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DeepSom: A CNN-based approach to somatic variant calling in WGS samples without a matched normal.
Bioinformatics 39:9 (2023)
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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Passenger Mutations; Signatures; Landscape; Framework; Cancer; Driver; Genome
ISSN (print) / ISBN
1367-4803
Journal
Bioinformatics
Quellenangaben
Volume: 39,
Issue: 1,
Article Number: 9
Publisher
Oxford University Press
Publishing Place
Oxford
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
Grants
German Ministry for Education and Research (BMBF)