BACKGROUND: Adaptive alleles may rise in frequency as a consequence of positive 
selection, creating a pattern of decreased variation in the neighboring loci, 
known as a selective sweep. When the region containing this pattern is compared 
to another population with no history of selection, a rise in variance of allele 
frequencies between populations is observed. One challenge presented by large 
genome-wide datasets is the ability to differentiate between patterns that are 
remnants of natural selection from those expected to arise at random and/or as a 
consequence of selectively neutral demographic forces acting in the population.
FINDINGS: SmileFinder is a simple program that looks for diversity and 
divergence patterns consistent with selection sweeps by evaluating allele 
frequencies in windows, including neighboring loci from two or more populations 
of a diploid species against the genome-wide neutral expectation. The program 
calculates the mean of heterozygosity and FST in a set of sliding windows of 
incrementally increasing sizes, and then builds a resampled distribution (the 
baseline) of random multi-locus sets matched to the sizes of sliding windows, 
using an unrestricted sampling. Percentiles of the values in the sliding windows 
are derived from the superimposed resampled distribution. The resampling can 
easily be scaled from 1 K to 100 M; the higher the number, the more precise the 
percentiles ascribed to the extreme observed values.
CONCLUSIONS: The output from SmileFinder can be used to plot percentile values 
to look for population diversity and divergence patterns that may suggest past 
actions of positive selection along chromosome maps, and to compare lists of 
suspected candidate genes under random gene sets to test for the 
overrepresentation of these patterns among gene categories. Both applications of 
the algorithm have already been used in published studies. Here we present a 
publicly available, open source program that will serve as a useful tool for 
preliminary scans of selection using worldwide databases of human genetic 
variation, as well as population datasets for many non-human species, from which 
such data is rapidly emerging with the advent of new genotyping and sequencing 
technologies.