Laura Carroll

DDLS Fellow, Umeå University

Key publications

Carroll LM, Gaballa A, Guldimann C, Sullivan G, Henderson LO, Wiedmann M. Identification of Novel Mobilized Colistin Resistance Gene mcr-9 in a Multidrug-Resistant, Colistin-Susceptible Salmonella enterica Serotype Typhimurium Isolate. mBio. 2019 May 7;10(3):e00853-19. doi: 10.1128/mBio.00853-19. PMID: 31064835; PMCID: PMC6509194.

Carroll LM, Wiedmann M, Kovac J. Proposal of a Taxonomic Nomenclature for the Bacillus cereus Group Which Reconciles Genomic Definitions of Bacterial Species with Clinical and Industrial Phenotypes. mBio. 2020 Feb 25;11(1):e00034-20. doi: 10.1128/mBio.00034-20. PMID: 32098810; PMCID: PMC7042689.

Carroll LM, Matle I, Kovac J, Cheng RA, Wiedmann M. Laboratory Misidentifications Resulting from Taxonomic Changes to Bacillus cereus Group Species, 2018-2022. Emerg Infect Dis. 2022 Sep;28(9):1877-1881. doi: 10.3201/eid2809.220293. PMID: 35997597; PMCID: PMC9423903.

Carroll LM, Wiedmann M, Mukherjee M, Nicholas DC, Mingle LA, Dumas NB, Cole JA, Kovac J. Characterization of Emetic and Diarrheal Bacillus cereus Strains From a 2016 Foodborne Outbreak Using Whole-Genome Sequencing: Addressing the Microbiological, Epidemiological, and Bioinformatic Challenges. Front Microbiol. 2019 Feb 12;10:144. doi: 10.3389/fmicb.2019.00144. PMID: 30809204; PMCID: PMC6379260.

Carroll LM, Pierneef R, Mathole M, Matle I. Genomic Characterization of Endemic and Ecdemic Non-typhoidal Salmonella enterica Lineages Circulating Among Animals and Animal Products in South Africa. Front Microbiol. 2021 Oct 4;12:748611. doi: 10.3389/fmicb.2021.748611. PMID: 34671335; PMCID: PMC8521152.

Carroll LM, Larralde M, Fleck JS, Ponnudurai R, Milanese A, Cappio E, Zeller G. Accurate de novo identification of biosynthetic gene clusters with GECCO. bioRxiv. 2021.05.03.442509; doi:

Laura Carroll’s lab develops and utilizes bioinformatic approaches to monitor and combat the spread of bacterial pathogens.

(Meta)genomic sequencing is playing an increasingly pivotal role in clinical and public health microbiology. As such, the amount of publicly available (meta)genomic data derived from microbes—including pathogens—is growing rapidly. As a computational microbiology group, we develop and utilize bioinformatic approaches, which can leverage these massive data sets to improve pathogen surveillance, source tracking, outbreak detection, and risk evaluation efforts.

Specifically, we develop and deploy (i) phylodynamic models, which can be used to track the evolution and transmission of zoonotic pathogens between animal reservoirs and the human population; (ii) machine learning approaches, which can be used to identify microbial genomic determinants associated with phenotypes of clinical and industrial importance (e.g., host disease states, antimicrobial resistance phenotypes); and (iii) multi-omics methods, which can be used to predict pathogen virulence potential. We are also passionate about bridging the gap between experimental and computational microbiologists by making (meta)genomic data analysis methods accessible and approachable to all through teaching and outreach.

Last updated: 2024-07-03

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