Know your RNA-Seq data in depth: a case study using data from early life stress in mouse

Next-generation sequencing (NGS) is a technology that enables rapid and high-throughput sequencing of entire genomes, transcriptomes or specific DNA/RNA populations. RNA-Seq is an NGS-based method that specifically targets the transcriptome and can be applied to bulk tissue or single cells. NGS produces large volumes of partial sequences (reads), which must be aligned, assembled and analyzed to extract meaningful biological information such as gene expression and genetic variants. However, NGS data often contain noise and errors due to technical factors like PCR bias, contamination or alignment inaccuracies. Understanding and managing this noise is important for ensuring the reliability of results, especially in clinical or diagnostic contexts. Quality control is a critical step in the data analysis process to ensure the accuracy, reliability and reproducibility of sequencing outcomes. In this study, a detailed quality assessment of RNA-Seq data is presented using a publicly available dataset from Usui et al. (2021). Read alignment was performed with the BWA-MEM2 tool. Quality control included analysis of reports generated by the FastQC and MultiQC tools, followed by in-depth examination of information contained in the resulting SAM/BAM files. Specifically, read alignments were evaluated for the FLAG status of paired reads, variant information extracted from CIGAR and MD strings, Mapped and Matched Identity metrics, chromosomal distribution of mapped reads and nucleotide-level mapping. This comprehensive analysis highlights the importance of variant profiling and alignment quality metrics in ensuring the reliability of RNA-Seq data.