WeGET: predicting new genes for molecular systems by weighted co-expression

Abstract

We have developed the Weighted Gene Expression Tool and database (WeGET, http://weget.cmbi.umcn.nl) for the prediction of new genes of a molecular system by correlated gene expression. WeGET utilizes a compendium of 465 human and 560 murine gene expression datasets that have been collected from multiple tissues under a wide range of experimental conditions. It exploits this abundance of expression data by assigning a high weight to datasets in which the known genes of a molecular system are harmoniously up- and down-regulated. WeGET ranks new candidate genes by calculating their weighted co-expression with that system. A weighted rank is calculated for human genes and their mouse orthologs. Then, an integrated gene rank and p-value is computed using a rank-order statistic. We applied our method to predict novel genes that have a high degree of co-expression with Gene Ontology terms and pathways from KEGG and Reactome. For each query set we provide a list of predicted novel genes, computed weights for transcription datasets used and cell and tissue types that contributed to the final predictions. The performance for each query set is assessed by 10-fold cross-validation. Finally, users can use the WeGET to predict novel genes that co-express with a custom query set.

Identification of Signaling Pathways Targeted by the Food Contaminant FB1: Transcriptome and Kinome Analysis of Samples from Pig Liver and Intestine

“…To further investigate the molecular mechanism of FB1, a protein–protein interaction (PPI) network of the differentially expressed genes and the differentially phosphorylated proteins was constructed (http://www.string-db.org/). Here, we selected gene–gene interactions with integrated scores greater than 0.4 (the default threshold in the STRING database) or highest confidence (0.9) to construct the PPI network and visualize it . After PPI building, results were analyzed and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment results of the highlighted network reported.…”

“…Here, we selected gene-gene interactions with integrated scores greater than 0.4 (the default threshold in the STRING database) or highest confidence (0.9) to construct the PPI network and visualize it. [31] After PPI building, results were analyzed and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment results of the highlighted network reported.…”

Large-scale analysis of disease pathways in the human interactome

“…The other two PPI networks are from the BioGRID database 25 and the STRING database. 26 Both of these networks are restricted to those edges that have been experimentally verified.…”

Identifying gene function and module connections by the integration of multi-species expression compendia

“…Traditionally, gene function has been elucidated through experimental approaches, including the evaluation of the phenotypic consequences of gain-or loss-of-function (G/LOF) mutations (Austin et al 2004;Dickinson et al 2016), or by genetic linkage or association studies (Williams and Auwerx 2015). A large number of bioinformatics tools have been developed to predict gene function based on sequence homology Radivojac et al 2013;Jiang et al 2016), protein structure (Roy et al 2010;Radivojac et al 2013;Jiang et al 2016), phylogenetic profiles Tabach et al 2013;Li et al 2014), protein-protein interactions (Rolland et al 2014;Hein et al 2015;Huttlin et al 2017), genetic interactions (Tong et al 2004;Costanzo et al 2010;Horlbeck et al 2018), and co-expression (Langfelder and Horvath 2008;Warde-Farley et al 2010;Greene et al 2015;van Dam et al 2015;Szklarczyk et al 2016;Li et al 2017;Obayashi et al 2019).…”

“…A few web resources, including GEO (Barrett et al 2013), ArrayExpress (Kolesnikov et al 2015), GeneNetwork (Chesler et al 2004), and Bgee (Bastian et al 2008) amongst others, have created repositories of such expression data for curation, reuse, and integration. Several tools, such as GeneMANIA (Warde-Farley et al 2010), GIANT , SEEK (Zhu et al 2015), GeneFriends (van Dam et al 2015), WeGET (Szklarczyk et al 2016), COXPRESdb (Obayashi et al 2019), WGCNA (Langfelder and Horvath 2008), and CLIC , are able to assign putative new functions to genes by means of correlations or co-expression networks. At their core, these methods rely on the concept of guilt-byassociation -that transcripts or proteins exhibiting similar expression patterns tend to be functionally related (Eisen et al 1998).…”