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CoNVaDING: Single Exon Variation Detection in Targeted NGS Data
Author(s) -
Johansson Lennart F.,
Dijk Freerk,
Boer Eddy N.,
DijkBos Krista K.,
Jongbloed Jan D.H.,
der Hout Annemieke H.,
Westers Helga,
Sinke Richard J.,
Swertz Morris A.,
Sijmons Rolf H.,
SikkemaRaddatz Birgit
Publication year - 2016
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.22969
Subject(s) - exon , copy number variation , biology , genetics , computational biology , structural variation , metric (unit) , dna sequencing , gene , genome , operations management , economics
ABSTRACT We have developed a tool for detecting single exon copy‐number variations (CNVs) in targeted next‐generation sequencing data: CoNVaDING (Copy Number Variation Detection In Next‐generation sequencing Gene panels). CoNVaDING includes a stringent quality control (QC) metric, that excludes or flags low‐quality exons. Since this QC shows exactly which exons can be reliably analyzed and which exons are in need of an alternative analysis method, CoNVaDING is not only useful for CNV detection in a research setting, but also in clinical diagnostics. During the validation phase, CoNVaDING detected all known CNVs in high‐quality targets in 320 samples analyzed, giving 100% sensitivity and 99.998% specificity for 308,574 exons. CoNVaDING outperforms existing tools by exhibiting a higher sensitivity and specificity and by precisely identifying low‐quality samples and regions.