Omics Phenotyping for Identifying Molecular Signatures of the Healthy and Failing Heart: An Integrated Data Science Platform

用于识别健康和衰竭心脏分子特征的组学表型分析：集成数据科学平台

• 批准号: 10560520
• 负责人: Peipei Ping
• 金额: $ 91.07万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10560520
• 关键词: Address; Area; Automobile Driving; Big Data to Knowledge; Biological; Biological Models; Cardiac; Cardiovascular system; Clinical Data; Cloud Computing; Collaborations; Communities; Data; Data Commons; Data Science; Data Set; Disease; Environment; Fostering; Foundations; Future; Genetic Variation; Genomics; Health; Heart; Human; Informatics; Infrastructure; Knowledge; Leadership; Link; Medicine; Mission; Mitochondria; Modeling; Molecular; Molecular Profiling; National Heart, Lung, and Blood Institute; Pathway interactions; Phenotype; Production; Proteins; Proteome; Proteomics; Research; Research Personnel; Resources; Services; Time; Training; United States National Institutes of Health; Vision; analytical tool; cohort; computerized tools; data acquisition; data ecosystem; data integration; data management; data resource; data-driven model; graphical user interface; heart cell; improved; innovation; interoperability; kinetic model; large datasets; metabolomics; novel; phenotypic data; precision medicine; prototype; regression algorithm; spatiotemporal; technology platform; tool

PROJECT SUMMARY/ABSTRACT The inception of the R35 mechanism is a testament to the foresight and vision of NHLBI leadership. Clearly, this will provide unparalleled opportunities for driving discovery and enhancing human health. Capitalizing on a 22-year track record of scientific innovation, training and service to the community, as well as unique abilities in leveraging the technical foundation built by the NIH BD2K Center of Excellence at UCLA, this application presents a multi-pronged strategy for identifying molecular signatures that drive cardiac phenotypes. This application addresses two critical biomedical challenges. Firstly, there is a knowledge gap in how we conceptualize proteins, including how they interplay with other omes, and how their dynamics contribute to functional phenotypes. Secondly, there is an inadequacy of computational tools for systematically linking phenotypic and molecular data, and the cardiovascular community lacks a shared informatics management environment where both datasets and resources are accessible and interoperable for integrative analyses. Accordingly, this R35 proposes two areas of focus for breaking new ground. The first focus area will be to unveil how cardiac mitochondrial spatio-temporal proteomes and their interplay with metabolomic and genomic information drive cardiac phenotypes. This involves the advancement of technological platforms to characterize global spatio-temporal dynamics of cardiac proteins, metabolites, and pathways, producing both valuable molecular datasets from model systems and human cohorts and optimized kinetic models for enabling global dynamic analyses. The second focus area will be to build analytical tools for integrating molecular and phenotypic data, and to construct a prototypic cardiovascular data commons for supporting on-demand interactions among data, tools, resources, and users. These efforts will enable the elucidation of interconnected biological networks from proteomic, metabolomic, genetic variation, and clinical data types through a novel mixed model regression algorithm. Moreover, this will result in novel components for supporting a specialized data commons in cardiovascular medicine, including new APIs, graphical user interface, cloud-computing infrastructure, and data management pipeline. In summary, this R35 proposes to build a translational data ecosystem of seamless data acquisition and informatics platforms for enabling a new model of data-driven knowledge production. Discoveries will propel the NHLBI mission forward, including unveiling molecular signatures of disease in model systems and humans, fostering the training of future biomedical professionals, and disseminating advances to the scientific community and public via a specialized cardiovascular commons, all in the realization of precision medicine.

项目总结/文摘

项目成果

Protocol for the prediction, interpretation, and mutation evaluation of post-translational modification using MIND-S.

使用Mind-S对翻译后修饰的预测，解释和突变评估的协议。

• DOI: 10.1016/j.xpro.2023.102682
• 发表时间: 2023-12-15
• 期刊: STAR protocols
• 作者: Yan Y;Wang D;Xin R;Soriano RA;Ng DCM;Wang W;Ping P
• 通讯作者: Ping P

A reference set of curated biomedical data and metadata from clinical case reports.

• DOI: 10.1038/sdata.2018.258
• 发表时间: 2018-11-20
• 期刊: Scientific data
• 影响因子: 9.8
• 作者: Caufield JH;Zhou Y;Garlid AO;Setty SP;Liem DA;Cao Q;Lee JM;Murali S;Spendlove S;Wang W;Zhang L;Sun Y;Bui A;Hermjakob H;Watson KE;Ping P
• 通讯作者: Ping P

TahcoRoll: fast genomic signature profiling via thinned automaton and rolling hash.

• DOI: 10.1515/mr-2021-0016
• 发表时间: 2021-12-20
• 期刊: Medical review (2021)

Integrated omics dissection of proteome dynamics during cardiac remodeling.

• DOI: 10.1038/s41467-017-02467-3
• 发表时间: 2018-01-09
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Lau E;Cao Q;Lam MPY;Wang J;Ng DCM;Bleakley BJ;Lee JM;Liem DA;Wang D;Hermjakob H;Ping P
• 通讯作者: Ping P

COVID-19 Surveiller: toward a robust and effective pandemic surveillance system basedon social media mining.

COVID-19-19S监视者：朝着基于社交媒体挖掘的基于强大而有效的大流行监视系统。

• DOI: 10.1098/rsta.2021.0125
• 发表时间: 2022-01-10
• 期刊: Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
• 作者: Jiang JY;Zhou Y;Chen X;Jhou YR;Zhao L;Liu S;Yang PC;Ahmar J;Wang W
• 通讯作者: Wang W