计算生物学
故障排除
可视化
协议(科学)
生物
基因组
功能基因组学
基因组浏览器
生物信息学
基因
计算机科学
基因组学
数据挖掘
遗传学
病理
操作系统
替代医学
医学
作者
Jüri Reimand,Ruth Isserlin,Véronique Voisin,Mike Kucera,Christian Tannus-Lopes,Asha Rostamianfar,Lina Wadi,Mona Meyer,Joseph Wong,Changjiang Xu,Daniele Merico,Gary D. Bader
出处
期刊:Nature Protocols
[Springer Nature]
日期:2019-01-21
卷期号:14 (2): 482-517
被引量:1156
标识
DOI:10.1038/s41596-018-0103-9
摘要
Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes; however, the principles can be applied to diverse types of omics data. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training. This protocol describes pathway enrichment analysis of gene lists from RNA-seq and other genomics experiments using g:Profiler, GSEA, Cytoscape and EnrichmentMap software.
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