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. 2022 Apr 12;23:127. doi: 10.1186/s12859-022-04674-2

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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. 3 — TAD inference performance via TAD callers across a number of sequencing depths at 5kb resolution using HiCNorm normalized Micro-C interaction dataset over human embryonic stem cell line. A Across multiple ratio of downsampled sequenced reads, the number of TADs inferred by each caller in all chromosomes. B The mean TAD size inferred by considered TAD callers across different ratio of downsampled sequenced reads over all chromosomes. C The comparison of TAD boundaries inferred by one TAD caller with the others across a number of sequencing depths over all chromosomes in terms of Jaccard Index similarity metric. We do not report CHDF results as CHDF has inferred each bin in the interaction matrix as an independent TAD after downsampling the dataset