis JW, McLellan MD, Larson DE, Kalicki JM, Pohl CS, McGrath SD, Wendl MC, Zhang QY, Locke DP. et al.BreakDancer: an algorithm for high-resolution mapping of genomic structural variation. Nat Methods. 2009;6:677–681. doi: 10.1038/nmeth.1363.
    1. Korbel JO, Abyzov A, Mu XJ, Carriero N, Cayting P, Zhang ZD, Snyder M, Gerstein MB. PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. Genome Biol. 2009;10:R23. doi: 10.1186/gb-2009-10-2-r23.
    1. Hormozdiari F, Alkan C, Eichler EE, Sahinalp SC. Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes. Genome Res. 2009;19:1270–1278. doi: 10.1101/gr.088633.108.
    1. Hormozdiari F, Hajirasouliha I, Dao P, Hach F, Yorukoglu D, Alkan C, Eichler EE, Sahinalp SC. Next-generation VariationHunter: combinatorial algorithms for transposon insertion discovery. Bioinformatics. 2010;26:i350–357. doi: 10.1093/bioinformatics/btq216.
    1. Hormozdiari F, Hajirasouliha I, McPherson A, Eichler EE, Sahinalp SC. Simultaneous structural variation discovery among multiple paired-end sequenced genomes. Genome Res. 2011;21:2203–2212. doi: 10.1101/gr.120501.111.
    1. Ye K, Schulz MH, Long Q, Apweiler R, Ning Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics. 2009;25:2865–2871. doi: 10.1093/bioinformatics/btp394.
    1. Abyzov A, Gerstein M. AGE: defining breakpoints of genomic structural variants at single-nucleotide resolution, through optimal alignments with gap excision. Bioinformatics. 2011;27:595–603. doi: 10.1093/bioinformatics/btq713.
    1. Smith TF, Waterman MS. Identification of common molecular subsequences. J Mol Biol. 1981;147:195–197. doi: 10.1016/0022-2836(81)90087-5.
    1. Zhang ZD, Du J, Lam H, Abyzov A, Urban AE, Snyder M, Gerstein M. Identification of genomic indels and structural variations using split reads. BMC Genomics. 2011;12:375. doi: 10.1186/1471-2164-12-375.
    1. Abel HJ, Duncavage EJ, Becker N, Armstrong JR, Magrini VJ, Pfeifer JD. SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data. Bioinformatics. 2010;26:2684–2688. doi: 10.1093/bioinformatics/btq528.
    1. Teo SM, Pawitan Y, Ku CS, Chia KS, Salim A. Statistical challenges associated with detecting copy number variations with next-generation sequencing. Bioinformatics. 2012;28:2711–2718. doi: 10.1093/bioinformatics/bts535.
    1. Yoon S, Xuan Z, Makarov V, Ye K, Sebat J. Sensitive and accurate detection of copy number variants using read depth of coverage. Genome Res. 2009;19:1586–1592. doi: 10.1101/gr.092981.109.
    1. Magi A, Tattini L, Pippucci T, Torricelli F, Benelli M. Read count approach for DNA copy number variants detection. Bioinformatics. 2012;28:470–478. doi: 10.1093/bioinformatics/btr707.
    1. Plagnol V, Curtis J, Epstein M, Mok KY, Stebbings E, Grigoriadou S, Wood NW, Hambleton S, Burns SO, Thrasher AJ. et al.A robust model for read count data in exome sequencing experiments and implications for copy number variant calling. Bioinformatics. 2012;28:2747–2754. doi: 10.1093/bioinformatics/bts526.
    1. Xie C, Tammi MT. CNV-seq, a new method to detect copy number variation using high-throughput sequencing. BMC Bioinformatics. 2009;10:80. doi: 10.1186/1471-2105-10-80.
    1. Gusnanto A, Wood HM, Pawitan Y, Rabbitts P, Berri S. Correcting for cancer genome size and tumour cell content enables better estimation of copy number alterations from next-generation sequence data. Bioinformatics. 2012;28:40–47. doi: 10.1093/bioinformatics/btr593.
    1. Chiang DY, Getz G, Jaffe DB, O'Kelly MJ, Zhao X, Carter SL, Russ C, Nusbaum C, Meyerson M, Lander ES. High-resolution mapping of copy-number alterations with massively parallel sequencing. Nat Methods. 2009;6:99–103. doi: 10.1038/nmeth.1276.
    1. Kim TM, Luquette LJ, Xi R, Park PJ. rSW-seq: algorithm for detection of copy number alterations in deep sequencing data. BMC Bioinformatics. 2010;11:432. doi: 10.1186/1471-2105-11-432.
    1. Alkan C, Kidd JM, Marques-Bonet T, Aksay G, Antonacci F, Hormozdiari F, Kitzman JO, Baker C, Malig M, Mutlu O. et al.Personalized copy number and segmental duplication maps using next-generation sequencing. Nat Genet. 2009;41:1061–1067. doi: 10.1038/ng.437.
    1. Campbell PJ, Stephens PJ, Pleasance ED, O'Meara S, Li H, Santarius T, Stebbings LA, Leroy C, Edkins S, Hardy C. et al.Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing. Nat Genet. 2008;40:722–729. doi: 10.1038/ng.128.
    1. Olshen AB, Venkatraman ES, Lucito R, Wigler M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics. 2004;5:557–572. doi: 10.1093/biostatistics/kxh008.
    1. Miller CA, Hampton O, Coarfa C, Milosavljevic A. ReadDepth: a parallel R package for detecting copy number alterations from short sequencing reads. PLoS One. 2011;6:e16327. doi: 10.1371/journal.pone.0016327.
    1. Wang LY, Abyzov A, Korbel JO, Snyder M, Gerstein M. MSB: a mean-shift-based approach for the analysis of structural variation in the genome. Genome Res. 2009;19:106–117.
    1. Abyzov A, Urban AE, Snyder M, Gerstein M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 2011;21:974–984. doi: 10.1101/gr.114876.110.
    1. Xi R, Hadjipanayis AG, Luquette LJ, Kim TM, Lee E, Zhang J, Johnson MD, Muzny DM, Wheeler DA, Gibbs RA. et al.Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion. Proc Natl Acad Sci USA. 2011;108:E1128–1136. doi: 10.1073/pnas.1110574108.
    1. Wang Z, Hormozdiari F, Yang W-Y, Halperin E, Eskin E. In: Research in Computational Molecular Biology. Chor B: Springer Berlin/Heidelberg, editor. Vol. 7262. 2012. CNVeM: Copy Number Variation Detection Using Uncertainty of Read Mapping; pp. 326–340. Lecture Notes in Computer Science.
    1. Hach F, Hormozdiari F, Alkan C, Birol I, Eichler EE, Sahinalp SC. mrsFAST: a cache-oblivious algorithm for short-read mapping. Nat Methods. 2010;7:576–577. doi: 10.1038/nmeth0810-576.
    1. Ivakhno S, Royce T, Cox AJ, Evers DJ, Cheetham RK, Tavare S. CNAseg--a novel framework for identification of copy number changes in cancer from second-generation sequencing data. Bioinformatics. 2010;26:3051–3058. doi: 10.1093/bioinformatics/btq587.
    1. Magi A, Benelli M, Yoon S, Roviello F, Torricelli F. Detecting common copy number variants in high-throughput sequencing data by using JointSLM algorithm. Nucleic Acids Res. 2011;39:e65. doi: 10.1093/nar/gkr068.
    1. Klambauer G, Schwarzbauer K, Mayr A, Clevert DA, Mitterecker A, Bodenhofer U, Hochreiter S. cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate. Nucleic Acids Res. 2012;40:e69. doi: 10.1093/nar/gks003.
    1. Zhang Q, Ding L, Larson DE, Koboldt DC, McLellan MD, Chen K, Shi X, Kraja A, Mardis ER, Wilson RK. et al.CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data. Bioinformatics. 2010;26:464–469. doi: 10.1093/bioinformatics/btp708.
    1. Ng SB, Buckingham KJ, Lee C, Bigham AW, Tabor HK, Dent KM, Huff CD, Shannon PT, Jabs EW, Nickerson DA. et al.Exome sequencing identifies the cause of a mendelian disorder. Nat Genet. 2010;42:30–35. doi: 10.1038/ng.499.
    1. Sathirapongsasuti JF, Lee H, Horst BA, Brunner G, Cochran AJ, Binder S, Quackenbush J, Nelson SF. Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV. Bioinformatics. 2011;27:2648–2654. doi: 10.1093/bioinformatics/btr462.
    1. Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, Miller CA, Mardis ER, Ding L, Wilson RK. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012;22:568–576. doi: 10.1101/gr.129684.111.
    1. Seshan VE OA. DNAcopy: A package for analyzing DNA copy data. 2010.
    1. Rigaill GJ, Cadot S, Kluin RJ, Xue Z, Bernards R, Majewski IJ, Wessels LF. A regression model for estimating DNA copy number applied to capture sequencing data. Bioinformatics. 2012;28:2357–2365. doi: 10.1093/bioinformatics/bts448.
    1. Boeva V, Zinovyev A, Bleakley K, Vert JP, Janoueix-Lerosey I, Delattre O, Barillot E. Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization. Bioinformatics. 2011;27:268–269. doi: 10.1093/bioinformatics/btq635.
    1. Krumm N, Sudmant PH, Ko A, O'Roak BJ, Malig M, Coe BP, Quinlan AR, Nickerson DA, Eichler EE. Copy number variation detection and genotyping from exome sequence data. Genome Res. 2012;22:1525–1532. doi: 10.1101/gr.138115.112.
    1. Fromer M, Moran JL, Chambert K, Banks E, Bergen SE, Ruderfer DM, Handsaker RE, McCarroll SA, O'Donovan MC, Owen MJ. et al.Discovery and Statistical Genotyping of Copy-Number Variation from Whole-Exome Sequencing Depth. Am J Hum Genet. 2012;91:597–607. doi: 10.1016/j.ajhg.2012.08.005.
    1. Coin LJ, Cao D, Ren J, Zuo X, Sun L, Yang S, Zhang X, Cui Y, Li Y, Jin X, Wang J. An exome sequencing pipeline for identifying and genotyping common CNVs associated with disease with application to psoriasis. Bioinformatics. 2012;28:i370–i374. doi: 10.1093/bioinformatics/bts379.
    1. Iqbal Z, Caccamo M, Turner I, Flicek P, McVean G. De novo assembly and genotyping of variants using colored de Bruijn graphs. Nat Genet. 2012;44:226–232. doi: 10.1038/ng.1028.
    1. Nijkamp JF, van den Broek MA, Geertman JM, Reinders MJ, Daran JM, de Ridder D. De novo detection of copy number variation by co-assembly. Bioinformatics. 2012.
    1. Mills RE, Walter K, Stewart C, Handsaker RE, Chen K, Alkan C, Abyzov A, Yoon SC, Ye K, Cheetham RK. et al.Mapping copy number variation by population-scale genome sequencing. Nature. 2011;470:59–65. doi: 10.1038/nature09708.
    1. Zeitouni B, Boeva V, Janoueix-Lerosey I, Loeillet S, Legoix-ne P, Nicolas A, Delattre O, Barillot E. SVDetect: a tool to identify genomic structural variations from paired-end and mate-pair sequencing data. Bioinformatics. 2010;26:1895–1896. doi: 10.1093/bioinformatics/btq293.
    1. Medvedev P, Fiume M, Dzamba M, Smith T, Brudno M. Detecting copy number variation with mated short reads. Genome Res. 2010;20:1613–1622. doi: 10.1101/gr.106344.110.
    1. Handsaker RE, Korn JM, Nemesh J, McCarroll SA. Discovery and genotyping of genome structural polymorphism by sequencing on a population scale. Nat Genet. 2011;43:269–276. doi: 10.1038/ng.768.
    1. Sindi SS, Onal S, Peng LC, Wu HT, Raphael BJ. An integrative probabilistic model for identification of structural variation in sequencing data. Genome Biol. 2012;13:R22. doi: 10.1186/gb-2012-13-3-r22.
    1. Qi J, Zhao F. inGAP-sv: a novel scheme to identify and visualize structural variation from paired end mapping data. Nucleic Acids Res. 2011;39:W567–575. doi: 10.1093/nar/gkr506.
    1. Sindi S, Helman E, Bashir A, Raphael BJ. A geometric approach for classification and comparison of structural variants. Bioinformatics. 2009;25:i222–230. doi: 10.1093/bioinformatics/btp208.
    1. Hajirasouliha I, Hormozdiari F, Alkan C, Kidd JM, Birol I, Eichler EE, Sahinalp SC. Detection and characterization of novel sequence insertions using paired-end next-generation sequencing. Bioinformatics. 2010;26:1277–1283. doi: 10.1093/bioinformatics/btq152.
    1. Quinlan AR, Clark RA, Sokolova S, Leibowitz ML, Zhang Y, Hurles ME, Mell JC, Hall IM. Genome-wide mapping and assembly of structural variant breakpoints in the mouse genome. Genome Res. 2010;20:623–635. doi: 10.1101/gr.102970.109.
    1. Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, Hall KP, Evers DJ, Barnes CL, Bignell HR. et al.Accurate whole human genome sequencing using reversible terminator chemistry. Nature. 2008;456:53–59. doi: 10.1038/nature07517.
    1. Mamanova L, Andrews RM, James KD, Sheridan EM, Ellis PD, Langford CF, Ost TW, Collins JE, Turner DJ. FRT-seq: amplification-free, strand-specific transcriptome sequencing. Nat Methods. 2010;7:130–132. doi: 10.1038/nmeth.1417.
    1. Wang Q, Qu JH, Cheng XX, Kang YJ, Wan L, Qian MP, Deng MH. A study of biases of DNA copy number estimation based on PICR model. Front Math China. 2011;6:1203–1216. doi: 10.1007/s11464-011-0125-x.
    1. Wang W, Wei Z, Lam TW, Wang J. Next generation sequencing has lower sequence coverage and poorer SNP-detection capability in the regulatory regions. Sci Rep. 2011;1:55.
    1. Minoche AE, Dohm JC, Himmelbauer H. Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and genome analyzer systems. Genome Biol. 2011;12:R112. doi: 10.1186/gb-2011-12-11-r112.
    1. Zhang J, Wu Y. SVseq: an approach for detecting exact breakpoints of deletions with low-coverage sequence data. Bioinformatics. 2011;27:3228–3234. doi: 10.1093/bioinformatics/btr563.
    1. Nord AS, Lee M, King MC, Walsh T. Accurate and exact CNV identification from targeted high-throughput sequence data. BMC Genomics. 2011;12:184. doi: 10.1186/1471-2164-12-184.
    1. Glenn TC. Field guide to next-generation DNA sequencers. Mol Ecol Resour. 2011;11:759–769. doi: 10.1111/j.1755-0998.2011.03024.x.
    1. Schadt EE, Turner S, Kasarskis A. A window into third-generation sequencing. Hum Mol Genet. 2010;19:R227–240. doi: 10.1093/hmg/ddq416.
    1. Li J, Lupat R, Amarasinghe KC, Thompson ER, Doyle MA, Ryland GL, Tothill RW, Halgamuge SK, Campbell IG, Gorringe KL. CONTRA: copy number analysis for targeted resequencing. Bioinformatics. 2012;28:1307–1313. doi: 10.1093/bioinformatics/bts146.
    1. Ramachandran A, Micsinai M, Pe'er I. CONDEX: Copy number detection in exome sequences. Bioinformatics and Biomedicine Workshops (BIBMW), 2011 IEEE International Conference on: 12-15 November 2011. 2011. pp. 87–93.
    1. Deng X. SeqGene: a comprehensive software solution for mining exome- and transcriptome- sequencing data. BMC Bioinformatics. 2011;12:267. doi: 10.1186/1471-2105-12-267.

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