论文信息 - EGAN: exploratory gene association networks

EGAN: exploratory gene association networks

UNLABELLED Exploratory Gene Association Networks (EGAN) is a Java desktop application that provides a point-and-click environment for contextual graph visualization of high-throughput assay results. By loading the entire network of genes, pathways, interactions, annotation terms and literature references directly into memory, EGAN allows a biologist to repeatedly query and interpret multiple experimental results without incurring additional delays for data download/integration. Other compelling features of EGAN include: support for diverse -omics technologies, a simple and interactive graph display, sortable/searchable data tables, links to external web resources including > or = 240 000 articles at PubMed, hypergeometric and GSEA-like enrichment statistics, pipeline-compatible automation via scripting and the ability to completely customize and/or supplement the network with new/proprietary data. AVAILABILITY Runs on most operating systems via Java; downloadable from http://akt.ucsf.edu/EGAN/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Taku A. Tokuyasu | Jesse Paquette

[1] Tom H. Pringle,et al. The human genome browser at UCSC. , 2002, Genome research.

[2] Michael L. Creech,et al. Integration of biological networks and gene expression data using Cytoscape , 2007, Nature Protocols.

[3] P. Shannon,et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[4] Alexandra Paillusson,et al. A GFP-based reporter system to monitor nonsense-mediated mRNA decay , 2005, Nucleic acids research.

[5] Gregory D. Schuler,et al. Database resources of the National Center for Biotechnology Information: update , 2004, Nucleic acids research.

[6] Zhenjun Hu,et al. VisANT 3.0: new modules for pathway visualization, editing, prediction and construction , 2007, Nucleic Acids Res..

[7] T. Jenssen,et al. A literature network of human genes for high-throughput analysis of gene expression , 2001 .

[8] Steven C. Lawlor,et al. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways , 2002, Nature Genetics.

[9] T. Tatusova,et al. Entrez Gene: gene-centered information at NCBI , 2010, Nucleic Acids Res..

[10] Claude Berge,et al. Graphs and Hypergraphs , 2021, Clustering.