Microbial genomic island discovery, visualization and analysis

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.

Keywords: antimicrobial resistance; genomic island; horizontal gene transfer; interactive visualization; microbial genomics.

Figure 1.

Accuracy assessment of genomic island prediction methods. Accuracy of genomic island (GI) predictors was assessed using a data set of GIs identified by comparative genomics analysis of 104 genomes [39]. Each genome is represented by a value, with the median, and the first and third quartiles represented in the boxplot as the lower and upper hinges, respectively. Outliers are shown as black dots, if they exceed 1.5 times the interquartile range.

Figure 2.

Summary of GI predictor characteristics with a multi-entry decision table. GI predictors were classified according to their interface and the requirements. Sensitivity (recall) and precision are color coded using a gradient from low-to-high as assessed using the comparative genomics data set. Methods that were not assessed (comparative genomics) or that we were unable to assess are shown in white.