Dataset Information

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap.

ABSTRACT: 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.

SUBMITTER: Reimand J

PROVIDER: S-EPMC6607905 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap.

Reimand Jüri J Isserlin Ruth R Voisin Veronique V Kucera Mike M Tannus-Lopes Christian C Rostamianfar Asha A Wadi Lina L Meyer Mona M Wong Jeff J Xu Changjiang C Merico Daniele D Bader Gary D GD

Nature protocols 20190201 2

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 ...[more]

PMID: 30664679

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Project description:To infer the biological meaning from transcriptome data, it is useful to focus on genes that are regulated by the same regulator, i.e., regulons. Unfortunately, current gene set enrichment analysis (GSEA) tools do not consider whether a gene is activated or repressed by a regulator. This distinction is crucial when analyzing regulons since a regulator can work as an activator of certain genes and as a repressor of other genes, yet both sets of genes belong to the same regulon. Therefore, simply averaging expression differences of the genes of such a regulon will not properly reflect the activity of the regulator. What makes it more complicated is the fact that many genes are regulated by different transcription factors, and current transcriptome analysis tools are unable to indicate which regulator is most likely responsible for the observed expression difference of a gene. To address these challenges, we developed the gene set enrichment analysis program GINtool. Additional features of GINtool are novel graphical representations to facilitate the visualization of gene set analyses of transcriptome data, the possibility to include functional categories as gene sets for analysis, and the option to analyze expression differences within operons, which is useful when analyzing prokaryotic transcriptome and also proteome data.IMPORTANCEMeasuring the activity of all genes in cells is a common way to elucidate the function and regulation of genes. These transcriptome analyses produce large amounts of data since genomes contain thousands of genes. The analysis of these large data sets is challenging. Therefore, we developed a new software tool called GINtool that can facilitate the analysis of transcriptome data by using prior knowledge of gene sets controlled by the same regulator, the so-called regulons. An important novelty of GINtool is that it can take into account the directionality of gene regulation in these analyses, i.e., whether a gene is activated or repressed, which is crucial to assess whether a regulon or functional category is affected. GINtool also includes new graphical methods to facilitate the visual inspection of regulation events in transcriptome data sets. These and additional analysis methods included in GINtool make it a powerful software tool to analyze transcriptome data.

Dataset Information

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap.

Publications

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap.

Similar Datasets

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