The Monarch Initiative: Linking diseases to model organism resources

君主倡议：将疾病与模型生物资源联系起来

• 批准号: 10693346
• 负责人: MELISSA A HAENDEL
• 金额: $ 132.37万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693346
• 关键词: Algorithms; Aloral; Animal Disease Models; Animal Model; Basic Science; Biological; Clinical; Collection; Communities; Computer software; Data; Data Sources; Diagnosis; Diagnostic; Disease; Disease model; Drosophila genus; Ensure; Evaluation; Feedback; Foundations; Genes; Genetic; Genetic Variation; Genotype; Goals; Graph; Human; Individual; Information Systems; Intuition; Libraries; Link; Machine Learning; Manuals; Methods; Modeling; Monitor; Mus; National Health Services; Ontology; Organism; Pathway interactions; Patients; Performance; Phase; Phenotype; Process; Quality Control; Rare Diseases; Reporting; Research; Research Personnel; Resources; Services; Structure; Techniques; Technology; Testing; Training; Translational Research; Unified Medical Language System; Variant; Visual; Visualization; Work; Zebrafish; bioinformatics tool; body system; clinical decision support; comparative; computable phenotypes; computerized tools; data access; data dissemination; data harmonization; data resource; deep learning; diagnostic algorithm; disease diagnosis; disease diagnostic; effective therapy; experience; gene discovery; gene function; improved; insight; knowledge graph; machine learning method; model organism; multimodal data; novel; operation; outreach; phenotypic data; precision medicine; success; tool; treatment optimization; web portal; web site

ABSTRACT Biomedical researchers need to identify novel disease genes and understand disease mechanisms; clinicians need to diagnose diseases and optimize treatments. An improved understanding of the genetic basis of disease helps achieve both goals. The Monarch Initiative makes this possible by integrating the fragmented data landscape into the most comprehensive open collection of genotype-phenotype data in the world. Our Knowledge Graph (KG) links together clinical, biomedical, and basic science research data spanning multiple organisms, and supports reasoning across a wide range of organisms, body systems, and diseases. Monarch has achieved demonstrable clinical and translational success using model organism data to perform rare disease diagnosis and gene-to-disease discovery, and our resources have become global standards. In Phase II, we integrated the Human Phenotype Ontology (HPO) into the UMLS; enhanced our variant prioritization algorithms and Exomiser tool, which was applied to 30,000 patients in the National Health Service (UK); developed the Biolink Model/API; released new ontologies (Mondo unified disease ontology, Unified Phenotype Ontology (uPheno), and the Environmental Conditions and Treatments Ontology (ECTO)), raised the number of harmonized data sources in our KG to 34; and overhauled our web Portal. We will leverage this foundation to make Monarch more intuitive for a diversity of users and contexts in phase III as follows: Augment the Monarch Portal with new visualizations and tools. Guided by user requirements, iterative user testing, and feedback, we propose to enhance the user experience and Portal functionality, focusing our work on improvements to Navigation, Visualization, and Query. Evaluate, optimize, and enhance algorithms for disease diagnostics, cross-species inference, and gene-disease discovery. We will develop a comprehensive, modular evaluation framework, ‘PhEval,’ that will allow us to monitor the diagnostic yield and performance of cross-species inference as our ontologies and data graphs evolve. This will assist basic science researchers and clinicians to reveal cross-species mechanistic evidence and evaluate potential precision disease modeling strategies. Disseminate computational tools, data, services, and tutorials to a broad translational community. We will expand access to our KG to enable users to process the KG for different domains and use cases, through simplified downloads, APIs, software libraries, R packages, Jupyter notebooks, and Dockerized resources, along with training materials. This will better support bioinformaticians and other researchers in leveraging our KG and phenotype data in their analyses. The Monarch Initiative aims to significantly improve the utilization, accessibility, and value of animal models for disease diagnosis and discovery.

摘要 生物医学研究人员需要识别新的疾病基因并了解疾病机制;临床医生 需要诊断疾病和优化治疗。更好地了解遗传基础 疾病有助于实现这两个目标。君主计划通过整合分散的 数据景观转化为世界上最全面的开放式基因型-表型数据集合。我们 知识图谱（KG）将临床、生物医学和基础科学研究数据链接在一起， 生物体，并支持跨各种生物体，身体系统和疾病的推理。君主 已经取得了可证明的临床和转化成功，使用模式生物数据进行罕见的 疾病诊断和基因对疾病的发现，我们的资源已成为全球标准。同相 第二，我们将人类表型本体（HPO）整合到UMLS中;增强了我们的变体优先级 算法和Exomiser工具，应用于国家卫生服务（英国）的30，000名患者; 开发了Biolink模型/API;发布了新的本体（Mondo统一疾病本体，统一 表型本体（uPheno）和环境条件和治疗本体（ECTO）），提出了 我们KG中的协调数据源数量达到34个;并彻底改革了我们的门户网站。我们会利用这个 在第三阶段，Monarch将为不同的用户和环境提供更直观的体验，具体如下： 使用新的可视化和工具增强Monarch Portal。以用户需求为导向，迭代 用户测试和反馈，我们建议增强用户体验和门户功能， 致力于改进导航、可视化和查询。 评估、优化和增强疾病诊断、跨物种推断和 基因-疾病发现我们将开发一个全面的模块化评估框架“PhEval”， 允许我们监控跨物种推理的诊断产量和性能，因为我们的本体和数据 图表在进化。这将有助于基础科学研究人员和临床医生揭示跨物种机制 证据和评估潜在的精确疾病建模策略。 向广泛的翻译社区传播计算工具、数据、服务和教程。我们 将扩大对我们KG的访问权限，使用户能够通过以下方式处理不同领域和用例的KG 简化的下载、API、软件库、R包、Quixyter笔记本和Dockerized资源， 沿着培训材料。这将更好地支持生物信息学家和其他研究人员利用我们的 KG和表型数据。 君主倡议旨在显著提高动物的利用率，可及性和价值。 疾病诊断和发现的模型。

项目成果

PhenoDigm: analyzing curated annotations to associate animal models with human diseases.

• DOI: 10.1093/database/bat025
• 发表时间: 2013
• 期刊: Database : the journal of biological databases and curation
• 作者: Smedley D;Oellrich A;Köhler S;Ruef B;Sanger Mouse Genetics Project;Westerfield M;Robinson P;Lewis S;Mungall C
• 通讯作者: Mungall C

CLO: The cell line ontology.

• DOI: 10.1186/2041-1480-5-37
• 发表时间: 2014
• 期刊: Journal of biomedical semantics
• 影响因子: 1.9
• 作者: Sarntivijai S;Lin Y;Xiang Z;Meehan TF;Diehl AD;Vempati UD;Schürer SC;Pang C;Malone J;Parkinson H;Liu Y;Takatsuki T;Saijo K;Masuya H;Nakamura Y;Brush MH;Haendel MA;Zheng J;Stoeckert CJ;Peters B;Mungall CJ;Carey TE;States DJ;Athey BD;He Y
• 通讯作者: He Y

Gateways to the FANTOM5 promoter level mammalian expression atlas.

通往Fantom5启动子级哺乳动物表达地图集的网关。

• DOI: 10.1186/s13059-014-0560-6
• 发表时间: 2015-01-05
• 期刊: Genome biology
• 影响因子: 12.3
• 作者: Lizio M;Harshbarger J;Shimoji H;Severin J;Kasukawa T;Sahin S;Abugessaisa I;Fukuda S;Hori F;Ishikawa-Kato S;Mungall CJ;Arner E;Baillie JK;Bertin N;Bono H;de Hoon M;Diehl AD;Dimont E;Freeman TC;Fujieda K;Hide W;Kaliyaperumal R;Katayama T;Lassmann T;Meehan TF;Nishikata K;Ono H;Rehli M;Sandelin A;Schultes EA;'t Hoen PA;Tatum Z;Thompson M;Toyoda T;Wright DW;Daub CO;Itoh M;Carninci P;Hayashizaki Y;Forrest AR;Kawaji H;FANTOM consortium
• 通讯作者: FANTOM consortium

SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing.

• DOI: 10.1186/s13073-022-01046-6
• 发表时间: 2022-04-28
• 期刊: Genome medicine
• 影响因子: 12.3

KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response.

KG-COVID-19：为 COVID-19 响应生成定制知识图的框架。

• DOI: 10.1101/2020.08.17.254839
• 发表时间: 2020
• 期刊: bioRxiv : the preprint server for biology
• 作者: Reese,Justin;Unni,Deepak;Callahan,TiffanyJ;Cappelletti,Luca;Ravanmehr,Vida;Carbon,Seth;Fontana,Tommaso;Blau,Hannah;Matentzoglu,Nicolas;Harris,NomiL;Munoz-Torres,MonicaC;Robinson,PeterN;Joachimiak,MarcinP;Mungall,Christopher
• 通讯作者: Mungall,Christopher