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RSEQREP: RNA-Seq Reports, an open-source cloud-enabled framework for reproducible RNA-Seq data processing, analysis,

Travis L Jensen1, Michael Frasketi2, Kevin Conway2

  • 1Vaccine and Infectious Disease Department , The Emmes Corporation, Rockville, MD, USA.

F1000Research
|July 24, 2018
PubMed
Summary
This summary is machine-generated.

RNA-Seq Reports (RSEQREP) is an open-source framework for comprehensive RNA sequencing analysis. It identifies treatment-responsive genes and provides insights into molecular mechanisms, biomarkers, and personalized medicine.

Keywords:
RNA-SeqRSEQREPcloud computingdifferential gene expressionpathway enrichmentreproducible researchtranscriptomicstrivalent influenza vaccine

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • RNA sequencing (RNA-Seq) is vital for genome-wide human RNA expression analysis.
  • Expression profiles reveal treatment-responsive genes, offering insights into molecular mechanisms, biomarkers, and personalized medicine.

Purpose of the Study:

  • Introduce RNA-Seq Reports (RSEQREP), an open-source, cloud-enabled framework for end-to-end gene-level RNA-Seq analysis.
  • Facilitate the identification and functional characterization of treatment-responsive genes.
  • Support personalized medicine by providing tools for molecular mechanism and biomarker discovery.

Main Methods:

  • RSEQREP framework executes customizable analysis steps: reference alignment, CRAM compression, QC, normalization, visualization, differential expression analysis, clustering, and pathway enrichment.
  • Supports various input FASTQ file types (unstranded, stranded, paired-end) and storage locations (local, S3, SRA).
  • Operates via an Amazon Machine Image (AMI) on AWS or a Docker container/installation script on Ubuntu Linux.

Main Results:

  • RSEQREP generates a comprehensive output collection, including a dynamic PDF report (R, knitr, LaTeX), publication-ready tables, and figures.
  • The framework accommodates complex experimental designs, including time-series data with pre- and post-treatment samples, multiple treatment groups, and specimen types.
  • Demonstrated utility with a trivalent influenza vaccine (TIV) study analyzing peripheral blood mononuclear cells and B-cells from 5 subjects over 10 days post-vaccination.

Conclusions:

  • RSEQREP provides a robust, user-friendly, and customizable platform for RNA-Seq data analysis.
  • Enables deeper understanding of treatment effects, biomarker identification, and advances personalized medicine.
  • Built with open-source R code and R/Bioconductor packages for maximum flexibility and extensibility.