Statistical Methods for Genome Characterization

项目3：基因组特征的统计方法 摘要 了解基因在生命中扮演的角色是生物医学科学中的一个关键问题，但 公共数据库中的绝大多数序列仍然没有特征。功能性 注释对于遗传数据的各种下游分析很重要。但仍处于实验阶段 函数的表征仍然昂贵而缓慢，使得计算预测成为一种 这是一项重要的努力。因此，该项目提出了三个侧重于功能基因组学的目标。 在我们的第一个具体目标中，我们建议开发一个概率进化模型，该模型建立在 允许自动化的系统发育树和实验性基因本体论功能注释 未注释基因的功能预测。我们将开发一种概率分层模型 框架，这将允许联合推理，并在相关家族中借用力量 树木。我们预计这将显著提高总体准确度。我们的方法将提供一个 一种可扩展的计算方法，使基因注释能够保持最新 新的实验数据的流动。我们的第二个目标是开发改进的 通径分析的统计方法。这类方法旨在检测过度表达的 超结构的成员，如遗传途径，在来自 实验或统计分析。然而，路径的定义并不一致 资源，不同资源上同一路径的两个定义之间的重叠 低至30%。在这个目标中，我们将开发专注于网络结构的方法 本身，它要健壮得多。我们的第三个目标是分析表观遗传保护。 表观基因组决定了细胞的表型，推测哪些基因是 通过测量细胞的表观基因组来沉默或表达的。癌症的特点是 与正常组织相比，多个基因同时表现出高甲基化和低甲基化。 我们将开发先进的统计方法来评估DNA甲基化的保守性 随着基因组的不同而变化，并使用从癌症中提取的“本质”的测量来验证 肿瘤药物敏感性基因组学中的依赖图谱和药物敏感性数据 (GDSC)项目。

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.