Stanford MoTrPAC Bioinformatics Center

摘要 很少有干预措施像体育锻炼那样对人类健康有益，但我们仍然 在很大程度上忽视了这些强大效应的传导机制。《分子》 体力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鳞片和多个组织中。这项研究将 结合最先进的表型和分子组学方法。以我们悠久的历史为基础 在高通量生物学方面的分析创新和在分析方面的经验可能是最大的 迄今为止，斯坦福大学MoTrPAC生物信息学中心将提供核心计算、存储 并向MoTrPAC调查人员提供分析专业知识。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 基于时间序列、训练相关、多组、生理和传感器的测量数据的安全共享。 目标1专注于为RAW创建本地测试计算环境和基于云的数据仓库 数据。我们将通过首先建立数据安全和隐私标准、数据协议来实现这一点 共享，以及公共数据元素词典。目标2描述了现有组学的扩展 管道和开发用于原始多组数据的新管道。此外，我们建议 为多尺度、高通量数据开发新的分析方法。基于以下方面的专业知识 斯坦福大学著名的统计系以及新的生物医学数据科学系，我们将 将我们团队开发的基于图形的方法扩展到跨多个数据集的时间维度，以 从根本上理解体育锻炼反应的系统生物学。此外，我们还将 专注于数据集成和新的可视化方法。目标3关注的是构建一个交互式的 供调查人员和参与者审问、交互、探索和安全下载的界面 数据。AIM 4将着眼于MoTrPAC之外的更广泛的与相关数据集的集成，如GTEx、GEO、 Precision Medicine Cohort、英国生物库和其他机构。我们相信我们团队的直接经验是 领导未诊断疾病网络、Encode、Clingen、BD2K等研究(动员 Center)和MyHeart Counts(Apple的ResearchKit框架的发布应用程序之一)将是 有利于最大限度地为世界各地的研究人员带来MoTrPAC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.