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

摘要 基因组数据的通路分析-利用基因在生物学中如何共同发挥作用的先验知识 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， 尤其是在癌症研究中。然而，即使是计算机可访问的生物途径的最丰富的来源， 基因本体（GO）信息是非常不完整的，阻碍了途径分析。过去三 多年来，GO联盟已经开发了一个项目，该项目表明，通过利用严格的系统发育 通过这种方法，我们可以通过仔细使用 在模式生物如小鼠、果蝇和酵母中获得的实验数据。GOC项目， 然而，它依赖于专业的人类生物学家，不会扩展到整个人类基因组。在这里，我们建议 开发一种计算方法，利用GOC项目中获得的经验。我们将 开发一个准确的，可扩展的计算解决方案的基因功能推断问题，这将 大大增加了可用于分析人类基因组规模的生物信息量。 数据集。简而言之，任务是整合从多个生物体的实验中获得的知识， 通过构建功能的概率模型， 守恒和发散概率模型的主要应用将是推断 人类基因，从其他生物体的实验中。虽然每个基因家族都有一个特定的模型， 根据其自身的独特历史，为了避免过度拟合，我们将仅估计少量参数 是所有家庭共有的我们建议使用同样的，严格的功能进化模型， 在GOC项目中使用，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、结合功能或甚至参与生物过程或途径），不仅使用 GO注释，但额外的信息，如蛋白质结构域结构和活性位点。我们将使用 来自GO联盟的手动策划的示例作为开发的训练集，以及 评估我们的计算推理方法。我们预计，这项工作将导致 人类基因的GO注释的数量，导致来自途径的更多信息结果 分析，从而产生对人类疾病风险，进展和潜在疗法的额外见解。 虽然我们的方法是通用的，但我们将专注于癌症相关途径的手动验证，以确保 特别是在癌症研究中的应用。

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.