Genomic Islands in bacteria are gene clusters often associated with specific traits and adaptations to different environments. Bioinformatics researchers present a novel method to identify these important genomic regions.
Bacteria are fascinating organisms as they can essentially be found wherever life is possible. However, different environments, habitats, energy sources, and niches demand particular traits (or “lifestyles”) of bacterial species for them to survive, reproduce and proliferate. It is reasonable to expect that these organisms possess a large and diverse genomic arsenal to withstand different environmental conditions. Interestingly, all this diversity is encoded in relatively small chromosomes.
To facilitate the identification of genomic features that might influence bacterial adaptation to specific niches, an international group of researchers has now introduced the Bioinformatics tool LifeStyle-Specific-Islands (LiSSI). LiSSI combines evolutionary sequence analysis with statistical learning. The approach aims to identify signature genes or “genomic islands” (specific gene clusters) that distinguish bacterial lifestyles.
LiSSI uses a comparative method: it is dedicated (and limited) to detecting genetic features that differ between two sets of species, i.e. genes or genomic islands appearing in most species of one lifestyle but rarely in any species of the other lifestyle. Genetic elements that are not conserved among species of one of the two sets may remain undetected.
In their recent paper, published in the Journal of Integrative Bioinformatics, the researchers not only present the tool but demonstrate its functionality by identifying genetic features for bacterial pathogenicity (the ability to cause disease) and tolerance for atmospheric oxygen.
See also: LiSSI Tutorial
Read the original article here:
Eudes Barbosa, Richard Röttger, Anne-Christin Hauschild, Siomar de Castro Soares, Sebastian Böcker, Vasco Azevedo, Jan Baumbach: LifeStyle-Specific-Islands (LiSSI): Integrated Bioinformatics Platform for Genomic Island Analysis, 05.07.2017