| Literature DB >> 34816127 |
Zhongshan Cheng1, Michael Vermeulen1, Micheal Rollins-Green1, Tomas Babak1, Brian DeVeale2.
Abstract
Identification of non-coding mutations driving tumorigenesis requires alternative approaches to coding mutations. Enriched associations between mutated regulatory elements and altered cis-regulation in tumors are a promising approach to stratify candidate non-coding driver mutations. Here we provide a bioinformatics pipeline to mine data from the Cancer Genomic Commons (GDC) for such associations. The pipeline integrates RNA and whole-genome sequencing with genotyping data to reveal putative non-coding driver mutations by cancer type. For complete information on the generation and use of this protocol, please refer to Cheng et al. (2021).Entities:
Keywords: Bioinformatics; Cancer; Genomics; RNAseq; Sequence analysis
Mesh:
Year: 2021 PMID: 34816127 PMCID: PMC8591365 DOI: 10.1016/j.xpro.2021.100934
Source DB: PubMed Journal: STAR Protoc ISSN: 2666-1667