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. 2018 Jan 15;9:222. doi: 10.1038/s41467-017-02595-w

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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. 3 — Loss of AICDA in GC B cells reduces DNA methylation heterogeneity and causes relative gain in methylation. a–c Scatterplots showing shift in mean methylation values (a), inter-individual methylation heterogeneity (b), and intra-individual methylation heterogeneity (c) of GC Aicda^−/− perturbation signature between Aicda^−/− and wild-type GC B cells. d Bar plot showing the distribution of GC Aicda^−/− perturbed CpGs relative to the distribution of all represented CpGs; Fisher’s exact test. e Bar plot showing the relative distribution GC Aicda^−/− perturbed CpGs within proximity to CpG islands; Fisher’s exact test. f Venn diagram showing the overlap between genes significantly over-representing GC Aicda^−/− signature CpGs and genes over-representing VavP-Bcl2+Aicda signature CpGs; hypergeometric test. (*P < 0.05, **P < 0.01, ***P < 0.001)