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Genomic Epidemiology with Mixed Samples

View ORCID ProfileTommi Mäklin, View ORCID ProfileTeemu Kallonen, View ORCID ProfileJarno Alanko, View ORCID ProfileVeli Mäkinen, View ORCID ProfileJukka Corander, View ORCID ProfileAntti Honkela
doi: https://doi.org/10.1101/2020.04.03.021501
Tommi Mäklin
1Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
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  • For correspondence: tommi.maklin@helsinki.fi antti.honkela@helsinki.fi
Teemu Kallonen
2Department of Biostatistics, University of Oslo, Oslo, Norway
3Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
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Jarno Alanko
4Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland
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Veli Mäkinen
4Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland
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Jukka Corander
1Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
2Department of Biostatistics, University of Oslo, Oslo, Norway
3Wellcome Sanger Institute, Hinxton, Cambridgeshire, UK
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Antti Honkela
4Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland
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  • ORCID record for Antti Honkela
  • For correspondence: tommi.maklin@helsinki.fi antti.honkela@helsinki.fi
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Abstract

Genomic epidemiology is an established tool for investigation of outbreaks of infectious diseases and wider public health applications. It traces transmission of pathogens based on whole-genome sequencing of colony picks from culture plates enriching the target organism(s). In this article, we introduce the mGEMS pipeline for performing genomic epidemiology directly with plate sweeps representing mixed samples of the target pathogen in a culture plate, skipping the colony pick step entirely. By requiring only a single culturing and library preparation step per analyzed sample, we address several key issues in the current approach relating to its cost, practical application and sensitivity. Our pipeline significantly improves upon the state-of-the-art in analysing mixed short-read sequencing data from bacteria, reaching accuracy levels in downstream analyses closely resembling colony pick sequencing data that allow reliable SNP calling and subsequent phylogenetic analyses. The fundamental novel parts enabling these analyses are the mGEMS read binner for probabilistic assignments of sequencing reads and the high-throughput exact pseudoaligner Themisto. In conjunction with recent advances in probabilistic modelling of mixed bacterial samples and genome assembly techniques, these tools form the mGEMS pipeline. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting for the three major pathogens Enterococcus faecalis, Escherichia coli and Staphylococcus aureus. Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.

Footnotes

  • https://github.com/PROBIC/mGEMS

  • https://github.com/algbio/themisto

  • https://zenodo.org/record/3724112

  • https://zenodo.org/record/3724101

  • https://zenodo.org/record/3724135

  • ↵1 The colexicographic order of strings is like the standard lexicographic order, but characters are compared starting starting from the end. The index can be build with either lexicographic or colexicographic sorting, but we choose to follow the colexicographic convention of the Wheeler graph framework. The indexed graph can be traversed in both directions.

  • ↵2 Assemblies from ftp://ftp.ncbi.nlm.nih.gov/genomes/genbank/bacteria/assembly_summary.txt with the organism name “Escherichia coli “.

  • ↵3 Hardware: Intel Xeon E7-8890 CPU (2.2GHz, 60M Cache, 9.6GT/s QPI 24C/48T, HT, Turbo 165W) with 48 × 64GB LRDIMM memory (2400MT/s, Quad Rank, x4 Data Width), running on top of a distributed Lustre file system.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 04, 2020.
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Genomic Epidemiology with Mixed Samples
Tommi Mäklin, Teemu Kallonen, Jarno Alanko, Veli Mäkinen, Jukka Corander, Antti Honkela
bioRxiv 2020.04.03.021501; doi: https://doi.org/10.1101/2020.04.03.021501
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Genomic Epidemiology with Mixed Samples
Tommi Mäklin, Teemu Kallonen, Jarno Alanko, Veli Mäkinen, Jukka Corander, Antti Honkela
bioRxiv 2020.04.03.021501; doi: https://doi.org/10.1101/2020.04.03.021501

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