YeastNet v3: a public database of data-specific and integrated functional gene networks forSaccharomyces cerevisiae

Hanhae Kim,Junha Shin,Jung Eun Shim,Sohyun Hwang,Hyojin Kim,Eiru Kim,Insuk Lee

doi:10.1093/nar/gkt981

Abstract

Saccharomyces cerevisiae, i.e. baker’s yeast, is a widely studied model organism in eukaryote genetics because of its simple protocols for genetic manipulation and phenotype profiling. The high abundance of publicly available data that has been generated through diverse ‘omics’ approaches has led to the use of yeast for many systems biology studies, including large-scale gene network modeling to better understand the molecular basis of the cellular phenotype. We have previously developed a genome-scale gene network for yeast, YeastNet v2, which has been used for various genetics and systems biology studies. Here, we present an updated version, YeastNet v3 (available at http://www.inetbio.org/yeastnet/), that significantly improves the prediction of gene–phenotype associations. The extended genome in YeastNet v3 covers up to 5818 genes (∼99% of the coding genome) wired by 362 512 functional links. YeastNet v3 provides a new web interface to run the tools for network-guided hypothesis generations. YeastNet v3 also provides edge information for all data-specific networks (∼2 million functional links) as well as the integrated networks. Therefore, users can construct alternative versions of the integrated network by applying their own data integration algorithm to the same data-specific links.

Highlights

The complete mapping of gene-to-phenotype associations, which is a fundamental goal in the field of genetics, seems unconquerable due in part to the complex functional relationship among genes
To determine whether the rewired and additional functional links of YeastNet v3 improved the prediction of pathways and phenotypes in yeast, we assessed the predictive power of YeastNet v3 on different data sets
YeastNet v3 was assessed by pathway annotations in the Kyoto Encyclopedia of Genes and Genomes (KEGG) [8] database to test whether the measured accuracy of cofunctional links can be generalized

Summary

Introduction

The complete mapping of gene-to-phenotype associations, which is a fundamental goal in the field of genetics, seems unconquerable due in part to the complex functional relationship among genes. YeastNet v2 included 5483 coding genes with 102 803 cofunctional links and showed high predictive power for independent pathway annotations and knockout (KO) phenotypes [4].

Results

Conclusion