Gene Co-expression Analysis for Functional Classification and Gene-disease Predictions

Overview

Journal Brief Bioinform

Publisher Oxford University Press

Specialty Biology

Date 2017 Jan 13

PMID 28077403

Citations 495

Authors

Sipko van Dam

Urmo Vosa

Adriaan van der Graaf

Lude Franke

Joao Pedro de Magalhaes

Affiliations

Soon will be listed here.

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.

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