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Detalle de: EcID. A database for the inference of functional interactions in E. coli.

Title EcID. A database for the inference of functional interactions in E. coli.
Abstract

The EcID database (Escherichia coli Interaction Database) provides a framework for the integration of information on functional interactions extracted from the following sources: EcoCyc (metabolic pathways, protein complexes and regulatory information), KEGG (metabolic pathways), MINT and IntAct (protein interactions). It also includes information on protein complexes from the two E. coli high-throughput pull-down experiments and potential interactions extracted from the literature using the web services associated to the iHOP text-mining system. Additionally, EcID incorporates results of various prediction methods, including two protein interaction prediction methods based on genomic information (Phylogenetic Profiles and Gene Neighbourhoods) and three methods based on the analysis of co-evolution (Mirror Tree, In Silico 2 Hybrid and Context Mirror). EcID associates to each prediction a specifically developed confidence score. The two main features that make EcID different from other systems are the combination of co-evolution-based predictions with the experimental data, and the introduction of E. coli-specific information, such as gene regulation information from EcoCyc. The possibilities offered by the combination of the EcID database information are illustrated with a prediction of potential functions for a group of poorly characterized genes related to yeaG. EcID is available online at http://ecid.bioinfo.cnio.es.

Apa Reference Andres Leon, E., Ezkurdia, I., Garcia, B., Valencia, A., & Juan, D. (2009). EcID. A database for the inference of functional interactions in E. coli. Nucleic Acids Research, 37(Database), D629–D635. 10.1093/nar/gkn853
Doi 10.1093/nar/gkn853
Publication Date 1 Jan 2009
Publication Type Article
Researcher Eduardo Andrés-León
Ontology Model organisms, Genomics, Functional annotation, Biological Databases, Computational models and simulations, Regulatory biology, Web-service, Phylogenetic Analysis, Pathway analysis, Text mining
Research Groups Computational Biomedicine and Health Data Science
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