Title:
EFICAz: a comprehensive approach for accurate genome-scale enzyme function inference
EFICAz: a comprehensive approach for accurate genome-scale enzyme function inference
| dc.contributor.author | Tian, Weidong | |
| dc.contributor.author | Arakaki, Adrian K. | |
| dc.contributor.author | Skolnick, Jeffrey | |
| dc.contributor.corporatename | Washington University (Saint Louis, Mo.). Dept. of Biology | |
| dc.contributor.corporatename | State University of New York at Buffalo. Center of Excellence in Bioinformatics | |
| dc.date.accessioned | 2009-01-27T21:11:03Z | |
| dc.date.available | 2009-01-27T21:11:03Z | |
| dc.date.issued | 2004-12-01 | |
| dc.description | ©2004 Oxford University Press. The definitive version is available online at: http://nar.oxfordjournals.org/cgi/content/full/32/21/6226. | |
| dc.description | doi:10.1093/nar/gkh956 | |
| dc.description.abstract | EFICAz (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.identifier.citation | Nucleic Acids Research 2004 32(21):6226-6239 | |
| dc.identifier.issn | 0305-1048 | |
| dc.identifier.uri | http://hdl.handle.net/1853/26728 | |
| dc.language.iso | en_US | en |
| dc.publisher | Georgia Institute of Technology | en |
| dc.publisher.original | Oxford University Press | |
| dc.subject | EFICAz | en |
| dc.subject | Enzyme function inference | en |
| dc.subject | Enzyme family classification | |
| dc.subject | Sequence identity threshold | |
| dc.subject | Enzyme Function Inference by Combined Approach | |
| dc.title | EFICAz: a comprehensive approach for accurate genome-scale enzyme function inference | en |
| dc.type | Text | |
| dc.type.genre | Article | |
| dspace.entity.type | Publication | |
| local.contributor.author | Skolnick, Jeffrey | |
| local.contributor.corporatename | College of Sciences | |
| local.contributor.corporatename | School of Biological Sciences | |
| local.contributor.corporatename | Center for the Study of Systems Biology | |
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