TaxaTreeMapper: A Novel Algorithm for Phylogenetic Ancestral State Reconstruction Using Set Theory

To determine evolutionary relationships, it is crucial to conduct phylogenetic ancestral state reconstruction. Although widely used, existing algorithms, such as Fitch’s, are challenged by the computational demands of complex datasets. This study introduces the TaxaTreeMapper algorithm, which presents a streamlined approach that optimizes phylogenetic analysis. TaxaTreeMapper reduces computational time without compromising accuracy by performing ancestral state reconstruction in a single ‘leaf-to-root’ traversal. Our comparative study shows that TaxaTreeMapper correlates strongly with the Fitch algorithm and demonstrates superior efficiency, especially in identifying global minima in extensive datasets. This makes it significant in large-scale evolutionary studies.