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

• Published in: Briefings in bioinformatics Vol. 19; no. 4; pp. 575 - 592
• Main Authors: van Dam, Sipko, Võsa, Urmo, van der Graaf, Adriaan, Franke, Lude, de Magalhães, João Pedro
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 10.01.2017; Oxford Publishing Limited (England)
• Subjects: Alternative splicing; Biological activity; Computational Biology - methods; Disease - classification; Disease - genetics; Gene expression; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; Gene sequencing; Genes; high-throughput nucleotide sequencing; Humans; Identification methods; Networks; Next-generation sequencing; Phenotype; Phenotypes; prediction; regulator genes; Ribonucleic acid; RNA; Transcription; transcription (genetics); transcriptomics; transcriptomics; functional genomics; network analysis; next-generation sequencing; disease gene prediction
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbw139

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.