Bayesian prediction of tissue-regulated splicing using RNA sequence and cellular context
Bayesian prediction of tissue-regulated splicing using RNA sequence and cellular context
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DOI：
10.1093/bioinformatics/btr444
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发表时间：
2011-09-15
期刊：
影响因子：
5.8
通讯作者：
Frey, Brendan J.
中科院分区：
文献类型：
--
作者：
Xiong, Hui Yuan;Barash, Yoseph;Frey, Brendan J.
动机：选择性剪接是哺乳动物组织中细胞多样性的主要贡献者，并与许多人类疾病有关。理解这一现象的一个重要目标是推断“剪接密码”，该密码预测剪接在不同细胞类型中是如何通过来自RNA、DNA和表观遗传修饰剂的特征进行调节的。我们将拼接代码的组装公式化为一个统计推断问题，并引入贝叶斯方法，该方法使用自适应选择的隐变量数量将联合收割机特征子组组合成网络，允许不同的组织共享特征亚组，并使用Gibbs采样器来对冲预测和确定所识别的特征的统计显著性。结果：使用来自27个小鼠组织的3665个盒外显子、1014个RNA特征和4种组织类型的数据（http：//genes.toronto.edu/wasp），我们对几种方法进行了基准测试。我们的方法优于所有其他人，并实现了相对改善的52%的剪接代码质量和高达22%的分类错误，与最先进的状态相比，使用我们的方法确定了新的组合的监管功能和新的组合的组织共享功能亚组。
Motivation: Alternative splicing is a major contributor to cellular diversity in mammalian tissues and relates to many human diseases. An important goal in understanding this phenomenon is to infer a 'splicing code' that predicts how splicing is regulated in different cell types by features derived from RNA, DNA and epigenetic modifiers.Methods: We formulate the assembly of a splicing code as a problem of statistical inference and introduce a Bayesian method that uses an adaptively selected number of hidden variables to combine subgroups of features into a network, allows different tissues to share feature subgroups and uses a Gibbs sampler to hedge predictions and ascertain the statistical significance of identified features.Results: Using data for 3665 cassette exons, 1014 RNA features and 4 tissue types derived from 27 mouse tissues (http://genes.toronto.edu/wasp), we benchmarked several methods. Our method outperforms all others, and achieves relative improvements of 52% in splicing code quality and up to 22% in classification error, compared with the state of the art. Novel combinations of regulatory features and novel combinations of tissues that share feature subgroups were identified using our method.