Identifying horizontally acquired elements in bacterial genomes

Contributed by Cory Schlesener
Bacterial genomes are composed of diverse genetic elements. Many of these elements are not exclusively passed down a parental lineage (vertically acquired), but can also be horizontally acquired from other bacteria of varying degrees of relatedness. As bacteria are asexual, mechanisms of horizontal transfer of genetic information provide ways to add diversity to a population. Novel genes and accompanying traits can allow a strain to take advantage of new niches (e.g. enzymes to break down new metabolites), endure formally inhospitable conditions (e.g. antimicrobial resistance genes), or improve fitness in other ways. These elements can often be the defining feature that make a strain stand out from typical members of its species (e.g. becoming a pathogen or one with enhanced virulence). As there are many mechanisms for bacteria to acquire genes horizontally (e.g. plasmids, bacteriophage, DNA uptake/integration) and differences in technical genetic trends (e.g. GC% content and codon usage) can homogenize overtime, they do not always stand out when integrated into the genome. Luckily, there are many programs that can bioinformatically predict regions that are horizontally acquired genomic islands. These programs utilize a variety of techniques that basically work off contrasts. They can contrast a genome to comparable genomes to assess differences, or they can contrast within a genome, comparing different regions to one another (some programs are enhanced when working with annotated gene data). There are still ambiguities when predicting horizontally acquired regions, with varying levels of prediction accuracy. The recent review article (given below) details how many of these programs work, compares their performance, and gives a schematic for how best to choose the program(s) for your needs (based on methodology, user interface, data inputs, precision and accuracy).
Microbial genomic island discovery, visualization and analysis
Claire Bertelli, Keith E Tilley, Fiona S L Brinkman
Briefings in Bioinformatics (September 2019)

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