![]() ![]() Pathways are statistically tested for over-representation in the experimental gene list relative to what is expected by chance, using several common statistical tests that consider the number of genes detected in the experiment, their relative ranking and the number of genes annotated to a pathway of interest. A standard approach to addressing this problem is pathway enrichment analysis, which summarizes the large gene list as a smaller list of more easily interpretable pathways. Analyses often result in long lists of genes that require an impractically large amount of manual literature searching to interpret. However, analysis and interpretation of these data represent a major challenge for many researchers. The resulting data are growing exponentially, and their analysis helps researchers discover novel biological functions, genotype-phenotype relationships and disease mechanisms 1, 2. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training.Ĭomprehensive quantification of DNA, RNA and proteins in biological samples is now routine. ![]() 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. 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 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 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. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. ![]()
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