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。分子传感器 的体力活动联合会在多个级别和 数以千计的人类和动物模型中的多种组织。联合式联合收割机的研究 分子组学方法的最新表型分析。以我们悠久的分析历史为基础 高通量生物学的创新和可能是最大规模的多组体研究的分析经验 到目前为止，斯坦福MoTrPAC生物信息学中心获得了资金，提供核心计算、存储和分析 向MoTrPAC调查人员提供专业知识。在本行政副刊中，MoTrPAC BIC建议 正式加入CFDE；为数据组织做出贡献，以增强MoTrPAC数据集的公平性；定期 与共同发展基金的其他实体互动；推进共同基金数据生态系统的使命。我们 提议与共同基金数据生态系统对接和协作，以改善 MoTrPAC数据和其他共同基金数据资源。目标1专注于开发数据标准化 与CFDCC合作，为各种OME提供可重复使用的分析流水线 由科学界重新调整用途和定制；我们将从转录数据处理开始 与多个CFDE实体协作进行优化。目标2建议统一联合国系统的数据目录 MoTrPAC BIC和CFDE数据模型，并记录和分析用户体验并部署工具和 培训研究界轻松利用现有数据集解决新的交叉生物学问题 问题 。我们期望通过上述目标和活动，我们将为CFDE的长期目标做出贡献 开发和部署资源和工具、培训材料，使研究社区能够 使用CF数据集进行新的科学研究、假设生成、发现和验证，从而产生新的 对健康和疾病的洞察。

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 examines the response to acute and chronic exercise at multiple scales and in multiple tissues across thousands of humans and in animal models. The studies of the Consortium 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 provides core compute, storage and analytic expertise to the MoTrPAC investigators. In this administrative supplement, the MoTrPAC BIC proposes to formally join the CFDE; to contribute to data organization to enhance MoTrPAC dataset FAIRness; to regular interact with other CFDE entities; and to advance the mission of the Common Fund Data Ecosystem. We propose to interface and collaborate with the Common Fund Data Ecosystem to improve the interaction of MoTrPAC data with other Common Fund data resources. Aim 1 is focused on develop data standardizations and reproducible analysis pipelines for various ‘omes in collaboration with the CF DCCs that can be repurposed and customized by the scientific community; we will start with transcriptomic data processing optimization in collaboration with several CFDE entities. Aim 2 proposes to harmonize the data catalog of the MoTrPAC BIC with the CFDE data model and to record and analyze user experience and deploy tools and training for the research community to easily use existing datasets to address novel cross-cutting biological questions . We expect that with the above aims and activities, we will contribute towards CFDE’s long term goal of developing and deploying resources and tools, training materials, empowering the research community to use CF data sets for novel scientific research, hypothesis generation, discovery, and validation, leading to new insights into health and disease.