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PathOGiST: A Novel Method for Clustering Pathogen Isolates by Combining Multiple Genotyping Signals

  • Mohsen KatebiEmail author
  • Pedro Feijao
  • Julius Booth
  • Mehrdad Mansouri
  • Sean La
  • Alex Sweeten
  • Reza Miraskarshahi
  • Matthew Nguyen
  • Johnathan Wong
  • William Hsiao
  • Cedric Chauve
  • Leonid Chindelevitch
Conference paper
  • 24 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12099)

Abstract

In this paper we study the problem of clustering bacterial isolates into epidemiologically related groups from next-generation sequencing data. Existing methods for this problem mainly use a single genotyping signal, and either use a distance-based method with a pre-specified number of clusters, or a phylogenetic tree-based method with a pre-specified threshold. We propose PathOGiST, an algorithmic framework for clustering bacterial isolates by leveraging multiple genotypic signals and calibrated thresholds. PathOGiST uses different genotypic signals, clusters the isolates based on these individual signals with correlation clustering, and combines the clusterings based on the individual signals through consensus clustering. We implemented and tested PathOGiST on three different bacterial pathogens - Escherichia coli, Yersinia pseudotuberculosis, and Mycobacterium tuberculosis - and we conclude by discussing further avenues to explore.

Keywords

Bacterial pathogens Whole-genome sequencing Correlation clustering Microbiology Public health 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mohsen Katebi
    • 1
    Email author
  • Pedro Feijao
    • 1
  • Julius Booth
    • 1
  • Mehrdad Mansouri
    • 1
  • Sean La
    • 1
  • Alex Sweeten
    • 1
  • Reza Miraskarshahi
    • 1
  • Matthew Nguyen
    • 1
  • Johnathan Wong
    • 1
  • William Hsiao
    • 2
  • Cedric Chauve
    • 1
  • Leonid Chindelevitch
    • 1
  1. 1.Simon Fraser UniversityBurnabyCanada
  2. 2.British Columbia Centre for Disease ControlVancouverCanada

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