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. 2020 Jun 1;13:76. doi: 10.1186/s12920-020-00731-y

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© The Author(s) 2020

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

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Fig. 2 — Genome segmentation by MetaCNV. a Segmentation of the genome according to the bins and breakpoints from the input callers. b Consensus segment prediction by MetaCNV is marked as a thick line. Bins c is predicted as amplified and bin d as deleted. Here there is no conflict between the input callers, hence the consensus is that c is amplified and d is deleted. Bin f is not predicted by SVDetect, but because ReadDepth predicts it as an amplification, this becomes the consensus. Bins b, e and g have conflicting ReadDepth and SVDetect predictions. CNVnator judges that e and g are amplifications, hence this becomes the consensus. Bin b is set to CN 2 because only ReadDepth predicts it as an amplification and CNVnator makes no prediction (Table S18)