RNASequest: An End-to-End Reproducible RNAseq Data Analysis and Publishing Framework

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA sequencing (RNAseq) is a crucial technique for gene expression analysis.
• Existing RNAseq analysis pipelines can be complex and lack integrated result presentation.
• Efficient data management and reproducible analysis are essential for omics research.

Purpose of the Study:

• To develop RNASequest, a customizable framework for RNA sequencing analysis, app management, and result publishing.
• To automate differential gene expression analysis and simplify statistical model design.
• To provide a centralized system for managing omics data and generated reports.

Main Methods:

• RNASequest integrates a reproducible expression analysis (EA) module.
• Results are presented via R Shiny, Bookdown documents, and online slide decks.
• A centralized data management system and the ShinyOne web tool are included for app and report management.

Main Results:

• RNASequest automates differential gene expression analysis and facilitates covariate testing.
• The framework offers multi-faceted result presentation, enhancing data visualization.
• ShinyOne enables centralized management of Quickomics apps and reports across multiple projects.

Conclusions:

• RNASequest offers a unified ecosystem for RNAseq data analysis, management, and publication.
• The framework enhances reproducibility and simplifies the interpretation of gene expression data.
• Researchers can utilize RNASequest for efficient processing and presentation of private RNAseq datasets.