RSEQREP

RSEQREP performs comprehensive gene-level RNA-Seq analysis to identify differentially expressed genes, characterize co-expression and pathway enrichment, and produce reproducible reports from unstranded, stranded, and paired-end FASTQ or archived data in the SRA or on Amazon S3.

Key Features:

• Automated workflow: Executes reference alignment, CRAM compression, quality control of alignments, data normalization, and multivariate data visualization in an automated pipeline.
• Differential expression analysis: Identifies differentially expressed genes to support discovery of treatment-responsive genes and functional characterization.
• Data visualization: Produces heatmaps, co-expressed gene cluster analyses, enriched pathway visualizations, and supports custom visualizations.
• Dynamic reporting: Generates publication-ready tables and figures and produces PDF reports via R, knitr, and LaTeX.
• Input data sources and formats: Accepts unstranded, stranded, and paired-end FASTQ and can process data stored locally, in the Sequence Read Archive (SRA), or on Amazon S3.
• Configuration and experimental design support: Uses a configuration file to capture metadata and supports complex experimental designs including time series, multiple treatment groups, specimen types, and pre- and post-treatment samples across subjects.
• Implementation and customization: Implemented in R using R/Bioconductor packages with open-source R code enabling customization.

Scientific Applications:

• Treatment response and mechanism discovery: Identification and characterization of molecular mechanisms underlying treatment effects and temporal responses.
• Biomarker identification: Discovery of candidate biomarkers from differential expression and co-expression analyses.
• Vaccine and immunological studies: Profiling of vaccine responses, exemplified by application to a trivalent influenza vaccine (TIV) pre- and post-vaccination RNA-Seq study across multiple subjects and specimen types.

Methodology:

Processes unstranded/stranded/paired-end FASTQ or archived SRA/S3 data, performs reference alignment, applies CRAM compression and alignment quality control, conducts data normalization and differential expression analysis, generates multivariate visualizations (heatmaps, co-expression clusters, pathway enrichment), and compiles reports using R, knitr, and LaTeX into PDF outputs.