BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources

By Chunlei Wu, C. Orozco, J. Boyer, Marc Leglise, James Goodale, Serge Batalov, C. L. Hodge, James Haase, Jeff Janes, Jon W. Huss and 1 more. Genome Biology, 2009

1,450 citations recorded by the source corpus.

Online gene annotation resources are indispensable for analysis of genomics data. However, the landscape of these online resources is highly fragmented, and scientists often visit dozens of these sites for each gene in a candidate gene list. Here, we introduce BioGPS http://biogps.gnf.org, a centralized gene portal for aggregating distributed gene annotation resources. Moreover, BioGPS embraces the principle of community intelligence, enabling any user to easily and directly contribute to the BioGPS platform.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2e9d9f0d6f9f72c76482bcfc2a93abb890fd560f

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Citations in the corpus

DGIdb 3.0: a redesign and expansion of the drug–gene interaction database (2017, neutral): “edu), BioGPS (23), Omics Pipe (24), GEMINI (25), StationX (www.”
Analysis of the Human Tissue-specific Expression by Genome-wide Integration of Transcriptomics and Antibody-based Proteomics* (2013, methodology): “On the RNA level, the FANTOM consortium has been initiated to map the transcriptional space of the human genome and several gene expression atlases for RNA expression data have been launched, such as the original work to create a gene atlas by integrating mouse and human expression data from multipl”
DGIdb 2.0: mining clinically relevant drug–gene interactions (2015, background): “In addition, a number of other bioinformatics tools have used DGIdb in developing their own platforms including PANDA (22), iCAGES (icages.usc.edu), BioGPS (23), OmicsPipe (24), GEMINI (25), StationX (www.stationxinc. com) and IHLDB.rf (www.lungcancerdatabase.com), highlighting the broad usefulness ”
TSGene: a web resource for tumor suppressor genes (2012, methodology): “Digital gene expressions for 184 tumor samples and 84 normal tissues were integrated from BioGPS (21).”
Reactome: a database of reactions, pathways and biological processes (2010, background): “This year, additional crossreferences to RSCB Protein Data Bank (34), Comparative Toxicogenomics Database (35), DockBlaster (36), BioGPS (37) and dbSNP (38) have been added to the protein pages.”
miRTarBase: a database curates experimentally validated microRNA–target interactions (2010, background): “Generally, web pages of the miRTarBase contain many effective quick links to several other web resources, including NCBI Entrez (21), UCSC Genome Browser (22), miRBase (2), BioGPS (23), iHOP (24) and HGNC (25).”
Gender bias in autoimmunity is influenced by microbiota (2013, neutral): “org) (Lattin et al., 2008; Wu et al., 2009).”
Gateways to the FANTOM5 promoter level mammalian expression atlas (2015, methodology): “We included BioMart, BioGPS plugin, and UCSC DataHub to disseminate our results across these user communities, and introduced the enhancer selector, BioLayout and TET to facilitate further analysis and inspection of our resources.”
Reuse of public genome-wide gene expression data (2012, methodology): “A well-known topical database is the Symatlas from the Genomics Institute of the Novartis Research Foundation, which is entirely based on two microarray-based studies of gene expression across 79 human and 61 mouse tissues 39 that have now become a resource provided through the BioGPS portal 40 .”
WikiPathways: building research communities on biological pathways (2011, background): “The WikiPathways content is already accessible from several external resources, such as NCBI BioSystems (7) and BioGPS (8).”
Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures (2016, result): “T gamma delta and T follicular helper cells were excluded from TIS and IIS because public gene expression maps from healthy tissues show that certain genes in the T gamma delta signature (C1orf61, FEZ1) and the T follicular helper signature (B3GAT1, HEY1, CHGB, CDK5R1) are expressed at elevated leve”
The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses (2016, methodology): “This section has the following subsections: a. mRNA expression graph: Provides normal tissue expression proﬁles for the gene, via experimental results from BioGPS (Wu et al., 2009), GTEx (Lonsdale et al., 2013[), Illumina Body Map, and SAGE.”