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Deep clustering of bacterial tree images
The field of genomic epidemiology is rapidly growing as many jurisdictions begin to deploy whole-genome sequencing (WGS) in their national or regional pathogen surveillance programmes. WGS data offer a rich view of the shared ancestry of a set of taxa, typically visualized with phylogenetic trees il...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393560/ https://www.ncbi.nlm.nih.gov/pubmed/35989604 http://dx.doi.org/10.1098/rstb.2021.0231 |
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author | Hayati, Maryam Chindelevitch, Leonid Aanensen, David Colijn, Caroline |
author_facet | Hayati, Maryam Chindelevitch, Leonid Aanensen, David Colijn, Caroline |
author_sort | Hayati, Maryam |
collection | PubMed |
description | The field of genomic epidemiology is rapidly growing as many jurisdictions begin to deploy whole-genome sequencing (WGS) in their national or regional pathogen surveillance programmes. WGS data offer a rich view of the shared ancestry of a set of taxa, typically visualized with phylogenetic trees illustrating the clusters or subtypes present in a group of taxa, their relatedness and the extent of diversification within and between them. When methicillin-resistant Staphylococcus aureus (MRSA) arose and disseminated widely, phylogenetic trees of MRSA-containing types of S. aureus had a distinctive ‘comet’ shape, with a ‘comet head’ of recently adapted drug-resistant isolates in the context of a ‘comet tail’ that was predominantly drug-sensitive. Placing an S. aureus isolate in the context of such a ‘comet’ helped public health laboratories interpret local data within the broader setting of S. aureus evolution. In this work, we ask what other tree shapes, analogous to the MRSA comet, are present in bacterial WGS datasets. We extract trees from large bacterial genomic datasets, visualize them as images and cluster the images. We find nine major groups of tree images, including the ‘comets’, star-like phylogenies, ‘barbell’ phylogenies and other shapes, and comment on the evolutionary and epidemiological stories these shapes might illustrate. This article is part of a discussion meeting issue ‘Genomic population structures of microbial pathogens’. |
format | Online Article Text |
id | pubmed-9393560 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-93935602022-08-30 Deep clustering of bacterial tree images Hayati, Maryam Chindelevitch, Leonid Aanensen, David Colijn, Caroline Philos Trans R Soc Lond B Biol Sci Articles The field of genomic epidemiology is rapidly growing as many jurisdictions begin to deploy whole-genome sequencing (WGS) in their national or regional pathogen surveillance programmes. WGS data offer a rich view of the shared ancestry of a set of taxa, typically visualized with phylogenetic trees illustrating the clusters or subtypes present in a group of taxa, their relatedness and the extent of diversification within and between them. When methicillin-resistant Staphylococcus aureus (MRSA) arose and disseminated widely, phylogenetic trees of MRSA-containing types of S. aureus had a distinctive ‘comet’ shape, with a ‘comet head’ of recently adapted drug-resistant isolates in the context of a ‘comet tail’ that was predominantly drug-sensitive. Placing an S. aureus isolate in the context of such a ‘comet’ helped public health laboratories interpret local data within the broader setting of S. aureus evolution. In this work, we ask what other tree shapes, analogous to the MRSA comet, are present in bacterial WGS datasets. We extract trees from large bacterial genomic datasets, visualize them as images and cluster the images. We find nine major groups of tree images, including the ‘comets’, star-like phylogenies, ‘barbell’ phylogenies and other shapes, and comment on the evolutionary and epidemiological stories these shapes might illustrate. This article is part of a discussion meeting issue ‘Genomic population structures of microbial pathogens’. The Royal Society 2022-10-10 2022-08-22 /pmc/articles/PMC9393560/ /pubmed/35989604 http://dx.doi.org/10.1098/rstb.2021.0231 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Articles Hayati, Maryam Chindelevitch, Leonid Aanensen, David Colijn, Caroline Deep clustering of bacterial tree images |
title | Deep clustering of bacterial tree images |
title_full | Deep clustering of bacterial tree images |
title_fullStr | Deep clustering of bacterial tree images |
title_full_unstemmed | Deep clustering of bacterial tree images |
title_short | Deep clustering of bacterial tree images |
title_sort | deep clustering of bacterial tree images |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9393560/ https://www.ncbi.nlm.nih.gov/pubmed/35989604 http://dx.doi.org/10.1098/rstb.2021.0231 |
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