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Microbial genomic island discovery, visualization and analysis

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of partic...

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Detalles Bibliográficos
Autores principales: Bertelli, Claire, Tilley, Keith E, Brinkman, Fiona S L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6917214/
https://www.ncbi.nlm.nih.gov/pubmed/29868902
http://dx.doi.org/10.1093/bib/bby042
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author Bertelli, Claire
Tilley, Keith E
Brinkman, Fiona S L
author_facet Bertelli, Claire
Tilley, Keith E
Brinkman, Fiona S L
author_sort Bertelli, Claire
collection PubMed
description Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.
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spelling pubmed-69172142019-12-20 Microbial genomic island discovery, visualization and analysis Bertelli, Claire Tilley, Keith E Brinkman, Fiona S L Brief Bioinform Review Articles Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions. Oxford University Press 2018-06-03 /pmc/articles/PMC6917214/ /pubmed/29868902 http://dx.doi.org/10.1093/bib/bby042 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Articles
Bertelli, Claire
Tilley, Keith E
Brinkman, Fiona S L
Microbial genomic island discovery, visualization and analysis
title Microbial genomic island discovery, visualization and analysis
title_full Microbial genomic island discovery, visualization and analysis
title_fullStr Microbial genomic island discovery, visualization and analysis
title_full_unstemmed Microbial genomic island discovery, visualization and analysis
title_short Microbial genomic island discovery, visualization and analysis
title_sort microbial genomic island discovery, visualization and analysis
topic Review Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6917214/
https://www.ncbi.nlm.nih.gov/pubmed/29868902
http://dx.doi.org/10.1093/bib/bby042
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