Gene co-expression analysis for functional classification and gene–disease predictions

By Sipko van Dam, Urmo Võsa, Adriaan van der Graaf, Lude Franke, João Pedro de Magalhães. Briefings Bioinform., 2017

923 citations recorded by the source corpus.

Abstract Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/defdda7f3a58a5c1bb228bebcbe2fffa5a9975be

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In Molecular Biology

Citations in the corpus

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, background): “255 referencebased) transcriptome assembly [45] , allelespecific expression analyses [46] , expression quantitative trait loci mapping [47] , splicing [48] , analysis of gene regulatory networks [49] or pathway analyses [50,51] .”