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

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.

Compact Integration of Multi-Network Topology for Functional Analysis of Genes

“…Even with an optimized parameter setting for CliXO (Method Details), Mashup-based ontology achieved substantially higher alignment scores than CliXO in all three ontology categories: Mashup had a combined score of 0.35 whereas CliXO, 0.18 (Figure 4b). Furthermore, we observed similar improvement for Mashup on the YeastNet networks (Kim et al, 2013), the original data set used by Kramer et al (2014) to evaluate CliXO (Figure S8). …”

“…We first extracted compact topological representations with Mashup from the same set of four binary yeast PPI networks used by Dutkowski et al (2013), which consists of a physical interaction network, a genetic interaction network, a co-expression network, and a functional association network from YeastNet (Kim et al, 2013). While Dutkowski et al (2013) simply took the union of all edges to construct a combined network for clustering, we used topological features from Mashup’s integration to construct a gene ontology via a standard hierarchical agglomerative clustering algorithm.…”

Integrating Information in Biological Ontologies and Molecular Networks to Infer Novel Terms

“…We used four public yeast networks that had also been used for inferring GO in previous studies1112, namely 1) correlation network of genetic interactions from DRYGIN (http://drygin.ccbr.utoronto.ca/DOWNLOAD/sgadata_costanzo2010_correlations.txt.gz)18, co-expression network from Stanford Microarray Database (SMD)(Provided by Michael Kramer)19, probabilistic functional gene network from YeastNet (v3)(http://www.inetbio.org/yeastnet/download.php?type=1)20, and network of physical interactions (of types “direct interaction” and “physical association”) from BioGRID (http://thebiogrid.org/downloads/archives/Release%20Archive/BIOGRID-3.3.122/BIOGRID-ORGANISM-3.3.122.mitab.zip)21. We considered only genes with at least one GO annotation.…”

SoyNet: a database of co-functional networks for soybeanGlycine max

“…(ii) Networks inferred by genomic context similarity based on either gene neighborhood across bacterial genomes (14) or similarity of phylogenetic profiles (15), using 396 eukaryotic genomes and 1748 prokaryotic genomes. (iii) Networks inferred from evolutionarily conserved functional associations (associalogs) (16) in seven other species: Arabidopsis thaliana (17), Caenorhabditis elegans (18), Drosophila melanogaster (19), Danio rerio, Homo sapiens (20), Saccharomyces cerevisiae (21) and Oryza sativa (22). We transferred co-expression links, protein-protein interactions derived from the literature and high-throughput analysis, and genetic interactions, based on orthology relationships as measured by Inpranoid software version 4.1 (23).…”