SCCT (Selection detection by Conditional Coalescent Tree) is a efficiency computational software developed to detect recent positive selection using deep sequencing data. It’s robust to various demographic events and also robust to the variations of mutation rates and recombination rates.
MFDM (Maximum Frequency of Derived Mutations) is designed to reliably detect recent positive selection in a varying size population, even if you only have polymorphism data from a single locus (i.e. a very short piece of DNA).
PoSeiDon is a pipeline to detect significant positively selected sites and possible recombination events in analignment of multiple coding sequences. Sites that undergo positive selection can give you insights in the evolutionary history of your sequences, for example showing you important mutation hot spots, accumulated as results of virus-host arms races during evolution.
InVEx (Introns Vs Exons) is a permutation-based method for ascertaining genes with a somatic mutation distribution showing evidence of positive selection for non-silent mutations. The method was developed for use in cancer genomics studies, with particular relevance to high mutation rate cancers. Mutations are permuted on a per-patient, per-trinucleotide-context basis across the exons, introns and UTRs of a gene, generating a null model of the distribution of mutations to which the observed distribution can be compared to determine statistical significance. Significant genes are of interest, as their somatic mutation is likely to be important in the formation of the cancer being studied. The method can operate on whole exome as well as whole genome sequencing data.