EPA STAR RD832720 (October 1, 2005 ~ September 30, 2010)
The objectives of the Carolina Environmental Bioinformatics Center are to enhance and advance the field of Computational Toxicology. The Center develops novel analytic and computational methods, creates efficient user-friendly tools to disseminate the methods to the wider community, and applies the computational methods to data from molecular toxicology and other studies.
The center is divided into three Research Projects and an Administrative Unit.
- Project 1 (Biostatistics in Computational Biology) provides biostatistical support to the Center, performs data analysis at the US EPA and develops new methods in collaboration with EPA personnel and the computational toxicology community.
- Project 2 (Chem-informatics) coordinates the compilation and mining of data from relevant external databases and performs analysis and methods development for investigating Quantitative Structure-Activity Relationships with burgeoning high-throughput chem-informatics data.
- Project 3 (Computational Infrastructure for Systems Toxicology) works to create a framework for merging data from various –omic technologies in a systems biology approach.
- The Administration Core provides staff and support to the Center, and provides oversight for each for the Functional Areas. Public Outreach and Translation Activity (POTA) ensures that the activities of the Center are translated into useable information and materials for the public and policy makers.
The Center is advancing the field of computational toxicology through the development of new methods and tools, as well as through direct collaborative efforts with EPA and other environmental scientists. In each Project, new methods are being developed and published that represent the state-of-the-art. The tools developed within each project are disseminated, and will be useful both to trained bioinformatics scientists and bench scientists. The synthesis of data from a variety of sources will move the field of computational toxicology from a hypothesis-driven science toward a predictive science.
- Fred Wright (PI)
- Fei Zhou (co-PI)
- Ivan Rusyn (co-PI)
- Leonard McMillan (co-PI)
- Genetic Diversity of Mus musculus Laboratory Strains
- FastANONA: an Efficient Algorithm for Genome-Wide Association Study
- Inferring missing genotypes in large SNP panels using fast nearest-neighbor searches over sliding windows, by Adam Roberts, Leonard McMillan, Wei Wang, Joel Parker, Ivan Rusyn, and David Threadgill, Proceedings of the 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 2007.
- The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics, by Adam Roberts, Fernando Pardo-Manuel de Villena, Wei Wang, Leonard McMillan, and David Threadgill, Mammalian Genome, Aug 3, 2007.
- FastANOVA: an efficient algorithm for genome-wide association study, by Xiang Zhang, Fei Zou, and Wei Wang. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’08).