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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    • "A range of FDR values from 0.01 to 0.20 were evaluated based on preliminary testing and recommendations by Ball (2013) and Gondro et al. (2013). The second method employed the LOSITAN selection detection workbench (Antao et al., 2008), which utilises a frequency-based approach to assess relationships between F st and H e . All LOSITAN outlier detection runs were computed within a 95% confidence interval under an infinite alleles model, with 50,000 iterations evaluating a range of FDR values from 0.01 to 0.20 to match the BayeScan 2.1 analyses. "
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    • "We used two methods to detect outlier loci across all populations . First , we used the method developed by Beaumont & Nichols ( 1996 ) and implemented in LOSITAN ( Antao et al . , 2008 ) . We conducted an initial run of 100 000 simulations assuming a stepwise mutation model and then computed the distribution of F ST for each microsatellite loci using putatively neutral loci derived from the simulations . The F ST values of all loci were compared with the neutral expectation at the 95% , 99% and 99 . 5% confidence leve"
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    • "The relationship is expected to be approximately linear in a unidimensional system if there is IBD. To identify putative loci under selection, both BayeScan 2.1 (Foll and Gaggiotti, 2008) and LOSITAN (Antao et al., 2008) were used. Methods implemented in both programmes assume an island model, but BayeScan allows for differences in population sizes, whereas LOSITAN does not. "
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