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. 2017 Nov 8;25(12):1354–1363. doi: 10.1038/s41431-017-0005-2

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, which permits any non-commercial 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. If you remix, transform, or build upon this article or a part thereof, you must distribute your contributions under the same license as the original. 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-nc-sa/4.0/.

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Fig. 3 — a Boxplot showing the size distribution (vertical axis) of calls made by the five CNV detection tools (horizontal axis). The annotated numbers next to boxplots show the median size for that tool. CoNIFER has the least fragmentation in its calls, and XHMM the most. CLAMMS calls are also fragmented and have a median size of only 1133 bp. To improve visibility of the box plots, outliers above 250,000 were cropped. Supplementary Fig. S5a shows this boxplot without the crop. b Number of amplifications and deletions per tool after thresholding, clearly showing CODEX’s huge number of calls compared to other tools.