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

Split scores: a tool to quantify phylogenetic signal in genome-scale data