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dc.contributor.authorTian, Weidong
dc.contributor.authorArakaki, Adrian K.
dc.contributor.authorSkolnick, Jeffrey
dc.date.accessioned2009-01-27T21:11:03Z
dc.date.available2009-01-27T21:11:03Z
dc.date.issued2004-12-01
dc.identifier.citationNucleic Acids Research 2004 32(21):6226-6239
dc.identifier.issn0305-1048
dc.identifier.urihttp://hdl.handle.net/1853/26728
dc.description©2004 Oxford University Press. The definitive version is available online at: http://nar.oxfordjournals.org/cgi/content/full/32/21/6226.
dc.descriptiondoi:10.1093/nar/gkh956
dc.description.abstractEFICAz (Enzyme Function Inference by Combined Approach) is an automatic engine for large-scale enzyme function inference that combines predictions from four different methods developed and optimized to achieve high prediction accuracy: (i) recognition of functionally discriminating residues (FDRs) in enzyme families obtained by a Conservation controlled HMM Iterative procedure for Enzyme Family classification (CHIEFc), (ii) pairwise sequence comparison using a family specific Sequence Identity Threshold, (iii) recognition of FDRs in Multiple Pfam enzyme families, and (iv) recognition of multiple Prosite patterns of high specificity. For FDR (i.e. conserved positions in an enzyme family that discriminate between true and false members of the family) identification, we have developed an Evolutionary Footprinting method that uses evolutionary information from homofunctional and heterofunctional multiple sequence alignments associated with an enzyme family. The FDRs show a significant correlation with annotated active site residues. In a jackknife test, EFICAz shows high accuracy (92%) and sensitivity (82%) for predicting four EC digits in testing sequences that are ,40% identical to any member of the corresponding training set. Applied to Escherichia coli genome, EFICAz assigns more detailed enzymatic function than KEGG, and generates numerous novel predictions.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectEFICAzen
dc.subjectEnzyme function inferenceen
dc.subjectEnzyme family classification
dc.subjectSequence identity threshold
dc.subjectEnzyme Function Inference by Combined Approach
dc.titleEFICAz: a comprehensive approach for accurate genome-scale enzyme function inferenceen
dc.typeArticleen
dc.contributor.corporatenameWashington University (Saint Louis, Mo.). Dept. of Biology
dc.contributor.corporatenameState University of New York at Buffalo. Center of Excellence in Bioinformatics
dc.publisher.originalOxford University Press


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