HCPC: A New Parsimonious Clustering Method based on Hierarchical Characters for Morphological Phylogenetic Reconstruction

Abstract Background: Phylogenetic trees are reconstructed frequently to provide a better interpretation of the evolutionary history of species. However, most traditional methods ignore the hierarchical relationships among characters and neglect the inapplicable state that frequently exists in the morphological data, resulting in poor performance of the phylogenetic analysis.Results: In this study, we propose a phylogenetic clustering method based on hierarchical characters. Accordingly, we call our method Hierarchical Characters Parsimonious Clustering(HCPC). To combine prior phylogenetic knowledge and treat the inapplicable state more reasonably, two stages are proposed, i.e., Phylogenetic reconstruction and parsimonious tree search. During phylogenetic reconstruction, HCPC is able to infer the shared ancestral relationships among species. For the search of the parsimonious tree, we use a simulated annealing algorithm to heuristically search the phylogenetic tree based on the parsimony criterion. In addition, HCPC combines asymmetric binary relationships and character hierarchies to solve the problem of the ambiguity of the inapplicable state.Conclusion: The experimental results show that the proposed method provides better performance of phylogenetic analysis than existing methods and a scientific and quantitative basis for biologists to study species evolution.