Project Summary (2025-2030)

The Data Management and Analysis Core (DMAC) will support the Baylor College of Medicine (BCM) - Rice University Superfund Research Program (SRP) by providing advanced tools and infrastructure for data management, exploratory and integrative data analysis, and hypothesis generation. DMAC will act as a hub for biomedical and engineering projects. The DMAC will be led by Dr. Cristian Coarfa; he has been Director of Multi-Omics Analysis Core for the BCM Advanced Technology Cores since 2014. DMAC will be co-led by Dr. Susan Hilsenbeck who has acted as Director of Biostatistics and Bioinformatics Shared Resource for the Dan L Duncan Comprehensive Cancer Center for the past 20 years.

The DMAC provides data management for all SRP projects, acting as a central SRP resource for storage and access for all chemical, physical, biological, phenotypic, and multi-omics data generated by the SRP projects. DMAC will define and execute quality assurance, using best practices per data type, and store QA/QC for primary data, secondary data, and analysis results. DMAC will define and implement a comprehensive Data Management Plan (DMP), leveraging the biobanking experience and resources at the Dan L Duncan Comprehensive Cancer Center, including sample metadata and omics data. DMAC will function as a data management hub for all the Center's Support Cores. DMAC will enable data integration across all SRP, harnessing methods such as GPU-supported deep learning. In coordination with Training Core, the DMAC will conduct training in biostatistics and bioinformatics for graduate students and postdocs working on BCM-Rice SRP projects and will provide the computing and web infrastructure for workshops and seminars. DMAC will interact with the Administrative Core and the Training Core to support training and technology transfer activities that deliver new findings and analytical tools to scientific community. DMAC will support the CEC to deposit community engagement documents and enable SRP data access by the Superfund sites communities.

To achieve its critical goals, DMAC has the following aims:

1. Implement a robust and comprehensive Data Management Plan;

2. Provide expert analysis and consultation in the specialized areas of statistics and bioinformatics for all Projects;

3. Develop and deploy of a framework supporting deep and multi-level data integration;

4. Conduct education and training in statistics, multi-omics bioinformatics, and data science for graduate students and postdoctoral fellows involved in Superfund related research.