The case for using mapped exonic non-duplicate reads when reporting RNA-sequencing depth: examples from pediatric cancer datasets

Abstract Background The reproducibility of gene expression measured by RNA sequencing (RNA-Seq) is dependent on the depth. While unmapped or non-exonic reads do not contribute to quantification, duplicate quantification but are informative for reproducibility. We show that mapped, exonic, non-duplicate (MEND) a useful measure RNA-Seq datasets used analysis. Findings In bulk from 2,179 tumors in 48 cohorts, fraction analysis varies greatly. Unmapped constitute 1–77% all (median [IQR], 3% [3–6%]); 3–100% mapped 27% [13–43%]); and 4–97% 25% [16–37%]). MEND 0–79% total 50% [30–61%]). Conclusions Because an dataset measurements varies, we propose reporting dataset's depth reads, which definitively inform expression, rather than total, exonic reads. provide Docker image containing (i) existing required tools (RSeQC, sambamba, samblaster) (ii) custom script calculate data files. recommend experiments, sensitivity studies, recommendations use units