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Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo
Comparing the pathogen genomes from several cases of an infectious disease has the potential to help us understand and control outbreaks. Many methods exist to reconstruct a phylogeny from such genomes, which represents how the genomes are related to one another. However, such a phylogeny is not dir...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995038/ https://www.ncbi.nlm.nih.gov/pubmed/33617114 http://dx.doi.org/10.1002/cpz1.60 |
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author | Didelot, Xavier Kendall, Michelle Xu, Yuanwei White, Peter J. McCarthy, Noel |
author_facet | Didelot, Xavier Kendall, Michelle Xu, Yuanwei White, Peter J. McCarthy, Noel |
author_sort | Didelot, Xavier |
collection | PubMed |
description | Comparing the pathogen genomes from several cases of an infectious disease has the potential to help us understand and control outbreaks. Many methods exist to reconstruct a phylogeny from such genomes, which represents how the genomes are related to one another. However, such a phylogeny is not directly informative about transmission events between individuals. TransPhylo is a software tool implemented as an R package designed to bridge the gap between pathogen phylogenies and transmission trees. TransPhylo is based on a combined model of transmission between hosts and pathogen evolution within each host. It can simulate both phylogenies and transmission trees jointly under this combined model. TransPhylo can also reconstruct a transmission tree based on a dated phylogeny, by exploring the space of transmission trees compatible with the phylogeny. A transmission tree can be represented as a coloring of a phylogeny where each color represents a different host of the pathogen, and TransPhylo provides convenient ways to plot these colorings and explore the results. This article presents the basic protocols that can be used to make the most of TransPhylo. © 2021 The Authors. Basic Protocol 1: First steps with TransPhylo Basic Protocol 2: Simulation of outbreak data Basic Protocol 3: Inference of transmission Basic Protocol 4: Exploring the results of inference |
format | Online Article Text |
id | pubmed-7995038 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79950382021-03-26 Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo Didelot, Xavier Kendall, Michelle Xu, Yuanwei White, Peter J. McCarthy, Noel Curr Protoc Protocol Comparing the pathogen genomes from several cases of an infectious disease has the potential to help us understand and control outbreaks. Many methods exist to reconstruct a phylogeny from such genomes, which represents how the genomes are related to one another. However, such a phylogeny is not directly informative about transmission events between individuals. TransPhylo is a software tool implemented as an R package designed to bridge the gap between pathogen phylogenies and transmission trees. TransPhylo is based on a combined model of transmission between hosts and pathogen evolution within each host. It can simulate both phylogenies and transmission trees jointly under this combined model. TransPhylo can also reconstruct a transmission tree based on a dated phylogeny, by exploring the space of transmission trees compatible with the phylogeny. A transmission tree can be represented as a coloring of a phylogeny where each color represents a different host of the pathogen, and TransPhylo provides convenient ways to plot these colorings and explore the results. This article presents the basic protocols that can be used to make the most of TransPhylo. © 2021 The Authors. Basic Protocol 1: First steps with TransPhylo Basic Protocol 2: Simulation of outbreak data Basic Protocol 3: Inference of transmission Basic Protocol 4: Exploring the results of inference John Wiley and Sons Inc. 2021-02-22 2021-02 /pmc/articles/PMC7995038/ /pubmed/33617114 http://dx.doi.org/10.1002/cpz1.60 Text en © 2021 The Authors. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Protocol Didelot, Xavier Kendall, Michelle Xu, Yuanwei White, Peter J. McCarthy, Noel Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo |
title | Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo |
title_full | Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo |
title_fullStr | Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo |
title_full_unstemmed | Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo |
title_short | Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo |
title_sort | genomic epidemiology analysis of infectious disease outbreaks using transphylo |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995038/ https://www.ncbi.nlm.nih.gov/pubmed/33617114 http://dx.doi.org/10.1002/cpz1.60 |
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