Machine learning methods to impute and annotate epigenomic maps

用于估算和注释表观基因组图谱的机器学习方法

• 批准号: 8814095
• 负责人: William Stafford Noble
• 金额: $ 28.51万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-10 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8814095
• 关键词: Accounting; Adult; Architecture; Big Data; Biochemical; Biological Assay; Biological Process; Cell Nucleus; Cells; ChIP-seq; Chromatin; Collection; Computer software; Computing Methodologies; DNA; Data; Data Analyses; Data Set; Deoxyribonucleases; Dependency; Disease; Elements; Evolution; Exhibits; Future; Generations; Genome; Genomics; Goals; Graph; Human; Human Genome; In Vitro; Investigation; Joints; Label; Link; Machine Learning; Maps; Measures; Methodology; Methods; Modeling; Pattern; Phenotype; Positron-Emission Tomography; Process; Recording of previous events; Resources; Role; Scheme; System; Techniques; Time; Tissues; United States National Institutes of Health; Virtual Library; Work; assay development; base; cell type; computer based statistical methods; epigenome; epigenomics; fetal; functional genomics; genome annotation; histone modification; human disease; improved; insight; markov model; novel; pressure; programs; public health relevance; research study; three dimensional structure; transcriptome sequencing; virtual

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.

描述（由申请人提供）：NIH路线图表观基因组学计划已产生了来自各种人类原代细胞和组织（包括多能细胞类型和体外分化形式），高度纯化的原始细胞以及一系列胎儿和成人组织的参考表观基因组图。拟议项目的目的是开发，验证和应用无监督的机器学习方法对这些表观基因组图的联合分析以及（1）由NIH编码协会生成的（1）数据，（2）多种可公开可用的数据集，这些数据集表征了核中DNA的三维结构的多种多样的数据集，并以交叉保护为代表的信息，代表了crossectians ander nna，代表了crossectians contecties contecies nna。该项目的第一个目的将使用数据插补方法进行虚拟功能基因组学实验。提出的方法基于在推荐系统的背景下开发的技术，但扩展到沿基因组轴的模型依赖性。通过同时分析各种细胞类型和测定类型的生化活性模式，提出的插补方法将准确预测测定的结果，例如在特定细胞类型中特定组蛋白修饰的chip-seq，尚未进行。我们将系统地将此方法应用于路线图表观基因组学和编码数据，并填充细胞类型和测定类型的矩阵中缺少的实验。其余的三个特定目的扩展并应用了我们现有的系统进行半自动基因组注释Segway，该系统将各种功能基因组数据集成到人类的基因组元素的可解释标记中。这些分析将在实际数据以及AIM 1的虚拟实验上进行。我们提出了一种基于图形的新颖正则化方案，并显示如何使用这种方法使用SEGWAY对跨单元类型进行数据进行集成分析，并从HI-C等测定中集成了3D基因组体系结构信息。我们还提出了一种后处理方法，以利用进化保守的模式，以识别所得注释中功能上重要的标签。主要的可交付成果将包括用于插补和注释的新型软件，以及可公开可用的虚拟实验和基因组注释。