Genome-scale gene function prediction using multiple sources of high-throughput data in yeast Saccharomyces cerevisiae.
Characterizing gene function is one of the major challenging tasks in the post-genomic era. To address this challenge, we have developed GeneFAS (Gene Function Annotation System), a new integrated probabilistic method for cellular function prediction by combining information from protein-protein interactions, protein complexes, microarray gene expression profiles, and annotations of known proteins through an integrative statistical model. Our approach is based on a novel assessment for the relationship between (1) the interaction/correlation of two proteins' high-throughput data and (2) their functional relationship in terms of their Gene Ontology (GO) hierarchy. We have developed a Web server for the predictions. We have applied our method to yeast Saccharomyces cerevisiae and predicted functions for 1548 out of 2472 unannotated proteins.
Joshi
Trupti
T
Digital Biology Laboratory, Computer Science Department, University of Missouri-Columbia, Columbia, Missouri 65211, USA.
Chen
Yu
Y
Becker
Jeffrey M
JM
Alexandrov
Nickolai
N
Xu
Dong
D
D001331:Automation; D001499:Bayes Theorem; D019992:Databases as Topic; D030562:Databases, Protein; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal; D016678:Genome; D016681:Genome, Fungal; D020407:Internet; D046911:Macromolecular Substances; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D016366:Open Reading Frames; D010802:Phylogeny; D011485:Protein Binding; D025941:Protein Interaction Mapping; D017434:Protein Structure, Tertiary; D040901:Proteomics / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry*; D012680:Sensitivity and Specificity; D012984:Software; D020798:Two-Hybrid System Techniques
D046911:Macromolecular Substances; D029701:Saccharomyces cerevisiae Proteins