Microbial genomic island discovery, visualization and analysis.

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Horizontal gene transfer (HGT) is a key driver of microbial genome evolution, facilitating adaptation to specific environments.
• Genomic islands (GIs) are segments of DNA acquired through HGT, often encoding important traits like virulence factors, antimicrobial resistance, and metabolic pathways.
• Accurate prediction and tracking of GIs are increasingly vital for genomic epidemiology, public health, and industrial applications.

Purpose of the Study:

• To review and assess computational methods for predicting genomic islands (GIs).
• To evaluate the performance of 20 GI prediction software tools using sequence-composition bias.
• To provide guidelines for researchers on effectively identifying GIs in microbial genomes.

Main Methods:

• Review of existing computational tools for GI prediction.
• Comparative analysis of 20 GI prediction software methods based on sequence-composition bias.
• Validation using a reference dataset of GIs from 104 microbial genomes derived from comparative genomics.

Main Results:

• Various computational approaches exist for GI prediction, each with distinct advantages and limitations.
• Performance assessment of 20 sequence-composition-based GI prediction tools was conducted.
• Guidelines for selecting and applying GI prediction methods are presented.

Conclusions:

• Accurate GI prediction is essential for understanding microbial adaptation and evolution.
• The review provides a valuable resource for researchers needing to identify GIs in microbial genomes.
• The assessment of prediction tools aids in the routine analysis of microbial genomes in the era of rapid sequencing.