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Title: Evaluation of CNV detection tools for NGS panel data in genetic diagnostics
Author: Moreno Cabrera, José Marcos
Valle Domínguez, Jesús del
Castellanos, Elisabeth
Feliubadaló i Elorza, Maria Lídia
Pineda Riu, Marta
Brunet, Joan
Serra, Eduard
Capellá, G. (Gabriel)
Lázaro García, Conxi
Gel, Bernat
Keywords: Malalties hereditàries
Cèl·lules germinals
Genetic disorders
Germ cells
Issue Date: 1-Dec-2020
Publisher: Springer Nature
Abstract: Although germline copy-number variants (CNVs) are the genetic cause of multiple hereditary diseases, detecting them from targeted next-generation sequencing data (NGS) remains a challenge. Existing tools perform well for large CNVs but struggle with single and multi-exon alterations. The aim of this work is to evaluate CNV calling tools working on gene panel NGS data and their suitability as a screening step before orthogonal confirmation in genetic diagnostics strategies. Five tools (DECoN, CoNVaDING, panelcn.MOPS, ExomeDepth, and CODEX2) were tested against four genetic diagnostics datasets (two in-house and two external) for a total of 495 samples with 231 single and multi-exon validated CNVs. The evaluation was performed using the default and sensitivity-optimized parameters. Results showed that most tools were highly sensitive and specific, but the performance was dataset dependant. When evaluating them in our diagnostics scenario, DECoN and panelcn.MOPS detected all CNVs with the exception of one mosaic CNV missed by DECoN. However, DECoN outperformed panelcn.MOPS specificity achieving values greater than 0.90 when using the optimized parameters. In our in-house datasets, DECoN and panelcn.MOPS showed the highest performance for CNV screening before orthogonal confirmation. Benchmarking and optimization code is freely available at
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It is part of: European Journal of Human Genetics, 2020, vol. 28, num. 12, p. 1645-1655
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Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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