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TLsub: A transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion

Mutation detecting is a routine work for sequencing data analysis and the trading of existing tools often involves the combinations of signals on a set of overlapped sequencing reads. However, the subclonal mutations, which are reported to contribute to tumor recurrence and metastasis, are sometimes...

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Detalles Bibliográficos
Autor principal: Zheng, Tian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9723383/
https://www.ncbi.nlm.nih.gov/pubmed/36482899
http://dx.doi.org/10.3389/fgene.2022.981269
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author Zheng, Tian
author_facet Zheng, Tian
author_sort Zheng, Tian
collection PubMed
description Mutation detecting is a routine work for sequencing data analysis and the trading of existing tools often involves the combinations of signals on a set of overlapped sequencing reads. However, the subclonal mutations, which are reported to contribute to tumor recurrence and metastasis, are sometimes eliminated by existing signals. When the clonal proportion decreases, signals often present ambiguous, while complicated interactions among signals break the IID assumption for most of the machine learning models. Although the mutation callers could lower the thresholds, false positives are significantly introduced. The main aim here was to detect the subclonal mutations with high specificity from the scenario of ambiguous sample purities or clonal proportions. We proposed a novel machine learning approach for filtering false positive calls to accurately detect mutations with wide spectrum subclonal proportion. We have carried out a series of experiments on both simulated and real datasets, and compared to several state-of-art approaches, including freebayes, MuTect2, Sentieon and SiNVICT. The results demonstrated that the proposed method adapts well to different diluted sequencing signals and can significantly reduce the false positive when detecting subclonal mutations. The codes have been uploaded at https://github.com/TrinaZ/TL-fpFilter for academic usage only.
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spelling pubmed-97233832022-12-07 TLsub: A transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion Zheng, Tian Front Genet Genetics Mutation detecting is a routine work for sequencing data analysis and the trading of existing tools often involves the combinations of signals on a set of overlapped sequencing reads. However, the subclonal mutations, which are reported to contribute to tumor recurrence and metastasis, are sometimes eliminated by existing signals. When the clonal proportion decreases, signals often present ambiguous, while complicated interactions among signals break the IID assumption for most of the machine learning models. Although the mutation callers could lower the thresholds, false positives are significantly introduced. The main aim here was to detect the subclonal mutations with high specificity from the scenario of ambiguous sample purities or clonal proportions. We proposed a novel machine learning approach for filtering false positive calls to accurately detect mutations with wide spectrum subclonal proportion. We have carried out a series of experiments on both simulated and real datasets, and compared to several state-of-art approaches, including freebayes, MuTect2, Sentieon and SiNVICT. The results demonstrated that the proposed method adapts well to different diluted sequencing signals and can significantly reduce the false positive when detecting subclonal mutations. The codes have been uploaded at https://github.com/TrinaZ/TL-fpFilter for academic usage only. Frontiers Media S.A. 2022-11-22 /pmc/articles/PMC9723383/ /pubmed/36482899 http://dx.doi.org/10.3389/fgene.2022.981269 Text en Copyright © 2022 Zheng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Zheng, Tian
TLsub: A transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion
title TLsub: A transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion
title_full TLsub: A transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion
title_fullStr TLsub: A transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion
title_full_unstemmed TLsub: A transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion
title_short TLsub: A transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion
title_sort tlsub: a transfer learning based enhancement to accurately detect mutations with wide-spectrum sub-clonal proportion
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9723383/
https://www.ncbi.nlm.nih.gov/pubmed/36482899
http://dx.doi.org/10.3389/fgene.2022.981269
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