title: ogaraK: a population genetics simulator for malaria.
abstract: BACKGROUND
  The evolution of resistance in Plasmodium falciparum malaria against most available treatments is a major global health threat. Population genetics approaches are commonly used to model the spread of drug resistance. Due to uncommon features in malaria biology, existing forward-time population genetics simulators cannot suitably model Plasmodium falciparum malaria.
  
  
  RESULTS
  Here we present ogaraK, a population genetics simulator for modelling the spread of drug-resistant malaria. OgaraK is designed to make malaria simulation computationally tractable as it models infections, not individual parasites. OgaraK is also able to model the life cycle of the parasite which includes both haploid and diploid phases and sexual and asexual reproduction. We also allow for the simulation of different inbreeding levels, an important difference between high and low transmission areas and a fundamental factor influencing the outcome of strategies to control or eliminate malaria.
  
  
  BACKGROUND
  OgaraK is available as free software (GPL) from the address http://popgen.eu/soft/ogaraK.
authors.affiliation: Department of Molecular and Biochemical Parasitology, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, UK. tra@popgen.eu
authors.forename: Tiago
authors.initials: T
authors.lastname: Antao
authors.forename: Ian M
authors.initials: IM
authors.lastname: Hastings
mesh_terms: D003198:Computer Simulation; D004351:Drug Resistance / Q000235:genetics*; D017125:Genes, Protozoan; D005828:Genetics, Population / Q000379:methods*; D016778:Malaria, Falciparum / Q000453:epidemiology / Q000469:parasitology; D010963:Plasmodium falciparum / Q000187:drug effects / Q000235:genetics*; D012984:Software