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.

MOTIVATION: High-density single nucleotide polymorphism (SNP) genotyping arrays are efficient and cost effective platforms for the detection of copy number variation (CNV). To ensure accuracy in probe synthesis and to minimize production costs, short oligonucleotide probe sequences are used. The use of short probe sequences limits the specificity of binding targets in the human genome. The specificity of these short probeset sequences has yet to be fully analysed against a normal reference human genome. Sequence similarity can artificially elevate or suppress copy number measurements, and hence reduce the reliability of affected probe readings. For the purpose of detecting narrow CNVs reliably down to the width of a single probeset, sequence similarity is an important issue that needs to be addressed. RESULTS: We surveyed the Affymetrix Human Mapping SNP arrays for probeset sequence similarity against the reference human genome. Utilizing sequence similarity results, we identified a collection of fine-scaled putative CNVs between gender from autosomal probesets whose sequence matches various loci on the sex chromosomes. To detect these variations, we utilized our statistical approach, Detecting REcurrent Copy number change using rank-order Statistics (DRECS), and showed that its performance was superior and more stable than the t-test in detecting CNVs. Through the application of DRECS on the HapMap population datasets with multi-matching probesets filtered, we identified biologically relevant SNPs in aberrant regions across populations with known association to physical traits, such as height, covered by the span of a single probe. This provided empirical confirmation of the existence of naturally occurring narrow CNVs as well as the sensitivity of the Affymetrix SNP array technology in detecting them. AVAILABILITY: The MATLAB implementation of DRECS is available at http://ww2.cs.mu.oz.au/ approximately gwong/DRECS/index.html.