YeastNet is

a probabilistic functional gene network for baker's yeast, Saccharomyces cerevisiae, which has been a major model organism for eukaryotic genetics and cell biology. Information from yeast biology has shed light on genetic of human diseases including cancer as well as core eukaryotic cellular processes such as transcription and translation. Huge amount of data derived from wide variety of experimental and computational studies have been infiltrated into the YeastNet. Therefore, this single gene network provides an integrated and comprehensive information platform to facilitate various biological studies. The current version of YeastNet (v3) maps 362,512 functional couplings among 5,818 coding genes (covers ~99% of all yeast coding genes) and a total number of links from all data-specific networks is about 2 millions. Edge information for all data-specific networks as well as the integrated network is available from Download page. Previous version of YeastNet(v2) is located here.

Data-specific networks were integrated by modified Bayesian integration method to construct YeastNet. Log likelihood scores were assigned for network links by Bayesian statistics framework (Lee et al. Science 2004, Lee et al. PLoS One 2007). LLS = 0 means the likelihood of two genes being functionally coupled is no better than random expectation. Gold standard gene associations (available from Download page) for likelihood scoring were derived from Gene Ontology biological process (IDA, IMP, IGI, IPI evidences only) and BioCyc/MetaCyc annotations.

Various network biology approaches such as guilt-by-association have been widely employed in generating novel hypotheses for gene functions and gene-phenotype associations (McGary et al. Genome Biology 2007, Li et al. PLoS Biology 2007). This web site also provides such network biology tools to (i) find new members of a pathway, (ii) infer functions from network neighbors, (iii) Find modulators for a cell state.

All edges have not only likelihood scores but also code for supporting evidence. The used evidence codes are listed with brief description below.

Evidence code Data set description
CC Inferred links by co-citation of two genes across 46,111 pubmed Medline article abstracts for yeast biology
CX Inferred links by co-expression pattern of two genes (based on high-dimensional gene expression data)
DC Inferred links by co-occurrence of protein domains between two coding genes
GN Inferred links by similar genomic context of bacterial orthologs of two yeast genes
GT Inferred links by similar profiles of genetic interaction partners
HT Links by high-throughput protein-protein interactions
LC Links by small/medium-scale protein-protein interactions (collected from protein-protein interaction data bases)
PG Inferred links by similar phylogenetic profiles between two yeast genes
TS Inferred links by 3-D protein structure of interacting orthologous proteins between two yeast proteins

Hanhae Kim, Junha Shin, Eiru Kim, Hyojin Kim, Sohyun Hwang, Jung Eun Shim, Insuk Lee, YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae. Nucl. Acids Res. (1 Januray 2014) 42 (D1): D731-D736 first published online October 27, 2013 (Link)

Contact information
Insuk Lee (insuklee (at)

This work was supported by the National Research Foundation of Korea grant (2010-0017649, 2012M3A9B4028641, 2012M3A9C7050151).