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. 2018 Feb 5;19:31. doi: 10.1186/s12859-018-2037-1

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© The Author(s). 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 3 — Performance comparison using all 16 Metilene’s benchmark datasets composed of RRBS and WGBS. Each point represents a data set. Precision is compared against FN (a) and recall against precision (b). In plot a, 15 points from Defiant, 15 from Metilene, 10 from RADMeth, and 5 from RnBeads overlap in the upper-left corner. Both Defiant and Metilene outperformed other predictors