Semantic Literature Annotation and Integrative Panomics Analysis for PTM-Disease Knowledge Network Discovery

项目总结 蛋白质翻译后修饰(PTM)在许多疾病中起着关键作用；然而，关键差距 留在研究基础设施中，以便对临时技术指标进行全球分析。关于酶的关键PTM信息- 底物关系、PTM酶的调节、PTM串扰和PTM的功能后果 PTM仍然埋藏在科学文献中。同时，虽然高通量泛组学(基因组， 转录组学、蛋白质组学、PTM蛋白质组学)数据为发现 PTM与疾病的关系，数据必须在集成的和易于获取的知识中进行分析 框架，以便研究人员和临床医生对疾病有一个分子上的了解。目标是 本应用的主要目的是为语义标注开发一个协同的知识环境 精确发现PTM疾病知识的科学文献和综合泛组学分析 医药。我们建议将文献挖掘和精选数据库中的PTM信息连接在一个 关于支持在上下文中分析泛组学数据的本体论框架的知识资源 对PTM网络的影响。为了扩大影响和促进合作发展，我们的资源将是公平的 (可查找、可访问、可互操作、可重复使用)和可与社区标准互操作。 具体目标是：(I)开发一种新的全面的自然语言处理系统 文献挖掘和PTM疾病知识提取；(Ii)开发PTM知识资源，用于 综合泛组学分析和网络发现；以及(3)提供公平的协作环境 用于可扩展的语义标注和知识集成。拟议的系统将建立在 NLP技术和文本挖掘工具已经由我们的团队和生物信息学开发 蛋白质信息资源(PIR)的基础设施。IPTMnet门户网站将允许搜索， 浏览、可视化和分析PTM网络和PTM相关突变 用户提供的组学数据，包括来自主要国家倡议的泛组学数据。使用场景将 包括鉴定致病基因变异体和分析细胞对激酶反应 抑制剂。我们的PTM知识库将通过RDF三库和SPARQL进行传播 端点用于语义查询，而我们的文本挖掘工具和全面的文献挖掘结果将是 在Bioc社区标准中传播，以便与其他文本挖掘管道无缝集成。 为了从事科学文献的社区语义标注，我们将举办一场黑客马拉松来开发 向语义网公开Bioc注释的文献语料库的工具，以及注释 探索用精确的本体论术语对科学文本进行标注。因此，该项目将提供一种 独特的研究资源，用于发现PTM-疾病网络以及可集成的协作 支持大数据到精准医疗知识的知识框架。

PROJECT SUMMARY Protein post-translational modification (PTM) plays a critical role in many diseases; however, critical gaps remain in research infrastructure for global analysis of PTMs. Key PTM information concerning enzyme- substrate relationships, regulation of PTM enzymes, PTM cross-talk, and functional consequences of PTM remains buried in the scientific literature. Meanwhile, while high-throughput panomics (genomic, transcriptomic, proteomic, PTM proteomic) data offer an unprecedented opportunity for the discovery of PTM-disease relationships, the data must be analyzed in an integrated and easily accessible knowledge framework in order for researchers and clinicians to gain a molecular understanding of disease. The goal of this application is to develop a collaborative knowledge environment for semantic annotation of scientific literature and integrative panomics analysis for PTM-disease knowledge discovery in precision medicine. We propose to connect PTM information from literature mining and curated databases in a knowledge resource on an ontological framework that supports analysis of panomics data in the context of PTM networks. To broaden impact and foster collaborative development, our resource will be FAIR (Findable, Accessible, Interoperable, Reusable) and interoperable with community standards. The specific aims are: (i) develop a novel NLP (natural language processing) system for full-scale literature mining and PTM-disease knowledge extraction; (ii) develop a PTM knowledge resource for integrative panomics analysis and network discovery; and (iii) provide a FAIR collaborative environment for scalable semantic annotation and knowledge integration. The proposed system will build upon the NLP technologies and text mining tools already developed by our team and the bioinformatics infrastructure at the Protein Information Resource (PIR). The iPTMnet web portal will allow searching, browsing, visualization and analysis of PTM networks and PTM-related mutations in conjunction with user-supplied omics data, including panomics data from major national initiatives. Use scenarios will include identification of disease-driving genetic variants and analysis of cellular responses to kinase inhibitors. Our PTM knowledgebase will be disseminated with an RDF triple-store and a SPARQL endpoint for semantic queries, while our text mining tools and full-scale literature mining results will be disseminated in the BioC community standard for seamless integration to other text mining pipelines. To engage the community semantic annotation of scientific literature, we will host a hackathon to develop tools to expose BioC-annotated literature corpora to the semantic web, as well as an annotation jamboree to explore tagging of scientific text with precise ontological terms. This project will thus offer a unique research resource for PTM-disease network discovery as well as an integrable collaborative knowledge framework to support Big Data to Knowledge in precision medicine.