Literature DB >> 25316676

Novel function discovery with GeneMANIA: a new integrated resource for gene function prediction in Escherichia coli.

James Vlasblom1, Khalid Zuberi1, Harold Rodriguez1, Roland Arnold1, Alla Gagarinova2, Viktor Deineko1, Ashwani Kumar2, Elisa Leung1, Kamran Rizzolo1, Bahram Samanfar1, Luke Chang1, Sadhna Phanse1, Ashkan Golshani1, Jack F Greenblatt2, Walid A Houry1, Andrew Emili2, Quaid Morris2, Gary Bader3, Mohan Babu1.   

Abstract

MOTIVATION: The model bacterium Escherichia coli is among the best studied prokaryotes, yet nearly half of its proteins are still of unknown biological function. This is despite a wealth of available large-scale physical and genetic interaction data. To address this, we extended the GeneMANIA function prediction web application developed for model eukaryotes to support E.coli.
RESULTS: We integrated 48 distinct E.coli functional interaction datasets and used the GeneMANIA algorithm to produce thousands of novel functional predictions and prioritize genes for further functional assays. Our analysis achieved cross-validation performance comparable to that reported for eukaryotic model organisms, and revealed new functions for previously uncharacterized genes in specific bioprocesses, including components required for cell adhesion, iron-sulphur complex assembly and ribosome biogenesis. The GeneMANIA approach for network-based function prediction provides an innovative new tool for probing mechanisms underlying bacterial bioprocesses. CONTACT: gary.bader@utoronto.ca; mohan.babu@uregina.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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Year:  2014        PMID: 25316676      PMCID: PMC4308668          DOI: 10.1093/bioinformatics/btu671

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  28 in total

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Journal:  PLoS Genet       Date:  2014-02-20       Impact factor: 5.917

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9.  Identification of Linkages between EDCs in Personal Care Products and Breast Cancer through Data Integration Combined with Gene Network Analysis.

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10.  Benchmarking selected computational gene network growing tools in context of virus-host interactions.

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