Microbial genomic island discovery, visualization and analysis. (3rd June 2018)

• Record Type: Journal Article
• Authors: Bertelli, Claire; Tilley, Keith E; Brinkman, Fiona S L
• Is Part Of: Briefings in bioinformatics. Volume 20:Number 5(2020)
• Page Start: 1685
• Page End: 1698
• Publication Date: 2018-06-03
• Subjects: genomic island -- horizontal gene transfer -- microbial genomics -- interactive visualization -- antimicrobial resistance; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bby042
• Languages: English
• ISSNs: 1467-5463

Abstract

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.