Journal Article10.1002/HUMU.22969
CoNVaDING: Single Exon Variation Detection in Targeted NGS Data
Lennart Johansson,Freerk van Dijk,Eddy N. de Boer,Krista K. van Dijk-Bos,Jan D. H. Jongbloed,Annemieke H. van der Hout,Helga Westers,Richard J. Sinke,Morris A. Swertz,Rolf H. Sijmons,Birgit Sikkema-Raddatz +10 more
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TL;DR: CoNVaDING outperforms existing tools by exhibiting a higher sensitivity and specificity and by precisely identifying low‐quality samples and regions.
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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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Citations
MET-dependent solid tumours - molecular diagnosis and targeted therapy
Robin Guo,Jia Luo,Jason C. Chang,Natasha Rekhtman,Maria E. Arcila,Alexander Drilon,Alexander Drilon +6 more
TL;DR: The utility of various diagnostic techniques and the roles of different classes of MET-targeted therapies in cancers with MET amplification, mutation and fusion, and MET overexpression are evaluated.
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Prevalence and properties of intragenic copy-number variation in Mendelian disease genes
Rebecca Truty,Joshua S. Paul,Michael Kennemer,Stephen E Lincoln,Eric Olivares,Robert L. Nussbaum,Swaroop Aradhya +6 more
TL;DR: Universal intragenic CNV analysis adds substantial clinical sensitivity to genetic testing and Clinically relevant CNVs have distinct properties that distinguish them from CNVs contributing to normal variation in human disease genes.
177
Rapid Targeted Genomics in Critically Ill Newborns
Cleo C. van Diemen,Wilhelmina S. Kerstjens-Frederikse,Klasien A. Bergman,Tom J. de Koning,Tom J. de Koning,Birgit Sikkema-Raddatz,Joeri van der Velde,Kristin M. Abbott,Johanna C. Herkert,Katharina Löhner,Patrick Rump,Martine T. Meems-Veldhuis,Pieter B. Neerincx,Jan D. H. Jongbloed,Conny M. A. van Ravenswaaij-Arts,Morris A. Swertz,Richard J. Sinke,Irene M. van Langen,Cisca Wijmenga +18 more
TL;DR: Rapid targeted genomics combined with copy number variant detection adds important value in the neonatal and pediatric intensive care setting and led to a fast diagnosis in 30% of critically ill children for whom the routine clinical workup was unsuccessful.
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Clinical Validation of Copy Number Variant Detection from Targeted Next-Generation Sequencing Panels
Jennifer Kerkhof,Laila C. Schenkel,Jack Reilly,Sheri McRobbie,Erfan Aref-Eshghi,Alan Graham Stuart,C. Anthony Rupar,C. Anthony Rupar,Paul C. Adams,Robert A. Hegele,Hanxin Lin,Hanxin Lin,David I. Rodenhiser,Joan H.M. Knoll,Joan H.M. Knoll,Peter Ainsworth,Peter Ainsworth,Bekim Sadikovic,Bekim Sadikovic +18 more
TL;DR: This NGS CNV pipeline enables stand-alone first-tier assessment for CNV and sequence variants in a clinical laboratory setting, dispensing with the need for parallel CNV analysis using classic techniques, such as microarray, long-range PCR, or multiplex ligation-dependent probe amplification.
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Evaluation of CNV detection tools for NGS panel data in genetic diagnostics
José Marcos Moreno-Cabrera,Jesús del Valle,Elisabeth Castellanos,Lídia Feliubadaló,Marta Pineda,Joan Brunet,Eduard Serra,Gabriel Capellá,Conxi Lázaro,Bernat Gel +9 more
TL;DR: Evaluating CNV calling tools working on gene panel NGS data and their suitability as a screening step before orthogonal confirmation in genetic diagnostics strategies showed that most tools were highly sensitive and specific, but the performance was dataset dependant.
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