Journal
FRONTIERS IN GENETICS
Volume 10, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2019.00607
Keywords
phylogenetic network; reticulate evolution; sextet; bacterial taxonomy; influenza reassortment
Categories
Funding
- Hainan Provincial Innovation research team [2019CXTD405]
- National Natural Science Foundation of China [61762034]
- Hainan Provincial Natural Science Foundation of China [618MS057, 617122]
- Hainan Provincial major scientific and technological plans [ZDKJ2017012]
- Natural Science Foundation of Hunan, China [2018JJ2461, 2018JJ3568]
- New Century Excellent Talents in university [NCET-10-0365]
- National Nature Science Foundation of China [11171369, 61272395, 61370171, 61300128, 61472127, 61572178, 61672214, 61702054]
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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 that they are highly accurate in reconstructing simple to moderate complex reticulate events, the performance decreases when several reticulate events are present 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 three artificial sequence data simulated from an evolutionary tree, an evolutionary network involving three reticulate events, and a complex evolutionary network involving five 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 it 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.
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