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. 2020 Oct 28;20:326. doi: 10.1186/s12866-020-01983-5

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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. 3 — Analyses of the selective barriers on HGT. The mean selective effects of 44 transferred S. Typhimurium genes expressed in E. coli background: a. divided into informational and operational genes based on functional categories; b. plotted against predicted number of PPIs; c. absolute deviation in %GC between orthologs; d. absolute deviation in codon usage between orthologs; e. gene length; f. number of disordered regions in the amino-acid sequences. All 5 factors given in a. to e. are used as explanatory variables in a multiple regression model (single p-values at the bottom right of each panel). e. We repeated the simple linear regression for the gene length as it was the only factor with a significant effect in the multiple regression. Black lines in e and f show the best fit for the simple linear regression between the two variables; gray dashed lines show the zero line