Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America.
Department of Computer Science & Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS Comput Biol. 2022 Jul 14;18(7):e1010265. doi: 10.1371/journal.pcbi.1010265. eCollection 2022 Jul.
Although poorly positioned nucleosomes are ubiquitous in the eukaryotic genome, they are difficult to identify with existing nucleosome identification methods. Recently available enhanced high-throughput chromatin conformation capture techniques such as Micro-C, DNase Hi-C, and Hi-CO characterize nucleosome-level chromatin proximity, probing the positions of mono-nucleosomes and the spacing between nucleosome pairs at the same time, enabling nucleosome profiling in poorly positioned regions. Here we develop a novel computational approach, NucleoMap, to identify nucleosome positioning from ultra-high resolution chromatin contact maps. By integrating nucleosome read density, contact distances, and binding preferences, NucleoMap precisely locates nucleosomes in both prokaryotic and eukaryotic genomes and outperforms existing nucleosome identification methods in both precision and recall. We rigorously characterize genome-wide association in eukaryotes between the spatial organization of mono-nucleosomes and their corresponding histone modifications, protein binding activities, and higher-order chromatin functions. We also find evidence of two tetra-nucleosome folding structures in human embryonic stem cells and analyze their association with multiple structural and functional regions. Based on the identified nucleosomes, nucleosome contact maps are constructed, reflecting the inter-nucleosome distances and preserving the contact distance profiles in original contact maps.
尽管在真核生物基因组中普遍存在定位不佳的核小体，但现有的核小体识别方法很难对其进行识别。最近出现的增强型高通量染色质构象捕获技术，如 Micro-C、DNase Hi-C 和 Hi-CO，可对核小体级别的染色质接近程度进行特征描述，同时探测单核小体的位置和核小体对之间的间隔，从而能够对定位不佳的区域进行核小体作图。在这里，我们开发了一种新的计算方法 NucleoMap，可从超高分辨率染色质接触图谱中识别核小体定位。通过整合核小体读取密度、接触距离和结合偏好，NucleoMap 能够精确地定位原核生物和真核生物基因组中的核小体，在精确性和召回率方面均优于现有的核小体识别方法。我们严格地描述了真核生物中单核小体的空间组织与其相应组蛋白修饰、蛋白质结合活性和更高阶染色质功能之间的全基因组关联。我们还在人类胚胎干细胞中发现了两种四联体核小体折叠结构的证据，并分析了它们与多个结构和功能区域的关联。基于识别出的核小体，构建了核小体接触图谱，反映了核小体之间的距离，并保留了原始接触图谱中的接触距离分布。
