The recently developed Hi-C technology enables a genome-wide view of chromosome spatial organizations, and has shed deep insights into genome structure and genome function. However, multiple sources of uncertainties make downstream data analysis and interpretation challenging. Specifically, statistical models for inferring three-dimensional (3D) chromosomal structure from Hi-C data are far from their maturity. Most existing methods are highly over-parameterized, lacking clear interpretations, and sensitive to outliers. In this study, we propose a parsimonious, easy to interpret, and robust piecewise helical model for the inference of 3D chromosomal structure of individual topologically associated domain from Hi-C data. When applied to a real Hi-C dataset, the piecewise helical model not only achieves much better model fitting than existing models, but also reveals that geometric properties of chromatin spatial organization are closely related to genome function.

中文翻译：

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通过分段螺旋模型推断单个拓扑相关域的空间组织。

最近开发的Hi-C技术使染色体空间组织的全基因组视图成为可能，并且对基因组结构和基因组功能有了深入的了解。但是，不确定性的多种来源使下游数据分析和解释具有挑战性。具体而言，用于从Hi-C数据推断三维（3D）染色体结构的统计模型还远远不够成熟。现有的大多数方法都高度参数化，缺乏清晰的解释，并且对异常值敏感。在这项研究中，我们提出了一个简约，易于解释且健壮的分段螺旋模型，用于从Hi-C数据推断单个拓扑相关域的3D染色体结构。当应用于实际的Hi-C数据集时，分段螺旋模型不仅比现有模型具有更好的模型拟合能力，