Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer

Kelsy C. Cotto, Yang-Yang Feng, Avinash Ramu, Megan Richters, Sharon L. Freshour, Zachary L. Skidmore, Huiming Xia, Joshua F. McMichael, Jason Kunisaki, Katie M. Campbell, Timothy Hung-Po Chen, Emily B. Rozycki, Douglas Adkins, Siddhartha Devarakonda, Sumithra Sankararaman, Yiing Lin, William C. Chapman, Christopher A. Maher, Vivek Arora, Gavin P. Dunn, Ravindra Uppaluri, Ramaswamy Govindan, Obi L. Griffith () and Malachi Griffith ()
Additional contact information
Kelsy C. Cotto: Washington University School of Medicine
Yang-Yang Feng: Washington University School of Medicine
Avinash Ramu: Washington University School of Medicine
Megan Richters: Washington University School of Medicine
Sharon L. Freshour: Washington University School of Medicine
Zachary L. Skidmore: Washington University School of Medicine
Huiming Xia: Washington University School of Medicine
Joshua F. McMichael: Washington University School of Medicine
Jason Kunisaki: Washington University School of Medicine
Katie M. Campbell: Washington University School of Medicine
Timothy Hung-Po Chen: Washington University School of Medicine
Emily B. Rozycki: Washington University School of Medicine
Douglas Adkins: Washington University School of Medicine
Siddhartha Devarakonda: Washington University School of Medicine
Sumithra Sankararaman: Washington University School of Medicine
Yiing Lin: Washington University School of Medicine
William C. Chapman: Washington University School of Medicine
Christopher A. Maher: Washington University School of Medicine
Vivek Arora: Washington University School of Medicine
Gavin P. Dunn: Mass General Hospital
Ravindra Uppaluri: Brigham and Women’s Hospital
Ramaswamy Govindan: Washington University School of Medicine
Obi L. Griffith: Washington University School of Medicine
Malachi Griffith: Washington University School of Medicine

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: Abstract 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37266-6

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DOI: 10.1038/s41467-023-37266-6

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