Control-independent mosaic single nucleotide variant detection with DeepMosaic

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• dc.contributor.author Yang, Xiaoxu
• dc.contributor.author Xu, Xin
• dc.contributor.author Breuss, Martin W.
• dc.contributor.author Antaki, Danny
• dc.contributor.author Ball, Laurel L.
• dc.contributor.author Chung, Changuk
• dc.contributor.author Shen, Jiawei
• dc.contributor.author Li, Chen
• dc.contributor.author George, Renee D.
• dc.contributor.author Wang, Yifan
• dc.contributor.author Bae, Taejeong
• dc.contributor.author Cheng, Yuhe
• dc.contributor.author Abyzov, Alexej
• dc.contributor.author Wei, Liping
• dc.contributor.author Alexandrov, Ludmil B.
• dc.contributor.author Sebat, Jonathan L.
• dc.contributor.author NIMH Brain Somatic Mosaicism Network
• dc.contributor.author Gleeson, Joseph G.
• dc.date.accessioned 2024-03-27T08:13:02Z
• dc.date.available 2024-03-27T08:13:02Z
• dc.date.issued 2023
• dc.description.abstract Mosaic variants (MVs) reflect mutagenic processes during embryonic development and environmental exposure, accumulate with aging and underlie diseases such as cancer and autism. The detection of noncancer MVs has been computationally challenging due to the sparse representation of nonclonally expanded MVs. Here we present DeepMosaic, combining an image-based visualization module for single nucleotide MVs and a convolutional neural network-based classification module for control-independent MV detection. DeepMosaic was trained on 180,000 simulated or experimentally assessed MVs, and was benchmarked on 619,740 simulated MVs and 530 independent biologically tested MVs from 16 genomes and 181 exomes. DeepMosaic achieved higher accuracy compared with existing methods on biological data, with a sensitivity of 0.78, specificity of 0.83 and positive predictive value of 0.96 on noncancer whole-genome sequencing data, as well as doubling the validation rate over previous best-practice methods on noncancer whole-exome sequencing data (0.43 versus 0.18). DeepMosaic represents an accurate MV classifier for noncancer samples that can be implemented as an alternative or complement to existing methods.
• dc.format.mimetype application/pdf
• dc.identifier.citation Yang X, Xu X, Breuss MW, Antaki D, Ball LL, Chung C, et al. Control-independent mosaic single nucleotide variant detection with DeepMosaic. Nat Biotechnol. 2023 Jun;41(6):870-7. DOI: 10.1038/s41587-022-01559-w
• dc.identifier.doi http://dx.doi.org/10.1038/s41587-022-01559-w
• dc.identifier.issn 1087-0156
• dc.identifier.uri http://hdl.handle.net/10230/59590
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Nat Biotechnol. 2023 Jun;41(6):870-7
• dc.rights © Springer Nature Publishing AG [Yang X, Xu X, Breuss MW, Antaki D, Ball LL, Chung C, et al. Control-independent mosaic single nucleotide variant detection with DeepMosaic. Nat Biotechnol. 2023 Jun;41(6):870-7. DOI: 10.1038/s41587-022-01559-w [http://dx.doi.org/10.1038/s41587-022-01559-w]
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Genomic analysis
• dc.subject.keyword Machine learning
• dc.subject.keyword Mutation
• dc.title Control-independent mosaic single nucleotide variant detection with DeepMosaic
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion