  RNA-Seq analysis has revolutionized researchers' understanding of the
transcriptome in biological research. Assessing the differences in
transcriptomic profiles between tissue samples or patient groups enables
researchers to explore the underlying biological impact of transcription.
RNA-Seq analysis requires multiple processing steps and huge computational
capabilities. There are many well-developed R packages for individual steps;
however, there are few R/Bioconductor packages that integrate existing software
tools into a comprehensive RNA-Seq analysis and provide fundamental end-to-end
results in pure R environment so that researchers can quickly and easily get
fundamental information in big sequencing data. To address this need, we have
developed the open source R/Bioconductor package, RNASeqR. It allows users to
run an automated RNA-Seq analysis with only six steps, producing essential
tabular and graphical results for further biological interpretation. The
features of RNASeqR include: six-step analysis, comprehensive visualization,
background execution version, and the integration of both R and command-line
software. RNASeqR provides fast, light-weight, and easy-to-run RNA-Seq analysis
pipeline in pure R environment. It allows users to efficiently utilize popular
software tools, including both R/Bioconductor and command-line tools, without
predefining the resources or environments. RNASeqR is freely available for
Linux and macOS operating systems from Bioconductor
(https://bioconductor.org/packages/release/bioc/html/RNASeqR.html).
