Quality control (QC) is a critical step in RNA sequencing (RNA-seq). Yet, it is often ignored or conducted on a limited basis. Here, Vanderbilt University researchers present a multi-perspective strategy for QC of RNA-seq experiments. The QC of RNA-seq can be divided into four related stages:
- RNA quality – the integrity of the RNA is the most important criterion for obtaining good quality data
- raw read data (FASTQ) – at the raw data level, the most common parameters to examine are the total number of reads sequenced, GC content and the overall base quality score, which are all commonly computed by standard raw data QC tools
- alignment – the distribution of MAPQ can paint a picture of overall alignment quality
- gene expression– using clustering as a QC measure will require an unbiased and unsupervised method
The researchers illustrate the importance of conducting QC at each stage of an RNA-seq experiment and demonstrate their recommended RNA-seq QC strategy. Furthermore, they discuss the major and often neglected quality issues associated with the three major types of RNA-seq: mRNA, total RNA and small RNA. This RNA-seq QC overview provides comprehensive guidance for researchers who conduct RNA-seq experiments.
The overall workflow of RNA-seq QC