A Software and Data Resource for Multidomain Homology and Protein Classification

用于多域同源性和蛋白质分类的软件和数据资源

• 批准号: 0641313
• 负责人: Marie Dannie Durand
• 金额: $ 67.73万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0641313&HistoricalAwards=false
• 关键词: Software; Data; Resource; Multidomain; Homology

Carnegie Mellon University is awarded a grant to develop general purpose software and data resource for homology detection and classification of multi-domain sequence families. Reliable homology identification is essential for accuracy in gene annotation, function prediction and comparative genomics. Homology identification is a well studied problem for single domain proteins, but remains unsolved for complex, multi-domain proteins. The project will develop a resource of handcurated validation data sets; high accuracy methods for detection of multi-domain homologs and orthologs; methods for automated classification of multi-domain families; and a publicly available database with a web-based interface and visualization tools for exploratory analysis of the data. A key component of the proposed platform is a novel and accurate method for inferring multi-domain family relationships by exploiting the local structure of the sequence similarity network. It will produce a platform for studying the evolutionary processes of domain shuffling; the histories of gene duplications and domain duplications, insertions, deletions and rearrangements in the evolution of specific of multi-domain families; and the functional roles these families play in contemporary organisms. Accurate methods to identify multi-domain homologs will enhance a broad range of applications in gene function prediction and comparative genomics, thus contributing to essential research infrastructure. Scientifically, the proposed research addresses basic problems of classification that are fundamental to a broad range of widely used genomic analyses, including protein function prediction, especially for complex, multi-domain proteins. Such proteins are of particular interest in vertebrate genomes, where they are implicated in development, neural function, tissue repair and the immune system. The research themes of the proposal will be incorporated into lectures for the Pittsburgh Supercomputing Center's workshop on Developing Bioinformatics Programs, a two-week course aimed at preparing MARC (Minority Access to Research Careers) program faculty to teach bioinformatics courses at their local campuses. Undergraduates will participate in research on individual multi-domain families through course projects in ''Evolution and the History of Life''.

卡内基梅隆大学获得一笔赠款，用于开发通用软件和数据资源，用于多结构域序列家族的同源性检测和分类。可靠的同源性鉴定对于基因注释、功能预测和比较基因组学研究的准确性至关重要。单结构域蛋白质的同源性鉴定是一个研究得很好的问题，但对于复杂的多结构域蛋白质仍然没有解决。该项目将开发一个手工验证数据集资源;检测多域同源物和直系同源物的高精度方法;多域家族自动分类方法;以及一个公开可用的数据库，该数据库具有基于网络的界面和可视化工具，用于数据的探索性分析。所提出的平台的一个关键组成部分是一个新的和准确的方法，通过利用序列相似性网络的局部结构来推断多域的家庭关系。它将产生一个平台，用于研究结构域改组的进化过程;特定多结构域家族进化中的基因复制和结构域复制，插入，缺失和重排的历史;以及这些家族在当代生物中发挥的功能作用。准确的方法来确定多域同源物将提高基因功能预测和比较基因组学的广泛应用，从而有助于必要的研究基础设施。科学上，拟议的研究解决了分类的基本问题，这些问题对于广泛使用的基因组分析至关重要，包括蛋白质功能预测，特别是对于复杂的多结构域蛋白质。这些蛋白质在脊椎动物基因组中特别令人感兴趣，其中它们涉及发育、神经功能、组织修复和免疫系统。该提案的研究主题将被纳入匹兹堡超级计算中心关于发展生物信息学计划的研讨会的讲座中，这是一个为期两周的课程，旨在为MARC（少数民族获得研究职业）计划教师在当地校园教授生物信息学课程做准备。本科生将通过“进化与生命史”课程项目参与对个人多领域家庭的研究。