Abstract
BackgroundWith advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1–4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1–4 exons), combining computational prediction algorithms and a high-resolution custom CGH array.ResultsWe used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate.ConclusionsIn this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1–4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-661) contains supplementary material, which is available to authorized users.
Highlights
With advances in generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases
Since the technologies used to generate the 30 exomes obtained from the 1000 genomes project can be grouped into three different categories (Additional file 1: Table S2), all the exomes were categorized and separate computational copy number variants (CNVs) predictions were performed for each group
In order to directly compare programs at this level, we identified individual exons covered by CNVs from each program
Summary
With advances in generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. Computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1–4 exons), combining computational prediction algorithms and a high-resolution custom CGH array. While computational approaches have limitations in predicting CNVs in exome sequence data, standard array comparative genomic hybridization (aCGH) used for genome-wide high-resolution CNV detection show restrictions in exonic CNV detection due to the low probe coverage over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including CNVs that cover 1 to 4 exons) from exome sequencing data by combining computational prediction algorithms and a high-resolution custom CGH array. We studied the clinical utility of the algorithms used in our study by computational prediction and array confirmation of CNVs in 9 exomes from primary immunodeficiency patients
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