Superfund Research Program

Data Management and Analysis Core

Project Leader: Katrina M. Waters (Pacific Northwest National Laboratory)
Co-Investigator: Sara Gosline (Pacific Northwest National Laboratory)
Grant Number: P42ES016465
Funding Period: 2009-2025

Project-Specific Links

Final Progress Reports

Year:   2019  2012 

 

Studies and Results

 

For the nonbiomedical projects, the core developed a statistical approach to assess locational variability for environmental studies that employ single sampling strategies. This is an important issue because historically differences between samples were determined statistically using technical variability of the analytical chemistry measurements, not true variability of the environmental sites being sampled. The assumption was that, particularly for rivers, the sites represented well-mixed systems, and therefore had low variability within several feet of sample collection. Using a designed pilot study, researchers measured true variability for the Portland Harbor Superfund site with 5 deployed passive samplers at each location. They also replicated this sampling during distinct seasons. The results demonstrated that the core could detect statistical differences between sites and between seasons using a simulated confidence interval from the pilot studies. The approach the core developed is applicable to any aquatic system and would potentially be useful in the field of environmental assessment, specifically for long-term monitoring of Superfund sites after remediation.

For the biomedical projects, the core developed data analysis workflows for the interpretation of gene expression and microRNA datasets based upon experiments performed and data collected by the research projects. These workflows include the use of publicly available databases for automated cross-species translations and miRNA target predictions. Where miRNA families are not conserved across species, miRNA sequence similarity can also be used to compare between zebrafish and human, because the human database is more complete. The core has incorporated these features into version 2.3 of their Bioinformatics Resource Manager software, released in January 2012.

 

Significance

 

The Research projects and cores in this SRP are collecting large amounts of molecular and chemical data to determine new toxicity endpoints for PAH mixtures, mechanism of action for these endpoints, and assessment of PAH mixtures and particulates through environmental exposure. Integration of traditional toxicity endpoints with high-throughput gene expression data and high-dimensional analytical chemistry data is essential for the identification of biomarkers of response and chemical fingerprints of exposure. The integration of real environmental exposure data with traditional toxicity testing ensures that estimates of risk are grounded in realistic exposures, and the PBPK component in Cross-Species Comparison of Transplacental Dosimetry PAHs project ensures that these exposure estimates are accurately translated to humans.

The Statistics and Bioinformatics Core greatly enhances the OSU SRP by providing expert statistical and bioinformatics data analysis support and software solutions for data management and interpretation. The standardization of data analysis methods will ensure that the best methods are utilized and enhance the utility of such information in future risk assessments and regulatory decisions by regulatory agencies. The centralized data management organization also promotes data sharing across research projects and facilitates the integration of exposure (source) to phenotype (outcome) linkages. In addition, the core is providing training to post-doctoral scientists and graduate students involved in the program who desire to learn the expert analysis methods needed for their projects.