A1 Refereed original research article in a scientific journal
Bacterial genomic epidemiology with mixed samples
Authors: Mäklin Tommi, Kallonen Teemu, Alanko Jarno, Samuelsen Ørjan, Hegstad Kristin, Mäkinen Veli, Corander Jukka, Heinz Eva, Honkela Antti
Publisher: Microbiology society
Publication year: 2021
Journal: Microbial genomics
Journal name in source: Microbial genomics
Journal acronym: Microb Genom
Volume: 7
Issue: 11
ISSN: 2057-5858
eISSN: 2057-5858
DOI: https://doi.org/10.1099/mgen.0.000691
Web address : https://www.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.000691
Abstract
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.
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.
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