Statistical Methods for Genome Characterization

基因组表征的统计方法

• 批准号: 10707463
• 负责人: Paul Marjoram
• 金额: $ 28.35万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707463
• 关键词: Biological; Cancer cell line; Cells; Collaborations; Computer software; Computing Methodologies; DNA Methylation; Data; Data Analyses; Databases; Dependence; Development; Epigenetic Process; Family; Gene Silencing; Genes; Genetic; Genome; Genomics; Health; Heterogeneity; Human; Hypermethylation; Joints; Knowledge; Libraries; Life; Malignant Neoplasms; Maps; Measures; Methods; Methylation; Modeling; Normal tissue morphology; Ontology; Pathologist; Pathway Analysis; Pathway interactions; Phenotype; Phylogenetic Analysis; Play; Recording of previous events; Research Personnel; Resources; Role; Science; Specificity; Statistical Data Interpretation; Statistical Methods; Structure; Translating; Translations; Trees; Variant; Work; bisulfite sequencing; cost; data to knowledge; drug sensitivity; epigenome; experimental analysis; functional genomics; gene function; gene interaction; genetic analysis; genome-wide; improved; interest; lens; member; model building; novel; personalized medicine; programs; public database; query tools; tool; tumor; whole genome

Project 3: Statistical Methods for Genome Characterization Abstract Understanding the role that genes play in life is a key issue in biomedical sciences, yet the overwhelming majority of sequences in public databases remain uncharacterized. Functional annotation is important for a variety of downstream analyses of genetic data. Yet experimental characterization of function remains costly and slow, making computational prediction an important endeavor. This project therefore proposes three Aims focused on functional genomics. In our first Specific Aim, we propose to develop a probabilistic evolutionary model built upon phylogenetic trees and experimental Gene Ontology functional annotations that allows automated prediction of function for unannotated genes. We will develop a probabilistic hierarchical modeling framework that that will allow joint inference, and borrowing of strength, across a family of related trees. We expect this to significantly improve overall accuracy. Our approach will provide a scalable computational method that will enable gene annotation to be kept up to date regardless of the flow of new experimental data. Our second Aim focuses on the development of improved statistical methods for pathway analysis. Such methods aim to detect over-representation of members of a super-structure, such as a genetic pathway, in a list of objects of interest from an experimental or statistical analysis. However, pathway definitions are not consistent between resources, with the overlap between two definitions of the same pathway on differing resources being as low as 30%. In this Aim we will develop methods that focus on the network structure itself, which is much more robust. Our third Aim focuses on analysis of epigenetic conservation. The epigenome dictates cell phenotype and it is increasingly possible to infer which genes are silenced or expressed by measuring the epigenome of a cell. Cancers are characterized by multiple genes that show both hypermethylation and hypomethylation relative to normal tissues. We will develop advanced statistical methods to assess how conservation of DNA methylation varies along the genome, and validated using measures of ‘essentiality’ taken from the Cancer Dependency Map and drug sensitivity data taken from the Genomics of Drug Sensitivity in Cancer (GDSC) Project.

项目3：基因组表征的统计方法 抽象的 了解基因在生命中的作用是生物医学科学中的关键问题，但是 公共数据库中的绝大多数序列仍然没有表征。功能 注释对于各种遗传数据的下游分析很重要。但是实验 功能的表征保持昂贵和缓慢，使计算预测成为 重要的努力。因此，该项目提出三个目的集中在功能基因组学上。 在我们的第一个特定目标中，我们建议开发建立在 系统发育树和实验基因本体论功能注释，允许自动化 未经注释的基因功能的预测。我们将开发概率分层建模 框架将允许在一个相关家庭中进行联合推断和借用力量 树木。我们希望这将显着提高整体准确性。我们的方法将提供 可扩展的计算方法将使基因注释能够保持最新状态 新实验数据的流量。我们的第二个目标重点是改进的发展 途径分析的统计方法。这样的方法旨在检测过多的代表 超级结构的成员，例如遗传途径，来自感兴趣的对象的列表 实验或统计分析。但是，途径定义之间不一致 资源，在不同资源上相同途径的两个定义之间的重叠 低至30％。在此目标中，我们将开发关注网络结构的方法 本身，这更强大。我们的第三个目标重点是分析表观遗传保护。 表观基因组决定细胞表型，并且越来越有可能推断哪些基因是 通过测量细胞的表观基因组沉默或表达。癌症的特征是 相对于正常组织，表现出高甲基化和低甲基化的多个基因。 我们将开发高级统计方法，以评估如何保存DNA甲基化 沿着基因组的多样性，并使用癌症采取的“本质性”测量验证 依赖图和药物敏感性数据从癌症药物敏感性的基因组学中获取 （GDSC）项目。