The Path-Label Reconciliation (PLR) Dissimilarity Measure for Gene Trees
<|reference_start|>The Path-Label Reconciliation (PLR) Dissimilarity Measure for Gene Trees: In this study, we investigate the problem of comparing gene trees reconciled with the same species tree using a novel semi-metric, called the Path-Label Reconciliation (PLR) dissimilarity measure. This approach not only quantifies differences in the topology of reconciled gene trees, but also considers discrepancies in predicted ancestral gene-species maps and speciation/duplication events, offering a refinement of existing metrics such as Robinson-Foulds (RF) and their labeled extensions LRF and ELRF. A tunable parameter {\alpha} also allows users to adjust the balance between its species map and event labeling components. We show that PLR can be computed in linear time and that it is a semi-metric. We also discuss the diameters of reconciled gene tree measures, which are important in practice for normalization, and provide initial bounds on PLR, LRF, and ELRF. To validate PLR, we simulate reconciliations and perform comparisons with LRF and ELRF. The results show that PLR provides a more evenly distributed range of distances, making it less susceptible to overestimating differences in the presence of small topological changes, while at the same time being computationally efficient. Our findings suggest that the theoretical diameter is rarely reached in practice. The PLR measure advances phylogenetic reconciliation by combining theoretical rigor with practical applicability. Future research will refine its mathematical properties, explore its performance on different tree types, and integrate it with existing bioinformatics tools for large-scale evolutionary analyses. The open source code is available at: https://pypi.org/project/parle/.<|reference_end|>
@article{sánchez2024the,
    title={The Path-Label Reconciliation (PLR) Dissimilarity Measure for Gene Trees},
    author={Alitzel L'opez S'anchez, Jos'e Antonio Ram'irez-Rafael, Alejandro
  Flores-Lamas, Maribel Hern'andez-Rosales, Manuel Lafond},
    journal={arXiv preprint arXiv:2407.06367},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06367},
    primaryClass={cs.DM q-bio.PE}
}