EGAN: Exploratory Gene Association Networks
Leverage organic intelligence
EGAN is a software tool that allows a bench biologist to visualize and interpret the results of high-throughput exploratory assays in an interactive hypergraph of genes, relationships (protein-protein interactions, literature co-occurrence, etc.) and meta-data (annotation, signaling pathways, etc.). EGAN provides comprehensive, automated calculation of meta-data coincidence (over-representation, enrichment) for user- and assay-defined gene lists, and provides direct links to web resources and literature (NCBI Entrez Gene, PubMed, KEGG, Gene Ontology, iHOP, Google, etc.).
In short, EGAN allows you to perform data-driven discovery of novel systems biology/disease pathways by integrating computational analysis output with a giant gene-centric knowledge base. Click here for a basic description of concepts.
EGAN functions as a module for exploratory investigation of analysis results from multiple high-throughput assay technologies, including but not limited to:
EGAN has been built using Cytoscape libraries for graph visualization and layout, and is comparable to DAVID, GSEA, Ingenuity IPA and Ariadne Pathway Studio. There are pre-collated EGAN networks available for human (Homo sapiens), mouse (Mus musculus), rat (Rattus norvegicus), chicken (Gallus gallus), zebrafish (Danio rerio), fruit fly (Drosophila melanogaster), nematode (Caenorhabditis elegans), mouse-ear cress (Arabidopsis thaliana), rice (Oryza sativa) and brewer's yeast (Saccharomyces cerevisiae). There is now an EGAN module available for GenePattern (human-only).
- Transcriptomics via expression microarrays or RNA-Seq
- Genomics via DNA-Seq, SNP arrays or aCGH
- Proteomics via MS/MS peptide identifications
- Epigenomics via DNA methylation, ChIP-on-Chip or ChIP-Seq
- In-silico analysis of sequences or literature
For more information, check out the paper. For a hands-on introduction to EGAN, try the demo and follow along with the tutorial.
EGAN has been developed by the Helen Diller Family Comprehensive Cancer Center Biostatistics and Computational Biology Core at the University of California, San Francisco, and is provided for use according to the terms of this license.
This EGAN-produced image shows a set of genes found to be up-regulated in breast cancer patients with poorer survival, connected by literature, protein-protein interactions, chromosomal adjacency and enriched gene sets: KEGG, Gene Ontology Process and NCI-Nature. These genes and terms/pathways are potential effectors of/biomarkers for poor survival.