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Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer
Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools (www.regtools.org), a computationally efficient, free, and open-source software package designed to integrate somatic var...
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/PMC10033906/ https://www.ncbi.nlm.nih.gov/pubmed/36949070 http://dx.doi.org/10.1038/s41467-023-37266-6 |
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author | Cotto, Kelsy C. Feng, Yang-Yang Ramu, Avinash Richters, Megan Freshour, Sharon L. Skidmore, Zachary L. Xia, Huiming McMichael, Joshua F. Kunisaki, Jason Campbell, Katie M. Chen, Timothy Hung-Po Rozycki, Emily B. Adkins, Douglas Devarakonda, Siddhartha Sankararaman, Sumithra Lin, Yiing Chapman, William C. Maher, Christopher A. Arora, Vivek Dunn, Gavin P. Uppaluri, Ravindra Govindan, Ramaswamy Griffith, Obi L. Griffith, Malachi |
author_facet | Cotto, Kelsy C. Feng, Yang-Yang Ramu, Avinash Richters, Megan Freshour, Sharon L. Skidmore, Zachary L. Xia, Huiming McMichael, Joshua F. Kunisaki, Jason Campbell, Katie M. Chen, Timothy Hung-Po Rozycki, Emily B. Adkins, Douglas Devarakonda, Siddhartha Sankararaman, Sumithra Lin, Yiing Chapman, William C. Maher, Christopher A. Arora, Vivek Dunn, Gavin P. Uppaluri, Ravindra Govindan, Ramaswamy Griffith, Obi L. Griffith, Malachi |
author_sort | Cotto, Kelsy C. |
collection | PubMed |
description | Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools (www.regtools.org), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes. |
format | Online Article Text |
id | pubmed-10033906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100339062023-03-24 Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer Cotto, Kelsy C. Feng, Yang-Yang Ramu, Avinash Richters, Megan Freshour, Sharon L. Skidmore, Zachary L. Xia, Huiming McMichael, Joshua F. Kunisaki, Jason Campbell, Katie M. Chen, Timothy Hung-Po Rozycki, Emily B. Adkins, Douglas Devarakonda, Siddhartha Sankararaman, Sumithra Lin, Yiing Chapman, William C. Maher, Christopher A. Arora, Vivek Dunn, Gavin P. Uppaluri, Ravindra Govindan, Ramaswamy Griffith, Obi L. Griffith, Malachi Nat Commun Article Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools (www.regtools.org), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes. Nature Publishing Group UK 2023-03-22 /pmc/articles/PMC10033906/ /pubmed/36949070 http://dx.doi.org/10.1038/s41467-023-37266-6 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 Cotto, Kelsy C. Feng, Yang-Yang Ramu, Avinash Richters, Megan Freshour, Sharon L. Skidmore, Zachary L. Xia, Huiming McMichael, Joshua F. Kunisaki, Jason Campbell, Katie M. Chen, Timothy Hung-Po Rozycki, Emily B. Adkins, Douglas Devarakonda, Siddhartha Sankararaman, Sumithra Lin, Yiing Chapman, William C. Maher, Christopher A. Arora, Vivek Dunn, Gavin P. Uppaluri, Ravindra Govindan, Ramaswamy Griffith, Obi L. Griffith, Malachi Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer |
title | Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer |
title_full | Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer |
title_fullStr | Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer |
title_full_unstemmed | Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer |
title_short | Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer |
title_sort | integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10033906/ https://www.ncbi.nlm.nih.gov/pubmed/36949070 http://dx.doi.org/10.1038/s41467-023-37266-6 |
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