PuSH - Publikationsserver des Helmholtz Zentrums München

Vilov, S. ; Heinig, M.

DeepSom: A CNN-based approach to somatic variant calling in WGS samples without a matched normal.

Bioinformatics 39:9 (2023)
Verlagsversion DOI PMC
Creative Commons Lizenzvertrag
MOTIVATION: Somatic mutations are usually called by analysing the DNA sequence of a tumor sample in conjunction with a matched normal. However, a matched normal is not always available, for instance, in retrospective analysis or diagnostic settings. For such cases, tumor-only somatic variant calling tools need to be designed. Previously proposed approaches demonstrate inferior performance on whole genome sequencing (WGS) samples. RESULTS: We present the convolutional neural network-based approach called DeepSom for detecting somatic single nucleotide polymorphism (SNP) and short insertion and deletion (INDEL) variants in tumor WGS samples without a matched normal. We validate DeepSom by reporting its performance on 5 different cancer datasets. We also demonstrate that on WGS samples DeepSom outperforms previously proposed methods for tumor-only somatic variant calling. AVAILABILITY: DeepSom is available as a GitHub repository at https://github.com/heiniglab/DeepSom. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Passenger Mutations; Signatures; Landscape; Framework; Cancer; Driver; Genome
ISSN (print) / ISBN 1367-4803
Zeitschrift Bioinformatics
Quellenangaben Band: 39, Heft: 1, Seiten: , Artikelnummer: 9 Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen German Ministry for Education and Research (BMBF)