Machine learning methods to impute and annotate epigenomic maps

描述（由申请人提供）：NIH表观基因组学计划路线图已经生成了来自多种人类原代细胞和组织的参考表观基因组图谱，包括多能细胞类型和体外分化形式，高度纯化的原代细胞，以及一系列胎儿和成人组织。拟议项目的目标是开发、验证和应用无监督机器学习方法来联合分析这些表观基因组图谱，以及(1)由NIH ENCODE联盟生成的数据，(2)各种公开可用的数据集，这些数据集表征了细胞核中DNA的三维结构，以及(3)关于进化守恒的信息，以跨物种DNA比对为代表。该项目的第一个目标是使用数据输入方法进行虚拟功能基因组学实验。提出的方法是基于在推荐系统背景下开发的技术，但扩展到沿基因组轴建模依赖关系。通过同时分析一系列细胞类型和检测类型的生化活性模式，所提出的插补方法将准确预测检测结果，例如针对特定细胞类型中特定组蛋白修饰的ChIP-seq，该检测尚未进行。我们将系统地将该方法应用于Roadmap表观基因组学和ENCODE数据，填补细胞类型和测定类型矩阵中缺失的实验。其余三个具体目标扩展并应用我们现有的半自动基因组注释系统Segway，该系统将各种功能基因组数据集成到人类可解释的基因组元素标记中。这些分析将在Aim 1的真实数据和虚拟实验上进行。我们提出了一种新颖的、基于图形的正则化方案，并展示了如何使用这种方法，我们可以使用Segway对跨细胞类型的数据进行集成分析，并集成来自Hi-C等分析的3D基因组结构信息。我们还提出了一种后处理方法来利用进化守恒模式来识别结果注释中功能重要的标签。主要的可交付成果将包括用于输入和注释的新型软件，以及公开可用的虚拟实验集和基因组注释。

DESCRIPTION (provided by applicant): The NIH Roadmap Epigenomics Program has produced reference epigenomic maps derived from a variety of human primary cells and tissues, including pluripotent cell types and in vitro differentiated forms, highly purified primar cells, and a range of fetal and adult tissues. The goal of the proposed project is to develop, validate and apply unsupervised machine learning methods to the joint analysis of these epigenomic maps along with (1) data generated by the NIH ENCODE Consortium, (2) a variety of publicly available data sets that characterize the three-dimensional structure of DNA in the nucleus, and (3) information about evolutionary conservation, represented by cross-species DNA alignments. The first aim of the project will use data imputation methods to carry out virtual functional genomics experiments. The proposed method is based on techniques developed in the context of recommender systems, but is extended to model dependencies along the genomic axis. By simultaneously analyzing the pattern of biochemical activity across a range of cell types and assay types, the proposed imputation method will accurately predict the results of an assay, such as ChIP-seq for a particular histone modification in a particular cell type, that has not yet been carried out. We will systematically apply this method to Roadmap Epigenomics and ENCODE data, filling in missing experiments in the matrix of cell types and assay types. The remaining three specific aims extend and apply our existing system for semi-automated genome annotation, Segway, which integrates a wide variety of functional genomics data into a human interpretable labeling of genomic elements. These analyses will be performed on real data as well as the virtual experiments from Aim 1. We propose a novel, graph-based regularization scheme and show how, using this approach, we can use Segway to perform integrated analysis of data across cell types and integrate 3D genome architecture information from assays such as Hi-C. We also propose a post-processing method to exploit patterns of evolutionary conservation to identify functionally important labels in the resulting annotations. The primary deliverables will include novel software for imputation and annotation, as well as publicly available sets of virtual experiments and genome annotations.