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Target-capture phylogenomics provide insights on gene and species tree discordances in Old World Treefrogs (Anura: Rhacophoridae)

Citation

Chan, Kin Onn et al. (2020), Target-capture phylogenomics provide insights on gene and species tree discordances in Old World Treefrogs (Anura: Rhacophoridae), Dryad, Dataset, https://doi.org/10.5061/dryad.8cz8w9gn7

Abstract

Genome-scale data have greatly facilitated the resolution of recalcitrant nodes that Sanger-based datasets have been unable to resolve. However, phylogenomic studies continue to utilize traditional methods such as bootstrapping to estimate branch support; and high bootstrap values are still interpreted as providing strong support for the correct topology. Furthermore, relatively little attention is given to assessing discordances between gene and species trees, and the underlying processes that produce phylogenetic conflict. We generated novel genomic datasets to characterize and determine the causes of discordance in Old World Treefrogs (Family: Rhacophoridae)—a group that is fraught with conflicting and poorly supported topologies among major clades. We showed that incomplete lineage sorting was present at all nodes that exhibited high levels of discordance, which was caused by extremely short internal branches. We also clearly demonstrate that bootstrap values do not reflect uncertainty or confidence for the correct topology, and hence, should not be used as a measure of branch support in phylogenomic datasets. Overall, we showed that species tree inference can be improved using a total-evidence and multi-faceted approach that utilizes the most amount of data and considers results from different analytical methods and datasets.

Usage Notes

This repository includes all relevant files required for reproducibility of the study:

1) Alignments (phylip format)

2) Partition files (for partitioned analysis of concatenated data)

3) IQ-TREE consensus trees

4) ASTRAL-III species trees

5) SVDQuartets species trees

6) Individual gene trees (estimated using IQ-TREE).