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Bertelli C, Gray KL, Woods N, Lim AC, Tilley KE, Winsor GL, Hoad GR, Roudgar A, Spencer A, Peltier J, Warren D, Raphenya AR, McArthur AG, Brinkman FSL. Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks. Microb Genom 2022; 8. [PMID: 35584003 PMCID: PMC9465072 DOI: 10.1099/mgen.0.000818] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
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).
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Affiliation(s)
- Claire Bertelli
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada.,Institute of Microbiology, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Kristen L Gray
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Nolan Woods
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Adrian C Lim
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Keith E Tilley
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Geoffrey L Winsor
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Gemma R Hoad
- Research Computing Group, Simon Fraser University, Burnaby, BC, Canada
| | - Ata Roudgar
- Research Computing Group, Simon Fraser University, Burnaby, BC, Canada
| | - Adam Spencer
- Research Computing Group, Simon Fraser University, Burnaby, BC, Canada
| | - James Peltier
- Research Computing Group, Simon Fraser University, Burnaby, BC, Canada
| | - Derek Warren
- Research Computing Group, Simon Fraser University, Burnaby, BC, Canada
| | - Amogelang R Raphenya
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, ON, Canada.,Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada.,Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
| | - Andrew G McArthur
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, ON, Canada.,Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada.,Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada
| | - Fiona S L Brinkman
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
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Bertelli C, Tilley KE, Brinkman FSL. Microbial genomic island discovery, visualization and analysis. Brief Bioinform 2020; 20:1685-1698. [PMID: 29868902 PMCID: PMC6917214 DOI: 10.1093/bib/bby042] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 04/30/2018] [Indexed: 12/27/2022] Open
Abstract
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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Affiliation(s)
- Claire Bertelli
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Keith E Tilley
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
| | - Fiona S L Brinkman
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada
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