Genome-wide Inference of Human Gene Function from Model Organism Data

从模式生物数据全基因组推断人类基因功能

• 批准号: 9359371
• 负责人: Paul D. Thomas
• 金额: $ 18.35万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9359371
• 关键词: Active Sites; Address; Animal Model; Automation; Binding; Bioinformatics; Biological; Biological Process; Cells; Communities; Computers; Computing Methodologies; Data; Data Set; Data Sources; Disease; Drosophila genus; Ensure; Evolution; Family; Gene Family; Genealogical Tree; Genes; Genomics; Human; Human Genome; Knowledge; Log-Linear Models; Malignant Neoplasms; Manuals; Markov Chains; Methods; Modeling; Mus; Ontology; Organism; Pathway Analysis; Pathway interactions; Phylogenetic Analysis; Play; Process; Proteins; Recording of previous events; Role; Source; Statistical Methods; Statistical Models; Structure; SwissProt; Tertiary Protein Structure; Testing; Training; Uncertainty; Validation; Work; Yeasts; anticancer research; base; biological systems; data resource; design; disorder risk; experience; experimental study; gene function; genome analysis; genome-wide; genomic data; human disease; improved; insight; programs; tumor progression

ABSTRACT Pathway analysis of genomic data—the use of prior knowledge about how genes function together in biological systems—plays an increasingly critical role in gaining biological insights from large-scale genomic studies, and particularly in cancer research. However, even the richest source of computer-accessible biological pathway information, the Gene Ontology (GO), is very incomplete, hampering pathway analyses. Over the past three years, the GO Consortium has developed a project that has shown that, by utilizing a rigorous phylogenetic approach, we can increase the amount of knowledge for human genes by five-fold through careful use of experimental data obtained in model organisms such as the mouse, fruit fly, and yeast. The GOC project, however, relies on expert human biologists, and will not scale to the entire human genome. Here, we propose to develop a computational approach that leverages the experience gained in the GOC project. We will develop an accurate, scalable computational solution to the gene function inference problem, which will dramatically increase the amount of biological information that can be used in analysis of genome-scale human datasets. In brief, the task is to integrate knowledge obtained from experiments across multiple organisms, in the context of the family tree that relates the genes, by constructing a probabilistic model of function conservation and divergence. The main application of the probabilistic model will be to infer the function of human genes, from experiments in other organisms. While each gene family will have a specific model depending on its own, unique history, to avoid overfitting we will estimate only a small number of parameters that are shared across all families. We propose to use the same, rigorous model of functional evolution as employed in the GOC project, which is based on evolutionary gain and loss of different kinds of functions (e.g. a catalytic function, binding function or even participation in a biological process or pathway), using not only GO annotations but additional information such as protein domain structure and active sites. We will use the manually-curated examples from the GO Consortium as a training set for developing, as well as a test set for assessing, our computational inference method. We expect that this work will result in a dramatic increase in the number of GO annotations for human genes, resulting in much more informative results from pathway analysis, thus generating additional insights into human disease risk, progression and potential therapies. While our approach is general, we will focus manual validation on cancer-related pathways in order to ensure applicability specifically in cancer research.

抽象的 基因组数据的路径分析——利用有关基因如何在生物中共同发挥作用的先验知识 系统——在从大规模基因组研究中获得生物学见解方面发挥着越来越重要的作用，并且 然而，即使是最丰富的计算机可访问的生物途径来源。 基因本体（GO）信息非常不完整，阻碍了过去三年的路径分析。 多年来，GO 联盟开发了一个项目，该项目表明，通过利用严格的系统发育 方法，通过仔细使用，我们可以将人类基因的知识量增加五倍 在小鼠、果蝇和酵母等模型生物中获得的实验数据， 然而，依靠人类生物学家专家，我们建议不会扩展到整个人类基因组。 我们将开发一种利用 GOC 项目中获得的经验的计算方法。 为基因功能推理问题开发一个准确的、可扩展的计算解决方案，这将 显着增加可用于分析人类基因组规模的生物信息量 简而言之，任务是将从多个生物体的实验中获得的知识整合到一起。 通过构建功能的概率模型，了解与基因相关的家谱背景 概率模型的主要应用是推断 的函数。 人类基因来自其他生物体的实验，而每个基因家族都有一个特定的模型。 根据其自身独特的历史，为了避免过度拟合，我们将仅估计少量参数 我们建议使用与所有家庭共享的相同、严格的功能进化模型。 GOC 项目中采用的方法，该项目基于不同类型功能的进化增益和损失（例如 催化功能、结合功能甚至参与生物过程或途径），不仅使用 GO 注释，但我们将使用蛋白质结构域结构和活性位点等附加信息。 来自 GO 联盟的手动示例示例作为开发训练集以及测试集 评估我们的计算推理方法，我们预计这项工作将导致显着增加。 人类基因的 GO 注释数量，从而从通路中获得更多信息丰富的结果 分析，从而对人类疾病风险、进展和潜在疗法产生更多见解。 虽然我们的方法是通用的，但我们将重点关注癌症相关途径的手动验证，以确保 特别是在癌症研究中的适用性。