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CoNVaDING: Single Exon Variation Detection in Targeted NGS Data.
Johansson, Lennart F; van Dijk, Freerk; de Boer, Eddy N; van Dijk-Bos, Krista K; Jongbloed, Jan D H; van der Hout, Annemieke H; Westers, Helga; Sinke, Richard J; Swertz, Morris A; Sijmons, Rolf H; Sikkema-Raddatz, Birgit.
Affiliation
  • Johansson LF; University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
  • van Dijk F; University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands.
  • de Boer EN; University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
  • van Dijk-Bos KK; University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands.
  • Jongbloed JD; University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
  • van der Hout AH; University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
  • Westers H; University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
  • Sinke RJ; University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
  • Swertz MA; University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
  • Sijmons RH; University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
  • Sikkema-Raddatz B; University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands.
Hum Mutat ; 37(5): 457-64, 2016 May.
Article de En | MEDLINE | ID: mdl-26864275
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.
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Analyse de séquence d'ADN / Prédisposition génétique à une maladie / Variations de nombre de copies de segment d'ADN / Séquençage nucléotidique à haut débit Type d'étude: Diagnostic_studies / Prognostic_studies Limites: Humans Langue: En Journal: Hum Mutat Sujet du journal: GENETICA MEDICA Année: 2016 Type de document: Article Pays d'affiliation: Pays-Bas

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Analyse de séquence d'ADN / Prédisposition génétique à une maladie / Variations de nombre de copies de segment d'ADN / Séquençage nucléotidique à haut débit Type d'étude: Diagnostic_studies / Prognostic_studies Limites: Humans Langue: En Journal: Hum Mutat Sujet du journal: GENETICA MEDICA Année: 2016 Type de document: Article Pays d'affiliation: Pays-Bas