Abstract
MotivationThe reconstruction of possible histories given a sample of genetic data in the presence of recombination and recurrent mutation is a challenging problem, but can provide key insights into the evolution of a population. We present KwARG, which implements a parsimony-based greedy heuristic algorithm for finding plausible genealogical histories (ancestral recombination graphs) that are minimal or near-minimal in the number of posited recombination and mutation events.ResultsGiven an input dataset of aligned sequences, KwARG outputs a list of possible candidate solutions, each comprising a list of mutation and recombination events that could have generated the dataset; the relative proportion of recombinations and recurrent mutations in a solution can be controlled via specifying a set of ‘cost’ parameters. We demonstrate that the algorithm performs well when compared against existing methods.Availability and implementationThe software is available at https://github.com/a-ignatieva/kwarg.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
For many species, the evolution of genetic variation within a population is driven by the processes of mutation and recombination in addition to genetic drift
Recombination can be undetectable unless mutations appear on specific branches of the genealogy (Hein et al, 2004, Section 5.11), and recombination events can produce patterns in the data that are indistinguishable from the effects of recurrent mutation (McVean et al, 2002); that is, two or more mutation events in a genealogical history that affect the same locus
Slightly cheaper costs are assigned to recurrent mutations if they happen on terminal branches, so the results show a bias towards solutions with more SE events for each given number of recombinations
Summary
The evolution of genetic variation within a population is driven by the processes of mutation and recombination in addition to genetic drift. This is a very important but challenging problem, as many possible histories might have generated a given sample.
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