Background: Targeted next-generation sequencing (NGS) has been widely used as a cost-effective way to identify the genetic basis of human disorders. Copy number variations (CNVs) contribute significantly to human genomic variability, some of which can lead to disease. However, effective detection of CNVs from targeted capture sequencing data remains challenging. Results: Here we present SeqCNV, a novel CNV calling method designed to use capture NGS data. SeqCNV extracts the read depth information and utilizes the maximum penalized likelihood estimation (MPLE) model to identify the copy number ratio and CNV boundary. We applied SeqCNV to both bacterial artificial clone (BAC) and human patient NGS data to identify CNVs. These CNVs were validated by array comparative genomic hybridization (aCGH). Conclusions: SeqCNV is able to robustly identify CNVs of different size using capture NGS data. Compared with other CNV-calling methods, SeqCNV shows a significant improvement in both sensitivity and specificity.
[1]Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China.
[2]Fudan Univ, Sch Comp Sci & Technol, Shanghai, Peoples R China.
[3]Baylor Coll Med, Struct & Computat Biol & Mol Biophys Grad Program, Houston, TX 77030 USA.
[4]Baylor Coll Med, Human Genome Sequencing Ctr, Houston, TX 77030 USA.
[5]Fudan Univ, Sch Life Sci, Shanghai, Peoples R China.
[6]Univ Texas Hlth Sci Ctr, Houston, TX USA.
[7]Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA.
[8]Univ Texas Hlth Sci Ctr, Dept Ophthalmol & Visual Sci, Houston, TX USA.
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