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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >DPNuc: Identifying Nucleosome Positions Based on the Dirichlet Process Mixture Model
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DPNuc: Identifying Nucleosome Positions Based on the Dirichlet Process Mixture Model

机译：DPNuc：基于Dirichlet过程混合模型识别核小体位置

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摘要

Nucleosomes and the free linker DNA between them assemble the chromatin. Nucleosome positioning plays an important role in gene transcription regulation, DNA replication and repair, alternative splicing, and so on. With the rapid development of ChIP-seq, it is possible to computationally detect the positions of nucleosomes on chromosomes. However, existing methods cannot provide accurate and detailed information about the detected nucleosomes, especially for the nucleosomes with complex configurations where overlaps and noise exist. Meanwhile, they usually require some prior knowledge of nucleosomes as input, such as the size or the number of the unknown nucleosomes, which may significantly influence the detection results. In this paper, we propose a novel approach for identifying nucleosome positions based on the Dirichlet process mixture model. In our method, Markov chain Monte Carlo (MCMC) simulations are employed to determine the mixture model with no need of prior knowledge about nucleosomes. Compared with three existing methods, our approach can provide more detailed information of the detected nucleosomes and can more reasonably reveal the real configurations of the chromosomes; especially, our approach performs better in the complex overlapping situations. By mapping the detected nucleosomes to a synthetic benchmark nucleosome map and two existing benchmark nucleosome maps, it is shown that our approach achieves a better performance in identifying nucleosome positions and gets a higher -score. Finally, we show that our approach can more reliably detect the size distribution of nucleosomes.
机译：核小体和它们之间的自由接头DNA组装染色质。核小体定位在基因转录调控，DNA复制和修复，可变剪接等方面起着重要作用。随着ChIP-seq的迅速发展，有可能通过计算检测染色体上核小体的位置。然而，现有方法不能提供关于检测到的核小体的准确和详细的信息，特别是对于具有重叠和噪声存在的复杂构型的核小体。同时，他们通常需要一些核小体的先验知识作为输入，例如未知核小体的大小或数量，这可能会大大影响检测结果。在本文中，我们提出了一种基于Dirichlet过程混合物模型的核小体位置识别新方法。在我们的方法中，采用马尔可夫链蒙特卡罗（MCMC）模拟来确定混合物模型，而无需事先了解核小体。与三种现有方法相比，我们的方法可以提供有关检测到的核小体的更详细信息，并且可以更合理地揭示染色体的真实构型；特别是在复杂的重叠情况下，我们的方法效果更好。通过将检测到的核小体映射到合成的基准核小体图和两个现有的基准核小体图，表明我们的方法在识别核小体位置方面获得了更好的性能并获得了更高的得分。最后，我们表明我们的方法可以更可靠地检测核小体的大小分布。

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