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

Insuk Lee, Zhihua Li, Edward M. Marcotte

Research output: Contribution to journalArticle

150 Citations (Scopus)

Abstract

Background: 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. Methodology/Principal Findings: We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] 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. Conclusions/Significance: 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.

Original languageEnglish
Article numbere988
JournalPloS one
Volume2
Issue number10
DOIs
Publication statusPublished - 2007 Oct 3

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bakers yeast
Fungal Proteins
Gene Regulatory Networks
Genomics
Yeast
Saccharomyces cerevisiae
Eukaryotic Large Ribosome Subunits
Genes
RNA-Binding Proteins
Statistical Models
Proteome
Oligonucleotide Array Sequence Analysis
Proteomics
yeasts
genomics
probabilistic models
RNA-binding proteins
Proteins
proteins
system optimization

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

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abstract = "Background: 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. Methodology/Principal Findings: We report a significantly improved version (v. 2) of a probabilistic functional gene network [1] 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. Conclusions/Significance: 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.",
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An improved, bias-reduced probabilistic functional gene network of baker's yeast, Saccharomyces cerevisiae. / Lee, Insuk; Li, Zhihua; Marcotte, Edward M.

In: PloS one, Vol. 2, No. 10, e988, 03.10.2007.

Research output: Contribution to journalArticle

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