title: Predicting genes for orphan metabolic activities using phylogenetic profiles.
abstract: Homology-based methods fail to assign genes to many metabolic activities present in sequenced organisms. To suggest genes for these orphan activities we developed a novel method that efficiently combines local structure of a metabolic network with phylogenetic profiles. We validated our method using known metabolic genes in Saccharomyces cerevisiae and Escherichia coli. We show that our method should be easily transferable to other organisms, and that it is robust to errors in incomplete metabolic networks.
authors.affiliation: Center for Computational Biology and Bioinformatics and Department of Biomedical Informatics, Columbia University, St Nicholas Avenue, Irving Cancer Research Center, New York, NY 10032, USA. lifeng.chen@dbmi.columbia.edu
authors.forename: Lifeng
authors.initials: L
authors.lastname: Chen
authors.forename: Dennis
authors.initials: D
authors.lastname: Vitkup
mesh_terms: D030561:Databases, Nucleic Acid; D004926:Escherichia coli / Q000145:classification / Q000235:genetics* / Q000378:metabolism*; D020869:Gene Expression Profiling; D005798:Genes, Bacterial; D005800:Genes, Fungal; D008957:Models, Genetic; D005810:Multigene Family; D010802:Phylogeny; D011237:Predictive Value of Tests; D012441:Saccharomyces cerevisiae / Q000145:classification / Q000235:genetics* / Q000378:metabolism*