PSE-HMM: Genome-wide CNV Detection from NGS Data Using an HMM with Position-Specific Emission Probabilities

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2016 Nov 5

PMID 27809781

Citations 1

Authors

Seyed Amir Malekpour

Hamid Pezeshk

Mehdi Sadeghi

Affiliations

Soon will be listed here.

Abstract

Background: Copy Number Variation (CNV) is envisaged to be a major source of large structural variations in the human genome. In recent years, many studies apply Next Generation Sequencing (NGS) data for the CNV detection. However, still there is a necessity to invent more accurate computational tools.

Results: In this study, mate pair NGS data are used for the CNV detection in a Hidden Markov Model (HMM). The proposed HMM has position specific emission probabilities, i.e. a Gaussian mixture distribution. Each component in the Gaussian mixture distribution captures a different type of aberration that is observed in the mate pairs, after being mapped to the reference genome. These aberrations may include any increase (decrease) in the insertion size or change in the direction of mate pairs that are mapped to the reference genome. This HMM with Position-Specific Emission probabilities (PSE-HMM) is utilized for the genome-wide detection of deletions and tandem duplications. The performance of PSE-HMM is evaluated on a simulated dataset and also on a real data of a Yoruban HapMap individual, NA18507.

Conclusions: PSE-HMM is effective in taking observation dependencies into account and reaches a high accuracy in detecting genome-wide CNVs. MATLAB programs are available at http://bs.ipm.ir/softwares/PSE-HMM/ .

Citing Articles

CIRCNV: Detection of CNVs Based on a Circular Profile of Read Depth from Sequencing Data.

Zhao H, Li Q, Tian Y, Chen Y, Alvi H, Yuan X Biology (Basel). 2021; 10(7).

PMID: 34202028 PMC: 8301091. DOI: 10.3390/biology10070584.

References

Li H, Ruan J, Durbin R . Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 2008; 18(11):1851-8. PMC: 2577856. DOI: 10.1101/gr.078212.108. View

Medvedev P, Fiume M, Dzamba M, Smith T, Brudno M . Detecting copy number variation with mated short reads. Genome Res. 2010; 20(11):1613-22. PMC: 2963824. DOI: 10.1101/gr.106344.110. View

Szatkiewicz J, ODushlaine C, Chen G, Chambert K, Moran J, Neale B . Copy number variation in schizophrenia in Sweden. Mol Psychiatry. 2014; 19(7):762-73. PMC: 4271733. DOI: 10.1038/mp.2014.40. View

Ding J, Shah S . A robust hidden semi-Markov model with application to aCGH data processing. Int J Data Min Bioinform. 2014; 8(4):427-42. DOI: 10.1504/ijdmb.2013.056616. View

Rueda O, Diaz-Uriarte R . Flexible and accurate detection of genomic copy-number changes from aCGH. PLoS Comput Biol. 2007; 3(6):e122. PMC: 1894821. DOI: 10.1371/journal.pcbi.0030122. View

Abyzov A, Gerstein M . AGE: defining breakpoints of genomic structural variants at single-nucleotide resolution, through optimal alignments with gap excision. Bioinformatics. 2011; 27(5):595-603. PMC: 3042181. DOI: 10.1093/bioinformatics/btq713. View

Kidd J, Cooper G, Donahue W, Hayden H, Sampas N, Graves T . Mapping and sequencing of structural variation from eight human genomes. Nature. 2008; 453(7191):56-64. PMC: 2424287. DOI: 10.1038/nature06862. View

Wang H, Nettleton D, Ying K . Copy number variation detection using next generation sequencing read counts. BMC Bioinformatics. 2014; 15:109. PMC: 4021345. DOI: 10.1186/1471-2105-15-109. View

Layer R, Chiang C, Quinlan A, Hall I . LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 2014; 15(6):R84. PMC: 4197822. DOI: 10.1186/gb-2014-15-6-r84. View

10.

Yavas G, Koyuturk M, Gould M, McMahon S, LaFramboise T . DB2: a probabilistic approach for accurate detection of tandem duplication breakpoints using paired-end reads. BMC Genomics. 2014; 15:175. PMC: 4234483. DOI: 10.1186/1471-2164-15-175. View

11.

McCarroll S, Kuruvilla F, Korn J, Cawley S, Nemesh J, Wysoker A . Integrated detection and population-genetic analysis of SNPs and copy number variation. Nat Genet. 2008; 40(10):1166-74. DOI: 10.1038/ng.238. View

12.

Rausch T, Zichner T, Schlattl A, Stutz A, Benes V, Korbel J . DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics. 2012; 28(18):i333-i339. PMC: 3436805. DOI: 10.1093/bioinformatics/bts378. View

13.

Sindi S, Onal S, Peng L, Wu H, Raphael B . An integrative probabilistic model for identification of structural variation in sequencing data. Genome Biol. 2012; 13(3):R22. PMC: 3439973. DOI: 10.1186/gb-2012-13-3-r22. View

14.

Zhang Z, Du J, Lam H, Abyzov A, Urban A, Snyder M . Identification of genomic indels and structural variations using split reads. BMC Genomics. 2011; 12:375. PMC: 3161018. DOI: 10.1186/1471-2164-12-375. View

15.

Peltonen L, Altshuler D, de Bakker P, Deloukas P, Gabriel S, Gwilliam R . Integrating common and rare genetic variation in diverse human populations. Nature. 2010; 467(7311):52-8. PMC: 3173859. DOI: 10.1038/nature09298. View

16.

Zhang Q, Ding L, Larson D, Koboldt D, McLellan M, Chen K . CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data. Bioinformatics. 2009; 26(4):464-9. PMC: 2852218. DOI: 10.1093/bioinformatics/btp708. View

17.

Abel H, Duncavage E, Becker N, Armstrong J, Magrini V, Pfeifer J . SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data. Bioinformatics. 2010; 26(21):2684-8. DOI: 10.1093/bioinformatics/btq528. View

18.

Xi R, Hadjipanayis A, Luquette L, Kim T, Lee E, Zhang J . Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion. Proc Natl Acad Sci U S A. 2011; 108(46):E1128-36. PMC: 3219132. DOI: 10.1073/pnas.1110574108. View

19.

Korbel J, Urban A, Affourtit J, Godwin B, Grubert F, Simons J . Paired-end mapping reveals extensive structural variation in the human genome. Science. 2007; 318(5849):420-6. PMC: 2674581. DOI: 10.1126/science.1149504. View

20.

Ye K, Schulz M, 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(21):2865-71. PMC: 2781750. DOI: 10.1093/bioinformatics/btp394. View