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/.

协作赠款将授予南达科他大学和北卡罗来纳大学，以开发本体驱动的工具，用于在大量表型数据上进行机器推理。 对“表型”特性（例如解剖和行为）的人类可读描述不适合计算分析。 然而，在进化生物学，遗传学和发展中，必须在文献和在线数据库中报告的大量描述性表型数据中发现模式。 本体是结构化的，受控的词汇，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的软件，该软件使用户可以在大型和多样的数据集中发现针对不同分类单元或突变基因的类似表型。将开发快速的语义相似性引擎，以允许搜索以相似表型特征为特征的进化过渡和突变基因。 将开发一个关于同源性推理的本体论框架，以允许对进化的各种谱系进行严格的推理。 将开发自然语言处理工具，以提高文献中采矿表型数据的效率并提高数据一致性。这套工具将在各种化石和现代脊椎动物的大量骨骼表型上进行测试。 将增强脊椎动物的分类学和解剖本体论，并正式编码解剖同源性的假设。 本体和软件工具以及从脊椎动物系统文献中提取的表型将与来自三种脊椎动物模型有机体的遗传和表型数据集成到知识库中：斑马鱼（danio rerio），非洲爪状froged（Xenopus laevis）（Xenopus laevis）和Mus Musculus（Xenopus laevis）。 知识库将使用语义Web标准接触通用推理器。 该系统将通过其在检索候选基因的成功中的成功来验证，用于研究良好的脊椎动物FIN-LIMB过渡和其他主要事件。 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，以从已发表的文献中可用的描述中有效捕获表型的本体论描述。 将通过恢复脊椎动物中从鳍到肢体的进化过渡的发展遗传途径来验证一系列工具，并通过与域生物信息学家的迭代测试以及更广泛的用户社区的生物学家进行了改进。 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，以提供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关键研究网络的协调来实现。 利益相关者的所有权将通过快速开放的各种产品的快速和开放释放，我们期望对更大的生物学社区具有直接和持久的价值，包括用于简化数据策划的工具，并进行大规模的语义相似性搜索，高质量的脊椎动物分类法和解剖学本体，以及针对具有词法的理论的标准。 我们将为学生，博士后和暑期实习生提供独特的培训环境，包括美国原住民在南达科他大学的外展以及少数民族和女学生，尽管与芝加哥大学的项目探索合作。 项目进度和成果将通过传统和在线渠道进行学术沟通（包括博客文章和邮件列表）进行传播；主要网络存在将在https://www.phenoscape.org/wiki/上。