The advent of high-resolution chromosome conformation capture assays (such as 5C, Hi-C and Pore-C) has allowed for unprecedented sequence-level investigations into the structure-function relationship of the genome. In order to comprehensively understand this relationship, computational tools are required that utilize data generated from these assays to predict 3D genome organization (the 3D genome reconstruction problem). Many computational tools have been developed that answer this need, but a comprehensive comparison of their underlying algorithmic approaches has not been conducted. This manuscript provides a comprehensive review of the existing computational tools (from November 2006 to September 2019, inclusive) that can be used to predict 3D genome organizations from high-resolution chromosome conformation capture data. Overall, existing tools were found to use a relatively small set of algorithms from one or more of the following categories: dimensionality reduction, graph/network theory, maximum likelihood estimation (MLE) and statistical modeling. Solutions in each category are far from maturity, and the breadth and depth of various algorithmic categories have not been fully explored. While the tools for predicting 3D structure for a genomic region or single chromosome are diverse, there is a general lack of algorithmic diversity among computational tools for predicting the complete 3D genome organization from high-resolution chromosome conformation capture data.

中文翻译：

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从高分辨率染色体构象捕获数据预测3D基因组组织的计算方法。

高分辨率染色体构象捕获测定法（例如5C，Hi-C和Pore-C）的出现允许对基因组的结构-功能关系进行前所未有的序列级研究。为了全面理解这种关系，需要使用从这些测定生成的数据来预测3D基因组组织（3D基因组重建问题）的计算工具。已经开发出许多计算工具来满足这一需求，但是尚未对其基础算法方法进行全面比较。该手稿提供了对现有计算工具（从2006年11月至2019年9月，包括在内）的全面综述，该工具可用于从高分辨率染色体构象捕获数据预测3D基因组组织。总体，发现现有工具使用以下类别中一个或多个类别中相对较小的一组算法：降维，图/网络理论，最大似然估计（MLE）和统计建模。每个类别中的解决方案还远远不够成熟，并且各种算法类别的广度和深度尚未得到充分探讨。尽管用于预测基因组区域或单个染色体的3D结构的工具多种多样，但是从高分辨率染色体构象捕获数据中预测完整3D基因组组织的计算工具之间普遍缺乏算法多样性。最大似然估计（MLE）和统计建模。每个类别中的解决方案还远远不够成熟，并且各种算法类别的广度和深度还没有得到充分探讨。尽管用于预测基因组区域或单个染色体的3D结构的工具多种多样，但是从高分辨率染色体构象捕获数据中预测完整3D基因组组织的计算工具之间普遍缺乏算法多样性。最大似然估计（MLE）和统计建模。每个类别中的解决方案还远远不够成熟，并且各种算法类别的广度和深度尚未得到充分探讨。尽管用于预测基因组区域或单个染色体的3D结构的工具多种多样，但是从高分辨率染色体构象捕获数据中预测完整3D基因组组织的计算工具之间普遍缺乏算法多样性。