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BISR-RNAseq: an efficient and scalable RNAseq analysis workflow with interactive report generation

Authors
  • Gadepalli, Venkat Sundar1, 1, 1
  • Ozer, Hatice Gulcin1, 1, 1
  • Yilmaz, Ayse Selen1, 1, 1
  • Pietrzak, Maciej1, 1, 1
  • Webb, Amy1, 1, 1
  • 1 The Ohio State University, Columbus, OH, USA , Columbus (United States)
Type
Published Article
Journal
BMC Bioinformatics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Dec 20, 2019
Volume
20
Issue
Suppl 24
Identifiers
DOI: 10.1186/s12859-019-3251-1
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundRNA sequencing has become an increasingly affordable way to profile gene expression patterns. Here we introduce a workflow implementing several open-source softwares that can be run on a high performance computing environment.ResultsDeveloped as a tool by the Bioinformatics Shared Resource Group (BISR) at the Ohio State University, we have applied the pipeline to a few publicly available RNAseq datasets downloaded from GEO in order to demonstrate the feasibility of this workflow. 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/.ConclusionThe 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.

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