Integrated bioinformatic pipeline using whole-exome and RNAseq data to identify germline variants correlated with cancer

Divya Sahu; Ajay Chatrath; Aakrosh Ratan; Anindya Dutta

doi:10.1016/j.xpro.2022.101273

Integrated bioinformatic pipeline using whole-exome and RNAseq data to identify germline variants correlated with cancer

STAR Protoc. 2022 Apr 4;3(2):101273. doi: 10.1016/j.xpro.2022.101273. eCollection 2022 Jun 17.

Authors

Divya Sahu¹, Ajay Chatrath², Aakrosh Ratan³, Anindya Dutta^{1

2}

Affiliations

¹ Department of Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
² Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22903, USA.
³ Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA.

Abstract

Germline Variants (GVs) are effective in predicting cancer risk and may be relevant in predicting patient outcomes. Here we provide a bioinformatic pipeline to identify GVs from the TCGA lower grade glioma cohort in Genomics Data Commons. We integrate paired whole exome sequences from normal and tumor samples and RNA sequences from tumor samples to determine a patient's GV status. We then identify the subset of GVs that are predictive of patient outcomes by Cox regression. For complete details on the use and execution of this protocol, please refer to Chatrath et al. (2019) and Chatrath et al. (2020).

Keywords: Bioinformatics; Cancer; Genetics; Genomics; RNAseq; Sequencing; Systems biology.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Computational Biology
Exome Sequencing
Exome* / genetics
Glioma* / genetics
Humans

Abstract

Publication types

MeSH terms

Grants and funding