Stanford MoTrPAC Bioinformatics Center

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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.