The PhyloFacts Phylogenomic Encyclopedia of Microbial Protein Families

由金门·肖兰德博士领导的伯克利系统基因组学小组与微生物学、计算结构生物学和基因组学领域的顶级研究人员合作，创建了一部关于微生物基因家族的在线系统基因组百科全书。这一资源将使生物学家能够预测微生物基因组中编码的数百万蛋白质的功能、生物过程和3D结构。当一个基因被测序时，这项工作才刚刚开始。基因组功能注释和分析从基因测序工作结束的地方开始。下一个(也是更具挑战性的)问题涉及了解该基因编码的蛋白质的功能：该基因(或蛋白质)参与哪些生物学过程或途径？它在这些过程或途径中扮演什么角色？这种蛋白质的三维结构是什么？系统基因组学是在进化框架内对基因组进行研究，是迄今为止回答这些问题的最有力的方法。它在技术和计算上也是极具挑战性的，需要在无数生物信息学任务中的专业知识。这些要求严重限制了系统发育学方法在预测基因功能方面的应用，尽管有压倒性的证据表明，标准的功能注释方法容易出现严重的系统错误，而系统发育学推断能够防止这些错误。根据这笔赠款将开发的资源--PhyloFact微生物百科全书--将通过提供数百万微生物基因的预先计算的系统基因组分析来解决这些问题。PhyloFact微生物百科全书将结合强大的生物信息学新方法来重建这些古老基因家族的进化史，预测蛋白质结构、分子功能和细胞定位，并将基因与代谢网络和信号通路联系起来。这些数据将有助于生物学家理解微生物基因组进化的生物学机制，帮助识别水平基因转移事件，并为理解基因家族在基因复制、结构域改组、基因融合和裂变事件后如何进化提供一个框架。PhyloFact资源途径发现和分析模块将帮助生物学家发现微生物生物化学、新陈代谢、发育和细胞生物学的新方面。所有数据将在网上提供，包括交互式图形用户界面，使生物学家能够查看蛋白质结构，操纵和注释系统发育树，并在其专业领域合作对蛋白质家族进行功能注释。基因组测序项目产生的新序列将使用隐马尔可夫模型数据库被分类为家族和亚家族，隐马尔可夫模型是代表这些大分子共识结构中每个位置的首选氨基酸的统计模型。微生物科学界的这一在线合作将为研究不同微生物物种和基因家族的科学家提供一个基础，以分享他们的专业知识，从而加快生物发现的步伐。

The Berkeley Phylogenomics Group, headed by Dr Kimmen Sjolander, has joined forces with top investigators in microbiology, computational structural biology, and genomics to create an online phylogenomic encyclopedia of microbial gene families. This resource will enable biologists to predict the function, biological process and 3D structure of millions of proteins encoded in microbial genomes. When a gene is sequenced, the work has only just begun. Genome functional annotation and analysis starts where gene sequencing efforts end. The next (and much more challenging) questions involve understanding the function of the protein the gene encodes: What biological processes or pathways does this gene (or protein) participate in? What is its role in these processes or pathways? What is the protein's 3D structure? Phylogenomics, the study of genomes in an evolutionary framework, is the most powerful approach to date to answer these questions. It is also extremely challenging technically and computationally, and requires expertise in myriad bioinformatics tasks. These requirements have severely limited the application of phylogenomic approaches to predicting gene function despite overwhelming evidence that standard approaches to functional annotation are prone to serious systematic error, and that phylogenomic inference is able to prevent these errors. The resource to be developed under this grant, the PhyloFacts Microbial Encyclopedia, will address these problems by providing pre-computed phylogenomic analyses of millions of microbial genes. The PhyloFacts Microbial Encyclopedia will incorporate powerful new bioinformatics methods to reconstruct the evolutionary histories of these ancient gene families, predict protein structure, molecular function and cellular localization, and link genes to metabolic networks and signalling pathways.The PhyloFacts microbial phylogenomic encyclopedia will contain tens of thousands of phylogenetic trees for microbial gene families. These data will help biologists understand the biological mechanisms underlying the evolution of microbial genomes, assist in the identification of horizontal gene transfer events, and provide a framework for understanding how gene families evolve following gene duplication, domain shuffling and gene fusion and fission events. The PhyloFacts resource pathway discovery and analysis modules will help biologists discover novel aspects of microbial biochemistry, metabolism, development and cellular biology. All data will be provided on the web, including interactive graphical user interfaces to enable biologists to view protein structures, manipulate and annotate phylogenetic trees, and collaborate in the functional annotation of protein families in their area of expertise. New sequences generated by genome sequencing projects will be classified to families and subfamilies using a database of hidden Markov models, statistical models representing the preferred amino acids at each position in the consensus structure of these macromolecules. This online collaboratory for the microbial scientific community will provide a foundation for scientists working on different microbial species and gene families to share their expertise and thus advance the pace of biological discovery.