GSEPD: a Bioconductor package for RNA-seq gene set enrichment and projection display

BMC Bioinformatics

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[1] 0000 0001 2111 8460, grid.30760.32, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA;0000 0001 2369 3143, grid.259670.f, Department of Mathematics, Statistics and Computer Science, Marquette University, Milwaukee, WI, USA;0000 0001 2369 3143, grid.259670.f, Department of Mathematics, Statistics and Computer Science, Marquette University, Milwaukee, WI, USA;0000 0001 2111 8460, grid.30760.32, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA;

关键词: RNA-Seq;    Transcriptome;    Gene ontology;    Differential gene expression;    Clustering;    Visualization;    Bioconductor;   

DOI  :  10.1186/s12859-019-2697-5

来源: publisher

【 摘 要 】

BackgroundRNA-seq, wherein RNA transcripts expressed in a sample are sequenced and quantified, has become a widely used technique to study disease and development. With RNA-seq, transcription abundance can be measured, differential expression genes between groups and functional enrichment of those genes can be computed. However, biological insights from RNA-seq are often limited by computational analysis and the enormous volume of resulting data, preventing facile and meaningful review and interpretation of gene expression profiles. Particularly, in cases where the samples under study exhibit uncontrolled variation, deeper analysis of functional enrichment would be necessary to visualize samples’ gene expression activity under each biological function.ResultsWe developed a Bioconductor package rgsepd that streamlines RNA-seq data analysis by wrapping commonly used tools DESeq2 and GOSeq in a user-friendly interface and performs a gene-subset linear projection to cluster heterogeneous samples by Gene Ontology (GO) terms. Rgsepd computes significantly enriched GO terms for each experimental condition and generates multidimensional projection plots highlighting how each predefined gene set’s multidimensional expression may delineate samples.ConclusionsThe rgsepd serves to automate differential expression, functional annotation, and exploratory data analyses to highlight subtle expression differences among samples based on each significant biological function.

【 授权许可】

CC BY