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

项目摘要 蛋白质翻译后修饰（PTM）在许多疾病中起着关键作用;然而， 保留在用于PTM全球分析的研究基础设施中。关于酶的关键PTM信息- 底物关系，PTM酶的调节，PTM串扰，以及 PTM仍然埋在科学文献中。同时，虽然高通量泛组学（基因组学， 转录组学、蛋白质组学、PTM蛋白质组学）数据为发现 PTM-疾病关系，必须以综合且易于获取的知识分析数据 为了使研究人员和临床医生获得对疾病的分子理解，目标 本申请的目的是开发一个协作知识环境， 科学文献和综合泛组学分析用于精确发现PTM疾病知识 药我们建议将文献挖掘和策展数据库中的PTM信息连接起来， 支持上下文中泛组学数据分析的本体框架上的知识资源 PTM网络。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、可扩展、可互操作、可重用），并可与社区标准互操作。 具体目标是：（i）开发一个新的NLP（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， 整合泛组学分析和网络发现;以及（iii）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 蛋白质信息资源（Protein Information Resource，PIR）iPTMnet门户网站将允许搜索， PTM网络和PTM相关突变的浏览、可视化和分析， 用户提供的组学数据，包括主要国家举措提供的泛组学数据。使用场景将 包括鉴定疾病驱动遗传变异和分析细胞对激酶的反应 抑制剂的我们的PTM知识库将通过RDF三重存储和SPARQL进行传播 端点的语义查询，而我们的文本挖掘工具和全面的文献挖掘结果将 在BioC社区标准中传播，以便无缝集成到其他文本挖掘管道中。 为了吸引科学文献的社区语义注释，我们将举办一个黑客会议， 将BioC注释的文献语料库暴露给语义网的工具，以及注释 探索用精确的本体论术语标记科学文本的盛会。该项目将提供一个 PTM疾病网络发现的独特研究资源以及可整合的协作 知识框架，以支持精准医疗中的大数据到知识。

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.