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. 2021 Jan 19;22:62. doi: 10.1186/s12864-020-07362-8

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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. 5 — Relationship between GC content, number of variants, and concordance of variant calls between experimental setups. The correlation between GC content and the number of SNPs (a) and indels (b) based on genomic bins of 100 kbp was evaluated with the Spearman correlation coefficient r. Regression lines were fitted using generalized additive models. The correlation was highly significant for all setups (p < 2 × 10^− 16). Heatmaps show the concordance between different experimental setups for SNPs (c, d, e) and indels (f, g, h) in genomic regions with low (< 37%), medium (between 37 and 47%), and high (> 47%) GC content. Values correspond to the Jaccard distance, which ranges between 0 and 1. Higher values indicate decreased concordance between the respective experimental setups. CG: Complete Genomics; MOL: Illumina HiSeq; HSX: Illumina HiSeq X