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. 2022 Jan 12;12:609. doi: 10.1038/s41598-021-04526-8

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© The Author(s) 2022

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 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/.

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Density plot of %GC Content vs Mean Depth for: (A) RSMU_exome + Agilent v6; (B) RSMU_exome + MGI v4; (C) MGIeasy capture + MGI v4. Here we show a 2D density plot of %GC Content vs Mean Depth parameters calculated by Picard HsMetrics v2.22.4. We obtained the data for this plot by merging all samples from the corresponding pools (1A + 1B, 2A + 2B, 3A + 3B) with Picard 2.22.4 . Density estimation was performed using 2D histograms. More specifically, we chose data points in a fixed rectangle (%GC Content ∈ (0;1) and Mean Depth ∈ (0;1000) ), then we split this rectangle into an evenly spaced grid of the size 200 × 100, and counted the number of data points in each cell of the grid. Finally, we normalized the grid to the (0,1) range and plotted it using "jet" colormap from the matplotlib library (https://matplotlib.org/).