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Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks
Outbreaks of virulent and/or drug-resistant bacteria have a significant impact on human health and major economic consequences. Genomic islands (GIs; defined as clusters of genes of probable horizontal origin) are of high interest because they disproportionately encode virulence factors, some antimi...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Microbiology Society
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465072/ https://www.ncbi.nlm.nih.gov/pubmed/35584003 http://dx.doi.org/10.1099/mgen.0.000818 |
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author | Bertelli, Claire Gray, Kristen L. Woods, Nolan Lim, Adrian C. Tilley, Keith E. Winsor, Geoffrey L. Hoad, Gemma R. Roudgar, Ata Spencer, Adam Peltier, James Warren, Derek Raphenya, Amogelang R. McArthur, Andrew G. Brinkman, Fiona S. L. |
author_facet | Bertelli, Claire Gray, Kristen L. Woods, Nolan Lim, Adrian C. Tilley, Keith E. Winsor, Geoffrey L. Hoad, Gemma R. Roudgar, Ata Spencer, Adam Peltier, James Warren, Derek Raphenya, Amogelang R. McArthur, Andrew G. Brinkman, Fiona S. L. |
author_sort | Bertelli, Claire |
collection | PubMed |
description | Outbreaks of virulent and/or drug-resistant bacteria have a significant impact on human health and major economic consequences. Genomic islands (GIs; defined as clusters of genes of probable horizontal origin) are of high interest because they disproportionately encode virulence factors, some antimicrobial-resistance (AMR) genes, and other adaptations of medical or environmental interest. While microbial genome sequencing has become rapid and inexpensive, current computational methods for GI analysis are not amenable for rapid, accurate, user-friendly and scalable comparative analysis of sets of related genomes. To help fill this gap, we have developed IslandCompare, an open-source computational pipeline for GI prediction and comparison across several to hundreds of bacterial genomes. A dynamic and interactive visualization strategy displays a bacterial core-genome phylogeny, with bacterial genomes linearly displayed at the phylogenetic tree leaves. Genomes are overlaid with GI predictions and AMR determinants from the Comprehensive Antibiotic Resistance Database (CARD), and regions of similarity between the genomes are also displayed. GI predictions are performed using Sigi-HMM and IslandPath-DIMOB, the two most precise GI prediction tools based on nucleotide composition biases, as well as a novel blast-based consistency step to improve cross-genome prediction consistency. GIs across genomes sharing sequence similarity are grouped into clusters, further aiding comparative analysis and visualization of acquisition and loss of mobile GIs in specific sub-clades. IslandCompare is an open-source software that is containerized for local use, plus available via a user-friendly, web-based interface to allow direct use by bioinformaticians, biologists and clinicians (at https://islandcompare.ca). |
format | Online Article Text |
id | pubmed-9465072 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Microbiology Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-94650722022-09-12 Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks Bertelli, Claire Gray, Kristen L. Woods, Nolan Lim, Adrian C. Tilley, Keith E. Winsor, Geoffrey L. Hoad, Gemma R. Roudgar, Ata Spencer, Adam Peltier, James Warren, Derek Raphenya, Amogelang R. McArthur, Andrew G. Brinkman, Fiona S. L. Microb Genom Methods Outbreaks of virulent and/or drug-resistant bacteria have a significant impact on human health and major economic consequences. Genomic islands (GIs; defined as clusters of genes of probable horizontal origin) are of high interest because they disproportionately encode virulence factors, some antimicrobial-resistance (AMR) genes, and other adaptations of medical or environmental interest. While microbial genome sequencing has become rapid and inexpensive, current computational methods for GI analysis are not amenable for rapid, accurate, user-friendly and scalable comparative analysis of sets of related genomes. To help fill this gap, we have developed IslandCompare, an open-source computational pipeline for GI prediction and comparison across several to hundreds of bacterial genomes. A dynamic and interactive visualization strategy displays a bacterial core-genome phylogeny, with bacterial genomes linearly displayed at the phylogenetic tree leaves. Genomes are overlaid with GI predictions and AMR determinants from the Comprehensive Antibiotic Resistance Database (CARD), and regions of similarity between the genomes are also displayed. GI predictions are performed using Sigi-HMM and IslandPath-DIMOB, the two most precise GI prediction tools based on nucleotide composition biases, as well as a novel blast-based consistency step to improve cross-genome prediction consistency. GIs across genomes sharing sequence similarity are grouped into clusters, further aiding comparative analysis and visualization of acquisition and loss of mobile GIs in specific sub-clades. IslandCompare is an open-source software that is containerized for local use, plus available via a user-friendly, web-based interface to allow direct use by bioinformaticians, biologists and clinicians (at https://islandcompare.ca). Microbiology Society 2022-05-18 /pmc/articles/PMC9465072/ /pubmed/35584003 http://dx.doi.org/10.1099/mgen.0.000818 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution. |
spellingShingle | Methods Bertelli, Claire Gray, Kristen L. Woods, Nolan Lim, Adrian C. Tilley, Keith E. Winsor, Geoffrey L. Hoad, Gemma R. Roudgar, Ata Spencer, Adam Peltier, James Warren, Derek Raphenya, Amogelang R. McArthur, Andrew G. Brinkman, Fiona S. L. Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks |
title | Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks |
title_full | Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks |
title_fullStr | Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks |
title_full_unstemmed | Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks |
title_short | Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks |
title_sort | enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9465072/ https://www.ncbi.nlm.nih.gov/pubmed/35584003 http://dx.doi.org/10.1099/mgen.0.000818 |
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