What is an ontology?
An ontology is a hierachical, organized dictionary of terms. It provides a standard set of labels for researchers to apply to genes, and its structure allows for reasoning about terms.
Which ontologies does GREAT provide?
GREAT uses numerous ontologies covering a wide range of topics, which allows you to examine many aspects of your input set. Currently, GREAT includes the Gene Ontology (GO) and other ontologies covering phenotypes and human disease, pathways, gene expression, regulatory motifs, and gene families.
To see a description and statistics for an ontology, choose from the list below:
Gene Ontology (GO)
Phenotype Data and Human Disease
- MGI Phenotype - data about mouse genotype - phenotype associations primarily obtained by curators from the literature
- MSigDB Cancer Neighborhood - gene sets defined by correlated expression profiles with cancer-associated genes
- MSigDB Cancer Modules - gene sets with an altered expression in a variety of cancer conditions
- Panther Pathway - primarily signaling pathways
- Pathway Commons - a collection of pathways from multiple sources
- BioCyc Pathway - metabolic pathways
- MSigDB Pathway - gene sets from pathway databases
- MGI Expression___Detected - genes detected as expressed in data with a focus on gene expression during mouse development
- MGI Expression___Not Detected - genes detected as not expressed in data with a focus on gene expression during mouse development
- MSigDB Perturbation - gene sets that change their expression after genetic and chemical perturbations
- MSigDB miRNA Motifs - gene sets that share a 3'-UTR microRNA binding motif
- MSigDB Transcription Factor Motifs - gene sets that share a transcription factor binding motif
- miRNA Targets - genes that are downregulated in a microRNA overexpression experiment
- Transcription Factor Targets - genes identified by a transcription factor ChIP-chip experiment
- InterPro - protein domains, families and functional sites
- TreeFam - gene families of paralogs
- HGNC Gene Families - gene sets based on sequence similarity, data from the literature, and manual curation
Can I use other ontologies?
Currently, GREAT only supports the listed ontologies.