WebGestalt

WebGestalt: An integrated system for exploring gene sets in various biological contexts. High-throughput technologies have led to the rapid generation of large-scale datasets about genes and gene products. These technologies have also shifted our research focus from ‘single genes’ to ‘gene sets’. We have developed a web-based integrated data mining system, WebGestalt (http://genereg.ornl.gov/webgestalt/), to help biologists in exploring large sets of genes. WebGestalt is composed of four modules: gene set management, information retrieval, organization/visualization, and statistics. The management module uploads, saves, retrieves and deletes gene sets, as well as performs Boolean operations to generate the unions, intersections or differences between different gene sets. The information retrieval module currently retrieves information for up to 20 attributes for all genes in a gene set. The organization/visualization module organizes and visualizes gene sets in various biological contexts, including Gene Ontology, tissue expression pattern, chromosome distribution, metabolic and signaling pathways, protein domain information and publications. The statistics module recommends and performs statistical tests to suggest biological areas that are important to a gene set and warrant further investigation. In order to demonstrate the use of WebGestalt, we have generated 48 gene sets with genes over-represented in various human tissue types. Exploration of all the 48 gene sets using WebGestalt is available for the public at http://genereg.ornl.gov/webgestalt/wg_enrich.php.


References in zbMATH (referenced in 7 articles )

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  1. Shomroni, Orr: Development of algorithms and next-generation sequencing data workflows for the analysis of gene regulatory networks (2017)
  2. Kasabov, Nikola (ed.): Springer handbook of bio-/neuro-informatics (2014)
  3. Panigrahi, Priya P.; Singh, Tiratha Raj: Computational studies on Alzheimer’s disease associated pathways and regulatory patterns using microarray gene expression and network data: revealed association with aging and other diseases (2013)
  4. Ding, Min; Wang, Haiyun; Chen, Jiajia; Shen, Bairong; Xu, Zhonghua: Identification and functional annotation of genome-wide ER-regulated genes in breast cancer based on ChIP-Seq data (2012)
  5. Lachmann, Alexander; Ma’ayan, Avi: Lists2networks: integrated analysis of gene/protein lists (2010) ioport
  6. Tseng, Huei-Hun E.; Tompa, Martin: Algorithms for locating extremely conserved elements in multiple sequence alignments (2009) ioport
  7. Zhang, Bing; Kirov, Stefan; Snoddy, Jay: Webgestalt: An integrated system for exploring gene sets in various biological contexts. (2005) ioport