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. 2019 Jul 17;9:10383. doi: 10.1038/s41598-019-46875-5

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© The Author(s) 2019

Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Overall performance comparison between library preparation methods. Heatmap displaying rankings of the three library preparation methods by main performance metric. The libraries are ranked from one to three, with one (red) for the worst performing method and three (green) for the best performing method. Where there is no statistically significant difference between any pairwise comparison for a particular metric, the two methods will be given the same rank equivalent to the average ranks e.g. for Q20 fraction, QIAseq was the worst performing method (hence given a rank score of 1), while both Swift and TruSeq had the same rank score of 2.5 = (2 + 3)/2 since there was no statistically significant difference between the two.