Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling.
Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling.
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DOI：
10.1093/bioinformatics/btr247
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发表时间：
2011-07-01
期刊：
影响因子：
--
通讯作者：
Kreil DP
中科院分区：
文献类型：
--
作者：
Łabaj PP;Leparc GG;Linggi BE;Markillie LM;Wiley HS;Kreil DP
动机：测量精度决定了任何分析可靠识别重要信号的能力，例如在差异表达的筛选中，与实验设计是否包含重复无关。然而，随着大规模RNA-Seq数据集与技术重复样本的编译，我们现在可以第一次对大规模平行测序技术的表达水平估计精度进行系统分析。然后，这允许考虑通过计算或实验手段对其进行改进。结果如下：我们报告的目标识别和测量精度，包括其依赖于转录表达水平，读取深度和其他参数的全面研究。特别是，可以用3.31亿个50 bp读段实现84%的估计真实转录物群体的令人印象深刻的召回，其中较长读段长度的回报递减，并且增加测序深度的收益甚至更少。然而，大部分测量能力（75%）仅花费在已知转录组的7%上，使得表达较弱的转录物更难测量。因此，<30%的转录本可以可靠地定量，相对误差<20%。基于已建立的工具，我们引入了一种新的方法来定位和分析测序读数，从而大大提高了基因表达谱的性能，将可以可靠量化的转录本数量增加到40%以上。外推到更高的测序深度突出了对有效互补步骤的需要。在讨论中，我们概述了可能的实验和计算策略，进一步提高定量精度。联系方式：rnaseq10@boku.ac.at补充信息：补充数据可从生物信息学在线网站获得。
Motivation: Measurement precision determines the power of any analysis to reliably identify significant signals, such as in screens for differential expression, independent of whether the experimental design incorporates replicates or not. With the compilation of large-scale RNA-Seq datasets with technical replicate samples, however, we can now, for the first time, perform a systematic analysis of the precision of expression level estimates from massively parallel sequencing technology. This then allows considerations for its improvement by computational or experimental means. Results: We report on a comprehensive study of target identification and measurement precision, including their dependence on transcript expression levels, read depth and other parameters. In particular, an impressive recall of 84% of the estimated true transcript population could be achieved with 331 million 50 bp reads, with diminishing returns from longer read lengths and even less gains from increased sequencing depths. Most of the measurement power (75%) is spent on only 7% of the known transcriptome, however, making less strongly expressed transcripts harder to measure. Consequently, <30% of all transcripts could be quantified reliably with a relative error <20%. Based on established tools, we then introduce a new approach for mapping and analysing sequencing reads that yields substantially improved performance in gene expression profiling, increasing the number of transcripts that can reliably be quantified to over 40%. Extrapolations to higher sequencing depths highlight the need for efficient complementary steps. In discussion we outline possible experimental and computational strategies for further improvements in quantification precision. Contact: rnaseq10@boku.ac.at Supplementary information: Supplementary data are available at Bioinformatics online.
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影响因子：
48
作者：
Cloonan, Nicole;Forrest, Alistair R. R.;Grimmond, Sean M.
通讯作者：
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影响因子：
14.9
作者：
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通讯作者：
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通讯作者：
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7
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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