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

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时效率低下。PI将使生物学家能够有效和自动地回答这些问题，通过开发一个模拟生物数据固有结构的计算基础设施，通过创建一个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，来模拟关系数据库中无法有效表示的关系（进化、相互作用、调节）。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缘可以表示“基因G编码蛋白质P”或“基因G受蛋白质P调控”。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。函数注释将是自动化的，结果将以机器可读和人类可读的格式生成。直观的基于网络的界面将提供给实验生物学家导航和解释数据。将提供预测功能的来源，使生物学家能够深入研究潜在的支持和证据。所有核心软件工具将以开源方式提供，数据将可下载。该项目将提供适合生物信息学、基因组学、系统基因组学和进化生物学等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。该项目将提供适合生物信息学、基因组学、系统基因组学和进化生物学等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。

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.