AUTHORS
Mathieu Quinodoz, Karolina Kaminska, Francesca Cancellieri, Ji Hoon Han, Virginie G Peter, Elifnaz Celik, Lucas Janeschitz-Kriegl, Nils Schärer, Daniela Hauenstein, Bence György, Giacomo Calzetti, Vincent Hahaut, Sónia Custódio, Ana Cristina Sousa, Yuko Wada, Yusuke Murakami, Almudena Avila Fernández, Cristina Rodilla Hernández, Pablo Minguez, Carmen Ayuso, Koji M Nishiguchi, Cristina Santos, Luisa Coutinho Santos, Viet H Tran, Veronika Vaclavik, Hendrik P N Scholl, Carlo Rivolta
Am J Hum Genet. 2024 Apr 4;111(4):701-713. doi: 10.1016/j.ajhg.2024.03.001. Epub 2024 Mar 25.
ABSTRACT
Copy-number variants (CNVs) play a substantial role in the molecular pathogenesis of hereditary disease and cancer, as well as in normal human interindividual variation. However, they are still rather difficult to identify in mainstream sequencing projects, especially involving exome sequencing, because they often occur in DNA regions that are not targeted for analysis. To overcome this problem, we developed OFF-PEAK, a user-friendly CNV detection tool that builds on a denoising approach and the use of “off-target” DNA reads, which are usually discarded by sequencing pipelines. We benchmarked OFF-PEAK on data from targeted sequencing of 96 cancer samples, as well as 130 exomes of individuals with inherited retinal disease from three different populations. For both sets of data, OFF-PEAK demonstrated excellent performance (>95% sensitivity and >80% specificity vs. experimental validation) in detecting CNVs from in silico data alone, indicating its immediate applicability to molecular diagnosis and genetic research.
Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.
PMID: 38531366 PMCID: PMC11023916 DOI: 10.1016/j.ajhg.2024.03.001
