  Locating recombination hotspots in genomic data is an important but difficult
task. Current methods frequently rely on estimating complicated models at high
computational cost. In this paper we develop an extremely fast, scalable method
for inferring recombination hot spots in a population of genomic sequences that
is based on the singular value decomposition. Our method performs well in
several synthetic data scenarios. We also apply our technique to a real data
investigation of the evolution of drug therapy resistance in a population of
HIV genomic sequences. Finally, we compare our method both on real and
simulated data to a state of the art algorithm.
