期刊
MOLECULAR BIOLOGY AND EVOLUTION
卷 37, 期 12, 页码 3632-3641出版社
OXFORD UNIV PRESS
DOI: 10.1093/molbev/msaa164
关键词
phylogenetic inference; maximum likelihood; parsimony; long-branch attraction; neural networks; Felsenstein zone
资金
- Medical University of Vienna
- University of Vienna
- Austrian Science Fund [FWF DOC 32-B28, FWF I 2805-B29, FWF I 4686-B]
Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently, resulting in unresolved or disputed phylogenies. We show that a neural network can distinguish between four-taxon alignments that were evolved under conditions susceptible to either long-branch attraction or long-branch repulsion. When likelihood and parsimony methods are discordant, the neural network can provide insight as to which tree reconstruction method is best suited to the alignment. When applied to the contentious case of Strepsiptera evolution, our method shows robust support for the current scientific view, that is, it places Strepsiptera with beetles, distant from flies.
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