Abstract

Phylogenetic analysis is important in understanding the process of biological evolution, and phylogenetic trees are used to represent the evolutionary history. Each taxon in a phylogenetic tree has not more than one parent, so phylogenetic trees cannot express the complex evolutionary information implicit in phylogeny. Phylogenetic networks can be used to express genome evolutionary histories. Therefore, it is great significance to research the construction of phylogenetic networks. Cass algorithm is an efficient method for constructing phylogenetic networks because it can construct a much simpler network. However, Cass relies heavily on the order of input data, i.e. different networks can be constructed for the same dataset with different input orders. Based on the frequency and incompatibility degree of taxa, we propose an efficiently improved algorithm of Cass, called as Frin. The experimental results show that the networks constructed by Frin are not only simpler than those constructed by other methods, but Frin can also construct more consistent phylogenetic networks when the treated data have different input orders. Furthermore, the phylogenetic network constructed by Frin is closer to the original information described by phylogenetic trees. Frin has been built as a Java software package and is freely available at https://github.com/wangjuanimu/Frin.

Highlights

  • Studying the evolution of species is helpful for humans to reveal biological secrets, prevent, and treat diseases

  • Frequency and incompatibility degree of taxa contribute a lot to the compatibility of a cluster set, which will affect the construction of phylogenetic networks

  • We propose an efficient method called Frin to construct phylogenetic networks

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Summary

Introduction

Studying the evolution of species is helpful for humans to reveal biological secrets, prevent, and treat diseases. There are a number of reticulate evolutionary events, such as reversal, translocation, and fusion, which have resulted in more than one parent of some taxa in the evolution (Gusfield et al, 2007a; Gusfield et al, 2007b; Kelk and Scornavacca, 2014; Wu, 2010; Van Iersel et al, 2017). Such a complex evolutionary history can be represented by the phylogenetic networks (Doolittle, 1999; Nakhle, 2010; Yu and Nakhleh, 2015; Huber et al, 2018).

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