  Detecting variation in the evolutionary process along chromosomes is
increasingly important as whole-genome data becomes more widely available. For
example, factors such as incomplete lineage sorting, horizontal gene transfer,
and chromosomal inversion are expected to result in changes in the underlying
gene trees along a chromosome, while changes in selective pressure and
mutational rates for different genomic regions may lead to shifts in the
underlying mutational process. We propose the split score as a general method
for quantifying support for a particular phylogenetic relationship within a
genomic data set. Because the split score is based on algebraic properties of a
matrix of site pattern frequencies, it can be rapidly computed, even for data
sets that are large in the number of taxa and/or in the length of the
alignment, providing an advantage over other methods (e.g., maximum likelihood)
that are often used to assess such support. Using simulation we explore the
properties of the split score, including its dependence on sequence length,
branch length, size of a split and its ability to detect true splits in the
underlying tree. Using a sliding window analysis, we show that split scores can
be used to detect changes in the underlying evolutionary process for
genome-scale data from primates, mosquitoes, and viruses in a computationally
efficient manner. Computation of the split score has been implemented in the
software package SplitSup.
