Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models
Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models
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DOI：
10.1093/bioinformatics/btu003
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发表时间：
2014-04-15
期刊：
影响因子：
5.8
通讯作者：
Dugas, Martin
中科院分区：
文献类型：
--
作者：
Klein, Hans-Ulrich;Schaefer, Martin;Dugas, Martin
动机：组蛋白修饰是激活或抑制基因转录的关键表观遗传机制。通过ChIP-seq获得的匹配的转录数据和组蛋白修饰数据的数据集是存在的，但是用于这两种数据类型的综合分析的方法仍然很少。在这里，我们提出了一种新的生物信息学方法来检测基因，显示不同的转录丰度之间的两个条件pupillance所造成的组蛋白modification.Results的改变：我们引入了一个相关的措施，通过RNA测序或微阵列测量的ChIP-seq和基因转录数据的综合分析，并证明一个适当的归一化ChIP-seq数据是至关重要的。我们建议应用不同类型分布的贝叶斯混合模型来进一步研究相关性测度的分布。混合模型的隐式分类用于检测在基因转录和组蛋白修饰两种条件之间具有差异的基因。该方法适用于不同的数据集，并证明了其优越性，一个天真的单独分析这两种数据类型。
Motivation: Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatics approach to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification.Results: We introduce a correlation measure for integrative analysis of ChIP-seq and gene transcription data measured by RNA sequencing or microarrays and demonstrate that a proper normalization of ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different types of distributions to further study the distribution of the correlation measure. The implicit classification of the mixturemodels is used to detect genes with differences between two conditions in both gene transcription and histone modification. The method is applied to different datasets, and its superiority to a naive separate analysis of both data types is demonstrated.