Statistical Methods for Genome Characterization

项目3：基因组表征的统计方法 摘要 了解基因在生命中的作用是生物医学科学的一个关键问题， 公共数据库中的绝大多数序列仍然未被表征。功能 注释对于遗传数据的各种下游分析是重要的。还在试验阶段 功能的表征仍然昂贵且缓慢，使得计算预测成为可能。 重要的奋进。因此，该项目提出了三个目标，重点是功能基因组学。 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， 系统发育树和实验基因本体功能注释，允许自动化 预测未注释基因的功能。我们将开发一个概率层次模型 框架，这将允许联合推理，并借用力量，在一个家庭的相关 树我们预计这将显着提高整体准确性。我们的方法将提供一个 可扩展的计算方法，使基因注释保持最新， 新的实验数据流。我们的第二个目标侧重于开发改进的 途径分析的统计方法。这些方法旨在检测过度表示的 超结构的成员，如遗传途径，在感兴趣的对象的列表中， 实验或统计分析。然而，路径定义在以下方面并不一致： 资源，同一途径在不同资源上的两个定义之间存在重叠 低至30%。在这个目标中，我们将开发专注于网络结构的方法 它本身更强大。我们的第三个目标集中在分析表观遗传保守。 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 多个基因相对于正常组织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 沿着基因组沿着变化，并使用从癌症中提取的“必要性”措施进行验证。 从癌症药物敏感性基因组学中获得的药物敏感性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.