Variability in estimated gene expression among commonly used RNA-seq pipelines

Abstract RNA-sequencing data is widely used to identify disease biomarkers and therapeutic targets using numerical methods such as clustering, classification, regression, and differential expression analysis. Such approaches rely on the assumption that mRNA abundance estimates from RNA-seq are reliable estimates of true expression levels. Here, using data from five RNA-seq processing pipelines applied to 6,690 human tumor and normal tissues, we show that nearly 88% of protein-coding genes have similar gene expression profiles across all pipelines. However, for >12% of protein-coding genes, current best-in-class RNA-seq processing pipelines differ in their abundance estimates by more than four-fold when applied to exactly the same samples and the same set of RNA-seq reads. Expression fold changes are similarly affected. Many of the impacted genes are widely studied disease-associated genes. We show that impacted genes exhibit diverse patterns of discordance among pipelines, suggesting that many inter-pipeline differences contribute to overall uncertainty in mRNA abundance estimates. A concerted, community-wide effort will be needed to develop gold-standards for estimating the mRNA abundance of the discordant genes reported here. In the meantime, our list of discordantly evaluated genes provides an important resource for robust marker discovery and target selection.