Unlocking the Potential of Low Quality Total RNA-seq Data: A Stepwise Mapping Approach for Improved Quantitative Analyses

High-throughput sequencing assays face persistent challenges when analyzing low-quality RNAs, often assessed by the RNA integrity number (RIN). Current preprocessing methods and pipelines designed for mRNA-seq presume high-quality RNAs, overlooking the nuanced complexities arising from degraded transcripts in low-quality samples. This study questions the applicability of standard analysis pipelines, especially when sequencing low-quality total RNAs, which are sometimes the sole recourse for specific inquiries. To address this, we conducted a comprehensive investigation using large sequencing reads obtained from blood biospecimens with varying RINs. Introducing a novel mapping approach, termed 'stepwise mapping', our systematic comparative analyses propose an optimal practice for total RNA-seq data analysis. Contrary to conventional mapping procedures, the 'stepwise mapping' approach unveils additional transcriptome information, crucial for stable differential expression analysis, even with total RNA-seq data from specimens with relatively low RINs. Our method proves particularly valuable when analyzing limited specimens with low RNA quality.