Detection of germline CNVs from gene panel data : benchmarking the state of the art
Munté, Elisabet (Universitat de Barcelona)
Roca, Carla (Universitat Autònoma de Barcelona)
Del Valle, Jesús (Instituto de Salud Carlos III)
Feliubadaló, Lidia (Instituto de Salud Carlos III)
Pineda, Marta (Instituto de Salud Carlos III)
Gel, Bernat (Institut Germans Trias i Pujol)
Castellanos, Elisabeth (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Rivera, Bárbara (Universitat de Barcelona)
Cordero, David (Instituto de Salud Carlos III)
Lázaro, Contxi (Instituto de Salud Carlos III)
Moreno-Cabrera, José Marcos (Institut Català d'Oncologia)
Universitat Autònoma de Barcelona

Date:	2025
Abstract:	Germline copy number variants (CNVs) play a significant role in hereditary diseases. However, the accurate detection of CNVs from targeted next-generation sequencing (NGS) gene panel data remains a challenging task. Several tools for calling CNVs within this context have been published to date, but the available benchmarks suffer from limitations, including testing on simulated data, testing on small datasets, and testing a small subset of published tools. In this work, we conducted a comprehensive benchmarking of 12 tools (Atlas-CNV, ClearCNV, ClinCNV, CNVkit, Cobalt, CODEX2, CoNVaDING, DECoN, ExomeDepth, GATK-gCNV, panelcn. MOPS, VisCap) on four validated gene panel datasets using their default parameters. We also assessed the impact of modifying 107 tool parameters and identified 13 parameter values that we suggest using to improve the tool F1 score. A total of 66 tool pair combinations were also evaluated to produce better meta-callers. Furthermore, we developed CNVbenchmarker2, a framework to help users perform their own evaluations. Our results indicated that in terms of F1 score, ClinCNV and GATK-gCNV were the best CNV callers. Regarding sensitivity, GATK-gCNV also exhibited particularly high performance. The results presented here provide an evaluation of the current state of the art in germline CNV detection from gene panel data and can be used as a reference resource when using any of the tools.
Grants:	Instituto de Salud Carlos III PI19/00553 Instituto de Salud Carlos III PI23/00017 Ministerio de Economía y Competitividad CB16/12/00234 Agència de Gestió d'Ajuts Universitaris i de Recerca 2023/SGR-01112 Fundació la Marató de TV3 202031-10 Generalitat de Catalunya SLT017/20/00012
Rights:	Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Published in:	Briefings in Bioinformatics, Vol. 26 Núm. 1 (january 2025) , p. bbae645, ISSN 1477-4054

DOI: 10.1093/bib/bbae645
PMID: 39668338

10 p, 957.4 KB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
Articles > Research articles
Articles > Published articles