GIPSy2: high-performance and scalable genomic island prediction software.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Genomic mobility presents challenges for current prediction tools.
• The increasing volume of sequenced genomes necessitates efficient software for multiple inputs.

Purpose of the Study:

• To introduce Genomic Island Prediction Software 2 (GIPSy2), an upgraded tool for predicting bacterial genomic islands and mobilome.
• To enhance scalability and data output for improved genomic analysis.

Main Methods:

• Utilized statistical methods including Fisher's exact test, Support Vector Machine (SVM), and Logistic Regression.
• Implemented improvements for simultaneous analysis of multiple genomes.
• Developed structured outputs to aid interpretation and reproducibility.

Main Results:

• GIPSy2 demonstrates performance comparable to its predecessor under default settings.
• The new version offers enhanced flexibility via user-defined parameterization.
• Comparative analyses confirm GIPSy2's effectiveness across diverse bacterial datasets.

Conclusions:

• GIPSy2 is a versatile and scalable tool for bacterial genomic island and mobilome prediction.
• The software facilitates more efficient and reproducible genomic analyses.
• GIPSy2 is accessible via the Zenodo repository.