RNA sequencing has become an increasingly affordable way to profile gene expression patterns. Here researchers from the Ohio State University introduce a workflow implementing several open-source softwares that can be run on a high performance computing environment.
Developed as a tool by the Bioinformatics Shared Resource Group (BISR) at the Ohio State University, the researchers have applied the pipeline to a few publicly available RNAseq datasets downloaded from GEO in order to demonstrate the feasibility of this workflow.
The overall design of the BISR RNASeq shiny app
a Data gathering: The 3 inputs files that BISR shiny app takes as inputs (1) config.json file, that defines the shiny UI (2) a .Rds object generated by custom R script run on RNAseq pipeline output (3) files relevant to the project that are generated as Rmarkdown or html files. These three items are sent into the app which is made up of the following components b A screen shot of BISR RNAseq report
Availability – Source code is available here: workflow: https://code.bmi.osumc.edu/gadepalli.3/BISR-RNAseq-ICIBM2019 and shiny: https://code.bmi.osumc.edu/gadepalli.3/BISR_RNASeq_ICIBM19. Example dataset is demonstrated here: https://dataportal.bmi.osumc.edu/RNA_Seq/.
The workflow allows for the analysis (alignment, QC, gene-wise counts generation) of raw RNAseq data and seamless integration of quality analysis and differential expression results into a configurable R shiny web application.