This study provides a bioinformatic workflow for quality control of bulk RNA-seq data
Bulk RNA-seq experiments are widely used in molecular plant science to study transcriptome changes and regulatory pathways, making high-quality and reproducible data essential for progress in the field. Despite this importance, quality control measures are often underused or inconsistently applied. To address this gap, we present a stand-alone tutorial and software pipeline tailored for wet-lab biologists with basic R knowledge, enabling systematic quality assessment of bulk RNA-seq data. The pipeline includes tests for common issues such as low read numbers, poor mapping rates, contamination, rRNA content, RNA degradation, and replicate variability, while applying statistical models that correct for technical biases. By identifying experimental shortcomings before downstream analysis, the workflow improves data reliability, enhances confidence in RNA-seq studies, and lays the groundwork for future quality-control guidelines.
Bulk RNA-seq experiments are widely used in molecular plant science to study transcriptome changes and regulatory pathways, making high-quality and reproducible data essential for progress in the field. Despite this importance, quality control measures are often underused or inconsistently applied. To address this gap, we present a stand-alone tutorial and software pipeline tailored for wet-lab biologists with basic R knowledge, enabling systematic quality assessment of bulk RNA-seq data. The pipeline includes tests for common issues such as low read numbers, poor mapping rates, contamination, rRNA content, RNA degradation, and replicate variability, while applying statistical models that correct for technical biases. By identifying experimental shortcomings before downstream analysis, the workflow improves data reliability, enhances confidence in RNA-seq studies, and lays the groundwork for future quality-control guidelines.