Data from: Homology-aware phylogenomics at gigabase scales

Obstacles to inferring species trees from whole genome data sets range
from algorithmic and data management challenges to the wholesale
discordance in evolutionary history found in different parts of a genome.
Recent work that builds trees directly from genomes by parsing them into
sets of small k-mer strings holds promise to streamline and simplify these
efforts, but existing approaches do not account well for gene tree
discordance. We describe a “seed and extend” protocol that finds nearly
exact matching sets of orthologous k-mers and extends them to construct
data sets that can properly account for genomic heterogeneity. Exploiting
an efficient suffix array data structure, sets of whole genomes can be
parsed and converted into phylogenetic data matrices rapidly, with
contiguous blocks of k-mers from the same chromosome, gene, or scaffold
concatenated as needed. Phylogenetic trees constructed from highly curated
rice genome data and a diverse set of six other eukaryotic whole genome,
transcriptome, and organellar genome data sets recovered trees nearly
identical to published phylogenomic analyses, in a small fraction of the
time, and requiring many fewer parameter choices. Our method’s ability to
retain local homology information was demonstrated by using it to
characterize gene tree discordance across the rice genome, and by its
robustness to the high rate of interchromosomal gene transfer found in
several rice species.