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. 2022 May 26;20:244. doi: 10.1186/s12967-022-03440-5

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© The Author(s) 2022

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. 1 — Identifying non-mutated genes. a Schematic of mutagenesis simulation. To approximate the data used in the patient exome sequencing, a reduced exon library was used consisting of the exons approximating those used in the TCGA trial. Simulated mutagenesis depicted as red lines is subjected to repeated trials using the observed background mutation frequencies. The mutation frequencies are then compared between the simulated and observed data. b Log ratio distribution of observed/simulated data. The inset limits the y-axis to 25 to see the distribution at the tails more clearly; the genes that are most extreme are labelled. Gray dashed lines show the top 50 non-mutated genes and top 50 mutated genes that are used for subsequent analysis (corresponding to the values less than 0.007 quantile and greater than the 99.23 quantile of the dataset)