Chakraborty Shounak, Canzar Stefan, Marschall Tobias, Schulz Marcel H
Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarland Informatics Campus E1.7, Saarbrücken, Germany.
Max Planck Institute for Informatics, Saarland Informatics Campus E1.4, Saarbrücken, Germany.
J Comput Biol. 2020 Mar;27(3):330-341. doi: 10.1089/cmb.2019.0457.
Measuring nucleosome positioning in cells is crucial for the analysis of epigenetic gene regulation. Reconstruction of nucleosome profiles of individual cells or subpopulations of cells remains challenging because most genome-wide assays measure nucleosome positioning and DNA accessibility for thousands of cells using bulk sequencing. In this study we use characteristics of the NOMe (nucleosome occupancy and methylation)-sequencing assay to derive a new approach, called ChromaClique, for deconvolution of different nucleosome profiles (chromatypes) from cell subpopulations of one NOMe-seq measurement. ChromaClique uses a maximal clique enumeration algorithm on a newly defined NOMe read graph that is able to group reads according to their nucleosome profiles. We show that the edge probabilities of that graph can be efficiently computed using hidden Markov models. We demonstrate using simulated data that ChromaClique is more accurate than a related method and scales favorably, allowing genome-wide analyses of chromatypes in cell subpopulations.
测量细胞中的核小体定位对于表观遗传基因调控分析至关重要。重建单个细胞或细胞亚群的核小体图谱仍然具有挑战性，因为大多数全基因组分析使用批量测序来测量数千个细胞的核小体定位和DNA可及性。在本研究中，我们利用NOMe（核小体占有率和甲基化）测序分析的特征，得出了一种称为ChromaClique的新方法，用于从一次NOMe-seq测量的细胞亚群中解卷积不同的核小体图谱（染色质类型）。ChromaClique在新定义的NOMe读段图上使用最大团枚举算法，该算法能够根据读段的核小体图谱对其进行分组。我们表明，使用隐马尔可夫模型可以有效地计算该图的边概率。我们使用模拟数据证明，ChromaClique比相关方法更准确，并且扩展性良好，能够对细胞亚群中的染色质类型进行全基因组分析。
