Systematic Analysis of Experimental Phenotype Data Reveals Gene Functions

High-throughput phenotyping projects in model organisms have the potential to improve our understanding of gene functions and their role in living organisms. We have developed a computational, knowledge-based approach to automatically infer gene functions from phenotypic manifestations and applied this approach to yeast (Saccharomyces cerevisiae), nematode worm (Caenorhabditis elegans), zebrafish (Danio rerio), fruitfly (Drosophila melanogaster) and mouse (Mus musculus) phenotypes. Our approach is based on the assumption that, if a mutation in a gene leads to a phenotypic abnormality in a process , then must have been involved in , either directly or indirectly. We systematically analyze recorded phenotypes in animal models using the formal definitions created for phenotype ontologies. We evaluate the validity of the inferred functions manually and by demonstrating a significant improvement in predicting genetic interactions and protein-protein interactions based on functional similarity. Our knowledge-based approach is generally applicable to phenotypes recorded in model organism databases, including phenotypes from large-scale, high throughput community projects whose primary mode of dissemination is direct publication on-line rather than in the literature.模式生物（model organism）的高通量表型（high-throughput phenotyping）研究项目，有望助力我们深化对基因功能及其在活有机体内作用机制的认知。我们开发了一种基于知识的计算方法，可从表型表征中自动推断基因功能，并将该方法应用于酵母（Saccharomyces cerevisiae）、秀丽隐杆线虫（Caenorhabditis elegans）、斑马鱼（Danio rerio）、黑腹果蝇（Drosophila melanogaster）以及小家鼠（Mus musculus）的表型数据。我们的方法基于如下假设：若某基因的突变导致某生命过程出现表型异常，则该基因必然直接或间接参与了该过程。我们借助为表型本体（phenotype ontology）构建的形式化定义，系统分析了模式生物模型中已记录的表型数据。我们通过手动验证推断出的基因功能有效性，并基于功能相似性验证了该方法在预测遗传相互作用与蛋白质-蛋白质相互作用方面的显著提升效果。我们提出的基于知识的计算方法可广泛适用于模式生物数据库中收录的表型数据，包括那些主要传播方式为直接在线发布而非传统学术文献发表的大规模高通量社区项目所产生的表型数据。