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. 2021 Feb 16;12:1046. doi: 10.1038/s41467-021-21368-0

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© The Author(s) 2021

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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Fig. 2 — a–d Model performance is evaluated by area under precision-recall curve (AUPRC) as parameters are varied individually (a–c), and by grid search (d). Model predictions (upper-right) compared to (e) Hi-C data (bottom left) and f ChIA-PET data (bottom left). CTCF ChIP-seq signal is also shown as purple tracks in (e, f). The model predicts most of the direct CTCF ChIA-PET loop interactions, and Hi-C picks up additional contacts within the loops (or TADs) that are not the result of direct CTCF–CTCF interactions. g In the same locus, loops called by our model and ChIA-PET data are quite similar, as visualized using the WashU epigenome browser.