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AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples
The detection of somatic DNA variants in tumor samples with low tumor purity or sequencing depth remains a daunting challenge despite numerous attempts to address this problem. In this study, we constructed a substantially extended set of actual positive variants originating from a wide range of tum...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10474289/ https://www.ncbi.nlm.nih.gov/pubmed/37524869 http://dx.doi.org/10.1038/s12276-023-01049-2 |
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author | Jeon, Hyeonseong Ahn, Junhak Na, Byunggook Hong, Soona Sael, Lee Kim, Sun Yoon, Sungroh Baek, Daehyun |
author_facet | Jeon, Hyeonseong Ahn, Junhak Na, Byunggook Hong, Soona Sael, Lee Kim, Sun Yoon, Sungroh Baek, Daehyun |
author_sort | Jeon, Hyeonseong |
collection | PubMed |
description | The detection of somatic DNA variants in tumor samples with low tumor purity or sequencing depth remains a daunting challenge despite numerous attempts to address this problem. In this study, we constructed a substantially extended set of actual positive variants originating from a wide range of tumor purities and sequencing depths, as well as actual negative variants derived from sequencer-specific sequencing errors. A deep learning model named AIVariant, trained on this extended dataset, outperforms previously reported methods when tested under various tumor purities and sequencing depths, especially low tumor purity and sequencing depth. |
format | Online Article Text |
id | pubmed-10474289 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104742892023-09-03 AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples Jeon, Hyeonseong Ahn, Junhak Na, Byunggook Hong, Soona Sael, Lee Kim, Sun Yoon, Sungroh Baek, Daehyun Exp Mol Med Article The detection of somatic DNA variants in tumor samples with low tumor purity or sequencing depth remains a daunting challenge despite numerous attempts to address this problem. In this study, we constructed a substantially extended set of actual positive variants originating from a wide range of tumor purities and sequencing depths, as well as actual negative variants derived from sequencer-specific sequencing errors. A deep learning model named AIVariant, trained on this extended dataset, outperforms previously reported methods when tested under various tumor purities and sequencing depths, especially low tumor purity and sequencing depth. Nature Publishing Group UK 2023-08-01 /pmc/articles/PMC10474289/ /pubmed/37524869 http://dx.doi.org/10.1038/s12276-023-01049-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Jeon, Hyeonseong Ahn, Junhak Na, Byunggook Hong, Soona Sael, Lee Kim, Sun Yoon, Sungroh Baek, Daehyun AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples |
title | AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples |
title_full | AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples |
title_fullStr | AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples |
title_full_unstemmed | AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples |
title_short | AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples |
title_sort | aivariant: a deep learning-based somatic variant detector for highly contaminated tumor samples |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10474289/ https://www.ncbi.nlm.nih.gov/pubmed/37524869 http://dx.doi.org/10.1038/s12276-023-01049-2 |
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