Data from: Selecting question-specific genes to reduce incongruence in phylogenomics: a case study of jawed vertebrate backbone phylogeny
Chen, Meng-Yun; Liang, Dan; Zhang, Peng (2015), Data from: Selecting question-specific genes to reduce incongruence in phylogenomics: a case study of jawed vertebrate backbone phylogeny, Dryad, Dataset, https://doi.org/10.5061/dryad.47v40
Incongruence between different phylogenomic analyses is the main challenge faced by phylogeneticists in the genomic era. To reduce incongruence, phylogenomic studies normally adopt some data filtering approaches, such as reducing missing data or using slowly evolving genes, to improve the signal quality of data. Here, we assembled a phylogenomic data set of 58 jawed vertebrate taxa and 4682 genes to investigate the backbone phylogeny of jawed vertebrates under both concatenation and coalescent-based frameworks. To evaluate the efficiency of extracting phylogenetic signals among different data filtering methods, we chose six highly intractable internodes within the backbone phylogeny of jawed vertebrates as our test questions. We found that our phylogenomic data set exhibits substantial conflicting signal among genes for these questions. Our analyses showed that non-specific data sets that are generated without bias toward specific questions are not sufficient to produce consistent results when there are several difficult nodes within a phylogeny. Moreover, phylogenetic accuracy based on non-specific data is considerably influenced by the size of data and the choice of tree inference methods. To address such incongruences, we selected genes that resolve a given internode but not the entire phylogeny. Notably, not only can this strategy yield correct relationships for the question, but it also reduces inconsistency associated with data sizes and inference methods. Our study highlights the importance of gene selection in phylogenomic analyses, suggesting that simply using a large amount of data cannot guarantee correct results. Constructing question-specific data sets may be more powerful for resolving problematic nodes.