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. 2018 Jun 27;1:79. doi: 10.1038/s42003-018-0085-8

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — RAiSD evaluation and comparison with SweepFinder2, SweeD, and OmegaPlus, for a subset of the bottleneck models. a Detection accuracy, measured as the average distance between the reported locations and the known target of selection (reported as a percentage over the region length). b Success rate, reported as the percentage of the runs with best-score location in the proximity (closer than 1% of the region length) of the known target of selection. c ROC curve for bottleneck model 45. d ROC curve for bottleneck model 60. The parameters that vary per model are provided below the plots in the form: “D[#]: [severity], [begin time], [duration]”