PLoS Computational BiologyBioinformaticsPeer-reviewed journal article
SavvyCNV: genome-wide CNV calling from off-target reads
Thomas William LaveriD, Elisa De FrancoiD, Matthew B. JohnsoniD, Kashyap Amratlal PateliD, Sian EllardiD, Michael N. WeedoniD, Sarah Elizabeth FlanaganiD, Matthew Neil WakelingiD
Abstract
Identifying copy number variants (CNVS) can provide diagnoses to patients and provide important biological insights into human health and disease. Current exome and targeted sequencing approaches cannot detect clinically and biologically-relevant CNVs outside their target area. We present SavvyCNV, a tool which uses off-target read data to call CNVs genome-wide. Up to 70% of sequencing reads from exome and targeted sequencing fall outside the targeted regions - SavvyCNV exploits this free data.\n\nWe benchmarked SavvyCNV using truth sets generated from genome sequencing data and Multiplex Ligation-dependent Probe Amplification assays. SavvyCNV called CNVs with high precision and recall, outperforming five state-of-the-art CNV callers at calling CNVs genome-wide using off-target or on-target reads from targeted panel and exome sequencing. Furthermore SavvyCNV was able to call previously undetected clinically-relevant CNVs from targeted panel data highlighting the utility of this tool within the diagnostic setting. SavvyCNV is freely available.
- organism: human
- assay: whole-exome sequencing
- research focus: variant calling
- approach: statistical model
- Citations
- 46
- FWCI
- 3.77
- Field percentile
- top 6%
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