Collaborative research: ABI Development: Ontology-enabled reasoning across phenotypes from evolution and model organisms

合作研究：ABI 开发：跨进化和模式生物表型的本体推理

• 批准号: 1062542
• 负责人: Paula Mabee
• 金额: $ 181.77万
• 依托单位: University of South Dakota Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1062542&HistoricalAwards=false
• 关键词: Collaborative; research; ABI; Development; Ontology

Collaborative grants are awarded to the University of South Dakota and the University of North Carolina to develop ontology-driven tools for machine reasoning over large volumes of phenotype data. Human-readable descriptions of "phenotypic" properties such as anatomy and behavior are not well-suited to computational analysis. Yet, in evolutionary biology, genetics and development, computational assistance is necessary to discover patterns within the enormous volumes of descriptive phenotype data that are being reported in the literature and in online databases. Ontologies are structured, controlled vocabularies that can be applied to collections of descriptive data to permit logical reasoning to be used. Using the evolutionary transition from fins to limbs as a test system, this project will develop ontologically-aware software that allows users to discover similar sets of phenotypes for different taxa or mutant genes within large and diverse datasets. A fast semantic similarity engine will be developed to allow searches for evolutionary transitions and mutant genes characterized by similar phenotypic profiles. An ontological framework for reasoning over homology will be developed to allow rigorous reasoning over evolutionary diverse lineages. Natural language processing tools will be developed to improve upon the efficiency of mining phenotype data from the literature and improving data consistency. This suite of tools will be tested on a large number of skeletal phenotypes from diverse fossil and modern vertebrates. Taxonomic and anatomical ontologies for vertebrates will be augmented and hypotheses of anatomical homology formally encoded. The ontologies and software tools, together with phenotypes extracted from the vertebrate systematic literature, will be integrated in the knowledgebase with genetic and phenotype data from three vertebrate model organisms: zebrafish (Danio rerio), African clawed frog (Xenopus laevis), and mouse (Mus musculus). The knowledge base will be exposed to generic reasoners using semantic web standards. The system will be validated by its success in retrieving candidate genes for the well-studied vertebrate fin-limb transition and other major events in skeletal evolution. The evolutionary breadth of the test data requires the development of a rigorous framework for reasoning over hypotheses of homology. Another goal is to develop and evaluate natural language processing tools for efficiently capturing ontological descriptions of phenotype from the descriptions available in the published literature. The suite of tools will be validated by recovering developmental genetic pathways that underlie the evolutionary transition from fin to limb in vertebrates, and refined by iterative testing with domain bioinformaticians on the project and biologists from the broader user community. A broad community of users will participate through the lifecycle of this project in the development of community standards and resources for the interoperability and computability of phenotypic knowledge. This will be achieved through workshops, usability testing sessions, and coordination with key research networks. Stakeholder ownership will be enhanced by rapid and open release of a variety of products that we anticipate to be of immediate and enduring value to the greater biology community, including tools for streamlining data curation and performing large-scale semantic similarity searches, high quality vertebrate taxonomy and anatomy ontologies, and standards for reasoning over homology. We will provide a unique training environment for students, postdocs and summer interns, including Native Americans through outreach at the University of South Dakota and minority and female students though a collaboration with Project Exploration at the University of Chicago. Project progress and outcomes will be disseminated through both traditional and online outlets for scholarly communication (including blog posts and mailing lists); the primary web presence will be at https://www.phenoscape.org/wiki/.

南达科他州大学和北卡罗来纳州大学获得了合作赠款，用于开发本体驱动的工具，用于对大量表型数据进行机器推理。 人类可读的“表型”属性（如解剖学和行为）的描述并不适合计算分析。 然而，在进化生物学，遗传学和发展，计算辅助是必要的，以发现在文献和在线数据库中报道的大量描述性表型数据中的模式。 本体是结构化的、受控的词汇表，其可以应用于描述性数据的集合以允许使用逻辑推理。使用从鳍到四肢的进化过渡作为测试系统，该项目将开发本体感知软件，允许用户在大型和多样化的数据集中发现不同分类群或突变基因的相似表型集。一个快速的语义相似性引擎将被开发，以允许搜索进化的转变和突变基因的特征相似的表型配置文件。 一个本体论的框架推理同源性将被开发，以允许严格的推理进化不同的血统。 将开发自然语言处理工具，以提高从文献中挖掘表型数据的效率，并提高数据的一致性。这套工具将在来自不同化石和现代脊椎动物的大量骨骼表型上进行测试。 脊椎动物的分类学和解剖学本体将被扩充，解剖学同源性的假设将被正式编码。 本体论和软件工具，连同从脊椎动物系统文献中提取的表型，将与三种脊椎动物模式生物的遗传和表型数据集成在知识库中：斑马鱼（Danio rerio），非洲爪蛙（Xenopus laevis）和小鼠（Mus musculus）。 知识库将暴露给使用语义网标准的通用推理机。 该系统将被验证其成功检索候选基因的研究脊椎动物鳍肢过渡和骨骼进化的其他主要事件。 测试数据的进化广度需要开发一个严格的框架来推理同源性假设。 另一个目标是开发和评估自然语言处理工具，用于有效地从已发表文献中的描述中捕获表型的本体描述。 这套工具将通过恢复脊椎动物从鳍到肢体进化过渡的发育遗传途径进行验证，并通过与项目领域生物信息学家和来自更广泛用户社区的生物学家进行迭代测试进行改进。 一个广泛的用户社区将通过该项目的生命周期参与社区标准和资源的开发，以实现表型知识的互操作性和可计算性。这将通过研讨会、可用性测试会议以及与主要研究网络的协调来实现。 利益相关者的所有权将通过快速和开放发布各种产品来增强，我们预计这些产品将对更大的生物学社区产生直接和持久的价值，包括简化数据管理和执行大规模语义相似性搜索的工具，高质量的脊椎动物分类学和解剖学本体，以及同源性推理标准。 我们将为学生，博士后和暑期实习生提供一个独特的培训环境，包括通过在南达科他州和少数民族和女性学生的大学推广美洲原住民，通过与项目探索在芝加哥大学的合作。 项目进展和成果将通过传统和在线学术交流渠道（包括博客文章和邮件列表）传播;主要网站将是https://www.phenoscape.org/wiki/。