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. 2020 Jul 8;17:95. doi: 10.1186/s12985-020-01344-8

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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. 4 — Detection of spontaneous reactivation in animal models. a Distribution of read counts for all viral genes in each time point. Viral genes with the mean raw count of three biological replicates value > 0 were selected for mapping. Each individual plot represents the value of log10 (mean + 1). b Distribution of read counts for all viral genes in each latent sample (28dpi and 58dpi), and parameter settings as described in (a). c The heat map shows viral genes that are significantly transcribed (log₂ fold change > 2) in each latent sample. The graphical layout is the same as in Fig. 2a. d RT-qPCR was used to validate the viral genes in the reactivated samples. Error bars denote SD of two biological replicates. 28dpi 1# & 58dpi 2# in the mouse samples, and 28dpi 3# & 58dpi 1# in the tree shrew samples were selected for this experiment