Interpretation of differential gene expression results of RNA-seq data: review and integration

• Published in: Briefings in bioinformatics Vol. 20; no. 6; pp. 2044 - 2054
• Main Authors: McDermaid, Adam, Monier, Brandon, Zhao, Jing, Liu, Bingqiang, Ma, Qin
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 27.11.2019
• Subjects: bioinformatics; Computational Biology - methods; Gene Expression; gene expression regulation; High-Throughput Nucleotide Sequencing - methods; humans; Malus domestica; Review; sequence analysis; Sequence Analysis, RNA; Vitis riparia; differentially expressed genes; visualization and interpretation; differential gene expression analysis; bioinformatics tools; R/Bioconductor package
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bby067

Abstract Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many applications of RNA-seq data analysis. Interpretation of the DGE results can be nonintuitive and time consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. Here we reviewed DGE results analysis from a functional point of view for various visualizations. We also provide an R/Bioconductor package, Visualization of Differential Gene Expression Results using R, which generates information-rich visualizations for the interpretation of DGE results from three widely used tools, Cuffdiff, DESeq2 and edgeR. The implemented functions are also tested on five real-world data sets, consisting of one human, one Malus domestica and three Vitis riparia data sets.