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

Celine Everaert (UGent) , Manuel Luypaert, Jesper LV Maag, Quek Xiu Cheng, Marcel E Dinger, Jan Hellemans and Pieter Mestdagh (UGent)
(2017) SCIENTIFIC REPORTS. 7.
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Abstract
RNA-sequencing has become the gold standard for whole-transcriptome gene expression quanti cation. 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-Cuflinks, 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 speci c 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.
Keywords
RNA-Sequencing quantification, TRANSCRIPT EXPRESSION, SEQ, QUANTIFICATION, TOPHAT, GENE

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MLA
Everaert, Celine, et al. “Benchmarking of RNA-Sequencing Analysis Workflows Using Whole-Transcriptome RT-QPCR Expression Data.” SCIENTIFIC REPORTS, vol. 7, 2017, doi:10.1038/s41598-017-01617-3.
APA
Everaert, C., Luypaert, M., Maag, J. L., Cheng, Q. X., Dinger, M. E., Hellemans, J., & Mestdagh, P. (2017). Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data. SCIENTIFIC REPORTS, 7. https://doi.org/10.1038/s41598-017-01617-3
Chicago author-date
Everaert, Celine, Manuel Luypaert, Jesper LV Maag, Quek Xiu Cheng, Marcel E Dinger, Jan Hellemans, and Pieter Mestdagh. 2017. “Benchmarking of RNA-Sequencing Analysis Workflows Using Whole-Transcriptome RT-QPCR Expression Data.” SCIENTIFIC REPORTS 7. https://doi.org/10.1038/s41598-017-01617-3.
Chicago author-date (all authors)
Everaert, Celine, Manuel Luypaert, Jesper LV Maag, Quek Xiu Cheng, Marcel E Dinger, Jan Hellemans, and Pieter Mestdagh. 2017. “Benchmarking of RNA-Sequencing Analysis Workflows Using Whole-Transcriptome RT-QPCR Expression Data.” SCIENTIFIC REPORTS 7. doi:10.1038/s41598-017-01617-3.
Vancouver
1.
Everaert C, Luypaert M, Maag JL, Cheng QX, Dinger ME, Hellemans J, et al. Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data. SCIENTIFIC REPORTS. 2017;7.
IEEE
[1]
C. Everaert et al., “Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data,” SCIENTIFIC REPORTS, vol. 7, 2017.
@article{8535197,
  abstract     = {{RNA-sequencing has become the gold standard for whole-transcriptome gene expression quanti cation. 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-Cuflinks, 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 speci c 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.}},
  articleno    = {{1559}},
  author       = {{Everaert, Celine and Luypaert, Manuel and Maag, Jesper LV and Cheng, Quek Xiu and Dinger, Marcel E and Hellemans, Jan and Mestdagh, Pieter}},
  issn         = {{2045-2322}},
  journal      = {{SCIENTIFIC REPORTS}},
  keywords     = {{RNA-Sequencing quantification,TRANSCRIPT EXPRESSION,SEQ,QUANTIFICATION,TOPHAT,GENE}},
  language     = {{eng}},
  pages        = {{11}},
  title        = {{Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data}},
  url          = {{http://doi.org/10.1038/s41598-017-01617-3}},
  volume       = {{7}},
  year         = {{2017}},
}
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