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Bob Chapman

South Carolina Department of Natural Resources

Address: Hollings Marine Lab, 331 Fort Johnson Road, Charleston, SC 29412
Phone: 843.762.8860
E-mail: chapmanr@dnr.sc.gov



Education

Ph.D., 1980, University of Georgia
M.S., 1977, University of West Florida
A.B., 1972, University of North Carolina


Research Interests

  • Genomics and analysis of Complex Systems
  • Transcript profiling
  • Reproduction and adaptation in fishes
  • Application of machine learning to biological systems

Current and Planned Research Projects

  • molecular approaches to assess future fertility in black sea bass, Centropristis striata 
  • managing reproductive failure in fisheries and on fish farms: a joint NC/SC Sea Grant project
  • evaluating the suspension of oogenesis in young female fishes including black sea bass, vermillion snapper and red porgy

Publications

  • Chapman, R.W., Reading, B.J., and Sullivan, C.V. (2014) Ovary transcriptome profiling via artificial intelligence reveals a transcriptomic fingerprint predicting egg quality in striped bass. PlosOne. (in press).
  • John Bowden, Dominic Colosi, Michael Napolitano, Whitney Stutts, Robert Chapman, Benjamin Parrott/(2014) Recent Advances and Outlook in Mass Spectrometry and Data Analysis/Processing for Lipidomics: Implications for High-Throughput Clinical Analyses - Part II, Analytical and Bioanalytical Chemistry (in press).
  • Bertin, M.J., Voronca, D.C., Chapman, R.W., & Moeller, P.D. (2014). The effect of pH on the toxicity of fatty acids and fatty acid amides to rainbow trout gill cells. Aquatic Toxicology, 146, 1-11.
  • Reading, B.J., Williams, V.N., Chapman, R.W., Williams, T.I., & Sullivan, C.V. (2013). Dynamics of the Striped Bass (Morone saxatilis) Ovary Proteome Reveal a Complex Network of the Translasome. Journal of proteome research, 12(4), 1691-1699.
  • Rathburn, C.K., Sharp, N.J., Ryan, J.C., Neely, M.G., Cook, M., Chapman, R.W., & Burnett, K.G. (2013). Transcriptomic responses of juvenile Pacific whiteleg shrimp, Litopenaeus vannamei, to hypoxia and hypercapnic hypoxia. Physiological genomics, 45 (17), 794-807.
  • Bertin, M.J., P. Moeller, L.J. Guillette, Jr., and Chapman, R.W. 2013. Using Machine Learning Tools to Model Complex Toxic Interactions with Limited Sampling Regimes. Environ. Sci. Technol. 47 (6), pp 2728-2736.