Advantages of CEMiTool for gene co-expression analysis of RNA-seq data. (October 2020)

• Record Type: Journal Article
• Authors: Cheng, Chew Weng; Beech, David J.; Wheatcroft, Stephen B.
• Is Part Of: Computers in biology and medicine. Volume 125(2020)
• Publication Date: 2020-10
• Subjects: RNA-seq -- Co-expression -- Ischemic cardiomyopathy -- Chronic obstructive pulmonary Disease -- WGCNA -- CEMiTool -- Coseq; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2020.103975
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Gene co-expression analysis is widely applied to transcriptomics data to associate clusters of genes with biological functions or identify therapeutic targets in diseases. Recently, the emergence of high-throughput technologies for gene expression analyses allows researchers to establish connections through gene co-expression analysis to identify clinical disease markers. However, gene co-expression analysis is complex and may be a daunting task. Here, we evaluate three co-expression analysis packages ( WGCNA, CEMiTool, and coseq ) using published RNA-seq datasets derived from ischemic cardiomyopathy and chronic obstructive pulmonary disease. Results show that the packages produced consensus co-expression clusters using default parameters. CEMiTool package outperformed the other two packages and required less computational resource and bioinformatics experience. This evaluation provides a basis on which data analysts can select bioinformatics tools for gene co-expression analysis. Highlights: Comprehensive analysis of three co-expression analysis packages; WGCNA, CEMiTool, and coseq. Three packages generated biologically relevant findings. CEMiTool outperforms other packages with high reproducibility, ease of use, and simple pipeline.