Abstract
Resolving the phylogenetic relationships among birds is a classical problem in systematics, and this is particularly so when it comes to understanding the relationships among Neoaves. Previous phylogenetic inference of birds has been limited to mitochondrial genomes or a few nuclear genes. Here, we apply deep brain transcriptome sequencing of nine bird species (several passerines, hummingbirds, dove, parrot, and emu), using next-generation sequencing technology to understand features of transcriptome evolution in birds and how this affects phylogenetic inference, and combine with data from two bird species using first generation technology. The phylogenomic data matrix comprises 1,995 genes and a total of 0.77 Mb of exonic sequence. First, we find an unexpected heterogeneity in the evolution of base composition among avian lineages. There is a pronounced increase in guanine + cytosine (GC) content in the third codon position in several independent lineages, with the strongest effect seen in passerines. Second, we evaluate the effect of GC content variation on phylogenetic reconstruction. We find important inconsistencies between the topologies obtained with or without taking GC variation into account, each supporting different conclusions of past studies and also influencing hypotheses on the evolution of the trait of vocal learning. Third, we demonstrate a link between GC content evolution and recombination rate and, focusing on the zebra finch lineage, find that recombination seems to drive GC content. Although we cannot reveal the causal relationships, this observation is consistent with the model of GC-biased gene conversion. Finally, we use this unparalleled amount of avian sequence data to study the rate of molecular evolution, calibrated by fossil evidence and augmented with data from alligator transcriptome sequencing. There is a 2- to 3-fold variation in substitution rate among lineages with passerines being the most rapidly evolving and ratites the slowest. This study illustrates the potential of next-generation sequencing for phylogenomic studies but also the pitfalls when using genome-wide data with heterogeneous base composition.