Project 2: UW-CNOF Data Analysis and Modeling

摘要-项目2：UW-CNOF数据分析和建模 有效利用UW-CNOF和其他机构产生的大量和多样化的核基因组数据集 4D基因组联盟的成员需要复杂的计算方法，通过 功能强大、用户友好的软件。在这里，我们建议创建和验证这些方法，并传播 由此产生的软件工具推广到更广泛的科学界。解释基因组和表观基因组数据需要 方法可以扩展到非常大的数据集，并处理不同的数据类型，每个方法都有自己的数据类型 统计相关性和噪声的特殊模式。此外，4D核组数据的类型将是 UW-CNOF产生的数据带来了新的挑战。首先，在成对的基因组上定义Hi-C数据 基于例如马尔可夫链的呈现时间序列分析的轨迹是不适用的。相反，数据是最好的 根据投影到三维坐标的理解，使用层次模型捕获 多层次的染色质构象。其次，4D基因组包括两个截然不同的时间概念： 相对较快的细胞周期时间，加上较慢的分枝分化时间。第三，AS 我们从大量的Hi-C数据转移到单个细胞的Hi-C数据，潜在地结合了测量RNA的并发数据 在同一细胞中的表达和染色质可及性，我们必须明确地考虑细胞之间的可变性 同时仍然保持计算的易操纵性和统计能力。该项目将产生两个互补项目 直接应对这些挑战的软件工具包。第一个工具包(目标1和2)使用分层结构 考虑3D和4D核组结构的概率混合建模方法 二倍体和细胞间的变异性。具体地说，我们使用了一个柱面“伪时间”投影，它联合 建立细胞周期和分化时间的模型。第二个工具包(Aim 3)为 将Hi-C数据或相应的3D或4D模型与更传统的基因组和表观基因组数据集相关联， 特别强调将4D基因组数据与基因调控和复制时机联系起来。建议数 该项目基于两名研究人员在Hi-C数据(Noble)和单细胞3D建模方面的专业知识 分析(特拉普内尔)。这些软件工具将与联合国其他调查人员密切合作开发。 UW-CNOF，帮助验证在项目1中开发的新检测方法，并通过 项目3中描述并应用于项目4中与疾病相关的系统的实验。软件工具 它们将在开源许可下提供，并将通过以下方式传播(AIM 4) 出版文章和协议，以及通过实际操作培训活动。

ABSTRACT – PROJECT 2: UW-CNOF DATA ANALYSIS AND MODELING Making effective use of the large and diverse nucleome data sets generated by the UW-CNOF and other members of the 4D Nucleome Consortium requires sophisticated computational methods deployed through robust, user-friendly software. Here, we propose to create and validate such methods and to disseminate the resulting software tools to the wider scientific community. Interpreting genomic and epigenomic data requires methods that scale to very large data sets and that handle heterogeneous data types, each with its own idiosyncratic patterns of statistical dependence and noise. In addition, 4D nucleome data of the type to be generated by the UW-CNOF gives rise to new challenges. First, Hi-C data are defined over pairs of genomic loci, rendering time series analyses based on, e.g., Markov chains, inapplicable. Instead, the data are best understood under a projection into three-dimensional coordinates, with a hierarchical model that captures multiple levels of chromatin conformation. Second, the 4D nucleome includes two distinct notions of time: the relatively fast, cyclic time of the cell cycle, coupled with the slower, branching time of differentiation. Third, as we move from bulk Hi-C data to single cell Hi-C data, potentially coupled with concurrent data measuring RNA expression and chromatin accessibility in the same cells, we must explicitly account for cell-to-cell variability while still retaining computational tractability and statistical power. The project will produce two complementary software toolkits that directly address these challenges. The first toolkit (Aims 1 and 2) uses a hierarchical probabilistic mixture modeling approach to model 3D and 4D nucleome architecture, taking into account diploidy and cell-to-cell variability. In particular, we employ a cylindrical “pseudotime” projection that jointly models cell cycle and differentiation time scales. The second toolkit (Aim 3) provides a general framework for relating Hi-C data or corresponding 3D or 4D models to more traditional genomic and epigenomic data sets, with particular emphasis on relating 4D nucleome data to gene regulation and replication timing. The proposed project builds upon the two investigators' expertise in 3D modeling of Hi-C data (Noble) and single-cell analyses (Trapnell). The software tools will be developed in close collaboration with other investigators in the UW-CNOF, helping to validate the novel assays developed in Project 1 and in turn being validated by the experiments described in Project 3 and applied to disease-relevant systems in Project 4. The software tools themselves will be made available under an open source license and will be disseminated (Aim 4) via published articles and protocols, as well as through hands-on training activities.