Format

Send to

Choose Destination
See comment in PubMed Commons below
Nucleic Acids Res. 2015 Jul 1;43(W1):W128-33. doi: 10.1093/nar/gkv486. Epub 2015 May 12.

IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks.

Author information

  • 1Department of Computer Science, Princeton University, Princeton, NJ 08540, USA Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA Simons Center for Data Analysis, Simons Foundation, NY 10010, USA.
  • 2Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.
  • 3Department of Computer Science, Princeton University, Princeton, NJ 08540, USA Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.
  • 4Department of Computer Science, Princeton University, Princeton, NJ 08540, USA Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA Simons Center for Data Analysis, Simons Foundation, NY 10010, USA ogt@cs.princeton.edu.

Abstract

IMP (Integrative Multi-species Prediction), originally released in 2012, 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 biologists with a framework to analyze their candidate gene sets in the context of functional networks, expanding or refining their sets using functional relationships predicted from integrated high-throughput data. IMP 2.0 integrates updated prior knowledge and data collections from the last three years in the seven supported organisms (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Caenorhabditis elegans, and Saccharomyces cerevisiae) and extends function prediction coverage to include human disease. IMP identifies homologs with conserved functional roles for disease knowledge transfer, allowing biologists to analyze disease contexts and predictions across all organisms. Additionally, IMP 2.0 implements a new flexible platform for experts to generate custom hypotheses about biological processes or diseases, making sophisticated data-driven methods easily accessible to researchers. IMP does not require any registration or installation and is freely available for use at http://imp.princeton.edu.

PMID:
25969450
PMCID:
PMC4489318
DOI:
10.1093/nar/gkv486
[PubMed - indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems Icon for PubMed Central
    Loading ...
    Support Center