PNNL

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, metabolomics, lipidomics, 3-D mass spectrometry image analysis, statistical machine learning, multi-omics integration, containerization with Docker, cloud computing, benchmark dose statistics, data visualization, natural language processing, large language models, and app/package development.

Since joining Pacific Northwest National Laboratory in 2019, Degnan has been an instructor at Environmental Molecular Sciences Laboratory (EMSL) Summer School and has been a featured speaker for the EMSL LEARN Webinar Series and the Data Science Bootcamp for Biologists. Degnan holds an MS in bioinformatics from the University of Oregon and a BS in cellular and molecular biology from George Fox University. He also teaches bioinformatics and biostatistics at the University of Oregon.

Research Interests

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

MS, bioinformatics, University of Oregon

BS, cellular and molecular biology, George Fox University

2025

Degnan, D. J., D. M. Claborne, A. M. White, S. M. Akers, N. M. Winans, Y. E. Corilo, C. W. Strauch, V. L. Bailey, L. A. McCue, K. G. Stratton, and L. M Bramer. 2025. “FREDA: A Web Application for Processing, Analysis, and Visualization of Fourier-Transform Mass Spectrometry Data.” Rapid Communications in Mass Spectrometry 39 (7): e9980. https://doi.org/10.1002/rcm.9980.

2024

Fulcher, J. M., L. M. Markillie, H. D. Mitchell, S. M. Williams, K. M. Engbrecht, D. J. Degnan, L. M. Bramer, R. J. Moore, W. B. Chrisler, J. Cantlon-Bruce, J. W. Bagnoli, W.-J. Qian, A. Seth, L. Paša-Tolić, and Y. Zhu. 2024. “Parallel Measurement of Transcriptomes and Proteomes from Same Single Cells Using Nanodroplet Splitting.” Nature Communications 15: 10614. https://doi.org/10.1038/s41467-024-54099-z.

Degnan, D. J., C. W. Strauch, M. Y. Obiri, E. D. VonKaenel, G. S. Kim, J. D. Kershaw, D. L. Novelli, K. TL Pazdernik, and L. M. Bramer. 2024. “Protein–Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools.” Journal of Proteome Research 23 (12): 5395–5404. https://doi.org/10.1021/acs.jproteome.4c00535.

Degnan, D. J., L. A. Lewis, L. M. Bramer, L. A. McCue, J. J. Pesavento, M. Zhou, and A. Bilbao. 2024. “IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data.” Journal of Proteome Research 23 (8): 3318–3321. https://doi.org/10.1021/acs.jproteome.3c00681.

Bramer, L. M., H. M. Dixon, D. J. Degnan, D. Rohlman, J. B. Herbstman, K. A. Anderson, and K. M. Waters. 2024. “Expanding the Access of Wearable Silicone Wristbands in Community-Engaged Research Through Best Practices in Data Analysis and Integration.” In Biocomputing 2024: Proceedings of the Pacific Symposium. Kohala Coast, Hawaii, January 3–7, 2024, pp. 170–186. https://doi.org/10.1142/9789811286421_0014.

Richardson, R. E., D. T. Leach, N. M. Winans, D. J. Degnan, A. V. Prymolenna, and L. M. Bramer. 2024. “Race-Specific Risk Factors for Homeownership Disparity in the Continental United States.” Journal of Data Science 22 (4): 591–604. https://doi.org/10.6339/23-JDS1116.

2023

Stratton, K. G., D. M. Claborne, D. J. Degnan, R. E. Richardson, A. M. White, L. A. McCue, B.-J. M. Webb-Robertson, and L. M. Bramer. 2023. “PMart Web Application: Marketplace for Interactive Analysis of Panomics Data.” Journal of Proteome Research 23 (8): 3310–3317. https://doi.org/10.1021/acs.jproteome.3c00512.

Degnan, D. J., J. E. Flores, E. R. Brayfindley, V. L. Paurus, B.-J. M. Webb-Robertson, C. S. Clendinen, and L. M. Bramer. 2023. “Characterizing Families of Spectral Similarity Scores and Their Use Cases for GC-MS Small Molecule Identification.” Metabolites 13 (10): 1101. https://doi.org/10.3390/metabo13101101.

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. 2023. “IsoMatchMS: Open-Source Software for Automated Annotation and Visualization of High Resolution MALDI-MS Spectra.” Journal of the American Society for Mass Spectrometry 34 (9): 2061–2064. https://doi.org/10.1021/jasms.3c00180.

Jain, S., L. Pei, J. M. Spraggins, M. Angelo, J. P. Carson, N. Gehlenborg, F. Ginty, J. P. Gonçalves, J. S. Hagood, J. W. Hickey, N. L. Kelleher, L. C. Laurent, S. Lin, Y. Lin, H. Liu, A. Naba, E. S. Nakayasu, W.-J. Qian, A. Radtke, P. Robson, B. R. Stockwell, R. Van de Plas, I. S. Vlachos, M. Zhou, HuBMAP Consortium, K. Börner, and M. P. Snyder. 2023. “Advances and Prospects for the Human BioMolecular Atlas Program (HuBMAP).” Nature Cell Biology 25: 1089–1100. https://doi.org/10.1038/s41556-023-01194-w.

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 (19): 7536–7544. https://doi.org/10.1021/acs.analchem.2c05783.

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 (6): 1096–1104. https://doi.org/10.1021/jasms.3c00039.

Gosline, S. J. C., D. N. Kim, P. Pande, D. G. Thomas, L. Truong, P. Hoffman, M. Barton, J. Loftus, A. Moran, S. Hampton, S. Dowson, L. Franklin, D. Degnan, L. Anderson, A. Thessen, R. L. Tanguay, K. A. Anderson, and K. M. Waters. 2023. “The Superfund Research Program Analytics Portal: Linking Environmental Chemical Exposure to Biological Phenotypes.” Scientific Data 10: 151. https://doi.org/10.1038/s41597-023-02021-5.

Liao, Y.-C., J. M. Fulcher, D. J. Degnan, S. M. Williams, L. M. Bramer, D. Veličković, and K. J. Zemaitis, M. Veličković, R. L. Sontag, R. J. Moore, L. Paša-Tolić, Y. Zhu, and M. Zhou. 2023. “Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform.” Molecular & Cellular Proteomics 22 (2): 100491. https://doi.org/10.1016/j.mcpro.2022.100491.

Degnan, D. J., K. G. Stratton, R. E. Richardson, D. M. Claborne, E. A. Martin, N. A. Johnson, D. T. Leach, B.-J. M. Webb-Robertson, and L. M. Bramer. 2023. “pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes.” Journal of Proteome Research 22 (2): 570–576. https://doi.org/10.1021/acs.jproteome.2c00610.

2022

Zhou, M., J. M. Fulcher, K. J. Zemaitis, D. J. Degnan, Y.-C. Liao, M. Veličković, D. Veličković, L. M. Bramer, W. R. Kew, G. Stacey, and L. Paša-Tolić. 2022. “Discovery Top-Down Proteomics in Symbiotic Soybean Root Nodules.” Frontiers in Analytical Science 2. https://doi.org/10.3389/frans.2022.1012707.

2021

Degnan, D. J., L. M. Bramer, A. M. White, M. Zhou, A. Bilbao, and L. A. McCue. 2021. “PSpecteR: A User-Friendly and Interactive Application for Visualizing Top-Down and Bottom-Up Proteomics Data in R.” Journal of Proteome Research 20 (4): 2014–2020. https://doi.org/10.1021/acs.jproteome.0c00857.