SM-RCNV: a Statistical Method to Detect Recurrent Copy Number Variations in Sequenced Samples

Overview

Journal Genes Genomics

Specialty Genetics

Date 2019 Feb 20

PMID 30779024

Citations 2

Authors

Yaoyao Li

Xiguo Yuan

Junying Zhang

Liying Yang

Jun Bai

Shan Jiang

Affiliations

Soon will be listed here.

Abstract

Background: Copy number variation (CNV) is an important form of genomic structural variation and is linked to dozens of human diseases. Using next-generation sequencing (NGS) data and developing computational methods to characterize such structural variants is significant for understanding the mechanisms of diseases.

Objective: The objective of this study is to develop a new statistical method of detection recurrent CNVs across multiple samples from genomic sequences.

Methods: A statistical method is carried out to detect recurrent CNVs, referred to as SM-RCNV. This method uses a statistic associated with each location by combining the frequency of variation at one location across whole samples and the correlation among consecutive locations. The weights of the frequency and correlation are trained using real datasets with known CNVs. P-value is assessed for each location on the genome by permutation testing.

Results: Compared with six peer methods, SM-RCNV outperforms the peer methods under receiver operating characteristic curves. SM-RCNV successfully identifies many consistent recurrent CNVs, most of which are known to be of biological significance and associated with diseased genes. The validation rate of SM-RCNV in the CEU call set and YRI call set with Database of Genomic Variants are 258/328 (79%) and (157/309) 51%, respectively.

Conclusion: SM-RCNV is a well-grounded statistical framework for detecting recurrent CNVs from multiple genomic sequences, providing valuable information to study genomes in human diseases. The source code is freely available at https://sourceforge.net/projects/sm-rcnv/ .

Citing Articles

A copy number variation detection method based on OCSVM algorithm using multi strategies integration.

Zhou M, Dong J, Jiang H, Zhao Z, Yuan T Sci Rep. 2025; 15(1):3526.

PMID: 39875521 PMC: 11775105. DOI: 10.1038/s41598-025-88143-9.

CNV-MEANN: A Neural Network and Mind Evolutionary Algorithm-Based Detection of Copy Number Variations From Next-Generation Sequencing Data.

Huang T, Li J, Jia B, Sang H Front Genet. 2021; 12:700874.

PMID: 34484298 PMC: 8415314. DOI: 10.3389/fgene.2021.700874.

A Cluster-Based Approach for the Discovery of Copy Number Variations From Next-Generation Sequencing Data.

Liu G, Zhang J Front Genet. 2021; 12:699510.

PMID: 34262604 PMC: 8273656. DOI: 10.3389/fgene.2021.699510.

References

Freeman J, Perry G, Feuk L, Redon R, McCarroll S, Altshuler D . Copy number variation: new insights in genome diversity. Genome Res. 2006; 16(8):949-61. DOI: 10.1101/gr.3677206. View

Xie C, Tammi M . CNV-seq, a new method to detect copy number variation using high-throughput sequencing. BMC Bioinformatics. 2009; 10:80. PMC: 2667514. DOI: 10.1186/1471-2105-10-80. View

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

Cock P, Fields C, Goto N, Heuer M, Rice P . The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res. 2009; 38(6):1767-71. PMC: 2847217. DOI: 10.1093/nar/gkp1137. View

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(9):1586-92. PMC: 2752127. DOI: 10.1101/gr.092981.109. View

Klambauer G, Schwarzbauer K, Mayr A, Clevert D, Mitterecker A, Bodenhofer U . 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(9):e69. PMC: 3351174. DOI: 10.1093/nar/gks003. View

Pabinger S, Dander A, Fischer M, Snajder R, Sperk M, Efremova M . A survey of tools for variant analysis of next-generation genome sequencing data. Brief Bioinform. 2013; 15(2):256-78. PMC: 3956068. DOI: 10.1093/bib/bbs086. View

Ni X, Zhuo M, Su Z, Duan J, Gao Y, Wang Z . Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients. Proc Natl Acad Sci U S A. 2013; 110(52):21083-8. PMC: 3876226. DOI: 10.1073/pnas.1320659110. View

Duan J, Deng H, Wang Y . Common copy number variation detection from multiple sequenced samples. IEEE Trans Biomed Eng. 2014; 61(3):928-37. PMC: 4165854. DOI: 10.1109/TBME.2013.2292588. View

10.

Duan J, Zhang J, Deng H, Wang Y . CNV-TV: a robust method to discover copy number variation from short sequencing reads. BMC Bioinformatics. 2013; 14:150. PMC: 3679874. DOI: 10.1186/1471-2105-14-150. View

11.

Ratan A, Miller W, Guillory J, Stinson J, Seshagiri S, Schuster S . Comparison of sequencing platforms for single nucleotide variant calls in a human sample. PLoS One. 2013; 8(2):e55089. PMC: 3566181. DOI: 10.1371/journal.pone.0055089. View

12.

Miller C, Hampton O, Coarfa C, Milosavljevic A . ReadDepth: a parallel R package for detecting copy number alterations from short sequencing reads. PLoS One. 2011; 6(1):e16327. PMC: 3031566. DOI: 10.1371/journal.pone.0016327. View

13.

Cheung M, Down T, Latorre I, Ahringer J . Systematic bias in high-throughput sequencing data and its correction by BEADS. Nucleic Acids Res. 2011; 39(15):e103. PMC: 3159482. DOI: 10.1093/nar/gkr425. View

14.

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N . The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009; 25(16):2078-9. PMC: 2723002. DOI: 10.1093/bioinformatics/btp352. View

15.

Xi J, Li A . Discovering Recurrent Copy Number Aberrations in Complex Patterns via Non-Negative Sparse Singular Value Decomposition. IEEE/ACM Trans Comput Biol Bioinform. 2015; 13(4):656-68. DOI: 10.1109/TCBB.2015.2474404. View

16.

Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T . Strong association of de novo copy number mutations with autism. Science. 2007; 316(5823):445-9. PMC: 2993504. DOI: 10.1126/science.1138659. View

17.

Li H, Durbin R . Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25(14):1754-60. PMC: 2705234. DOI: 10.1093/bioinformatics/btp324. View

18.

Yang Y, Chung E, Wu Y, Savelli S, Nagaraja H, Zhou B . Gene copy-number variation and associated polymorphisms of complement component C4 in human systemic lupus erythematosus (SLE): low copy number is a risk factor for and high copy number is a protective factor against SLE susceptibility in European.... Am J Hum Genet. 2007; 80(6):1037-54. PMC: 1867093. DOI: 10.1086/518257. View

19.

Smith S, Kawash J, Grigoriev A . GROM-RD: resolving genomic biases to improve read depth detection of copy number variants. PeerJ. 2015; 3:e836. PMC: 4369336. DOI: 10.7717/peerj.836. View

20.

Yuan X, Zhang J, Yang L . IntSIM: An Integrated Simulator of Next-Generation Sequencing Data. IEEE Trans Biomed Eng. 2016; 64(2):441-451. DOI: 10.1109/TBME.2016.2560939. View