| GSEA [98] (see
Note 15) |
Calculates a normalized enrichment score quantifying the overrepresentation of sets of genes in normalized ranked gene sets representing different phenotypes. GenePattern modules also include Bowtie, Tophat, RNA-SeQC, among others. |
| Enrichr [99, 100] (see
Note 16) |
Produces a ranked list of enrichment terms determined by degree of overlap between quantitative or crisp gene sets and the unranked input data. |
| Revigo [101] (see
Note 17) |
Summarizes gene ontologies through semantic similarity and visualizes clusters of similar ontologies to emphasize the most significant sets of ontologies in a large dataset. |
| AmiGO [86, 102] |
Database to search the gene ontology and annotated genes from other associated databases. Includes the full gene set and GO hierarchies for each term. Includes an enrichment tool. |
| Gorilla [103] |
An enrichment tool to determine novel GO terms that are well represented in the top of a p-value ranked gene list or GO terms that are enriched as compared to a background dataset. |
| CEMiTool [104] |
Identifies co-expression modules within a gene dataset and performs enrichment analysis of the modules. Module expression activity can be compared between different samples and co-expression data can be integrated with protein–protein interaction data to develop an interaction network. |
| KEGG [105] |
Database with KEGG pathway maps determined from published literature and an enrichment analysis through KEGG mapping, a system to compare an input list of genes or proteins with KEGG pathway maps and KEGG modules. |
| DAVID [106] |
A functional tool that can identify gene groups based on annotation similarity and ranked by importance. Significant biological processes are then determined based on the similarity of genes between different gene groups. |
