Data Scientist
Data Scientist


David Degnan is a biological data scientist who develops bioinformatic and statistical pipelines for multi-omics data. He has experience with top-down and bottom-up proteomics analysis, genomics and transcriptomics, metabolomic scoring approaches, 3D mass spectrometry image analysis, statistical machine learning, containerization with docker, cloud computing, benchmark dose statistics, data visualization, and app/package development.  

Since joining Pacific Northwest National Laboratory in 2019, David has been an instructor at EMSL Summer School and has been a featured speaker for the EMSL LEARN Webinar Series and the Data Science Bootcamp for Biologists. David holds an MS in bioinformatics from the University of Oregon, and a BS in cellular and molecular biology from George Fox University. He is working on a second MS in statistics from Washington State University with a long-term goal of earning a PhD in statistics or a related field.  

Research Interests 

  • Bioinformatics 
  • Biology 
  • Chemistry 
  • Statistical Modeling 
  • Data Science & Visualization 


Master of Science, Bioinformatics, University of Oregon  

Bachelor of Science, Cell & Molecular Biology, George Fox University 


  • Degnan D.J., K.G. Stratton, R.E. Richardson, D.M. Claborne, E.A. Martin, N.A. Johnson, and D.T. Leach, et al. 2023. "pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes." Journal of Proteome Research 22, no. 2:570-576. PNNL-SA-177968. doi:10.1021/acs.jproteome.2c00610 

  • Flores J.E., L.M Bramer, D.J. Degnan, V.L Paurus, Y.E. Corilo, and C.S. Clendinen, 2023. “Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics.” Journal of the American Society for Mass Spectrometry 34, no.6:1096-1104. doi:10.1021/jasms.3c00039 

  • Degnan D.J., L.M. Bramer, J.E. Flores, V.L. Paurus, Y. Eberlim de Corilo, and C.S. Clendinen, 2023. "Evaluating Retention Index Score Assumptions to Refine GC-MS Metabolite Identification." Analytical Chemistry 95, no. 19:7536-7544. PNNL-SA-181303. doi:10.1021/acs.analchem.2c05783 

  • Gosline S., D. Kim, P. Pande, D.G. Thomas, L. Truong, P. Hoffman, and M. Barton, et al. 2023. "The Superfund Research Program Analytics Portal: linking environmental chemical exposure to biological phenotypes." Scientific Data 10. PNNL-SA-177557. doi:10.1038/s41597-023-02021-5 

  • Liao Y., J.M. Fulcher, D.J. Degnan, S.M. Williams, L.M. Bramer, D. Velickovic, and K. Zemaitis, et al. 2023. "Spatially resolved top-down proteomics of tissue sections based on a microfluidic nanodroplet sample preparation platform." Molecular & Cellular Proteomics 22, no. 2:Art. No. 100491. PNNL-SA-174522. doi:10.1016/j.mcpro.2022.100491 

  • Zhou M., J.M. Fulcher, K.J. Zemaitis, D.J. Degnan, Y.C. Liao, M. Velickovic, D. Velickovic, L.M. Bramer, W.R. Kew, G. Stacey, and L. Pasa-Tolic. “Discovery top-down proteomics in symbiotic soybean root nodules.” Frontiers in Analytical Science 2. doi: 10.3389/frans.2022.1012707 

  • Degnan D.J., L.M. Bramer, A.M. White, M. Zhou, A. Bilbao, and L.A. McCue, 2021. “PSpecteR: A User-Friendly and Interaction application for Visualizing Top-Down and Bottom-Up Proteomics Data in R.” Journal of Proteome Research 20, no. 4:2014-2020. doi:10.1021/acs.jproteome.0c00857