(Strategies), which we list in S

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We as a result sought to assess the energy of S/HIC and otherPLOS Genetics | DOI:10.1371/journal.pgen.March 15,15 /Robust Identification of Soft and Hard Sweeps Using Machine LearningFig four. Heatmaps showing the fraction of product of Sgpp2, sphingosine 1-phosphate phosphatase 2, is {likely regions at varying distances from sweeps inferred to belong to every class by S/HIC, SFselect+, and evolBoosting+. The location of any sweep relative towards the classified window (or "Neutral" if there's no sweep) is shown around the y-axis, although the inferred class around the x-axis. Right here, U(two.502, 2.503). A) Results for S/HIC. B) SFselect+. C) evolBoosting+. doi:10.1371/journal.pgen.1005928.g004 PLOS Genetics | DOI:ten.1371/journal.pgen.1005928 March 15, 2016 16 /Robust Identification of Soft and Really hard Sweeps Using Machine LearningFig 5. Heatmaps showing the fraction of regions at varying distances from robust sweeps inferred to belong to each class by S/HIC, SFselect+, and evolBoosting+. The location of any sweep relative for the classified window (or "Neutral" if there is no sweep) is shown on the y-axis, though the inferred class on the xaxis. Right here, U(two.503, two.504). A) Final results for S/HIC. B) SFselect+. C) evolBoosting+. doi:10.1371/journal.pgen.1005928.g005 PLOS Genetics | DOI:10.1371/journal.pgen.1005928 March 15, 2016 17 /Robust Identification of Soft and Tough Sweeps Applying Machine Learningmethods to detect selection occurring in populations experiencing dramatic changes in population size. To this finish we educated and tested our classifiers beneath 4 demographic scenarios (S1 Table): two very simple population bottlenecks of varying severity (one of which models European Drosophila), a model of recent exponential population size development, and lastly a far more complicated model that describes out-of-Africa populations of humans. Human demographic models. We examined the overall performance of S/HIC below demographic models that have been recently estimated for African and European human populations by Tennessen et al. [44]. The African model from Tennessen et al. consists of recent exponential development in population size. The European model from Tennessen et al. (S1 Table) contains recurrent population contractions followed by first slow after which accelerated population growth. Performance of those models is shown in S8 Fig, from which it could be noticed that S/HIC has the highest accuracy of all strategies that we examined. For these two scenarios both education and testing data had been drawn in the similar demographic model. A extra pessimistic scenario is a single where the true demographic history of your population is not recognized, and thus misspecified during education. Most demographic events should influence patterns of variation genome-wide as opposed to smaller regions (but see refs. [55, 56]). Thus, approaches that look for spatial patterns of polymorphism consistent with selective sweeps might be a lot more robust to demographic misspecification than methods examining regional Egregation pattern had been set aside {and the levels of variation only (as demonstrated by ref. [28]). To test this, we educated S/HIC and also other classifiers on equilibrium datasets, and measured their accuracy on test information simulated under the n.(Approaches), which we list in S2 Table. Commonly, we find that characteristics close to the center ^ of your window possess a greater contribution, and that relative values of y and usually havewgreater importance than other statistics.The influence of population size modify and demographic misspecificationNon-equilibrium demographic histories have the prospective to confound population genetic scans for selective sweeps [53, 54].