AUTHOR=Tan Ming , Long Haixia , Liao Bo , Cao Zhi , Yuan Dawei , Tian Geng , Zhuang Jujuan , Yang Jialiang 

TITLE=QS-Net: Reconstructing Phylogenetic Networks Based on Quartet and Sextet

JOURNAL=Frontiers in Genetics

VOLUME=Volume 10 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.00607

DOI=10.3389/fgene.2019.00607

ISSN=1664-8021

ABSTRACT=Phylogenetic networks are used to estimate evolutionary relationships among biological entities or taxa involving reticulate events such as horizontal gene transfer, hybridization, recombination and reassortment. In the past decade, many phylogenetic tree and network reconstruction methods have been proposed. Despite their great accuracy in reconstructing simple to moderate complex reticulate events, the performance decreases when there are several reticulate events simultaneously.
In this paper, we proposed QS-Net, a phylogenetic network reconstruction method taking advantage of information on the relationship among six taxa. To evaluate the performance of QS-Net, we conducted experiments on 3 artificial sequence data simulated respectively from an evolutionary tree, an evolutionary network involving 3 reticulate events, and a complex evolutionary network involving 5 reticulate events. Comparison with popular phylogenetic methods including Neighbor-Joining, Split-Decomposition, Neighbor-Net and Quartet-Net suggests that QS-Net is comparable with other methods in reconstructing tree-like evolutionary histories, while outperforms them in reconstructing reticulate events. In addition, we also applied QS-Net in real data including a bacterial taxonomy data consisting of 36 bacterial species and the whole genome sequences of 22 H7N9 influenza A viruses. The results indicate that QS-Net is capable of inferring commonly believed bacterial taxonomy and influenza evolution as well as identifying novel reticulate events. The software QS-Net is publically available at https://github.com/Tmyiri/QS-Net .