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 ar

SUBMITTER: Arora S 

PROVIDER: S-EPMC7026138 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature