An improved, bias-reduced probabilistic functional gene network of baker's yeast, Saccharomyces cerevisiae

• Published in: PloS one 2007-10, Vol.2 (10), p.e988-e988
• Main Authors: Lee, Insuk, Li, Zhihua, Marcotte, Edward M
• Format: Article
• Language: English
• Subjects: Baking yeast; Bias; Biochemistry/Bioinformatics; Biochemistry/Macromolecular Assemblies and Machines; Bioinformatics; Biosynthesis; Cell cycle; Cellular communication; Cluster analysis; Comparative analysis; Computational Biology; Computational Biology/Genomics; Computational Biology/Literature Analysis; Computational Biology/Systems Biology; Cytochrome; Data processing; Datasets; Deoxyribonucleic acid; DNA; DNA microarrays; False Positive Reactions; Gene expression; Genes; Genetics and Genomics; Genetics and Genomics/Bioinformatics; Genetics and Genomics/Comparative Genomics; Genetics and Genomics/Functional Genomics; Genetics and Genomics/Gene Function; Genetics and Genomics/Genomics; Genome, Fungal; Genomes; Genomics; Genomics - methods; Information retrieval; Linkages; Messenger RNA; Metabolism; Models, Biological; Models, Genetic; Models, Statistical; Molecular biology; Molecular Biology/Bioinformatics; mRNA; Mutation; Oligonucleotide Array Sequence Analysis; Optimization; Oxidative stress; Probabilistic models; Probability; Protein arrays; Protein binding; Protein interaction; Protein Interaction Mapping; Proteins; Proteomics; Proteomics - methods; Reduction; Ribonucleic acid; RNA; RNA, Messenger - metabolism; RNA-binding protein; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - physiology; Synthetic biology; Telecommunications systems; Trends; Yeast
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0000988

Probabilistic functional gene networks are powerful theoretical frameworks for integrating heterogeneous functional genomics and proteomics data into objective models of cellular systems. Such networks provide syntheses of millions of discrete experimental observations, spanning DNA microarray experiments, physical protein interactions, genetic interactions, and comparative genomics; the resulting networks can then be easily applied to generate testable hypotheses regarding specific gene functions and associations. We report a significantly improved version (v. 2) of a probabilistic functional gene network of the baker's yeast, Saccharomyces cerevisiae. We describe our optimization methods and illustrate their effects in three major areas: the reduction of functional bias in network training reference sets, the application of a probabilistic model for calculating confidences in pair-wise protein physical or genetic interactions, and the introduction of simple thresholds that eliminate many false positive mRNA co-expression relationships. Using the network, we predict and experimentally verify the function of the yeast RNA binding protein Puf6 in 60S ribosomal subunit biogenesis. YeastNet v. 2, constructed using these optimizations together with additional data, shows significant reduction in bias and improvements in precision and recall, in total covering 102,803 linkages among 5,483 yeast proteins (95% of the validated proteome). YeastNet is available from http://www.yeastnet.org.