GeneFriends: a human RNA-seq-based gene and transcript co-expression database

Abstract

Co-expression networks have proven effective at assigning putative functions to genes based on the functional annotation of their co-expressed partners, in candidate gene prioritization studies and in improving our understanding of regulatory networks. The growing number of genome resequencing efforts and genome-wide association studies often identify loci containing novel genes and there is a need to infer their functions and interaction partners. To facilitate this we have expanded GeneFriends, an online database that allows users to identify co-expressed genes with one or more user-defined genes. This expansion entails an RNA-seq-based co-expression map that includes genes and transcripts that are not present in the microarray-based co-expression maps, including over 10 000 non-coding RNAs. The results users obtain from GeneFriends include a co-expression network as well as a summary of the functional enrichment among the co-expressed genes. Novel insights can be gathered from this database for different splice variants and ncRNAs, such as microRNAs and lincRNAs. Furthermore, our updated tool allows candidate transcripts to be linked to diseases and processes using a guilt-by-association approach. GeneFriends is freely available from http://www.GeneFriends.org and can be used to quickly identify and rank candidate targets relevant to the process or disease under study.

Integrative methods for analyzing big data in precision medicine

“…Link Content NCBI Gene [66] www.ncbi.nlm.nih.gov/gene Atlas of 59,500 human genes GOA [67] www.ebi.ac.uk/GOA 487,409 Gene Ontology annotations for 48,569 human gene products ENCODE [68] www.encodeproject.org Functional annotations of coding/non-coding DNA elements NCBI Epigenomics [69] www.ncbi.nlm.nih.gov/epigenomics 5,110 epigenetic modifications 4DGenome [70] 4dgenome [73] www.ebi.ac.uk/gxa Differential and baseline gene expression data CMAP [74] www.broadinstitute.org/cmap ∼ 7,000 expression profiles for 1,309 perturbagen compounds COXPRESdb [75] coxpresdb.jp Co-expression of 19,803 human genes GeneFriends [76] genefriends.org Co-expression of 159,184 human genes and transcripts UniProt [77] www.uniprot.org Information about human proteome (69,693 proteins) NeXtProt [78] www.nextprot.org Knowledgebase on 20,066 human proteins RCSB PDB [79] www.rcsb.org/pdb Portal to 113,494 biological macromolecular 3D-structures HPA [80] www.thehpp.org Maps of human proteome on 44 normal and 20 cancer type tissues IntAct [81] www.ebi.ac.uk/intact 209,852 human protein-protein interactions BioGrid [82] thebiogrid.org 215,952 human protein-protein interactions I2D [83] ophid.utoronto.ca 183,524 (+ 55,985 predicted) protein-protein interactions STRING [84] string-db.org 8,548,005 interactions between 20,457 proteins HMDB [85] www.hmdb.ca Atlas of 41,993 human metabolites KEGG Pathway [86] www.genome.jp/kegg/pathway 298 human pathways SMPD [87] www.smpdb.ca ∼700 human metabolic and disease pathways Reactome [88] www.reactome.org 8,770 reactions in 1,887 human pathways SugarBindDB [89] sugarbind.expasy.org 1,256 interactions between 200 glycans and 551 pathogenic agents UniCarbKB [90] www.unicarbkb.org 3,740 glycan structure entries and 400 glycoproteins KEGG Glycan [91] www.genome.jp/kegg/glycan/ Glycan metabolic pathways OMIM [92] www.omim.org Catalog of mendelian disorders and over 15,000 genes NCBI dbGaP [93] www.ncbi.nlm.nih.gov/gap Database of genotypes and phenotypes GWAS Catalog [94] www.ebi.ac.uk/gwas/ Genome wide association studies, assaying ∼ 100,000 SNPs COSMIC...…”

Proteome-level assessment of origin, prevalence and function of Leucine-Aspartic Acid (LD) motifs

“…4). We additionally assessed the LDMF predictions using four computational methods, namely PrePPI (a Bayesian framework that combines structural, functional, evolutionary and expression information 44 ), GeneFriends (an RNAseq-based gene co-expression network 45 ), CoCiter (which evaluates the significance of literature co-citations 46 ), and a GO term analysis. ( Supplementary Table 3).…”

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).…”

“…The key polarity of interactions is often lost among gene products and linked modules (Warde-Farley et al 2010;Greene et al 2015;van Dam et al 2015;Zhu et al 2015;Li et al 2017). Gene set analyses, such as gene set enrichment analysis (GSEA) (Subramanian et al 2005), have been developed to identify processes or modules that are affected by certain genetic or environmental perturbations (Khatri et al 2012).…”