LOSITAN: a workbench to detect molecular adaptation based on a FST-outlier method. BMC Bioinforma 9:323

Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK.
BMC Bioinformatics (Impact Factor: 2.58). 02/2008; 9(1):323. DOI: 10.1186/1471-2105-9-323
Source: DBLP


Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to use, leaving many non-trivial, error-prone tasks to the user.
Here we present LOSITAN, a selection detection workbench based on a well evaluated Fst-outlier detection method. LOSITAN greatly facilitates correct approximation of model parameters (e.g., genome-wide average, neutral Fst), provides data import and export functions, iterative contour smoothing and generation of graphics in a easy to use graphical user interface. LOSITAN is able to use modern multi-core processor architectures by locally parallelizing fdist, reducing computation time by half in current dual core machines and with almost linear performance gains in machines with more cores.
LOSITAN makes selection detection feasible to a much wider range of users, even for large population genomic datasets, by both providing an easy to use interface and essential functionality to complete the whole selection detection process.

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Available from: Ricardo Jorge Lopes, Oct 07, 2015
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    • "To investigate whether selection affected any of the loci analyzed , we used the Lositan software (Antao et al. 2008). Adaptive differentiation has traditionally been identified from the differences in allele frequencies among different populations , summarized by an estimate of the F ST (Beaumont 2005). "
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    • "Both total average missing data and error rate in this study were at levels considered acceptable for microsatellite data (Selkoe and Toonen, 2006). To test each locus for neutrality, we used an F ST outlier method implemented by LOSITAN (Antao et al., 2008) with 10 runs of 50 000 simulations and 99.5% confidence interval under an infinite allele mutation mode. The results indicated that all loci in this study were likely to be neutral. "
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    • "The infinite allele model was used with a 95% and 99% confidence interval. We ran 10 5 simulations as recommended by Antao et al. (2008). The outlier detection software implemented in Arlequin uses the same island model, but adds on the option for hierarchical clustering. "
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