GLYCO, A HIGHLY EXPRESSIVE ONTOLOGY FOR THE GLYCOBIOLOGY DOMAIN

GLYCO，糖生物学领域的高度表达本体论

• 批准号: 8363019
• 负责人: WILLIAM YORK
• 金额: $ 5.16万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8363019
• 关键词: Amino Acids; Anabolism; Biochemical; Biological Process; Carbohydrates; Catabolism; Cereals; Chemicals; Collection; Complex; Databases; Enzymes; Funding; Genes; Genome; Glycobiology; Goals; Grant; Knowledge; Knowledge Discovery; Link; Location; Logic; National Center for Research Resources; Ontology; Peptide N-glycohydrolase F; Peptides; Polysaccharides; Population; Principal Investigator; Process; Research; Research Infrastructure; Resources; Retrieval; Signal Transduction; Source; Specific qualifier value; Structure; Structure-Activity Relationship; Techniques; Technology; Transferase; United States National Institutes of Health; Web Ontology Language; base; cost; information processing; knowledge base

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. GlycO is a highly expressive ontology that embodies knowledge of glycan structure and the relationships between the structure of glycans and their participation in biological processes. With GlycO we are aiming at a general description of the glycobiology domain that consists of a robust schema and large knowledgebase. The schema conceptually defines classes (e.g., "the class containing all N-glycans") to which specific instances are assigned and the knowledgebase is comprised of instances (e.g., a specific glycan structure) and specific relationships between instances. The schema allows reasoning about the concepts by exploiting the Web Ontology Language OWL-DL (based on Description Logic) to place restrictions on relationships. This provides the basis for automated population of the knowledge base, a process in which new instances are added and classified. The information needed to populate the GlycO knowledgebase can be automatically extracted from several partially overlapping sources, including the Kyoto Encyclopedia of Genes and Genomes (KEGG), Glycosciences.de databases (SweetDB), and the Complex Carbohydrate Structural Database (CARBBANK). In order to avoid multiple entries of identical structures, transformation and disambiguation techniques are applied. The ultimate goal is to generate a large ontology that can be used for the annotation, retrieval and processing of information regarding glycan structure-function relationships and the discovery of the knowledge implicit in that information. In GlycO, structural information is modularized at the instance level. That is, structures are composed of canonical building blocks that can be reused in different chemical contexts. Larger structures (e.g., "glycans") are composed of smaller canonical building blocks (carbohydrate_residues), which are, in turn, composed of even smaller canonical building blocks (carbohydrate_residue_atoms). The building blocks contain specific structural information, such as the absolute configuration, ring form, and anomeric configuration of a carbohydrate residue, and contextual information, such as the location of the residue in the glycan. For collections of glycans, such as N-glycans, that have significant overlap in their biosynthesis, each of the constituent canonical residues embodies contextual information that can be correlated to its interactions with biosynthetic enzymes and other biochemical entities. Thus, simply listing the residues that make up a glycan provides a description of the glycan structure and an implicit description of the biological processes (e.g., biosynthesis, catabolism, signal transduction) that it participates in. In GlycO, the links between canonical residues are expressed as full-fledged nodes. Such promotion of an edge to a node is often referred to as "reification" of a relationship. This schema allows the reified links to be specified in hierarchical terms. That is, the link between two canonical residues (e.g., the proximal core ¿-GlcpNAc residue and an Asn residue) explicitly embodies a more finely grained link between two atoms (i.e., the canonical carbohydrate_residue_C1_atom and the canonical amino_acid_residue_N4_atom). These links are themselves canonical objects that can be reused in a modular fashion. For example, the connection between a particular N-glycan and a given peptide corresponds to a higher level link, which, in turn, embodies the canonical link between the canonical core ¿-GlcNAc residue and the canonical Asn residue. Thus, the schema provides for the complete, unambiguous specification of a complex structure simply by listing its highest level components and links. This not only provides enhanced computability and logical consistency, it provides new classes of objects (links) that can be associated with particular processes. For example, the link described above is biosynthetically formed and hydrolyzed by specific enzymes (oligosaccharyl transferase and PNGase-F, respectively).

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 Glyco是一种高度表达的本体论，它体现了关于多糖结构的知识以及多糖结构与其参与生物过程之间的关系。通过Glyco，我们的目标是对糖生物学领域的一般描述，包括一个健壮的模式和庞大的知识库。该模式在概念上定义了被分配了特定实例的类(例如，包含所有N-糖链的类)，并且知识库由实例(例如，特定的多糖结构)和实例之间的特定关系组成。该模式通过利用Web本体语言OWL-DL(基于描述逻辑)来对关系进行限制，从而允许对概念进行推理。这为知识库的自动填充提供了基础，这是一个添加新实例并对其进行分类的过程。填充Glyco知识库所需的信息可以从几个部分重叠的来源中自动提取，包括京都基因和基因组百科全书(KEGG)、糖类科学数据库(SweetDB)和复杂碳水化合物结构数据库(CARBBANK)。为了避免相同结构的多个条目，采用了转换和歧义消除技术。最终目标是生成一个大型本体，可用于注释、检索和处理有关多糖结构-功能关系的信息，并发现该信息中隐含的知识。在Glyco中，结构信息在实例级别模块化。也就是说，结构是由可以在不同化学环境中重复使用的规范构建块组成的。较大的结构(如“多聚糖”)由较小的正则积木(碳水化合物残基)组成，而正则积木又由更小的正则积木(碳水化合物残基原子)组成。构建块包含特定的结构信息，例如碳水化合物残基的绝对构型、环形式和异构体构型，以及上下文信息，例如糖链中残基的位置。对于生物合成有显著重叠的多聚糖，如N-多聚糖，每个组成的典型残基都包含与其与生物合成酶和其他生化实体相互作用相关的上下文信息。因此，简单地列出组成多糖的残基提供了对多糖结构的描述和对其参与的生物过程(例如，生物合成、分解代谢、信号转导)的隐含描述。在Glyco中，正则残基之间的链接被表示为完整的节点。这种边到节点的提升通常被称为关系的“具体化”。该模式允许以分层术语指定具体化的链接。也就是说，两个正则残基(例如，最近的核心-GlcpNAc残基和Asn残基)之间的链接明确地体现了两个原子(即，正则碳水化合物_残基_C1_原子和正则氨基酸_残基_N4_原子)之间更细粒度的链接。这些链接本身就是规范的对象，可以模块化的方式重用。例如，特定N-葡聚糖和给定肽之间的连接对应于更高水平的连接，这反过来又体现了典型的核心-GlcNAc残基和典型的Asn残基之间的典型联系。因此，该模式只需列出其最高级别的组件和链接，即可为复杂结构提供完整、明确的规范。这不仅提供了增强的可计算性和逻辑一致性，还提供了可以与特定进程相关联的新的对象类(链接)。例如，上述连接是由特定的酶(分别是寡糖基转移酶和PNGase-F)以生物合成的方式形成和水解的。