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    Area of Science:

    • Genomics
    • Bioinformatics

    Background:

    • Comparative analysis of chromosomal gene orders aids in predicting gene clusters in bacterial and fungal genomes.
    • Current models rely on gene family assignments, which can be computationally predicted and introduce errors that impact cluster prediction accuracy.

    Purpose of the Study:

    • To present a new dynamic model and computational methods for gene cluster prediction.
    • To enable gene cluster prediction with or without prior gene family knowledge, including a gene family-free approach.

    Main Methods:

    • Development of a dynamic model for gene cluster prediction.
    • Implementation of efficient computational approaches for gene family-based and gene family-free analyses.
    • Evaluation of the gene family-free model against a gene family-based model using a dataset of 93 bacterial genomes.

    Main Results:

    • The proposed model successfully detects gene clusters comparable to established gene family-based methods.
    • The gene family-free model identifies conserved genomic regions missed by traditional methods due to inaccurate or incomplete gene family assignments.

    Conclusions:

    • The new model offers a robust alternative for gene cluster prediction, particularly in cases with uncertain gene family annotations.
    • This approach enhances the accuracy and scope of detecting conserved gene regions in bacterial genomes.