Bacterial genomic epidemiology with mixed samples.

Genome, Bacterial Genomics Sequence Analysis Whole Genome Sequencing
genomic epidemiology pathogen surveillance plate sweeps probabilistic modelling pseudoalignment

Journal

Microbial genomics
ISSN: 2057-5858
Titre abrégé: Microb Genom
Pays: England
ID NLM: 101671820

Informations de publication

Date de publication:
11 2021
Historique:
pubmed: 16 11 2021
medline: 5 4 2022
entrez: 15 11 2021
Statut: ppublish

Résumé

Genomic epidemiology is a tool for tracing transmission of pathogens based on whole-genome sequencing. We introduce the mGEMS pipeline for genomic epidemiology with plate sweeps representing mixed samples of a target pathogen, opening the possibility to sequence all colonies on selective plates with a single DNA extraction and sequencing step. The pipeline includes the novel mGEMS read binner for probabilistic assignments of sequencing reads, and the scalable pseudoaligner Themisto. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting, obtaining results that are comparable to those based on single-colony picks. Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.

Identifiants

DOI: 10.1099/mgen.0.000691 PMID: 34779765 PMC: PMC8743562
pubmed: 34779765
doi: 10.1099/mgen.0.000691
pmc: PMC8743562
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

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Auteurs

Tommi Mäklin (T)

Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.

Teemu Kallonen (T)

Department of Biostatistics, University of Oslo, Oslo, Norway.
Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK.

Jarno Alanko (J)

Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.

Ørjan Samuelsen (Ø)

Norwegian National Advisory Unit on Detection of Antimicrobial Resistance, Department of Microbiology and Infection Control, University Hospital of North Norway, Tromsø, Norway.
Department of Pharmacy, UT The Arctic University of Norway, Tromsø, Norway.

Kristin Hegstad (K)

Norwegian National Advisory Unit on Detection of Antimicrobial Resistance, Department of Microbiology and Infection Control, University Hospital of North Norway, Tromsø, Norway.
Research group for Host-Microbe Interactions, Department of Medical Biology, Faculty of Health Sciences, UT The Arctic University of Norway, Tromsø, Norway.

Veli Mäkinen (V)

Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.

Jukka Corander (J)

Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
Department of Biostatistics, University of Oslo, Oslo, Norway.
Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK.

Eva Heinz (E)

Department of Biostatistics, University of Oslo, Oslo, Norway.
Liverpool School of Tropical Medicine, Liverpool, UK.

Antti Honkela (A)

Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.

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Classifications MeSH

questionsmedicales.fr › Phénomènes génétiques › Structures génétiques › Génome › Génome microbien › Génome bactérien › Bacterial genomic epidemiology with mixed samples.
questionsmedicales.fr › Disciplines des sciences naturelles › Disciplines des sciences biologiques › Biologie › Génétique › Génomique › Bacterial genomic epidemiology with mixed samples.
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de séquence › Bacterial genomic epidemiology with mixed samples.
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de séquence › Analyse de séquence d'ADN › Séquençage du génome entier › Bacterial genomic epidemiology with mixed samples.