Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data.
Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data.
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DOI：
10.1038/s41598-017-01617-3
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发表时间：
2017-05-08
影响因子：
4.6
通讯作者：
Mestdagh P
中科院分区：
文献类型：
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作者：
Everaert C;Luypaert M;Maag JLV;Cheng QX;Dinger ME;Hellemans J;Mestdagh P
rna测序已成为全转录组基因表达量化的金标准。已经开发了多种算法来从测序读取中获得基因计数。虽然已经进行了许多基准研究，但问题仍然是个体方法如何从rna测序读数中准确定量基因表达水平。我们使用来自完善的MAQCA和MAQCB参考样本的rna测序数据进行了独立的基准研究。rna测序reads使用五种工作流程（Tophat-HTSeq、Tophat-Cufflinks、STAR-HTSeq、Kallisto和Salmon）进行处理，并将得到的基因表达测量结果与所有蛋白质编码基因的湿实验室验证qPCR检测产生的表达数据进行比较。所有方法均显示与qPCR数据高度相关的基因表达。在比较MAQCA和MAQCB样品的基因表达折叠变化时，约85%的基因在rna测序和qPCR数据之间显示一致的结果。值得注意的是，每种方法都揭示了一个小而特定的基因集，表达测量结果不一致。这些方法特异性不一致基因的很大一部分在独立的数据集中可重复地鉴定出来。这些基因通常较小，具有较少的外显子，并且与具有一致表达测量的基因相比表达较低。我们建议在评估这一特定基因集的RNA-seq表达谱时，需要仔细验证。
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.