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 




Bramer L., H.M. Dixon, D.J. Degnan, D. Rohlman, J.B. Herbstman, K.M. Waters, and K.A. Anderson. “Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration.” Biocomputing, 2023, 170-186.

Richardson R.E., D.T. Leach, A.V. Prymolenna, D.J. Degnan, N.W. Winans, and L.M. Bramer. “Race-Specific Risk Factors for Homeownership Disparity in the Continental United States.” Journal of Data Science, 2023.

Stratton K., D.M. Claborne, D.J. Degnan, R.E. Richardson, A.M. White, L.A. McCue, B.M. Webb-Robertson, L.M. Bramer. “PMart Web Application: Marketplace for Interactive Analysis of Panomics Data.” Journal of Proteome Research, 2023.

Degnan D.J., J.E. Flores, E.R. Brayfindley, V. Paurus, B.M. Webb-Robertson, C.S. Clendinen, and L.M. Bramer. “Characterizing Families of Spectral Similarity Scores and their Use Cases for GC-MS Small Molecule Identification.” Metabolites, 2023, 13(10), 1101.

Degnan, D.J, K.J. Zemaitis, L.A. Lewis, L.A. McCue, L.M. Bramer, J.M. Fulcher, D. Veličković, L. Paša-Tolić, and M. Zhou. “IsoMatchMS: Open-Source Software for Automated Annotation and Visualization of High Resolution MALDI-MS Spectra.” Journal of the American Society for Mass Spectrometry, 2023, 34 (9), 2061-2064.

Jain S., L. Pei, J.M. Spraggins, M. Angelo, J.P. Carson, et al. “Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP).” Nature Cell Biology, 2023, 25, 1089-1100.

Degnan D.J., L.M. Bramer, J.E. Flores, V.L. Paurus, Y. Eberlim de Corilo, and C.S. Clendinen, “Evaluating Retention Index Score Assumptions to Refine GC-MS Metabolite Identification.” Analytical Chemistry, 2023, 95 (19), 7536-7544.

Flores J.E., L.M Bramer, D.J. Degnan, V.L Paurus, Y.E. Corilo, and C.S. Clendinen. “Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics.” Journal of the American Society for Mass Spectrometry, 2023, 34 (6), 1096-1104.

Gosline S., D. Kim, P. Pande, D.G. Thomas, L. Truong, P. Hoffman, and M. Barton, et al. “The Superfund Research Program Analytics Portal: linking environmental chemical exposure to biological phenotypes.” Scientific Data, 2023, 10 (1), 151.

Liao Y., J.M. Fulcher, D.J. Degnan, S.M. Williams, L.M. Bramer, D. Velickovic, and K. Zemaitis, et al. “Spatially resolved top-down proteomics of tissue sections based on a microfluidic nanodroplet sample preparation platform.” Molecular & Cellular Proteomics, 2023, 22 (2), 100491.

Degnan D.J., K.G. Stratton, R.E. Richardson, D.M. Claborne, E.A. Martin, N.A. Johnson, and D.T. Leach, et al. “pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes.” Journal of Proteome Research, 2023, 22 (2), 570-576.


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, 2022, 2, 1012707.


Degnan D.J., L.M. Bramer, A.M. White, M. Zhou, A. Bilbao, and L.A. McCue. “PSpecteR: A User-Friendly and Interaction application for Visualizing Top-Down and Bottom-Up Proteomics Data in R.” Journal of Proteome Research, 2021, 20 (4), 2014-2020.