M
Mette Voldby Larsen
Researcher at Technical University of Denmark
Publications - 42
Citations - 12005
Mette Voldby Larsen is an academic researcher from Technical University of Denmark. The author has contributed to research in topics: Epitope & Human leukocyte antigen. The author has an hindex of 25, co-authored 42 publications receiving 9077 citations. Previous affiliations of Mette Voldby Larsen include University of Southern Denmark & University of Bergen.
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Journal ArticleDOI
Identification of acquired antimicrobial resistance genes
Ea Zankari,Henrik Hasman,Salvatore Cosentino,Martin Vestergaard,Simon Rasmussen,Ole Lund,Frank Møller Aarestrup,Mette Voldby Larsen +7 more
TL;DR: A web server providing a convenient way of identifying acquired antimicrobial resistance genes in completely sequenced isolates was created, and the method was evaluated on WGS chromosomes and plasmids of 30 isolates.
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In Silico Detection and Typing of Plasmids using PlasmidFinder and Plasmid Multilocus Sequence Typing
Alessandra Carattoli,Ea Zankari,Aurora García-Fernández,Mette Voldby Larsen,Ole Lund,Laura Villa,Frank Møller Aarestrup,Henrik Hasman +7 more
TL;DR: Two easy-to-use Web tools for in silico detection and characterization of whole-genome sequence (WGS) and whole-plasmid sequence data from members of the family Enterobacteriaceae are designed and developed.
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Multilocus Sequence Typing of Total-Genome-Sequenced Bacteria
Mette Voldby Larsen,Salvatore Cosentino,Simon Rasmussen,Carsten Friis,Henrik Hasman,Rasmus L. Marvig,Lars Jelsbak,Thomas Sicheritz-Pontén,David W. Ussery,Frank Møller Aarestrup,Ole Lund +10 more
TL;DR: A Web-based method for MLST of 66 bacterial species based on whole-genome sequencing data that enables investigators to determine the sequence types of their isolates on the basis of WGS data.
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Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction
TL;DR: In the large-scale benchmark calculation consisting of 216 known HIV epitopes covering all 12 recognized HLA supertypes, the NetCTL-1.2 method was shown to have a sensitivity among the 5% top-scoring peptides above 0.72, which is higher than any of the other methods.
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PathogenFinder--distinguishing friend from foe using bacterial whole genome sequence data.
TL;DR: PathogenFinder is described, a web-server for the prediction of bacterial pathogenicity by analysing the input proteome, genome, or raw reads provided by the user, that relies on groups of proteins, created without regard to their annotated function or known involvement in pathogenicicity.