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. 2021 Apr 26;21:460. doi: 10.1186/s12885-021-08210-y

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© The Author(s) 2021

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 — Screening genomic variation candidate genes using multi-omics data. a, Data flow of the candidate genes screening process. b, d, The examples show the copy number alteration of the candidate KD genes DERL1 (b) and NAA15 (d) on DNA and the expression level on the mRNA, as well as the expression level of its dysregulation factors. c, e, Examples showing the effects of candidate KD genes DERL1 (c) and NAA15 (e) copy number alteration on expression level (Wilcoxon Rank Sum Test). f, g, sample distribution of candidate KD genes, including before (f) and after (g) the addition of mutation information