染色质
计算生物学
贝叶斯概率
核小体
贝叶斯推理
基因组
蒙特卡罗方法
生物
计算机科学
基因
物理
人工智能
遗传学
数学
统计
出处
期刊:Methods in molecular biology
日期:2024-09-16
卷期号:: 309-324
标识
DOI:10.1007/978-1-0716-4136-1_19
摘要
Polymer modeling has been playing an increasingly important role in complementing 3D genome experiments, both to aid their interpretation and to reveal the underlying molecular mechanisms. This chapter illustrates an application of Hi-C metainference, a Bayesian approach to explore the 3D organization of a target genomic region by integrating experimental contact frequencies into a prior model of chromatin. The method reconstructs the conformational ensemble of the target locus by combining molecular dynamics simulation and Monte Carlo sampling from the posterior probability distribution given the data. Using prior chromatin models at both 1 kb and nucleosome resolution, we apply this approach to a 30 kb locus of mouse embryonic stem cells consisting of two well-defined domains linking several gene promoters together. Retaining the advantages of both physics-based and data-driven strategies, Hi-C metainference can provide an experimentally consistent representation of the system while at the same time retaining molecular details necessary to derive physical insights.
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