Resolving the systematic positions of enigmatic taxa: Manipulating the chloroplast genome data of Saxifragales - Dong, Wenpan; Xu, Chao; Wu, Ping; Cheng, Tao; Yu, Jing; Zhou, Shiliang; Hong, De-Yuan (Molecular phylogenetics and evolution, 2018)
[Display omitted] •Contrasting substitution rate heterogeneity among Saxifragales chloroplast genomes.•Paeoniaceae closer to herbaceous groups from Maximum Parsimony analyses and homogeneous models.•Partitioning has no obvious effect on tree inference.•Paeoniaceae closer to woody groups from site heterogeneous models. Accurately resolving the phylogeny of enigmatic taxa is always a challenge in phylogenetic inference. Such uncertainties could be due to systematic errors or model violations. Here, we provide an example demonstrating how these factors affect the positioning of Paeoniaceae within Saxifragales based on chloroplast genome data. We newly assembled 14 chloroplast genomes from Saxifragales, and by combining these genomes with those of 63 other angiosperms, three datasets were assembled to test different hypotheses proposed by recent studies. These datasets were subjected to maximum parsimony, maximum likelihood and Bayesian analyses with site-homogeneous/heterogeneous models, different data partitioning strategies, and the inclusion/exclusion of weak phylogenetic signals. Three datasets exhibited remarkable heterogeneity among sites and among taxa of Saxifragales. Phylogenetic analyses under homogeneous models or maximum parsimony showed a closer relationship of Paeoniaceae with herbaceous families in the order. Data partitioning strategies did not change the general tree topology. However, PhyloBayes analysis under the CAT+GTR model resulted in a relationship closer to woody families. We conclude that although genomic data significantly increase the phylogenetic resolution of enigmatic taxa with high support, the phylogenetic results inferred from such data might be analysis or signal dependent. The analytical pipeline outlined here combines phylogenomic inference methods with evaluation of lineage-specific rates of substitution, model selection, and assessment of systematic error. These methods would be applicable to resolve similar difficult questions in the tree of life.