Computational Scientist
Computational Scientist

Biography

Jason McDermott, a senior research scientist, has extensive experience in molecular and structural virology and data resource design, data integration and prediction of biological networks, and bridging experimental and computational biology. Currently, his research interests include data integration of high-throughput omics data for biomarker discovery, developing systems biology models in a number of systems focusing on host-pathogen interactions, characterizing phylogenetic and functional relationships in complex eukaryotic microbial communities, and using network inference and topology to characterize organism-level phenotypes.

A summary of his research projects is below.

Cancer biology and pathway analysis

Cancer is a pathway-centric disease. The emergence of high-throughput molecular profiling methods, such as transcriptomics and proteomics, has revolutionized scientists’ ability to interrogate signaling and functional pathways involved in cancer. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a National Cancer Institute program tasked with the proteomic characterization of tumors from The Cancer Genome Atlas. McDermott has worked on the PNNL-led CPTAC project to characterize nearly 200 high-grade serous ovarian cancer tumors using proteomics and phosphoproteomics. He has also developed several methods for integration of data types to determine pathway activity, and pathway-centric survival prediction methods. This work builds on his extensive experience in this area from other domains. In addition, McDermott has actively participated in the consortium, which holds bi-weekly teleconferences and meets in person twice per year. He currently serves as the chair of the biology working group and the chair of the data analysis working group.

Biological network analysis and predictive modeling

Networks are an abstract representation of biological systems and have been shown to be very useful for gaining biological insight and forming hypotheses. Many network studies work with experimentally derived networks of protein-protein interactions, regulatory interactions, or metabolic interactions—which are sparse and expensive to produce. McDermott’s research extends topological analysis of biological networks to networks inferred from high-throughput data. These networks are easy to generate from compendia of transcriptomics, proteomics, or metabolomics data. He showed that a number of systems with topologically important locations (high centrality and high degree proteins, e.g.) in these inferred networks are more likely to be important for functioning of the system as evaluated by a number of different phenotypes. This work extends to develop methods for inference of dynamic predictive models that could be used to simulate the behavior of a system over time and predict important nodes (proteins or genes) that are postulated to be gatekeepers for transitions between different system states.

Integrative omics for biomarker discovery

The field of biomarker discovery has been greatly expanded by the advent of high-throughput methods for data acquisition, such as transcriptomics, proteomics, and metabolomics. However, this rapid advance has driven the need for novel methods to integrate disparate types of data to identify biomarkers that are robust from study-to-study. McDermott and his team have found in the studies listed below, and others, that integration of different data types can improve phenotypic predictions dramatically. They also developed methods to make better use of data coming from the proteomics and metabolomics platforms. Finally, they’ve developed approaches for integrating high-throughput data with existing knowledge about the biological problems to improve the robustness and reproducibility of the resulting predictions and insights. McDermott works very closely with biologists (e.g. Dr. Rodland), statisticians (e.g. Dr. Webb-Robertson), and mass-spectrometrists (e.g. Dr. Metz) to develop the ideas for these approaches, implement methods in software, and analyze integrated data to provide predictions and biological insight. Another important component he has recently been working on is reproducibility and robustness, which pervades the rest of his work as well.

Computational prediction of problematic protein families

Prediction of protein functions has been accomplished by exploiting evolutionary similarities between sequences, which can be used to map functions to uncharacterized protein sequences. This has been, and continues to be, a very effective approach. However, it fails for important protein functions with diverse sequences that cannot be connected using existing methods, or where the sequences are highly related but specific functions can’t be assigned. McDermott has used machine learning methods to produce one of the first computational methods to predict type III secreted bacterial effectors, which have diverse sequences. This has spawned a series of related papers, covering both improvements on type III secreted effector predictions and for prediction of other types of secreted effectors, such as type IV substrates. Additionally, the algorithm, which can be found on a publicly accessible webserver, has been used many times and the results have been included in studies by collaborators and others. This work has recently been extended to predict multi-drug resistance transporters, which are related by sequence in very large families of transporters, but for which substrate specificity remains unknown. He originally worked with Dr. Fred Heffron to develop this approach and validate results. McDermott has done all the algorithm development and participated in experimental study design for the validation and related experimental studies. In addition, he led an effort to experimentally characterize the structural properties of secretion signal peptides.

Systems biology of infectious disease

The interactions between pathogen and host are very important to understand from a human disease perspective, yet they remain poorly understood. The advent of high-throughput methods has allowed gathering of large amounts of data on these systems. However, methods to utilize this data to develop systems biology models that can provide actionable hypotheses is an area of great need. McDermott has worked with several National Institute of Allergy and Infection Diseases-funded projects to develop regulatory networks of Salmonella Typhimurium, Yersinia pestis, and host response to HCV, influenza, and SARS viruses. These studies revealed important aspects of each host-pathogen interaction allowing identification, and subsequent validation, of critical genes and proteins involved in virulence or response to pathogens.

Science art

Red Pen Black Pen

McDermott has pursued an interest in art and science communication to draw comics and cartoons that are often (though not always) related to scientific themes, including: science infographics, publishing and peer review, silly science puns, bioinformatics, and microbiology. This is a completely personal endeavor and completely for his own entertainment, but many of the comics drawn by McDermott have resonated with scientists and academics throughout the world. See @redpenblackpen for the rest of the story.

Disciplines and Skills

  • Systems biology
  • Network inference
  • Data integration
  • Biological networks
  • Machine learning

Education

  • Post-doctoral Research Associate in Bioinformatics, University of Washington, 2006
  • PhD in Structural Virology, Oregon Health & Science University (OHSU), Structural Virology, 2000
  • BA in Biology, Reed College, 1993

Affiliations and Professional Service

  • Affiliate Associate Professor, Department of Molecular Microbiology and Immunology, OHSU

Publications

2020

  • McDermott J.E. and J.S. Tregoning. 2020. "Ten Simple Rules to becoming a Principal Investigator." PLoS Computational Biology 16, no. 2:Article No. e1007448. PNNL-SA-146497. doi:10.1371/journal.pcbi.1007448
  • McDermott J.E., O.A. Arshad, V.A. Petyuk, Y. Fu, M.A. Gritsenko, T.R. Clauss, and R.J. Moore, et al. 2020. "Proteogenomic characterization of ovarian HGSC implicates mitotic kinases, replication stress in observed chromosomal instability." Cell Reports Medicine 1, no. 1:Article No. 100004. PNNL-SA-145681. doi:10.1016/j.xcrm.2020.100004

2019

  • Arshad O.A., V.G. Danna, V.A. Petyuk, P.D. Piehowski, T. Liu, K.D. Rodland, and J.E. McDermott. 2019. "An integrative analysis of tumor proteomic and phosphoproteomic profiles to examine the relationships between kinase activity and phosphorylation." Molecular and Cellular Proteomics 18, no. 8 suppl 1:S26-S36. PNNL-SA-145629. doi:10.1074/mcp.RA119.001540
  • Cuesta R., M.A. Gritsenko, V.A. Petyuk, A.K. Shukla, C. Tsai, T. Liu, and J.E. McDermott, et al. 2019. "Phosphoproteome Analysis Reveals Estrogen-ER pathway as a modulator of mTOR activity via DEPTOR." Molecular and Cellular Proteomics 18, no. 8:1607-1618. PNNL-SA-145628. doi:10.1074/mcp.RA119.001506
  • McClure R.S., J.P. Wendler, J.N. Adkins, J.A. Swanstrom, R. Baric, B. Kaiser, and K.L. Oxford, et al. 2019. "Unified Feature Association Networks through Integration of Transcriptomic and Proteomic Data." PLoS Computational Biology 15, no. 9:Article No. e1007241. PNNL-SA-137420. doi:10.1371/journal.pcbi.1007241
  • McDermott J.E., J.R. Cort, E.S. Nakayasu, J.N. Pruneda, C.C. Overall, and J.N. Adkins. 2019. "Prediction of Bacterial E3 Ubiquitin Ligase Effectors using Reduced Amino Acid Peptide Fingerprinting." PeerJ 7:e7055. PNNL-SA-138492. doi:10.7717/peerj.7055
  • Minutoli M., M. Halappanavar, A. Kalyanaraman, A. Visweswara Sathanur, R.S. McClure, and J.E. McDermott. 2019. "Fast and Scalable Implementations of Influence Maximization Algorithms." In IEEE International Conference on Cluster Computing (CLUSTER 2019), September 23-26, 2019, Albuquerque, NM. Piscataway, New Jersey:IEEE. PNNL-SA-141177. doi:10.1109/CLUSTER.2019.8890991
  • Mitchell H.D., A.J. Eisfeld, K.G. Stratton, N.C. Heller, L.M. Bramer, J. Wen, and J.E. McDermott, et al. 2019. "The Role of EGFR in Influenza Pathogenicity: Multiple Network-based Approaches To Identify a Key Regulator of Non-Lethal Infections." Frontiers in Cell and Developmental Biology 7:Article No. 200. PNNL-SA-142083. doi:10.3389/fcell.2019.00200

2018

  • Colby S.M., R.S. McClure, C.C. Overall, R.S. Renslow, and J.E. McDermott. 2018. "Improving network inference algorithms using resampling methods." BMC Bioinformatics 19:Article No. 376. PNNL-SA-134995. doi:10.1186/s12859-018-2402-0
  • Duhen T., R. Duhen, R. Montler, J. Moses, T. Moudgil, N.F. de Miranda, and C.P. Goodall, et al. 2018. "Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors." Nature Communications 9, no. 1:Article No. 2724. PNNL-SA-135245. doi:10.1038/s41467-018-05072-0
  • Hosseini M.M., S.E. Kurtz, S. Abdelhamed, S. Mahmood, M.A. Davare, A. Kaempf, and J. Elferich, et al. 2018. "Inhibition of interleukin-1 receptor-associated kinase-1 is a therapeutic strategy for acute myeloid leukemia subtypes." Leukemia 32, no. 11:2374-2387. PNNL-SA-137772. doi:10.1038/s41375-018-0112-2
  • McDermott J.E., M. Partridge, and Y. Bromberg. 2018. "Ten Simple Rules for Drawing Scientific Comics." PLoS Computational Biology 14, no. 1:Article No. e1005845. PNNL-SA-127907. doi:10.1371/journal.pcbi.1005845
  • Moghieb A.M., G. Clair, H.D. Mitchell, J. Kitzmiller, E.M. Zink, Y. Kim, and V.A. Petyuk, et al. 2018. "Time-resolved Proteome Profiling of Normal Lung Development." American Journal of Physiology-Lung Cellular and Molecular Physiology 315, no. 1:L11-L24. PNNL-SA-127218. doi:10.1152/ajplung.00316.2017

2017

  • Ardini-Poleske M.E., R.F. Clark, C.K. Ansong, J.P. Carson, R.A. Corley, G. Deutsch, and J.S. Hagood, et al. 2017. "LungMAP: The Molecular Atlas of Lung Development Program." American Journal of Physiology-Lung Cellular and Molecular Physiology 313, no. 5:L733-L740. PNNL-SA-136379. doi:10.1152/ajplung.00139.2017
  • Maier T.V., M. Lucio, L.H. Lee, N. VerBerkmoes, C.J. Brislawn, J. Bernhardt, and R. Lamendella, et al. 2017. "Impact of Dietary Resistant Starch on the Human Gut Microbiome, Metaproteome and Metabolome." mBio 8, no. 5:Article No. e01343-17. PNNL-SA-126751. doi:10.1128/mBio.01343-17
  • McDermott J.E. 2017. "Working Life: Drawing Connections." Science 356, no. 6343:1202. PNNL-SA-125843. doi:10.1126/science.356.6343.1202
  • Wang J., Z. Ma, S.A. Carr, P. Mertins, H. Zhang, Z. Zhang, and D.W. Chan, et al. 2017. "Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction." Molecular & Cellular Proteomics. MCP 16, no. 1:121-134. PNNL-SA-124098. doi:10.1074/mcp.M116.060301

2016

  • Bernstein H.C., R.S. McClure, E.A. Hill, L.M. Markillie, W.B. Chrisler, M.F. Romine, and J.E. McDermott, et al. 2016. "Unlocking the Constraints of Cyanobacterial Productivity: Acclimations Enabling Ultrafast Growth." mBio 7, no. 4:Article No. e00949-16. PNNL-SA-112394. doi:10.1128/mBio.00949-16
  • McClure R.S., C.C. Overall, J.E. McDermott, E.A. Hill, L.M. Markillie, L.A. McCue, and R.C. Taylor, et al. 2016. "Network Analysis of Transcriptomics Expands Regulatory Landscapes in Synechococcus sp. PCC 7002." Nucleic Acids Research 44, no. 18:8810-8825. PNNL-SA-113421. doi:10.1093/nar/gkw737
  • McDermott J.E., H.D. Mitchell, L. Gralinski, A.J. Eisfeld, L. Josset, A. Bankhead, and G. Neumann, et al. 2016. "The Effect of inhibition of PP1 and TNFa signaling on pathogenesis of SARS coronavirus." BMC Systems Biology 10, no. 1:93. PNNL-SA-118784. doi:10.1186/s12918-016-0336-6
  • Mitchell H.D., L. Markillie, W.B. Chrisler, M.J. Gaffrey, D. Hu, C.J. Szymanski, and Y. Xie, et al. 2016. "Cells Respond to Distinct Nanoparticle Properties with Multiple Strategies as Revealed by Single-Cell RNA-Seq." ACS Nano 10, no. 11:10173-10185. PNNL-SA-121323. doi:10.1021/acsnano.6b05452
  • Oxford K.L., J.P. Wendler, J.E. McDermott, R.A. White, J.D. Powell, J.M. Jacobs, and J.N. Adkins, et al. 2016. "The Landscape of Viral Proteomics and Its Potential to Impact Human Health." Expert Review of Proteomics 13, no. 6:579-591. PNNL-SA-116259. doi:10.1080/14789450.2016.1184091
  • Shi T., M. Niepel, J.E. McDermott, Y. Gao, C.D. Nicora, W.B. Chrisler, and L.M. Markillie, et al. 2016. "Conservation of Protein Abundance Patterns Reveals the Regulatory Architecture of the of the EGFR-MAPK Pathway." Science Signaling 9, no. 436:rs6. PNNL-SA-115056. doi:10.1126/scisignal.aaf0891
  • Tabb D.L., X. Wang, S.A. Carr, K. Clauser, P. Mertins, M.C. Chambers, and J.D. Holman, et al. 2016. "Reproducibility of differential proteomic technologies in CPTAC fractionated xenografts." Journal of Proteome Research 15, no. 3:691-706. PNNL-SA-113210. doi:10.1021/acs.jproteome.5b00859
  • Zhang H., T. Liu, Z. Zhang, S.H. Payne, B. Zhang, J.E. McDermott, and J. Zhou, et al. 2016. "Integrated proteogenomic characterization of human high grade serous ovarian cancer." Cell 166, no. 3:755-765. PNNL-SA-107151. doi:10.1016/j.cell.2016.05.069

2015

  • Li J., C.C. Overall, E.S. Nakayasu, A.S. Kidwai, M.B. Jones, R. Johnson, and N.T. Nguyen, et al. 2015. "Analysis of the Salmonella regulatory network suggests involvement of SsrB and H-NS in sE-regulated SPI-2 gene expression." Frontiers in Microbiology 6:Article No. 27. PNNL-SA-101010. doi:10.3389/fmicb.2015.00027
  • Li J., C.C. Overall, R. Johnson, M.B. Jones, J.E. McDermott, F. Heffron, and J.N. Adkins, et al. 2015. "ChIP-Seq Analysis of the s E Regulon of Salmonella enterica Serovar Typhimurium Reveals New Genes Implicated in Heat Shock and Oxidative Stress Response." PLoS One 10, no. 9:Article No. e0138466. PNNL-SA-115504. doi:10.1371/journal.pone.0138466
  • Li J., E.S. Nakayasu, C.C. Overall, R. Johnson, A.S. Kidwai, J.E. McDermott, and C. Ansong, et al. 2015. "Global analysis of Salmonella alternative sigma factor E on protein translation." Journal of Proteome Research 14, no. 4:1716-1726. PNNL-SA-105707. doi:10.1021/pr5010423
  • Webb-Robertson B.M., H.K. Wiberg, M.M. Matzke, J.N. Brown, J. Wang, J.E. McDermott, and R.D. Smith, et al. 2015. "Review, Evaluation, and Discussion of the Challenges of Missing Value Imputation for Mass Spectrometry-Based Label-Free Global Proteomics." Journal of Proteome Research 14, no. 5:1993-2001. PNWD-SA-10158. doi:10.1021/pr501138h

2014

  • Aevermann B., B.E. Pickett, S. Kumar, E.B. Klem, S. Agnihothram, P.S. Askovich, and A. Bankhead, et al. 2014. "A Comprehensive Collection of Systems Biology Data Characterizing the Host Response to Viral Infection." Scientific Data 1:Article No. 140033. PNNL-SA-101269. doi:10.1038/sdata.2014.33
  • Kang S., S.H. Kahan, J.E. McDermott, N.S. Flann, and I. Shmulevich. 2014. "Biocellion: Accelerating Computer Simulation of Multicellular Biological System Models." Bioinformatics 30, no. 21:3101-3108. PNNL-SA-99077. doi:10.1093/bioinformatics/btu498
  • McDermott J.E., Y. Huang, B. Zhang, H. Xu, and Z. Zhao. 2014. "Integrative Genomics and Computational Systems Medicine." BioMed Research International 2014:Article No. 945253. PNNL-SA-105051. doi:10.1155/2014/945253
  • Webb-Robertson B.M., M.M. Matzke, S. Datta, S.H. Payne, J. Kang, L.M. Bramer, and C.D. Nicora, et al. 2014. "Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements." Molecular and Cellular Proteomics 13, no. 12:3639-3646. PNNL-SA-95335. doi:10.1074/mcp.M113.030932

2013

  • Hafen R.P., L.J. Gosink, J.E. McDermott, K.D. Rodland, K. Kleese-Van Dam, and W.S. Cleveland. 2013. "Trelliscope: A System for Detailed Visualization in Analysis of Large Complex Data." In IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV 2013), October 13-14, 2013, Atlanta, Georgia, 105-112. Piscataway, New Jersey:IEEE. PNNL-SA-95831. doi:10.1109/LDAV.2013.6675164
  • Kim Y., B. Schmidt, A.S. Kidwai, M.B. Jones, B.L. Deatherage, H.M. Brewer, and H.D. Mitchell, et al. 2013. "Salmonella Modulates Metabolism During Growth under Conditions that Induce Expression of Virulence Genes." Molecular Biosystems 9, no. 6:1522-1534. PNNL-SA-89810. doi:10.1039/C3MB25598K
  • Matzke M.M., J.N. Brown, M.A. Gritsenko, T.O. Metz, J.G. Pounds, K.D. Rodland, and A.K. Shukla, et al. 2013. "A Comparative Analysis of Computational Approaches to Relative Protein Quantification Using Peptide Peak Intensities in Label-free LC-MS Proteomics Experiments." Proteomics 13, no. 3-4:493-503. PNNL-SA-88866. doi:10.1002/pmic.201200269
  • McDermott J.E., J. Wang, H.D. Mitchell, B.M. Webb-Robertson, R.P. Hafen, J.A. Ramey, and K.D. Rodland. 2013. "Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data." Expert Opinion on Medical Diagnostics 7, no. 1:37-51. PNNL-SA-86517. doi:10.1517/17530059.2012.718329
  • Mitchell H.D., A.J. Eisfeld, A. Sims, J.E. McDermott, M.M. Matzke, B.M. Webb-Robertson, and S.C. Tilton, et al. 2013. "A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses." PLoS One 8, no. 7:e69374. PNNL-SA-92748. doi:10.1371/journal.pone.0069374
  • Niemann G., R.N. Brown, I.T. Mushamiri, N.T. Nguyen, R. Taiwo, A. Stufkens, and R.D. Smith, et al. 2013. "RNA Type III Secretion Signals that require Hfq." Journal of Bacteriology 195, no. 10:2119-2125. PNNL-SA-90112. doi:10.1128/JB.00024-13
  • Sanfilippo A.P., J.N. Haack, J.E. McDermott, S. Stevens, and M. Stenzel-Poore. 2013. "Modeling Emergence in Neuroprotective Regulatory Networks." In Complex Sciences: Second International Conference COMPLEX 2012, December 5-7, 2012, Santa Fe, New Mexico. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, edited by K Glass, et al, 126, 291-302. New York, New York:Springer. PNNL-SA-88379. doi:10.1007/978-3-319-03473-7_26
  • Wang J., B.M. Webb-Robertson, M.M. Matzke, S.M. Varnum, J.N. Brown, R.M. Riensche, and J.N. Adkins, et al. 2013. "A Semiautomated Framework for Integrating Expert Knowledge into Disease Marker Identification." Disease Markers 35, no. 5:513-523. PNNL-SA-94068. doi:10.1155/2013/613529
  • Webb-Robertson B.M., M.M. Matzke, T.O. Metz, J.E. McDermott, J. Walker, K.D. Rodland, and J.G. Pounds, et al. 2013. "Sequential Projection Pursuit Principal Component Analysis - Dealing with Missing Data Associated with New -Omics Technologies." BioTechniques 54, no. 3:165-168. PNNL-SA-87092.
  • Zhang B., Y. Huang, J.E. McDermott, R.H. Posey, H. Xu, and Z. Zhao. 2013. "Interdisciplinary Dialogue for Education, Collaboration, and Innovation: Intelligent Biology and Medicine In and Beyond 2013." BMC Genomics 14, no. Suppl 8:Article No. S1. PNNL-SA-99148. doi:10.1186/1471-2164-14-S8-S1

2012

  • Acquaah-Mensah G., D. Malhotra, M. Vulimiri, J.E. McDermott, S. Biswal, and S. Biswal. 2012. "Suppressed Expression of T-Box Transcription Factors is Involved in Senescence in Chronic Obstructive Pulmonary Disease." PLoS Computational Biology 8, no. 7:Article No. e1002597. PNNL-SA-86177. doi:10.1371/journal.pcbi.1002597
  • Ansong C.K., B.L. Deatherage, D.R. Hyduke, B. Schmidt, J.E. McDermott, M.B. Jones, and S. Chauhan, et al. 2012. "Studying Salmonellae and Yersiniae Host-Pathogen Interactions Using Integrated ‘Omics and Modeling." In Systems Biology. Current Topics in Microbiology and Immunology, edited by M Katze. 21-41. PNNL-SA-135981. doi:10.1007/82_2012_247
  • Archuleta M.N., J.E. McDermott, J.S. Edwards, and H. Resat. 2012. "An Adaptive Coarse Graining Method for Signal Transduction in Three-Dimensions." Fundamenta informaticae 118, no. 4:371-384. PNWD-SA-9566. doi:10.3233/FI-2012-720
  • Diamond D.L., A. Krasnoselski, K.E. Burnum, M.E. Monroe, B.M. Webb-Robertson, J.E. McDermott, and M.M. Yeh, et al. 2012. "Proteome and Computational Analyses Reveal New Insights into the Mechanisms of Hepatitis C Virus Mediated Liver Disease Posttransplantation." Hepatology 56, no. 1:28-38. PNWD-SA-9552. doi:10.1002/hep.25649
  • McDermott J.E., D.L. Diamond, C.D. Corley, A. Rasmussen, M.G. Katze, and K.M. Waters. 2012. "Topological Analysis of Protein Co-Abundance Networks Identifies Novel Host Targets Important for HCV Infection and Pathogenesis." BMC Systems Biology 6, no. 1:28. PNWD-SA-9359. doi:10.1186/1752-0509-6-28
  • McDermott J.E., K.B. Vartanian, H.D. Mitchell, S. Stevens, A.P. Sanfilippo, and M. Stenzel-Poore. 2012. "Identification and Validation of Ifit1 as an Important Innate Immune Bottleneck." PLoS One 7, no. 6:e36465. PNNL-SA-82946. doi:10.1371/journal.pone.0036465
  • McDermott J.E., K.D. Jarman, R.C. Taylor, M.J. Lancaster, H. Shankaran, K.B. Vartanian, and S. Stevens, et al. 2012. "Modeling Dynamic Regulatory Processes in Stroke." PLoS Computational Biology 8, no. 10:e1002722. PNNL-SA-81643. doi:10.1371/journal.pcbi.1002722

2011

  • Aderem A., J.N. Adkins, C. Ansong, J. Galagan, S. Kaiser, M.J. Korth, and G.L. Law, et al. 2011. "A Systems Biology Approach to Infectious Disease Research: Innovating the Pathogen-Host Research Paradigm." mBio 2, no. 1:Article No. e00325. PNNL-SA-77026. doi:10.1128/mBio.00325-10
  • Heffron F., G. Niemann, H. Yoon, A.S. Kidwai, R.N. Brown, J.E. McDermott, and R.D. Smith, et al. 2011. "Salmonella-secreted Virulence Factors." In Salmonella: From Genome to Function, edited by S Porwollik. 187-223. Norfolk:Caister Academic Press. PNNL-SA-71401.
  • McDermott J.E., A.L. Corrigan, E.S. Peterson, C.S. Oehmen, G. Niemann, E. Cambronne, and D. Sharp, et al. 2011. "Computational prediction of type III and IV secreted effectors in Gram-negative bacteria." Infection and Immunity 79, no. 1:23-32. PNNL-SA-72872. doi:10.1128/IAI.00537-10
  • McDermott J.E., C.S. Oehmen, L.A. McCue, E.A. Hill, D.M. Choi, J. Stockel, and M.L. Liberton, et al. 2011. "A Model of Cyclic Transcriptomic Behavior in Cyanobacterium Cyanothece sp. ATCC 51142." Molecular Biosystems 7, no. 8:2407-2418. PNNL-SA-74409. doi:10.1039/C1MB05006K
  • McDermott J.E., H. Shankaran, A.J. Eisfeld, S. Belisle, G. Neumann, C. Li, and S.K. Mcweeney, et al. 2011. "Conserved Host Response to Highly Pathogenic Avian Influenza Virus Infection in Human Cell Culture, Mouse and Macaque Model Systems." BMC Systems Biology 5:Article No. 190. PNWD-SA-9377. doi:10.1186/1752-0509-5-190
  • McDermott J.E., H. Yoon, E.S. Nakayasu, T.O. Metz, D.R. Hyduke, A.S. Kidwai, and B.O. Palsson, et al. 2011. "Technologies and Approaches to Elucidate and Model the Virulence Program of Salmonella." Frontiers in Microbiology 2:Article No. 121. PNNL-SA-78291. doi:10.3389/fmicb.2011.00121
  • McDermott J.E., M.N. Archuleta, B.D. Thrall, J.N. Adkins, and K.M. Waters. 2011. "Controlling the Response: Predictive Modeling of a Highly Central, Pathogen-Targeted Core Response Module in Macrophage Activation." PLoS One 6, no.2:e14673. PNWD-SA-9011. doi:10.1371/journal.pone.0014673
  • McDermott J.E., M.N. Costa, S. Stevens, M. Stenzel-Poore, and A.P. Sanfilippo. 2011. "Defining the Players in Higher-Order Networks: Predictive Modeling for Reverse Engineering Functional Influence Networks. " In Biocomputing 2011: Proceedings of the Pacific Symposium on Biocomputing, January 3-7, 2011, Kohala Coast, Hawaii, edited by RB Altman, et al, 314-25. London:World Scientific. PNNL-SA-74078. doi:10.1142/9789814335058_0033
  • McDermott J.E., P. Braun, R.A. Bonneau, and D.R. Hyduke. 2011. "Modeling Host-Pathogen Interactions: Computational Biology and Bioinformatics for Infectious Disease Research (Session introduction)." In Pacific Syposium on Biocomputing 2012, January 3-7, 2012, Honolulu, Hawaii, 17, 283-286. Stanford, California:Stanford University. PNNL-SA-83019.
  • Niemann G., R.N. Brown, J.K. Gustin, A. Stufkens, A.S. Shaikh-Kidwai, J. Li, and J.E. McDermott, et al. 2011. "Discovery of Novel Secreted Virulence Factors from Salmonella enterica Serovar Typhimurium by Proteomic Analysis of Culture Supernatants." Infection and Immunity 79, no. 1:33-43. PNNL-SA-71385. doi:10.1128/IAI.00771-10
  • Rasmussen A., D.L. Diamond, J.E. McDermott, X. Gao, T.O. Metz, M.M. Matzke, and V. Carter, et al. 2011. "Systems Virology Identifies a Mitochondrial Fatty Acid Oxidation Enyzme, Dodecenoyl Coenzyme A Delta Isomerase, Required for Hepatitis C Virus Replication and Likely Pathogenesis." Journal of Virology 85, no. 22:11646-11654. PNWD-SA-9324. doi:10.1128/JVI.05605-11
  • Taylor R.C., A.P. Sanfilippo, J.E. McDermott, R.L. Baddeley, R.M. Riensche, R.S. Jensen, and M. Verhagen, et al. 2011. "Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts." International Journal of Computational Biology and Drug Design 4, no. 1:56-82. PNNL-SA-76542. doi:10.1504/IJCBDD.2011.038657
  • Yoon H., C. Ansong, J.E. McDermott, M.A. Gritsenko, R.D. Smith, F. Heffron, and J.N. Adkins. 2011. "Systems analysis of multiple regulator perturbations allows discovery of virulence factors in Salmonella." BMC Systems Biology 5:Article No. 100. PNNL-SA-77197. doi:10.1186/1752-0509-5-100

2010

  • Buchko G.W., G. Niemann, E.S. Baker, M.E. Belov, R.D. Smith, F. Heffron, and J.N. Adkins, et al. 2010. "A multi-pronged search for a common structural motif in the secretion signal of Salmonella enterica serovar Typhimurium type III effector proteins." Molecular Biosystems 6, no. 12:2448-2458. PNNL-SA-73691. doi:10.1039/c0mb00097c
  • Diamond D.L., A.J. Syder, J.M. Jacobs, C.M. Sorensen, K. Walters, S. Proll, and J.E. McDermott, et al. 2010. "Temporal Proteome and Lipidome Profiles Reveal HCV-Associated Reprogramming of Hepatocellular Metabolism and Bioenergetics." PLoS Pathogens 6, no. 1:Article No. e1000719. PNWD-SA-8678. doi:10.1371/journal.ppat.1000719
  • Lawrence P., W. Kittichotirat, J.E. McDermott, and R.E. Bumgarner. 2010. "A Three-way Comparative Genomic Analysis of Mannheimia haemolytica Isolates." BMC Genomics 11:Article No. 535. PNNL-SA-75660. doi:10.1186/1471-2164-11-535
  • Lawrence P., W. Kittichotirat, R.E. Bumgarner, J.E. McDermott, D. Herndon, D.P. Knowles, and S. Srikumaran. 2010. "Genome sequences of Mannheimia haemolytica serotype A2: ovine and bovine isolates." Journal of Bacteriology 192, no. 4:1167-1168. PNWD-SA-8800. doi:10.1128/JB.01527-09
  • McDermott J.E., A.P. Sanfilippo, R.C. Taylor, R.L. Baddeley, R.M. Riensche, and R.S. Jensen. 2010. "An Integrated Approach to Predictive Genomic Analytics." In Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology, 390-393. New York, New York:Association for Computing Machinery. PNNL-SA-72871. doi:10.1145/1854776.1854837
  • McDermott J.E., M.N. Costa, D.B. Janszen, M. Singhal, and S.C. Tilton. 2010. "Separating the Drivers from the Driven: Integrative Network and Pathway Approaches Aid Identification of Disease Biomarkers from High-Throughput Data." Disease Markers 28, no. 4:253-266. PNNL-SA-70130. doi:10.3233/DMA-2010-0695
  • Taylor R.C., A.P. Sanfilippo, J.E. McDermott, R.L. Baddeley, R.M. Riensche, R.S. Jensen, and M. Verhagen. 2010. "Learning Biological Networks via Bootstrapping with Optimized GO-based Gene Similarity." In Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology, 515-519. New York, New York:Association for Computing Machinery. PNNL-SA-71923. doi:10.1145/1854776.1854875
  • Wang J., J. Zhang, R. Li, H. Zheng, J. Li, Y. Zhang, and H. Li, et al. 2010. "Evolutionary transients in the rice transcriptome." Genomics, Proteomics & Bioinformatics 8, no. 4:211-228. PNNL-SA-58635. doi:10.1016/S1672-0229(10)60023-X

2009

  • Cannon W.R., B.M. Webb-Robertson, A.R. Willse, M. Singhal, L.A. McCue, J.E. McDermott, and R.C. Taylor, et al. 2009. "An Integrative Computational Framework for Hypotheses-Driven Systems Biology Research in Proteomics and Genomics." In Computational and Systems Biology: Methods and Applications. 63-85. Trivandrum:Research Signpost. PNNL-SA-57999.
  • Frazier Z., J.E. McDermott, M. Guerquin, and R. Samudrala. 2009. "Computational representation of biological systems." In Computational Systems Biology, Methods in Molecular Biology. 535-550. Totowa, New Jersey:Humana Press. PNNL-SA-56401.
  • Guerquin M., J.E. McDermott, Z. Frazier, and R. Samudrala. 2009. "The Bioverse API and Web Application." In Computational Systems Biology, Methods in Molecular Biology. 511-534. Totowa, New Jersey:Humana Press. PNNL-SA-56404.
  • McDermott J.E., J. Wang, J. Yu, G. Wong, and R. Samudrala. 2009. "Prediction and Annotation of Plant Protein Interaction Networks." In Plant Genomics and Bioinformatics, edited by GP Rao, C Wagner, RK Singh and ML Sharma. 207-238. Houston, Texas:Studium Press LLC. PNNL-SA-57922.
  • McDermott J.E., R. Samudrala, R.E. Bumgarner, K. Montogomery, R. Ireton, and R. Ireton. 2009. Computational Systems Biology. Totowa, New Jersey:Humana Press. PNNL-SA-57221.
  • McDermott J.E., R.C. Taylor, H. Yoon, and F. Heffron. 2009. "Bottlenecks and Hubs in Inferred Networks Are Important for Virulence in Salmonella typhimurium." Journal of Computational Biology 16, no. 2:169-180. PNNL-SA-61800. doi:10.1089/cmb.2008.04TT
  • Rashid I., I. Rashid, J.E. McDermott, and R. Samudrala. 2009. "Inferring molecular interactions pathways from eQTL data." In Computational Systems Biology, Methods in Molecular Biology. 211-224. Totowa, New Jersey:Humana Press. PNNL-SA-56402.
  • Samudrala R., F. Heffron, and J.E. McDermott. 2009. "Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems." PLoS Pathogens 5, no. 4. PNNL-SA-61526. doi:10.1371/journal.ppat.1000375
  • Sanfilippo A.P., R.L. Baddeley, N. Beagley, J.E. McDermott, R.M. Riensche, R.C. Taylor, and B. Gopalan. 2009. "Using the Gene Ontology to Enrich Biological Pathways." International Journal of Computational Biology and Drug Design 2, no. 3:221-235. PNNL-SA-69383. doi:10.1504/IJCBDD.2009.030114
  • Shi L., C. Ansong, H.S. Smallwood, L.M. Rommereim, J.E. McDermott, H.M. Brewer, and A.D. Norbeck, et al. 2009. "Proteome of Salmonella enterica serotype Tyhimurium Grown in Low Mg2+/pH Medium." Journal of Proteomics and Bioinformatics 2, no. 9:388-397. PNNL-SA-68770. doi:10.4172/jpb.1000099
  • Shi L., S.M. Chowdhury, H.S. Smallwood, H. Yoon, H.M. Mottaz-Brewer, A.D. Norbeck, and J.E. McDermott, et al. 2009. "Proteomic Investigation of the Time Course Responses of RAW 264.7 Macrophages to Infection with Salmonella enterica." Infection and Immunity 77, no. 8:3227-3233. PNNL-SA-62432. doi:10.1128/IAI.00063-09
  • Taylor R.C., M. Singhal, D.S. Daly, J.M. Gilmore, W.R. Cannon, K.O. Domico, and A.M. White, et al. 2009. "An analysis pipeline for the inference of protein-protein interaction networks." International Journal of Data Mining and Bioinformatics 3, no. 4, (Sp. Iss. SI:409-430. PNNL-SA-59756. doi:10.1504/IJDMB.2009.029204
  • Taylor R.C., M. Singhal, J.B. Weller, J.B. Weller, S. Khoshnevis, L. Shi, and J.E. McDermott. 2009. "A Network Inference Workflow Applied to Virulence-Related Processes in Salmonella typhimurium." In Annals of the New York Academy of Sciences. 143-158. New York, New York:PubMed. PNNL-SA-60416.
  • Toepel J., J.E. McDermott, T. Summerfield, and L.A. Sherman. 2009. "Transcriptional analysis of the unicellular, diazotrophic cyanobacterium Cyanothece sp. ATCC 51142 grown under short day/night cycles." Journal of Phycology 45, no. 3:610-620. PNNL-SA-62083. doi:10.1111/j.1529-8817.2009.00674.x
  • Webb-Robertson B.M., L.A. McCue, N. Beagley, J.E. McDermott, D.S. Wunschel, S.M. Varnum, and J.Z. Hu, et al. 2009. "A Bayesian Integration Model of High-Throughput Proteomics and Metabolomics Data for Improved Early Detection of Microbial Infections." In Pacific Symposium on Biocomputing, 14, 451-463. Singapore:World Scientific Publishing Co. PNNL-SA-61531.
  • Wichadakul D., J.E. McDermott, and R. Samudrala. 2009. "Prediction and integration of regulatory and protein-protein interactions." In Computational Systems Biology, Methods in Molecular Biology. 101-144. Totowa, New Jersey:Humana Press. PNNL-SA-56403.
  • Yoon H., J.E. McDermott, S. Porwollik, M. Mcclelland, and F. Heffron. 2009. "Coordinated Regulation of Virulence during Systemic Infection of Salmonella enterica serovar Typhimurium." PLoS Pathogens 5, no. 2:1-16. PNNL-SA-61974. doi:10.1371/journal.ppat.1000306

2008

  • Ansong C., H. Yoon, A.D. Norbeck, J.K. Gustin, J.E. McDermott, H.M. Mottaz, and J. Rue, et al. 2008. "Proteomics Analysis of the Causative Agent of Typhoid Fever." Journal of Proteome Research 7, no. 2:546-557. PNNL-SA-56065. doi:10.1021/pr070434u
  • Gorton I., C.S. Oehmen, and J.E. McDermott. 2008. "It Takes Glue to Tango: MeDICi integration framework creates data-intensive computing pipeline." Scientific Computing 25, no. 7:16-24. PNNL-SA-62541.
  • McDermott J.E., and R. Samudrala. 2008. "Bioinformatic characterization of plant networks." In Proceedings of the Asia Pacific Conference on Plant Tissue Culture and Agrobiotechnology (APaCPA) 2007. Bedong:Aimst University. PNNL-SA-55710.

2007

  • Taylor R.C., M. Singhal, D.S. Daly, K.O. Domico, A.M. White, D.L. Auberry, and K.J. Auberry, et al. 2007. "SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction." In Sixth International Conference on Machine Learning and Applications, (ICMLA 2007), 587-593. Washington Dc:IEEE Computer Society. PNNL-SA-55941. doi:10.1109/ICMLA.2007.63