EAGER: Towards a self-organizing map and hyper-dimensional information network for the human genome

EAGER：迈向人类基因组的自组织图谱和超维信息网络

• 批准号: 1355632
• 负责人: Kimmen Sjolander
• 金额: $ 25万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1355632&HistoricalAwards=false
• 关键词: EAGER; Towards; self; organizing; map

Genome data enable scientists to pose a host of compelling questions spanning diverse disciplines. However, relational databases are inefficient at modeling the complex relationships between genes and the proteins they encode. The PI will enable biologists to answer these questions efficiently and automatically by developing a computational infrastructure that models the inherent structure of biological data, by creating a graphical database of genome and proteome data for the human genome and related eukaryotic genomes to model relationships (evolutionary, interaction, regulatory) that cannot be represented effectively in relational databases. Nodes will represent different biological entities - genes, proteins, species - and edges between nodes will represent different relationships between these entities. For example, edges between genes and proteins can represent "Gene G encodes protein P" or "Gene G is regulated by protein P". Edges between proteins can represent physical interaction or homology. Different types of features for these entities at each node will be stored and the team will use the network structure and statistical modeling methods to enable precise predictions of various aspects of "function" -- molecular function, metabolic pathway, biological process, cellular localization, inter-molecular interactions, protein 3D structure, etc. Functional annotation will be automated, with results produced in both machine-readable and human-readable formats. Intuitive web-based interfaces will be provided for navigation and interpretation of data by experimental biologists. Provenance of predicted functions will be provided, allowing biologists to drill down to examine the underlying support and evidence. All core software tools will be provided in open source, and data will be downloadable. This project will contribute curriculum materials suitable for inclusion in undergraduate and graduate courses in bioinformatics, genomics, phylogenomics and evolutionary biology and provide a resource for researchers in vertebrate genomes.This project will contribute curriculum materials suitable for inclusion in undergraduate and graduate courses in bioinformatics, genomics, phylogenomics and evolutionary biology,and provide a resource for researchers in vertebrate genomes.

基因组数据使科学家能够提出一系列跨越不同学科的引人注目的问题。 然而，关系数据库在对基因和它们编码的蛋白质之间的复杂关系进行建模方面效率低下。PI将使生物学家能够有效地自动回答这些问题，通过开发一个模拟生物数据固有结构的计算基础设施，通过创建人类基因组和相关真核生物基因组的基因组和蛋白质组数据的图形数据库，以模拟关系数据库中无法有效表示的关系（进化，相互作用，监管）。 节点将代表不同的生物实体-基因，蛋白质，物种-节点之间的边将代表这些实体之间的不同关系。例如，基因和蛋白质之间的边可以表示“基因G编码蛋白质P”或“基因G受蛋白质P调节”。蛋白质之间的边缘可以表示物理相互作用或同源性。 每个节点上这些实体的不同类型的特征将被存储，团队将使用网络结构和统计建模方法来精确预测“功能”的各个方面-分子功能，代谢途径，生物过程，细胞定位，分子间相互作用，蛋白质3D结构等。功能注释将自动化，其结果以机器可读和人类可读的格式产生。将提供直观的基于网络的界面，供实验生物学家导航和解释数据。将提供预测功能的来源，允许生物学家深入研究以检查潜在的支持和证据。所有核心软件工具都将以开放源代码提供，数据将可下载。 本项目将提供适合于生物信息学、基因组学、基因组学和进化生物学本科和研究生课程的课程材料，并为脊椎动物基因组研究人员提供资源。