SM-RCNV: a statistical method to detect recurrent copy number variations in sequenced samples

다국어 초록 (Multilingual 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 ://sourc eforg e.net/proje cts/sm-rcnv/.

참고문헌 (Reference)

1 Klambauer G, "cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate" 40 : e69-e69, 2012

2 Li H, "The sequence Alignment/Map format and SAMtools" 25 : 2078-2079, 2009

3 MacDonald JR, "The database of genomic variants: a curated collection of structural variation in the human genome" 42 : D986-D992, 2014

4 Cock PJA, "The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants" 38 : 1767-1771, 2010

5 Nguyen HT, "The CNVrd2 package:Measurement of copy number at complex loci using high-throughput sequencing data" 5 : 248-, 2014

6 Cheung M-S, "Systematic bias in high-throughput sequencing data and its correction by BEADS" 39 : e103-e103, 2011

7 Benjamini Y, "Summarizing and correcting the GC content bias in high-throughput sequencing" 40 : e72-e72, 2012

8 Sebat J, "Strong association of de novo copy number mutations with autism" 316 : 445-, 2007

9 Yoon S, "Sensitive and accurate detection of copy number variants using read depth of coverage" 19 : 1586-1592, 2009

10 Ni X, "Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients" 110 : 21083-21088, 2013

11 Miller CA, "ReadDepth:a parallel R package for detecting copy number alterations from short sequencing reads" 6 : e16327-, 2011

12 Alkan C, "Personalized copy number and segmental duplication maps using next-generation sequencing" 41 : 1061-, 2009

13 Yuan X, "IntSIM: an integrated simulator of next-generation sequencing data" 64 : 441-451, 2017

14 Redon R, "Global variation in copy number in the human genome" 444 : 444-, 2006

15 Yang Y, "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 American" 80 : 1037-1054, 2007

16 Smith SD, "GROM-RD: resolving genomic biases to improve read depth detection of copy number variants" 3 : e836-, 2015

17 Li H, "Fast and accurate short read alignment with Burrows–Wheeler transform" 25 : 1754-1760, 2009

18 Xi J, "Discovering recurrent copy number aberrations in complex patterns via non-negative sparse singular value decomposition" 13 : 656-668, 2016

19 Yuan X, "Detection of significant copy number variations from multiple samples in next-generation sequencing data" 17 : 12-20, 2018

20 Xi R, "Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion" 108 : E1128-, 2011

21 Freeman JL, "Copy number variation : new insights in genome diversity" 16 : 949-961, 2006

22 Boeva V, "Control-FREEC : a tool for assessing copy number and allelic content using next-generation sequencing data" 28 : 423-425, 2011

23 Ratan A, "Comparison of sequencing platforms for single nucleotide variant calls in a human sample" 8 : e55089-, 2013

24 Duan J, "Common copy number variation detection from multiple sequenced samples" 61 : 928-937, 2014

25 Abyzov A, "CNVnator : An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing" 21 : 974-984, 2011

26 Xie C, "CNV-seq, a new method to detect copy number variation using high-throughput sequencing" 10 : 80-, 2009

27 Duan J, "CNV-TV : A robust method to discover copy number variation from short sequencing reads" 14 : 150-, 2013

28 Brouwers N, "Alzheimer risk associated with a copy number variation in the complement receptor 1 increasing C3b/C4b binding sites" 17 : 223-, 2011

29 Haimes J, "Abstract 1381 : NGS-based CNV detection sensitivity is dependent upon nucleic acid input quality" 76 : 1381-, 2016

30 Pabinger S, "A survey of tools for variant analysis of next-generation genome sequencing data" 15 : 256-278, 2014

31 Trost B, "A comprehensive workflow for read depth-based identification of copy-number variation from whole-genome sequence data" 102 : 142-155, 2018