Stanford MoTrPAC Bioinformatics Center

摘要 很少有干预措施被证明是有益于人类健康的体育锻炼，但我们仍然 很大程度上不知道这些强效作用的传导机制。分子 身体活动联盟的传感器将检查对急性和慢性运动的反应， 在数千人和动物模型中的多个尺度和多个组织中。这项研究将 联合收割机最新的表型与分子组学方法相结合。在我们悠久的历史上， 高通量生物学的分析创新和分析经验，也许是最大的多， 到目前为止，斯坦福大学MoTrPAC生物信息学中心将提供核心计算、存储和分析服务。 和分析专业知识。利用我们与斯坦福大学的密切联系 高性能计算中心和谷歌云工程团队，我们将为 安全共享时间序列、培训相关、多组学、生理和传感器测量数据。 Aim 1专注于为原始数据创建本地测试计算环境和基于云的数据仓库 数据我们将首先建立数据安全和隐私的标准， 共享和公共数据元素字典。目标2描述了现有组学的扩展 管道和开发新的原始多组学数据管道。此外，我们建议 为多尺度、高通量数据开发新的分析方法。利用来自 斯坦福大学著名的统计学系以及生物医学数据科学的新部门，我们将 将我们团队开发的基于图形的方法扩展到多个数据集的时间维度， 了解系统生物学的反应，体育训练在一个基本的水平。此外，我们将 专注于数据集成和新颖的可视化方法。目标3是建立一个互动的 供研究者和参与者询问、互动、探索和安全下载的界面 数据Aim 4将在MoTrPAC之外寻找与GTeX，GEO， 精准医疗队列、英国生物银行等。我们相信我们的团队在 未诊断疾病网络、ENCODE、ClinGen、BD2K（Mobilize）等研究的领导 Center）和MyHeartCounts（苹果ResearchKit框架的启动应用程序之一）将在 这有利于使MoTrPAC为世界各地的调查人员带来最大的好处。

Abstract Few interventions have been shown to be as beneficial to human health as physical exercise, yet we remain largely ignorant of the mechanisms by which those potent effects are transduced. The Molecular Transducers of Physical Activity Consortium will examine the response to acute and chronic exercise at multiple scales and in multiple tissues across thousands of humans and in animal models. The study will combine state of the art phenotyping with molecular omics approaches. Building on our long history of analytical innovation in high throughput biology and experience in the analysis of perhaps the largest multi- omic study funded to date, the Stanford MoTrPAC Bioinformatics Center will provide core compute, storage and analytic expertise to the MoTrPAC investigators. Taking advantage of our close links with the Stanford high performance computing center and the Google cloud engineering team, we will build a platform for the secure sharing of time-series, training-related, multi-omic, physiological and sensor based measurement data. Aim 1 is focused on creating a local test computing environment and a cloud-based data warehouse for raw data. We will achieve this by first establishing standards for data security and privacy, protocols for data sharing, and a common data element dictionary. Aim 2 describes both the extension of existing omics pipelines and the development of new pipelines for raw multi-omic data. In addition, we propose the development of novel analytic approaches for multi-scale, high-throughput data. Building on expertise from Stanford's renowned statistics department as well as the new department of Biomedical Data Science, we will extend graph based approaches developed by our team into the time dimension across multiple data sets to understand the systems biology of the response to physical training at a fundamental level. In addition, we will focus on data integration and novel visualization approaches. Aim 3 is concerned with building an interactive interface for investigators and participants to interrogate, interact with, explore, and securely download the data. Aim 4 will look outside of MoTrPAC to broader integration with relevant datasets such as GTeX, GEO, the Precision Medicine Cohort, the UK Biobank and others. We believe our team's direct experience in the leadership of studies such as the Undiagnosed Diseases Network, ENCODE, ClinGen, BD2K (Mobilize Center), and MyHeart Counts (one of the launch applications for Apple's ResearchKit framework) will be advantageous in maximizing the benefit of MoTrPAC for investigators around the world.