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Peter E. Larsen

Assistant Computational Biologist

Peter Larsen focuses on analysis methods for high-throughput genomic data for the functional & structural annotation of genomes, high-throughput expression analysis, the construction of biological interaction networks, and novel computational approaches.


Research Interests:

Analysis methods for high-throughput genomic data for the functional and structural annotation of genomes, high-throughput expression analysis, the construction of biological interaction networks, and novel computational approaches to systems biology.


  1. LR Thompson, JG Sanders, D McDonald, A Amir, J Ladau, KJ Locey, … A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551 (7681) (2017).
  2. S Lax, D Smith, N Sangwan, K Handley, P Larsen, M Richardson, … Bacterial colonization and succession in a newly opened hospital. Science Translational Medicine 9 (391) (2017).
  3. S Shinde, JR Cumming, FR Collart, PH Noirot, PE Larsen. Pseudomonas fluorescens Transportome Is Linked to Strain-Specific Plant Growth Promotion in Aspen Seedlings under Nutrient Stress. Frontiers in Plant Science 8 (2017).
  4. Luke R Thompson, Gareth J Williams, Mohamed F Haroon, Ahmed Shibl, Peter Larsen, Joshua Shorenstein, Rob Knight, Ulrich Stingl (2016).  Metagenomic covariation along densely sampled environmental gradients in the Red Sea.  The ISME journal 11 (1), 138-151 (2017).
  5. Peter E Larsen.  More of an art than a science: Using microbial DNA sequences to compose music.  Journal of microbiology & biology education 17 (1), 129 (2016).
  6. Jessica L. Metcalf, Zhenjiang Zech Xu, Sophie Weiss, Simon Lax,  Will Van Treuren, Embriette R. Hyde, Se Jin Song, Amnon Amir, Peter Larsen, Naseer Sangwan, Daniel Haarmann, Greg C. Humphrey, Gail Ackermann, Luke R. Thompson, Christian Lauber, Alexander Bibat, Catherine Nicholas, Matthew J. Gebert, Joseph F. Petrosino, Sasha C. Reed, Jack A. Gilbert, Aaron M. Lynne, Sibyl R. Bucheli, David O. Carter, Rob Knight (2015). Microbial community assembly and metabolic function during mammalian corpse decomposition. Science DOI: 10.1126/science.aad2646
  7. Peter E Larsen, Y Dai. Metabolome of human gut microbiome is predictive of host dysbiosis. GigaScience 4 (1), 1-16 (2015).
  8. Peter E Larsen, FR Collart, Y Dai. Predicting ecological roles in the rhizosphere using metabolome and transportome modeling. PloS one 10 (9), e0132837 (2015)
  9. E Zielazinski, S Zerbs, P Larsen, F Collart, PD Laible. Methionine Importers in Soil Bacteria: Potential for Transporter-Component Crosstalk. Biophysical Journal. 108 (2), 146a (2015).
  10. Simon Lax, Daniel P Smith, Jarrad Hampton-Marcell, Sarah Owens, Kim M. Handley, Nicole Scott, Sean M Gibbons, Peter Larsen, Benjamin D Shogan, Sophie Weiss, Jessica L. Metcalf, Luke K. Ursell, Yoshiki Vázquez-Baeza, Will Van Treuren, Nur A. Hasan, Molly K. Gibson, Rita Colwell, Gautam Dantas, Rob Knight, Jack A. Gilbert. Longitudinal analysis of microbial interaction between humans and the indoor environment.  2014Science 345 (6200), 1048-1052.
  11. Peter E Larsen, Leland J Cseke, R M Miller, Frank R Collart.  Modeling forest ecosystem responses to elevated carbon dioxide and ozone using artificial neural networks. Journal of Theoretical Biology, Volume 359, 21 October 2014, Pages 6171.
  12. Peter E. Larsen, Frank Collart, Yang Dai.  Using metabolomic and transportomic modeling and machine learning to identify putative novel therapeutic targets for antibiotic resistant Pseudomonad infections.  2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 08/2014; 2014:314-7. DOI10.1109/EMBC.2014.6943592
  13. Peter E. Larsen, Dawn Field, Yuki Hamada, Jack A. Gilbert.  Satellite remote sensing data can be used to model marine microbial metabolite turnover.  ISME Journal. 2014; doi: 10.1038/ismej.2014.107..
  14. Yuki Hamada, Jack A. Gilbert, Peter E. Larsen, and Madeline J. Norgaard. Toward Linking Aboveground Vegetation Properties and Soil Microbial Communities Using Remote Sensing. Photogrammetric Engineering & Remote Sensing. Vol. 80, No. 4, April 2014, pp. 311321.
  15. Lopez, J., M. Cuvelier, J. A. Gilbert, P. Larsen, D. Willoughby, Y. Wu, P. Blackwelder, P. J. Mccarthy, E. Smith, and Vega R. Thurber. Synergistic Effects of Crude Oil and Corexit Dispersant on a Sponge Holobiont System.” In INTEGRATIVE AND COMPARATIVE BIOLOGY, vol. 53, pp. E130-E130. JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA: OXFORD UNIV PRESS INC, 2013.
  16. Stephen Lumayag, Caroline E Haldin, Colleen Cowan, Beatrix Kovacs, Peter Larsen, Dane P. Witmer, David Valle, Shunbin Xu.  Inactivation of the miR-183/96/182 Cluster Results in Syndromic Retinal Degeneration.  Accepted at PNAS 12/21/2012.
  17. Peter E. Larsen, Jack A. Gilbert.  Microbial Bebop:  creating music from complex dynamics in microbial ecology.  PLoS ONE, 2013, 8(3): e58119.
  18. Peter E Larsen and Frank R Collart. BowStrap v1.0: Assigning statistical significance to expressed genes using short-read transcriptome data. BMC Research Notes 2012, 5:275. [HIGHLY ACCESSED]
  19. Peter E. Larsen, Sean M. Gibbons and Jack A. Gilbert. Modeling Microbial Community Structure and Functional Diversity Across Time And Space. FEMS Microbiology Letters. Accepted manuscript online: 3 MAY 2012 09:14PM EST | DOI: 10.1111/j.1574-6968.2012.02588.x.
  20. Larsen PE, Field D, Gilbert JA. Predicting bacterial community assemblages using an artificial neural network approach. Nat Methods. 2012 Apr 15. doi: 10.1038/nmeth.1975. [Manuscript reviewed in June 2012 Nature Methods, News and Views]
  21. Larsen P, Hamada Y, Gilbert J. Modeling microbial communities: Current, developing, and future technologies for predicting microbial community interaction. J Biotechnol. 2012 Mar 23.
  22. Larsen PE, Collart F, Field D, Meyer F, Keegan KP, Henry CS, McGrath J, Quinn J, Gilbert JA. 2011. Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset. Microbial Informatics and Experimentation 2011, 1:4. [HIGHLY ACCESSED]
  23. Larsen, Peter E., Frank Collart, Folker Meyer, and Jack A. Gilbert. Predicted Relative Metabolomic Turnover-Predicting Changes in the Environmental Metabolome from the Metagenome.” In BIOINFORMATICS, pp. 337-345. 2011.
  24. Havel VE, Wool NK, Ayad D, Downey KM, Wilson CFLarsen P, Djordjevic JT, Panepinto JC. Ccr4 Promotes Resolution of the ER Stress Response during Host Temperature Adaptation in Cryptococcus neoformans. Eukaryot Cell. 2011 May 20.
  25. Peter E Larsen, Avinash Sreedasyam, Geetika Trivedi, Gopi K Podila, Leland J Cseke and Frank R Collart. Using Next Generation Transcriptome Sequencing to Predict an Ectomycorrhizal Metabolome. BMC Systems Biology (2011), 5:70. [HIGHLY ACCESSED]
  26. Adler A, Park YDLarsen P, Nagarajan V, Wollenberg K, Qiu J, Myers TG, Williamson PR. A novel specificity protein 1 (SP1)-like gene, regulating protein kinase C-1 (PKc1)-dependent cell-wall integrity and virulence factors in Cryptococcus neoformans. J Biol Chem. 2011 Apr 12.
  27. Henry CS, Overbeek R, Xia F, Best AA, Glass E, Gilbert J, Larsen P, Edwards R, Disz T, Meyer F, Vonstein V, Dejongh M, Bartels D, Desai N, D’Souza M, Devoid S, Keegan KP, Olson R, Wilke A, Wilkening J, Stevens RL. Connecting genotype to phenotype in the era of high-throughput sequencing. Biochim Biophys Acta. 2011 Mar 21.
  28. Peter Larsen, Frank Collart and Yang Dai, Incorporating network topology improves prediction of protein interaction networks from transcriptomic data”. International Journal of Knowledge discovery and Bioinformatics, 1(3), pp.1-19. 2010.
  29. Park YD, Panepinto J, Shin S, Larsen P, Giles S, Williamson PR. Mating pheromone in Cryptococcus neoformans is regulated by a transcriptional/degradative futile” cycle. J Biol Chem. 2010 Nov 5;285(45):34746-56. Epub 2010 Aug 27.
  30. Peter E Larsen, Trivedi G, Sreedasyam A, Lu V, Podila GK, Collart FR. Using deep RNA sequencing for the structural annotation of the Laccaria bicolor mycorrhizal transcriptome. PLoS One. 2010 Jul 6;5(7):e9780.
  31. Peter Larsen and Yang Dai, Using Gene Expression Modeling to Determine Biological Relevance of Putative Regulatory Networks, Proceeding of the 5th International Symposium on Bioinformatics Research and Applications (eds. I. Mandoiu, G. Narasimhan, and Y. Zhang), Lecture Notes in Bioinformatics, Springer Verlag, Vol. 5542 (2009) pp. 40-51, 2009.
  32. Kedar Kulkarni, Peter Larsen and Andreas A. Linninger, Assessing chronic liver toxicity based on relative gene expression data”, Journal of Theoretical Biology (2008), doi:10.1016/j.jtbi.2008.05.032.
  33. Peter Larsen, Eyad Almasri, Guanrao Chen and Yang Dai,” Incorporating Knowledge of Topology Improves Reconstruction of Interaction Networks from Microarray Data”, Lecture Notes in Bioinformatics, Vol. 4983 (eds​.by I.I. Mandoiu, Raj Sunderraman, and A. Xelikovsky), Springer Verlag, pp. 434-443, 2008.
  34. Eyad Almasri, Peter Larsen, Guanrao Chen and Yang Dai, Incorporating Literature Knowledge in Baysian Network for Inferring Gene Networks with Gene Expression Data”, Lecture Notes in Bioinformatics, Vol. 4983 (eds. by I.I. Mandoiu, Raj Sunderraman, and A. Xelikovsky), Springer Verlag, pp. 184-195, 2008.
  35. Guanrao Chen, Peter Larsen, Eyad Almasri, Yang Dai, Rank-based edge reconstruction for scale-free genetic regulatory networks”, BMC Bioinformatics (2008), 9:75.
  36. Peter Larsen, Eyad Almasri, Guanrao Chen, Yang Dai, A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments”, BMC Bioinformatics (2007), 8:317. [HIGHLY ACCESSED]
  37. Peter Larsen, E. Almasri, G. Chen and Y. Dai, Correlated discretized expression score: a method for identifying gene interaction networks from time course microarray expression data” Proceedings of the 28th International Conference of IEEE Engineering in Medicine and Biology Society (EMBS) (2006). pp. 5842-5845.
  38. G. Chen, P. Larsen, E. Almasri and Y. Dai, Sample scale-free gene regulatory network using gene ontology”, Proceedings of the 28th International Conference of IEEE Engineering in Medicine and Biology Society (EMBS) (2006). pp.5523-5526.
  39. Robert Folberg, Zarema Arbieva, Jonas Moses, Amin Hayee, Tone Sandal, ShriHari Kadkol, Amy Lin, Klara Valyi-Nagy, Suman Setty, Lu Leach, Patricia Chevez-Barrios, Peter Larsen, Dibyen Majumdar, Jacob Pe’er, Andrew Maniotis. The generation of vasculogenic mimicry patterns dampens the invasive melanoma cell genotype and phenotype”. Am J Pathol (2006), 166:1187-203.
  40. Hessler, PEPE Larsen, AI Constantinou, KH Schram, and JM Weber. Isolation of isoflavones from soy-based fermentations of the erythromycin-producing bacterium Saccharopolyspora erythraea”.  Appl. Microbiol. Biotechnol.  1997 47(4) P398-404.


Book Chapters

  1. Peter E. Larsen. Statistical Tools for Study Design: Replication. Springer Protocols Handbooks. Humana Press, 10.1007/8623_2015_95http://​dx​.doi​.org/​10​.​1007​/​8623​_​2015_95).
  2. Peter E. Larsen, Frank R. Collart, Yang Dai. Predicting Bacterial Community Assemblages using an Artificial Neural Network Approach.  Artificial Neural Networks: Methods and Applications, Springer, New York.
  3. Leland J. Cseke, Stan D. Wullschleger, Avinash Shreedasyam, Geetika Trivedi, Peter Larsen, Frank Collart.  Chapter 12: Carbon Sequestration.  Genomics & Breeding for Climate-Resilient Crops (ed. Chittaranjan Kole). Springer, New York.
  4. Andreas Wilke, Peter Larsen, Jack A Gilbert. Chapter 43.  Next Generation Sequencing and the Future of Microbial Metagenomics.  Horizon Scientific Press / Caister Academic Press.
  5. Peter Larsen, Leland Cseke, Frank R Collart.  Using Next Generation Transcriptome Sequencing to Predict an Ectomycorrhizal Metabolome.   Molecular Microbial Ecology of the Rhizosphere. (Frans J. de Bruijn ed.), INRA/CNRS Laboratory of Plant-Microbe Interactions.
  6. Yang Dai, Eyad Almasri, Peter Larsen, Guanrao Chen, Structure Learning of Genetic Regulatory Networks Based on Knowledge Derived from Literature and Microarray Gene Expression Measurements, Computational Methodologies in Gene Regulatory Networks, (S. Das, D. Caragea, W. H. Hsu, S. M. Welch eds.), IGI Global, pp.289-309, 2009.