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. 2022 Apr 22;23:319. doi: 10.1186/s12864-022-08541-5

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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. 5 — Correlation between genome coverage and Ct score for samples sequenced on the GridION and MiSeq: A correlation was performed to determine the effect of Ct score on the consensus genome coverage obtained from the GridION and the MiSeq. Genome coverage was plotted on the y-axis, whilst the sample’s average Ct score was plotted on the X-axis. GridION runs are represented by graphs A (Run101), B (Run111), and C (Run123), which are represented as green, blue, and red, respectively. MiSeq runs are represented by graphs D (Run100), E (Run109), and F (Run122) and are represented as black, purple, and gold, respectively. Statistical significance (Spearman’s rank correlation test) is represented by “*” (ns: non-significant, *: p < 0.05, **: p < 0.01, ***: p < 0.001, ****: p < 0.0001). For both platforms, as the Ct score increased, there was a decrease in genomic coverage