Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data

By Celine Everaert, Manuel Luypaert, Jesper L. V. Maag, Quek Xiu Cheng, Marcel E. Dinger, Jan Hellemans, Pieter Mestdagh. Scientific Reports, 2017

336 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/69ef2affee4f6f19a1126d7d1d60b1d975eab48d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Andrzej Zielezinski
2. W. Karłowski
3. Jonas S. Almeida
4. Susana Vinga
5. Nicolas J.-B. Brunel

Citations in the corpus

• Alignment-free sequence comparison: benefits, applications, and tools (2017, methodology): “This approach to quantify gene/ transcript expression levels from RNA sequencing reads is both 10 – 100 times faster than any of the alignment-based methods and at least as accurate as best-performing alignment-based workflows (e.g., TopHat-Cufflinks) [87, 88].”