title: Systematic condition-dependent annotation of metabolic genes.
abstract: The task of deriving a functional annotation for genes is complex as their involvement in various processes depends on multiple factors such as environmental conditions and genetic backup mechanisms. This study employs a large-scale model of the metabolism of Saccharomyces cerevisiae to investigate the function of yeast genes and derive a condition-dependent annotation (CDA) for their involvement in major metabolic processes under various genetic and environmental conditions. The resulting CDA is validated on a large scale and is shown to be superior to the corresponding Gene Ontology (GO) annotation, by showing that genes annotated with the same CDA term tend to be more coherently conserved in evolution and display greater expression coherency than those annotated with the same GO term. The CDA gives rise to new kinds of functional condition-dependent metabolic pathways, some of which are described and further examined via substrate auxotrophy measurements of knocked-out strains. The CDA presented is likely to serve as a new reference source for metabolic gene annotation.
authors: School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. shlomito@post.tau.ac.il
authors: Tomer
authors: T
authors: Shlomi
authors: Markus
authors: M
authors: Herrgard
authors: Vasiliy
authors: V
authors: Portnoy
authors: Efrat
authors: E
authors: Naim
authors: Bernhard Ø
authors: BØ
authors: Palsson
authors: Roded
authors: R
authors: Sharan
authors: Eytan
authors: E
authors: Ruppin
mesh_terms: D019295:Computational Biology; D030541:Databases, Genetic; D030562:Databases, Protein; D005800:Genes, Fungal; D010802:Phylogeny; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism*