MSSD: An Efficient Method for Constructing Accurate and Stable Phylogenetic Networks by Merging Subtrees of Equal Depth

Background:: Systematic phylogenetic networks are essential for studying the evolutionary relationships and diversity among species. These networks are particularly important for capturing non-tree-like processes resulting from reticulate evolutionary events. However, existing methods for constructing phylogenetic networks are influenced by the order of inputs. The different orders can lead to inconsistent experimental results. Moreover, constructing a network for large datasets is time-consuming and the network often does not include all of the input tree nodes. Aims: This paper aims to propose a novel method, called as MSSD, which can construct a phylogenetic network from gene trees by Merging Subtrees with the Same Depth in a bottom-up way. background: Phylogenetic trees can represent the evolutionary history of genes vertically. There is a difference between phylogenetic trees of different genes due to the reticulate evolution events of species. Phylogenetic networks can represent reticulate evolutionary processes and show the difference between rooted gene trees. Methods:: The MSSD first decomposes trees into subtrees based on depth. Then it merges subtrees with the same depth from 0 to the maximum depth. For all subtrees of one depth, it inserts each subtree into the current networks by means of identical subtrees. Results:: We test the MSSD on the simulated data and real data. The experimental results show that the networks constructed by the MSSD can represent all input trees and the MSSD is more stable than other methods. The MSSD can construct networks faster and the constructed networks have more similar information with the input trees than other methods. Conclusion:: MSSD is a powerful tool for studying the evolutionary relationships among species in biologyand is free available at https://github.com/xingjiajie2023/MSSD. conclusion: The MSSD can construct networks faster and the constructed networks have more similar information with the input trees than other methods.