A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Bacterial genomic epidemiology with mixed samples
Tekijät: Mäklin Tommi, Kallonen Teemu, Alanko Jarno, Samuelsen Ørjan, Hegstad Kristin, Mäkinen Veli, Corander Jukka, Heinz Eva, Honkela Antti
Kustantaja: Microbiology society
Julkaisuvuosi: 2021
Journal: Microbial genomics
Tietokannassa oleva lehden nimi: Microbial genomics
Lehden akronyymi: Microb Genom
Vuosikerta: 7
Numero: 11
ISSN: 2057-5858
eISSN: 2057-5858
DOI: https://doi.org/10.1099/mgen.0.000691
Verkko-osoite: https://www.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.000691
Tiivistelmä
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.
Turun yliopiston Tutkimustietojärjestelmän portaali