Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.
Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells.
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DOI：
10.1371/journal.pone.0078644
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Liu X
中科院分区：
文献类型：
--
作者：
Zhao S;Fung-Leung WP;Bittner A;Ngo K;Liu X
为了证明在转录组谱分析中RNA-Seq优于微阵列的益处，对来自人T细胞活化实验的RNA样品进行RNA-Seq和微阵列分析。与其他报告相比，我们的分析集中在RNA测序和微阵列技术在转录组分析中的差异，而不是相似性。使用同一组样品对来自RNA-Seq和Affyphase平台的数据集进行比较，显示出两个平台生成的基因表达谱之间的高度相关性。然而，它也证明了RNA-Seq在检测低丰度转录物、区分生物学关键同种型和允许鉴定遗传变体方面具有上级优势。RNA-Seq还表现出比微阵列更宽的动态范围，其允许检测具有更高倍数变化的更多差异表达基因。两个数据集的分析还显示了来自避免微阵列探针性能固有的技术问题的益处，例如交叉杂交、非特异性杂交和单个探针的有限检测范围。因为RNA-Seq不依赖于预先设计的互补序列检测探针，所以它没有与探针冗余和注释相关的问题，这简化了数据的解释。尽管RNA-Seq具有上级优势，但在进行转录谱分析实验时，微阵列仍然是研究人员更常见的选择。这可能是因为RNA-Seq测序技术对大多数研究人员来说是新的，比微阵列更昂贵，数据存储更具挑战性，分析更复杂。我们预计，一旦这些障碍被克服，RNA-Seq平台将成为转录组分析的主要工具。
To demonstrate the benefits of RNA-Seq over microarray in transcriptome profiling, both RNA-Seq and microarray analyses were performed on RNA samples from a human T cell activation experiment. In contrast to other reports, our analyses focused on the difference, rather than similarity, between RNA-Seq and microarray technologies in transcriptome profiling. A comparison of data sets derived from RNA-Seq and Affymetrix platforms using the same set of samples showed a high correlation between gene expression profiles generated by the two platforms. However, it also demonstrated that RNA-Seq was superior in detecting low abundance transcripts, differentiating biologically critical isoforms, and allowing the identification of genetic variants. RNA-Seq also demonstrated a broader dynamic range than microarray, which allowed for the detection of more differentially expressed genes with higher fold-change. Analysis of the two datasets also showed the benefit derived from avoidance of technical issues inherent to microarray probe performance such as cross-hybridization, non-specific hybridization and limited detection range of individual probes. Because RNA-Seq does not rely on a pre-designed complement sequence detection probe, it is devoid of issues associated with probe redundancy and annotation, which simplified interpretation of the data. Despite the superior benefits of RNA-Seq, microarrays are still the more common choice of researchers when conducting transcriptional profiling experiments. This is likely because RNA-Seq sequencing technology is new to most researchers, more expensive than microarray, data storage is more challenging and analysis is more complex. We expect that once these barriers are overcome, the RNA-Seq platform will become the predominant tool for transcriptome analysis.
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影响因子：
14.9
作者：
Hansen KD;Brenner SE;Dudoit S
通讯作者：
Dudoit S
DOI：
10.1621/nrs.07003
发表时间：
2009
期刊：
Nuclear receptor signaling
影响因子：
--
作者：
Jetten AM
通讯作者：
Jetten AM
影响因子：
14.9
作者：
Nurtdinov RN;Vasiliev MO;Ershova AS;Lossev IS;Karyagina AS
通讯作者：
Karyagina AS
影响因子：
14.9
作者：
Nookaew I;Papini M;Pornputtapong N;Scalcinati G;Fagerberg L;Uhlén M;Nielsen J
通讯作者：
Nielsen J
影响因子：
4.4
作者：
McIntyre LM;Lopiano KK;Morse AM;Amin V;Oberg AL;Young LJ;Nuzhdin SV
通讯作者：
Nuzhdin SV