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A GRASP-Based Heuristic for the Sorting by Length-Weighted Inversions Problem.

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    This study introduces a novel Greedy Randomized Adaptive Search Procedure (GRASP) algorithm to efficiently model genome rearrangements, specifically DNA inversions. The new method significantly outperforms existing approaches and aids in constructing accurate phylogenetic trees.

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    Area of Science:

    • Genomics and Evolutionary Biology
    • Bioinformatics and Computational Biology

    Background:

    • Genome rearrangements, including inversions, are significant mutational events driving genome evolution.
    • Understanding these large-scale mutations is crucial for deciphering evolutionary processes and relationships between species.

    Purpose of the Study:

    • To develop and evaluate a new algorithm for modeling genome inversions, a type of large-scale mutation.
    • To apply the developed algorithm to construct phylogenetic trees for the Yersinia genus.

    Main Methods:

    • Implementation of a novel algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic.
    • The algorithm models inversions as reversals of DNA segments, with cost proportional to segment length.
    • An iterative process was used, investigating the neighborhood of feasible solutions in each iteration.

    Main Results:

    • The proposed GRASP-based algorithm demonstrates superior performance compared to existing methods for modeling genome inversions.
    • The algorithm was successfully applied to generate phylogenetic trees for a subset of Yersinia species.
    • Comparative analysis showed favorable results when compared to previously published phylogenetic trees for Yersinia.

    Conclusions:

    • The novel GRASP algorithm provides a powerful and efficient tool for analyzing genome rearrangements, particularly inversions.
    • This approach offers advancements in phylogenetic reconstruction, contributing to a better understanding of evolutionary relationships.