TY - JOUR
T1 - IMP
T2 - A multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks
AU - Wong, Aaron K.
AU - Park, Christopher Y.
AU - Greene, Casey S.
AU - Bongo, Lars A.
AU - Guan, Yuanfang
AU - Troyanskaya, Olga G.
N1 - Funding Information:
National Science Foundation (NSF) CAREER [award DBI-0546275]; National Institutes of Health (NIH) [R01 GM071966, R01 HG005998 and T32 HG003284]; National Institute of General Medical Sciences (NIGMS) Center of Excellence [P50 GM071508]. Funding for open access charge: NIH.
PY - 2012/7
Y1 - 2012/7
N2 - Integrative multi-species prediction (IMP) is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides a framework for biologists to analyze their candidate gene sets in the context of functional networks, as they expand or focus these sets by mining functional relationships predicted from integrated high-throughput data. IMP integrates prior knowledge and data collections from multiple organisms in its analyses. Through flexible and interactive visualizations, researchers can compare functional contexts and interpret the behavior of their gene sets across organisms. Additionally, IMP identifies homologs with conserved functional roles for knowledge transfer, allowing for accurate function predictions even for biological processes that have very few experimental annotations in a given organism. IMP currently supports seven organisms (Homo sapiens, Mus musculus, Rattus novegicus, Drosophila melanogaster, Danio rerio, Caenorhabditis elegans and Saccharomyces cerevisiae), does not require any registration or installation and is freely available for use at http://imp.princeton.edu.
AB - Integrative multi-species prediction (IMP) is an interactive web server that enables molecular biologists to interpret experimental results and to generate hypotheses in the context of a large cross-organism compendium of functional predictions and networks. The system provides a framework for biologists to analyze their candidate gene sets in the context of functional networks, as they expand or focus these sets by mining functional relationships predicted from integrated high-throughput data. IMP integrates prior knowledge and data collections from multiple organisms in its analyses. Through flexible and interactive visualizations, researchers can compare functional contexts and interpret the behavior of their gene sets across organisms. Additionally, IMP identifies homologs with conserved functional roles for knowledge transfer, allowing for accurate function predictions even for biological processes that have very few experimental annotations in a given organism. IMP currently supports seven organisms (Homo sapiens, Mus musculus, Rattus novegicus, Drosophila melanogaster, Danio rerio, Caenorhabditis elegans and Saccharomyces cerevisiae), does not require any registration or installation and is freely available for use at http://imp.princeton.edu.
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U2 - 10.1093/nar/gks458
DO - 10.1093/nar/gks458
M3 - Article
C2 - 22684505
AN - SCOPUS:84864455716
SN - 0305-1048
VL - 40
SP - W484-W490
JO - Nucleic acids research
JF - Nucleic acids research
IS - W1
ER -