Accurate detection of differential RNA processing.
Accurate detection of differential RNA processing.
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DOI：
10.1093/nar/gkt211
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发表时间：
2013-05-01
影响因子：
14.9
通讯作者：
Rätsch G
中科院分区：
文献类型：
--
作者：
Drewe P;Stegle O;Hartmann L;Kahles A;Bohnert R;Wachter A;Borgwardt K;Rätsch G
深度转录组测序（Deep transcriptome sequencing, RNA-Seq）已经成为研究细胞在不同环境、基因型和其他因素下状态的重要工具。RNA-Seq分析数据能够鉴定新的同工异构体，定量已知的同工异构体，并检测转录或rna加工活性的变化。现有的检测样本之间差异异构体丰度的方法要么需要完整的异构体注释，要么无法提供统计上稳健和校准的显著性估计。在这里，我们提出了一套统计测试来解决这些开放的需求：一个参数测试，使用已知的异构体注释来检测相对异构体丰度的变化，一个非参数测试，检测差异读取覆盖率，可以在异构体注释不可用时应用。这两种方法都说明了读取计数的离散性质和固有的生物可变性。我们证明，这些测试比较有利的以前的方法，无论是在准确性和统计校准。我们利用这些技术分析了拟南芥和果蝇的RNA-Seq文库。鉴定的差异RNA加工事件与RT-qPCR测量和先前的研究一致。建议的工具包可从http://bioweb.me/rdiff获得，并允许对转录组进行深入分析，有或没有可用的异构体注释。
Deep transcriptome sequencing (RNA-Seq) has become a vital tool for studying the state of cells in the context of varying environments, genotypes and other factors. RNA-Seq profiling data enable identification of novel isoforms, quantification of known isoforms and detection of changes in transcriptional or RNA-processing activity. Existing approaches to detect differential isoform abundance between samples either require a complete isoform annotation or fall short in providing statistically robust and calibrated significance estimates. Here, we propose a suite of statistical tests to address these open needs: a parametric test that uses known isoform annotations to detect changes in relative isoform abundance and a non-parametric test that detects differential read coverages and can be applied when isoform annotations are not available. Both methods account for the discrete nature of read counts and the inherent biological variability. We demonstrate that these tests compare favorably to previous methods, both in terms of accuracy and statistical calibrations. We use these techniques to analyze RNA-Seq libraries from Arabidopsis thaliana and Drosophila melanogaster. The identified differential RNA processing events were consistent with RT–qPCR measurements and previous studies. The proposed toolkit is available from http://bioweb.me/rdiff and enables in-depth analyses of transcriptomes, with or without available isoform annotation.
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影响因子：
46.9
作者：
Trapnell C;Williams BA;Pertea G;Mortazavi A;Kwan G;van Baren MJ;Salzberg SL;Wold BJ;Pachter L
通讯作者：
Pachter L
影响因子：
12.3
作者：
Robinson MD;Oshlack A
通讯作者：
Oshlack A
影响因子：
48
作者：
Katz, Yarden;Wang, Eric T.;Airoldi, Edoardo M.;Burge, Christopher B.
通讯作者：
Burge, Christopher B.
影响因子：
7
作者：
Brooks, Angela N.;Yang, Li;Graveley, Brenton R.
通讯作者：
Graveley, Brenton R.
影响因子：
5.8
作者：
Borgwardt, Karsten M.;Gretton, Arthur;Smola, Alex J.
通讯作者：
Smola, Alex J.