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Bacterial genomic epidemiology with mixed samples.

Tommi Mäklin1, Teemu Kallonen2,3, Jarno Alanko4

  • 1Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.

Microbial Genomics
|November 15, 2021
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Summary
This summary is machine-generated.

Genomic epidemiology now includes mixed pathogen samples using the mGEMS pipeline. This method sequences multiple colonies from single DNA extractions, matching traditional single-colony pick accuracy.

Keywords:
genomic epidemiologypathogen surveillanceplate sweepsprobabilistic modellingpseudoalignment

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Area of Science:

  • Microbiology
  • Genomics
  • Epidemiology

Background:

  • Genomic epidemiology utilizes whole-genome sequencing to track pathogen transmission.
  • Current methods often require isolating single pathogen colonies, which can be labor-intensive.
  • Analyzing mixed infections directly presents a significant challenge in pathogen surveillance.

Purpose of the Study:

  • To introduce the mGEMS pipeline, a novel approach for genomic epidemiology.
  • To enable the analysis of mixed pathogen samples from plate sweeps.
  • To provide a scalable and efficient method for pathogen transmission tracing.

Main Methods:

  • Development of the mGEMS pipeline incorporating the mGEMS read binner and Themisto pseudoaligner.
  • Application of the pipeline to mixed pathogen samples from plate sweeps.
  • Comparison of results with traditional single-colony sequencing methods.

Main Results:

  • The mGEMS pipeline successfully sequences all colonies on selective plates from a single DNA extraction.
  • Probabilistic read assignment by the mGEMS binner accurately differentiates between pathogens in mixed samples.
  • Results obtained from mixed infection samples are comparable to those from single-colony picks.
  • The approach was validated in a nosocomial setting with closely related pathogen samples.

Conclusions:

  • The mGEMS pipeline offers an effective method for genomic epidemiology of mixed infections.
  • This approach simplifies sample processing by eliminating the need for individual colony isolation.
  • Widespread adoption of genomic epidemiology with mixed infection samples is encouraged.