RNA-sequencing has become the gold standard for whole-transcriptome gene 
expression quantification. Multiple algorithms have been developed to derive 
gene counts from sequencing reads. While a number of benchmarking studies have 
been conducted, the question remains how individual methods perform at 
accurately quantifying gene expression levels from RNA-sequencing reads. We 
performed an independent benchmarking study using RNA-sequencing data from the 
well established MAQCA and MAQCB reference samples. RNA-sequencing reads were 
processed using five workflows (Tophat-HTSeq, Tophat-Cufflinks, STAR-HTSeq, 
Kallisto and Salmon) and resulting gene expression measurements were compared to 
expression data generated by wet-lab validated qPCR assays for all protein 
coding genes. All methods showed high gene expression correlations with qPCR 
data. When comparing gene expression fold changes between MAQCA and MAQCB 
samples, about 85% of the genes showed consistent results between RNA-sequencing 
and qPCR data. Of note, each method revealed a small but specific gene set with 
inconsistent expression measurements. A significant proportion of these 
method-specific inconsistent genes were reproducibly identified in independent 
datasets. These genes were typically smaller, had fewer exons, and were lower 
expressed compared to genes with consistent expression measurements. We propose 
that careful validation is warranted when evaluating RNA-seq based expression 
profiles for this specific gene set.