CleanUpRNAseq: An R/Bioconductor Package for Detecting and Correcting DNA Contamination in RNA-Seq Data

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• RNA sequencing (RNA-seq) is crucial for gene expression profiling.
• Genomic DNA (gDNA) contamination in RNA-seq libraries compromises data integrity.
• Accurate analysis is vital, especially for scarce RNA samples.

Purpose of the Study:

• To develop and validate a tool for detecting and correcting gDNA contamination in RNA-seq data.
• To address limitations in existing gDNA contamination correction methods.
• To enhance the utility of RNA-seq data by ensuring its integrity.

Main Methods:

• Development of the CleanUpRNAseq package with multiple correction algorithms.
• Implementation of distinct correction strategies for stranded and unstranded RNA-seq data.
• Rigorous validation using public datasets with known contamination levels and real-world data.

Main Results:

• CleanUpRNAseq effectively identifies and corrects gDNA contamination across various RNA-seq library protocols.
• Demonstrated efficacy in improving data quality for both stranded and unstranded RNA-seq.
• Validation confirmed the tool's performance on diverse datasets.

Conclusions:

• CleanUpRNAseq is a valuable tool for post-alignment quality control in RNA-seq.
• Integration into routine workflows, such as OneStopRNAseq, is recommended.
• The package significantly enhances the accuracy of gene expression and differential expression analyses.