Data from: GHOST: Recovering Historical Signal from Heterotachously-evolved Sequence Alignments

Molecular sequence data that have evolved under the influence of
heterotachous evolutionary processes are known to mislead phylogenetic
inference. We introduce the General Heterogeneous evolution On a Single
Topology (GHOST) model of sequence evolution, implemented under a
maximum-likelihood framework in the phylogenetic program IQ-TREE
(http://www.iqtree.org). Simulations show that using the GHOST model,
IQ-TREE can accurately recover the tree topology, branch lengths, and
substitution model parameters from heterotachously evolved sequences. We
investigate the performance of the GHOST model on empirical data by
sampling phylogenomic alignments of varying lengths from a plastome
alignment. We then carry out inference under the GHOST model on a
phylogenomic data set composed of 248 genes from 16 taxa, where we find
the GHOST model concurs with the currently accepted view, placing turtles
as a sister lineage of archosaurs, in contrast to results obtained using
traditional variable rates-across-sites models. Finally, we apply the
model to a data set composed of a sodium channel gene of 11 fish taxa,
finding that the GHOST model is able to elucidate a subtle component of
the historical signal, linked to the previously established convergent
evolution of the electric organ in two geographically distinct lineages of
electric fish. We compare inference under the GHOST model to partitioning
by codon position and show that, owing to the minimization of model
constraints, the GHOST model offers unique biological insights when
applied to empirical data.