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

Modern approaches in molecular plant science often require bulk RNA-seq experiments, for example, to track global changes in transcriptomes upon treatments or to identify key components of regulatory pathways. Consequently, diverse areas of plant sciences rely on high-quality and reproducible bulk RNA-seq data for scientific advancement. However, from our experience, knowledge about and application of quality control measures in RNA-seq datasets are often lacking. Here, we introduce Rup (RNA-seq usability assessment pipeline) for the quality control of bulk RNA-seq data, for subsequent gene expression analyses, that is stand-alone and readily applicable for wet-lab biologists with basic knowledge of R. Rup helps to discriminate between sequencing data of high-quality, suitable for downstream gene expression experiments, and those unsuitable for general further analysis. Rup includes tests for several commonly encountered problems such as insufficient read numbers or mapping, identification of contaminations, quantification of rRNA fractions in the total RNA-seq data, replicate similarity testing, using real data for demonstration and offering intuitive visualization. Rup provides a suite of tools to identify experimental shortcomings before standardized transcriptome analysis, thereby improving data quality for individual researchers and the field. This enhances confidence in bulk RNA-seq data analysis and provides a foundation for future guidelines defining minimum quality control criteria, thus improving the reliability and transparency of published RNA-seq data. Rup and test data are available at https://github.com/oliverrupp/rup.