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

Abstract 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 pe...

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Autores principales:	Celine Everaert, Manuel Luypaert, Jesper L. V. Maag, Quek Xiu Cheng, Marcel E. Dinger, Jan Hellemans, Pieter Mestdagh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/07f441eb893f4c27925393cbc057c840
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spelling	oai:doaj.org-article:07f441eb893f4c27925393cbc057c8402021-12-02T11:41:01ZBenchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data10.1038/s41598-017-01617-32045-2322https://doaj.org/article/07f441eb893f4c27925393cbc057c8402017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01617-3https://doaj.org/toc/2045-2322Abstract 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.Celine EveraertManuel LuypaertJesper L. V. MaagQuek Xiu ChengMarcel E. DingerJan HellemansPieter MestdaghNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Celine Everaert Manuel Luypaert Jesper L. V. Maag Quek Xiu Cheng Marcel E. Dinger Jan Hellemans Pieter Mestdagh Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data
description	Abstract 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.
format	article
author	Celine Everaert Manuel Luypaert Jesper L. V. Maag Quek Xiu Cheng Marcel E. Dinger Jan Hellemans Pieter Mestdagh
author_facet	Celine Everaert Manuel Luypaert Jesper L. V. Maag Quek Xiu Cheng Marcel E. Dinger Jan Hellemans Pieter Mestdagh
author_sort	Celine Everaert
title	Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data
title_short	Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data
title_full	Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data
title_fullStr	Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data
title_full_unstemmed	Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data
title_sort	benchmarking of rna-sequencing analysis workflows using whole-transcriptome rt-qpcr expression data
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/07f441eb893f4c27925393cbc057c840
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Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data

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