QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model.
QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model.
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QNB：使用四阴性二项式模型对基于计数的小样本测序数据进行差异 RNA 甲基化分析
DOI：
10.1186/s12859-017-1808-4
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发表时间：
2017-08-31
影响因子：
3
通讯作者：
Meng J
中科院分区：
文献类型：
--
作者：
Liu L;Zhang SW;Huang Y;Meng J
背景RNA表观遗传学作为一个新兴的研究领域，由于其甲基化和其他修饰参与了许多重要的生物学过程，近年来受到越来越多的关注。由于高通量测序技术，如MeRIP-Seq，转录组范围的RNA甲基化谱现在可以以基于计数的数据的形式获得，利用该数据，研究表位转录组层的动力学通常是有意义的。然而，RNA甲基化实验的样本量通常是非常小的，由于其成本;此外，通常存在大量的基因，其甲基化水平不能准确估计，由于其低表达水平，使差异RNA甲基化分析一个困难的task.ResultsWe提出QNB，差异RNA甲基化分析与计数为基础的小样本测序数据的统计方法。与以往的DRME模型只对IP样本进行2个负二项分布的统计检验相比，QNB模型基于4个独立的负二项分布，其方差和均值均通过局部回归连接，同时也适当地考虑了输入控制样本。此外，不同于DRME方法，它只依赖于输入控制样本只用于估计背景，QNB使用一个更强大的估计基因表达，通过结合输入和IP样本的信息，这可以大大提高测试性能非常低expressedgenes.ConclusionQNB模拟和真实的MeRIP-Seq数据集相比，竞争算法的性能有所改善。QNB模型也适用于其他与RNA修饰相关的数据集，包括但不限于RNA亚硫酸氢盐测序、m1A-Seq、Par-CLIP、RIP-Seq等。
BackgroundAs a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task.ResultsWe present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes.ConclusionQNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m1A-Seq, Par-CLIP, RIP-Seq, etc.
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影响因子：
64.5
作者：
Meyer KD;Saletore Y;Zumbo P;Elemento O;Mason CE;Jaffrey SR
通讯作者：
Jaffrey SR
影响因子：
12.3
作者：
Kim D;Pertea G;Trapnell C;Pimentel H;Kelley R;Salzberg SL
通讯作者：
Salzberg SL
使用 MeRIP-Seq 数据和 exomePeak R/Bioconductor Package 进行 RNA 甲基化差异分析的方案
影响因子：
4.8
作者：
Meng, Jia;Lu, Zhiliang;Liu, Hui;Zhang, Lin;Zhang, Shaowu;Chen, Yidong;Rao, Manjeet K.;Huang, Yufei
通讯作者：
Huang, Yufei
影响因子：
64.8
作者：
Harcourt EM;Kietrys AM;Kool ET
通讯作者：
Kool ET
影响因子：
64.8
作者：
Dominissini, Dan;Moshitch-Moshkovitz, Sharon;Rechavi, Gideon
通讯作者：
Rechavi, Gideon