BACKGROUND: One of the main types of genetic variations in cancer is Copy Number Variations (CNV). Whole exome sequencing (WES) is a popular alternative to whole genome sequencing (WGS) to study disease specific genomic variations. However, finding CNV in Cancer samples using WES data has not been fully explored. RESULTS: We present a new method, called CoNVEX, to estimate copy number variation in whole exome sequencing data. It uses ratio of tumour and matched normal average read depths at each exonic region, to predict the copy gain or loss. The useful signal produced by WES data will be hindered by the intrinsic noise present in the data itself. This limits its capacity to be used as a highly reliable CNV detection source. Here, we propose a method that consists of discrete wavelet transform (DWT) to reduce noise. The identification of copy number gains/losses of each targeted region is performed by a Hidden Markov Model (HMM). CONCLUSION: HMM is frequently used to identify CNV in data produced by various technologies including Array Comparative Genomic Hybridization (aCGH) and WGS. Here, we propose an HMM to detect CNV in cancer exome data. We used modified data from 1000 Genomes project to evaluate the performance of the proposed method. Using these data we have shown that CoNVEX outperforms the existing methods significantly in terms of precision. Overall, CoNVEX achieved a sensitivity of more than 92% and a precision of more than 50%.

Our proposed method, to detect copy number variations in whole exome sequencing data [1] was published in BMC Bioinformatics as a special issue containing the proceedings of The Eleventh Asia Pacific Bioinformatics Conference (APBC 2013). After the publication, it was brought to our attention that name of our software has a conflict with another software developed by a project carried out at Wellcome Trust Sanger Institute (http://www.uk10k.org/assets/ashg_vijayarangakannan_etal_2012.pdf). We were not aware of this project when we first named our method as CoNVEX, in mid 2012. Therefore, we would like to thank Dr. P. Vijayarangakannan, one of the developers of the other method, for bringing this to our attention. We would like to mention that, although both software share the same name, they are different in terms of computational methods and types of exome sequencing data used. To avoid any confusion associated with the software name, we would no longer call our software as CoNVEX. Our software will be further developed with a new project name, Aberration Detection in Tumour Exome (ADTEx).

Abstract Motivation: In light of the increasing adoption of targeted resequencing (TR) as a cost-effective strategy to identify disease-causing variants, a robust method for copy number variation (CNV) analysis is needed to maximize the value of this promising technology. Results: We present a method for CNV detection for TR data, including whole-exome capture data. Our method calls copy number gains and losses for each target region based on normalized depth of coverage. Our key strategies include the use of base-level log-ratios to remove GC-content bias, correction for an imbalanced library size effect on log-ratios, and the estimation of log-ratio variations via binning and interpolation. Our methods are made available via CONTRA (COpy Number Targeted Resequencing Analysis), a software package that takes standard alignment formats (BAM/SAM) and outputs in variant call format (VCF4.0), for easy integration with other next-generation sequencing analysis packages. We assessed our methods using samples from seven different target enrichment assays, and evaluated our results using simulated data and real germline data with known CNV genotypes. Availability and implementation: Source code and sample data are freely available under GNU license (GPLv3) at http://contra-cnv.sourceforge.net/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.