IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks.
Bottom Line: 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 identifies homologs with conserved functional roles for disease knowledge transfer, allowing biologists to analyze disease contexts and predictions across all organisms.
Affiliation: Department 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.Show MeSH
Mentions: Biologists can query IMP with a gene set or biological context of interest to retrieve putative gene-pathway assignments. We have extended IMP's biological contexts to include human diseases, in addition to GO biological processes. Biologists can now analyze disease contexts and predictions across organisms. IMP applies the same machine-learning method for predicting genes to biological processes (2,3) as it does to diseases, which uses a functional network as input to a Support Vector Machine (SVM) to classify genes (Figure 1). We showed previously that this method is accurate and competitive among state-of-the-art methods in predicting genes to biological processes (2,3). Disease gene predictions are inferred directly in human—from disease genes curated by Online Mendelian Inheritance in Man (OMIM) (16) and using the human functional network—and in the six model organisms. The disease predictions inferred in the other organisms leverage biological knowledge from human by transferring OMIM knowledge using our previously described method to identify the appropriate homologs for gene annotation transfer (2,9). These human-transferred gene-disease annotations are then used as training data for prediction with the organisms’ functional network, and the subsequent gene predictions are specific to that organism. By applying a model organism's functional network to predict disease genes, IMP can help biologists address an important challenge in the study of human disease: identifying the best model system for a given disease and the appropriate orthologs for a disease of interest.
Affiliation: Department 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.