Gene set analysis methods for the functional interpretation of non-mRNA data-Genomic range and ncRNA data

Brief Bioinform. 2020 Sep 25;21(5):1495-1508. doi: 10.1093/bib/bbz090.

Abstract

Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to analyze mRNA (microarray, RNA-Seq) data. The following review includes an update regarding general methods and resources for GSA and then emphasizes GSA methods and tools for non-mRNA omics datasets, specifically genomic range data (ChIP-Seq, SNP and methylation) and ncRNA data (miRNAs, lncRNAs and others). In the end, the state of the GSA field for non-mRNA datasets is discussed, and some current challenges and trends are highlighted, especially the use of network approaches to face complexity issues.

Keywords: ChIP-Seq; SNP; gene set analysis; lncRNA; methylation; miRNA; pathway analysis.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Gene Regulatory Networks*
  • Genomics*
  • Humans
  • Machine Learning*
  • RNA, Long Noncoding / metabolism*
  • RNA, Messenger / metabolism*

Substances

  • RNA, Long Noncoding
  • RNA, Messenger