Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 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

Aaron K Wong  1 , Arjun Krishnan  2 , Victoria Yao  3 , Alicja Tadych  2 , Olga G Troyanskaya  4
Affiliations

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

Aaron K Wong et al. Nucleic Acids Res. .

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.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
A schematic for IMP disease knowledge transfer and prediction. (A) IMP constructs a functional network for each of seven organisms by integrating heterogeneous genomic data. (B) Disease-gene annotations from human are transferred to the functionally similar homologs in other organisms. (C) Additional disease genes are predicted using the human-transferred disease genes in the organism-specific functional networks.
Figure 2.
Figure 2.
Disease result pages for ‘hypertrophic cardiomyopathy’ in IMP. (A) Querying ‘hypertrophic cardiomyopathy’ in human returns a list of genes predicted to be involved in the disease, sorted by probability. IMP applies known hypertrophic cardiomyopathy genes in human (from OMIM) to predict additional genes from the human functional network. (B) The same disease query can be performed in mouse, returning predicted mouse genes. These predictions were learned using human disease genes transferred to mouse with the mouse functional network.
Figure 3.
Figure 3.
Diagram for submitting custom user predictions. (A) The input form for entering a gene set of interest. Genes can be pasted, selected from a saved gene set, or chosen from a pre-defined set. (B) IMP applies an SVM with the provided gene set as positive examples and predicts additional genome-wide genes likely to be functionally related. (C) The output is a list of genome-wide genes, ranked by their probability of functional relationship with the provided gene set. This result can be emailed to the user or accessed directly on the web server.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Ozsolak F., Milos P.M. RNA sequencing: advances, challenges and opportunities. Nat. Rev. Genet. 2011;12:87–98. - PMC - PubMed
    1. Park C.Y., Wong A.K., Greene C.S., Rowland J., Guan Y., Bongo L.A., Burdine R.D., Troyanskaya O.G. Functional knowledge transfer for high-accuracy prediction of under-studied biological processes. PLoS Comput. Biol. 2013;9:e1002957. - PMC - PubMed
    1. Wong A.K., Park C.Y., Greene C.S., Bongo L.A., Guan Y., Troyanskaya O.G. IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Res. 2012;40:W484–W490. - PMC - PubMed
    1. Zuberi K., Franz M., Rodriguez H., Montojo J., Lopes C.T., Bader G.D., Morris Q. GeneMANIA prediction server 2013 update. Nucleic Acids Res. 2013;41:W115–W122. - PMC - PubMed
    1. Guan Y., Gorenshteyn D., Burmeister M., Wong A.K., Schimenti J.C., Handel M.A., Bult C.J., Hibbs M.A., Troyanskaya O.G. Tissue-specific functional networks for prioritizing phenotype and disease genes. PLoS Comput. Biol. 2012;8:e1002694. - PMC - PubMed

Publication types

LinkOut - more resources