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!align=left {{table heading style}} |Link/URL:
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http://ecoli.med.utoronto.ca/
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!align=left {{table heading style}} |What:
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Gene function prediction and annotation of ''Escherichia coli'' K12 proteins using physical protein-protein interactions, genomic context methods and machine learning algorithms
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!align=left {{table heading style}} |Who:
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Emili Lab @ University of Toronto, and
Moreno-Hagelsieb Lab @ Wilfrid Laurier University
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!align=left {{table heading style}} |Updates:
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Javier Diaz (Jdime) - Jun 29, 2009
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!align=left {{table heading style}} |Upcoming events:
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Published paper (Hu et al. 2009) <ref name='PMID:19402753'/>
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!align=left {{table heading style}} |Web Services:
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==About eNet==
[http://ecoli.med.utoronto.ca/ eNet] is a database of '''gene function prediction in ''Escherichia coli'' K12'''. It exploits the topological properties of physical and functional protein interaction networks integrated via a machine learning algorithm explicitly developed for eNet. eNet provides specific protein functional prediction 'labels' using ontology resources (e.g. [http://www.geneontology.org/ GO], [http://genprotec.mbl.edu/ MultiFun] and [http://www.ncbi.nlm.nih.gov/COG/grace/uni.html COGs] terms) for hypothesis-driven following-up studies. eNet also provides the underlying actual protein interactions and companion groupings of proteins (e.g. multimeric complexes and functional modules) delimited optimizing the [http://www.micans.org/mcl/ Markov clustering algorithm]. All eNet datasets and predictions are freely available and can be searched, browsed and downloaded in eNet dedicated web portal [http://ecoli.med.utoronto.ca/ http://ecoli.med.utoronto.ca/] <ref name='PMID:19402753'/>.

==Content==
Over one-third of the 4,225 protein-coding genes of ''E. coli'' K-12 remain functionally unannotated (orphans). Many map to distant clades like Archaea, suggesting involvement in basic prokaryotic traits, while others appear restricted to E. coli, including pathogenic strains. '''To elucidate biological roles of these orphans, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome''' consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated genes, while '''a machine-learning strategy based on network integration implicated the orphans in specific biological processes''', such as protein synthesis, amino acid metabolism, biofilm formation, motility, assembly of the bacterial cell envelope, etc. This resource provides a "systems-wide" functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins.

From this website, you can access and download annotations, interactions (proteomic and genomic context-derived) and function predictions for all ''E. coli'' proteins analyzed in this study. Also you can explore protein complexes (from proteomic network), functional modules (from genomic context), and functional neighborhoods (from function predictions and preexisting annotations).

For more details you can visit eNet webpage:
http://ecoli.med.utoronto.ca/

or take a look at our publication:
http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1000096

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==External Links==
eNet: http://ecoli.med.utoronto.ca/<br />
Emili lab @ University of Toronto: http://www.utoronto.ca/emililab/<br />
Moreno-Hagelsieb lab @ Wilfrid Laurier University: http://popolvuh.wlu.ca/
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