Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Area of Science:

• Molecular Plant Science
• Genomics
• Bioinformatics

Background:

• Bulk RNA-sequencing (RNA-seq) is crucial for plant science research, enabling global transcriptome analysis.
• High-quality and reproducible RNA-seq data are essential for scientific advancement.
• Current quality control (QC) practices for RNA-seq datasets are often insufficient.

Purpose of the Study:

• To introduce Rup (RNA-seq usability assessment pipeline), a tool for QC of bulk RNA-seq data.
• To provide wet-lab biologists with an accessible R-based pipeline for assessing RNA-seq data quality.
• To improve the reliability and transparency of published RNA-seq data in plant science.

Main Methods:

• Rup is a stand-alone pipeline developed in R for ease of use by biologists.
• It incorporates tests for common RNA-seq issues: read counts, mapping rates, contamination, rRNA fraction, and replicate similarity.
• The pipeline includes intuitive visualizations and uses real data for demonstration.

Main Results:

• Rup effectively discriminates between high-quality and unsuitable RNA-seq data for downstream analyses.
• The pipeline identifies experimental shortcomings prior to standardized transcriptome analysis.
• It aids in quantifying rRNA contamination and assessing replicate similarity.

Conclusions:

• Rup enhances confidence in bulk RNA-seq data analysis by identifying experimental issues.
• The pipeline improves data quality for individual researchers and the broader scientific community.
• Rup provides a foundation for establishing minimum QC criteria for RNA-seq data publication.