Computational Methods for Directed Functional Genomics

描述（由申请人提供）：基因功能（或表型）的计算预测可以通过将注意力集中在可能的实验子集上来减少实验问题的规模。当前函数注释数据库——例如：例如，使用基因本体（GO）功能对基因进行注释的酵母菌基因组数据库（SGD）是非常重要的资源，但不是为承载计算预测而设计的。尽管SGD和其他几个注释数据库将预测标记为这样，但它们没有提供信心措施。定量预测的需求是存在的，这与“有人这么说”的定性预测截然不同。概率评分系统可能是最有用的，其中分数传达了准确性的概率。我们将通过开发预测功能和表型的概率模型来生成概率预测。出于数据可用性的考虑，我们使用酿酒线虫和秀丽隐杆线虫作为模型系统。

DESCRIPTION (provided by applicant): Computational prediction of gene function (or phenotype) can reduce the scale of an experimental problem by focusing attention on a subset of possible experiments. Current function annotation databases-e.g., the Saccharomyces Genome Database (SGD) annotation of genes with Gene Ontology (GO) functions-are critically important resources, but were not designed to host computational predictions Although SGD and several other annotation databases label predictions as such, they provide no measures of confidence. The need exists for quantitative predictions, as distinct from qualitative "somebody said so" predictions. Probabilistic scoring systems, in which the score communicates the probability of veracity, are likely to be the most useful. We will generate probabilistic predictions by developing probabilistic models for predicting function and phenotype. For reasons of data availability, we use S. cerevisiae and C. elegans as model systems. We will also generate probabilistic models to predict protein and genetic interactions. We will exploit probabilistic networks of protein and genetic interaction in several ways. We will apply ideas from communication theory (2-terminal network reliability) to predict new members of protein complexes from probabilistic protein networks. We will develop computational methods to guide efficient discovery of genetic interactions in S. cerevisiae, as a model for guiding future high-throughput studies in metazoans. We will exploit probabilistic synthetic lethal interaction networks to identify drug mechanism of action. We will disseminate predictions to the broader biomedical community. We propose a distributed quantitative prediction resource inspired by the DAS system of distributed genome annotation. We will adapt previously developed interfaces for browsing, searching, and retrieving probabilistic annotations to enhance their utility. In Aim 1. we develop, apply, and validate methods for predicting function, phenotype, physical and genetic interaction in S. cerevisiae and C. elegans. In Aim 2. we exploit probabilistic networks of protein and genetic interaction in S. cerevisiae to elucidate network structure, to guide functional genomic experiments, and to reveal drug mechanism of action. In Aim, 3. we disseminate probabilistic predictions within a simple, generic, distributed software framework for sharing and browsing quantitative predictions.