Journal Articles:

  1. Crowl, S., Jordan, B., Ma, C., and Naegle, K. M. "KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data."  Nat Commun 13, 4283 (2022).
  2. Naegle, Kristen M. "Ten simple rules for effective presentation slides" PLOS Computational Biology 17, no.12 (2021) doi:10.1371/journal.pcbi.1009554
  3. Sloutsky, Roman, and Naegle, K. M. “ASPEN, a methodology for reconstructing protein evolution with improved accuracy using ensemble models.” eLife vol. 8 e47676. (October 2019), doi10.7554/eLife.47676
  4. Zhu, W., Mazzanti, A., Voelker, T. L., Hou, P., Moreno, J.D., Angsutararux, P., Naegle, K. M., Priori, S. G., and Silva, J. R. (2018). Predicting Patient Response to the Antiarrhythmic Mexiletine Based on Genetic Variation: Personalized Medicine for Long QT Syndrome. Circulation Research, (November 2018), CIRCRESAHA.118.314050.
  5. Ronan, T., Anastasio, S., Qi, Z., Vieira Tavares, P.H.S., Sloutsky, R., and Kristen M. Naegle."OpenEnsembles: A Python Resource for Ensemble Clustering", Journal of Machine Learning Research, 26: 1-6 (2018)
  6. Sloutsky, R. and Naegle, K. M. Proteome-Level Analysis Indicates Global Mechanisms for Post- Translational Regulation of RRM Domains. J. Mol. Biol. 1–4 (2017). doi:10.1016/j.jmb.2017.11.001
  7. Mooradian, Arshag D., Jason M. Held, and Naegle, K. M. Using ProteomeScout: A Resource of Post-Translational Modifications, Their Experiments, and the Proteins they Annotate. Current Protocols Bioinformatics. 13, 13.32.1-13.32.27 (2017).
  8. Schaberg, Katherine E., Venktesh S Shirure, Elizabeth A Worley, Steven C George, and Kristen M Naegle. “Ensemble Clustering of Phosphoproteomic Data Identifies Differences in Protein Interactions and Cell-Cell Junction Integrity of HER2-Overexpressing Cells.” Integrative Biology. 9 (2017): 539–47.
  9. Sloutsky, Roman, and Kristen M. Naegle. “High-Resolution Identification of Specificity Determining Positions in the LacI Protein Family Using Ensembles of Sub-Sampled Alignments.” Plos One 11, no. 9 (2016): e0162579.
  10. Noren, David P., Byron L. Long, Raquel Norel, Kahn Rrhissorrakrai, Kenneth Hess, Chenyue Wendy Hu, Alex J. Bisberg, et al. “A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis.” PLOS Computational Biology 12, no. 6 (2016): e1004890. *Naegle lab (Tom Ronan, Jennifer Flynn, Kristen M. Naegle) participated as a team in the AML consortium.
  11. Ronan, Thomas, Zhijie Qi, and Kristen M Naegle, “Avoiding pitfalls when clustering biological data”, Science Signaling, 9, no. 432 (2016): re6. Invited Review.
    1. Made the banner of the home page of Science the week of publication.
    2. Most tweeted Science Signaling article.
  12. Ronan, Thomas, Jennifer L. McDonnell-Obermann, Laurel Huelsmann, *Kristen M. Naegle, and  *Linda J. Pike. “The seven EGF receptor agonists each elicit a unique signature of recruitment of downstream signaling proteins”, Journal of Biological Chemistry 291, no. 12 (2016): 5528-5540 *co-corresponding
    1. Article of the week in March 2016.
    2. 2016 Highlights issue
    3. Rated “Exceptional” by Faculty of 1000
  13. Holehouse, Alex S, and Kristen M. Naegle. “Reproducible Analysis of Post-Translational Modifications in Proteomes—Application to Human Mutations.” PLoS ONE 10, no. 12 (2015): 1–19.
  14. *Naegle, Kristen M., Nancy R Gough, and *Michael B Yaffe. “Criteria for Biological Reproducibility : What Does ‘n’ Mean ?” Science Signaling 8, no. 371 (2015): 2–5. *co-corresponding
  15. Matlock, Matthew K, Alex S Holehouse, and Kristen M Naegle. “ProteomeScout: A Repository and Analysis Resource for Post-Translational Modifications and Proteins.” Nucleic Acids Research 43, no. D1 (November 20, 2015): D521–30.
  16. Cho, Yongcheol, Roman Sloutsky, Kristen M Naegle, and Valeria Cavalli. “Injury-Induced HDAC5 Nuclear Export Is Essential for Axon Regeneration.” Cell 155, no. 4 (November 2013): 894–908.
  17. Iwai, Leo K, Leo S Payne, Maciej T Luczynski, Francis Chang, Huifang Xu, Ryan W Clinton, Angela Paul, Edward A. Esposito, Scott Gridley, Birgit Leitinger, Kristen M Naegle, and Paul H. Huang.  “Phosphoproteomics of Collagen Receptor Networks Reveals SHP-2 Phosphorylation Downstream of Wild-Type DDR2 and Its Lung Cancer Mutants.” The Biochemical Journal 454, no. 3 (September 15, 2013): 501–13.
  18. Sloutsky, Roman, Nicolas Jimenez, S Joshua Swamidass, and Kristen M Naegle. “Accounting for Noise When Clustering Biological Data.” Briefings in Bioinformatics 14, no. 4 (July 2013): 423–36. doi:10.1093/bib/bbs057.
  19. Naegle, Kristen M, Forest M White, Douglas A Lauffenburger, and Michael B Yaffe. “Robust Co-Regulation of Tyrosine Phosphorylation Sites on Proteins Reveals Novel Protein Interactions.” Molecular BioSystems 8, no. 10 (August 1, 2012): 2771–82.
  20. Naegle, Kristen M, Roy E Welsch, Michael B Yaffe, Forest M White, and Douglas A Lauffenburger. “MCAM: Multiple Clustering Analysis Methodology for Deriving Hypotheses and Insights from High-Throughput Proteomic Datasets.” PLoS Computational Biology 7, no. 7 (July 2011): e1002119.
  21. *Naegle, Kristen M, *Melissa Gymrek, Brian A Joughin, Joel P Wagner, Roy E Welsch, Michael B Yaffe, Douglas A Lauffenburger, and Forest M White. “PTMScout, a Web Resource for Analysis of High Throughput Post-Translational Proteomics Studies.” Molecular & Cellular Proteomics : MCP 9, no. 11 (November 2010): 2558–70. *authors contributed equally
  22. *Joughin, Brian A, *Kristen M Naegle, *Paul H Huang, Michael B Yaffe, Douglas A Lauffenburger, and Forest M White. “An Integrated Comparative Phosphoproteomic and Bioinformatic Approach Reveals a Novel Class of MPM-2 Motifs Upregulated in EGFRvIII-Expressing Glioblastoma Cells.” Molecular BioSystems 5, no. 1 (January 2009): 59–67. *authors contributed equally


Our Research Areas

  • Databases and resources for proteome-level PTM information

    A foundation of our work is the ability to have proteome information at our fingertips. This includes the current knowledge of tyrosine phosphorylation, quantitative measurements measured on those sites, and related protein annotations.  In enabling this research for our own lab, we also construct tools that can be used by the broader research community, with a focus on extendibility and reproducibility.

  • Inferring biological insight from high-dimensional data

    Kristen Naegle developed ensemble approaches to clustering of biological data in her Ph.D. work that demonstrated that one can infer function of tyrosine phosphorylation from quantitative measurements of the dynamic changes of network phosphorylation in cells in response to growth factor stimulation.  During her post-doctoral work, Dr. Naegle went on to show that robustness in clustering was predictive of protein interactions and inferred novel interactions in the epidermal growth factor receptor network.

  • SH2 domain binding

    A major piece of ongoing work in the lab is to develop methods that will allow us to identify what phosphotyrosines will be recognized by a binding domain. Specifically, we hope to push this area of research into arenas that allow us to predict the relative competition between domains for phosphotyrosine sequences and phosphotyrosine sequences for domains. This information will enable us to begin to predict the consequence of context differences between cells in response to the same extracellular cue. We will feel we have succeeded when these predictions can be used to explain complex network phenomena.

  • Engineering enzymatic interactions

    A major barrier to the study of protein phosphorylation is the ability to create phosphorylated proteins for in vitro study. The Naegle lab has been developing a cheap and fast method for producing phosphorylated proteins that capitalizes on observations made of enzymatic specificity.

Inferring biological insight from high-dimensional data

Science Home Page banner the week our review article was published (June 16, 2016).