Abstract
Motivation: 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.
Availability: OgaraK is available as free software (GPL) from the address http://popgen.eu/soft/ogaraK.
| Original language | English |
|---|---|
| Article number | btr139 |
| Pages (from-to) | 1335-1336 |
| Number of pages | 2 |
| Journal | Bioinformatics |
| Volume | 27 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - 1 May 2011 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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