RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
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DOI：
10.1101/gr.079558.108
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发表时间：
2008-09-01
期刊：
影响因子：
7
通讯作者：
Gilad, Yoav
中科院分区：
文献类型：
--
作者：
Marioni, John C.;Mason, Christopher E.;Gilad, Yoav
超高通量测序正在作为用于基因分型，甲基化模式分析和转录因子结合位点的微阵列的有吸引力的替代品。在这里，我们描述了Illumina测序（以前是Solexa测序）平台来研究mRNA表达水平的应用。我们的目标是估计与Illumina测序相关的技术差异，并比较其与现有数组技术鉴定差异表达基因的能力。为此，我们使用多个测序重复估计了肝脏和肾脏RNA样品之间的基因表达差异，并将测序数据与使用相同的RNA样品从Affymetrix阵列获得的结果进行了比较。我们发现Illumina测序数据是高度可复制的，技术变化相对较少，因此，出于许多目的，仅对每个mRNA样本进行测序一次（即使用一个车道）。单一泳道中的Illumina测序数据中的信息似乎与单个阵列中的信息相当，从而可以鉴定差异表达的基因，同时允许进行其他分析，例如检测低表达基因，替代剪接变体和新的转录本。根据我们的观察，我们提出了一种经验方案和使用超高通量测序技术分析基因表达的统计框架。
Ultra-high-throughput sequencing is emerging as an attractive alternative to microarrays for genotyping, analysis of methylation patterns, and identification of transcription factor binding sites. Here, we describe an application of the Illumina sequencing (formerly Solexa sequencing) platform to study mRNA expression levels. Our goals were to estimate technical variance associated with Illumina sequencing in this context and to compare its ability to identify differentially expressed genes with existing array technologies. To do so, we estimated gene expression differences between liver and kidney RNA samples using multiple sequencing replicates, and compared the sequencing data to results obtained from Affymetrix arrays using the same RNA samples. We find that the Illumina sequencing data are highly replicable, with relatively little technical variation, and thus, for many purposes, it may suffice to sequence each mRNA sample only once (i.e., using one lane). The information in a single lane of Illumina sequencing data appears comparable to that in a single array in enabling identification of differentially expressed genes, while allowing for additional analyses such as detection of low-expressed genes, alternative splice variants, and novel transcripts. Based on our observations, we propose an empirical protocol and a statistical framework for the analysis of gene expression using ultra-high-throughput sequencing technology.