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

被引：6

作者：

Mora, Antonio ^{[1
,2
]}

机构：

[1] Guangzhou Med Univ, Joint Sch Life Sci, Guangzhou, Peoples R China

[2] Chinese Acad Sci, Guangzhou Inst Biomed & Hlth, Beijing, Peoples R China

来源：

BRIEFINGS IN BIOINFORMATICS | 2020年 / 21卷 / 05期

关键词：

pathway analysis; gene set analysis; ChIP-Seq; SNP; methylation; miRNA; lncRNA; ENRICHMENT ANALYSIS; PATHWAY ANALYSIS; R PACKAGE; WIDE ASSOCIATION; EXPRESSION DATA; NONCODING RNAS; SNP DATA; RESOURCE; LISTS; METAANALYSIS;

D O I：

10.1093/bib/bbz090

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：1495 / 1508

页数：14

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