A supervised learning framework for chromatin loop detection in genome-wide contact maps
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Abstract
Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of orthogonal data types such as ChIA-PET, HiChIP, Capture Hi-C, and high-throughput imaging, a supervised learning approach could facilitate the discovery of a comprehensive set of chromatin interactions. Here, we present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps. We compare Peakachu with current enrichment-based approaches, and find that Peakachu identifies a unique set of short-range interactions. We show that our models perform well in different platforms, across different sequencing depths, and across different species. We apply this framework to predict chromatin loops in 56 Hi-C datasets, and release the results at the 3D Genome Browser.
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Peakachu detects loops as CTCF or H3K27ac loops depending on a pre-trained CTCF model or H3K27ac model.
“Peakachu detects loops as CTCF or H3K27ac loops depending on a pre-trained CTCF model or H3K27ac model.”
From Subtype-specific 3D genome alteration in acute myeloid leukaemia (2022) · cites this paper as [27]
Peakachu reports a probability score associated with each loop that demarcates confidence of loop calling and is positively associated with loop intensity.
“Peakachu reports a probability score associated with each loop which demarcates confidence of loop calling. The probability score is also positively associated with the loop intensity, making it convenient for differential loop analysis.”
From Subtype-specific 3D genome alteration in acute myeloid leukaemia (2022) · cites this paper as [27]
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A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping
Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.021 · Cited by 9,212 · see citing works
Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898 · see citing works
Capturing Chromosome Conformation
Dekker, J., Rippe, K., Dekker, M., & Kleckner, N. (2002) · Science · doi:10.1126/science.1067799 · Cited by 3,652 · see citing works
Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments
Durand, N. C., Shamim, M., Machol, I., Rao, S. S. P., Huntley, M. H., Lander, E., & Aiden, E. L. (2016) · Cell Systems · doi:10.1016/j.cels.2016.07.002 · Cited by 3,553 · see citing works
ChromHMM: automating chromatin-state discovery and characterization
Ernst, J., & Kellis, M. (2012) · Nature Methods · doi:10.1038/nmeth.1906 · Cited by 2,489 · see citing works
Detection of nonneutral substitution rates on mammalian phylogenies
Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R., & Siepel, A. (2010) · Genome Research · doi:10.1101/gr.097857.109 · Cited by 2,353 · see citing works
Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338 · see citing works
Formation of chromosomal domains by loop extrusion
Fudenberg, G., Imakaev, M., Lu, C., Goloborodko, A., Abdennur, N., & Mirny, L. A. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.04.085 · Cited by 2,003 · see citing works
Cohesin Loss Eliminates All Loop Domains
Rao, S. S. P., Huang, S.-C., Glenn St Hilaire, B., Engreitz, J. M., Perez, E. M., Kieffer-Kwon, K.-R., Sanborn, A. L., Johnstone, S. E., Bascom, G. D., Bochkov, I. D., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.026 · Cited by 1,883 · see citing works
Sanborn, A. L., Rao, S. S. P., Huang, S.-C., Durand, N. C., Huntley, M. H., Jewett, A. I., Bochkov, I. D., Chinnappan, D., Cutkosky, A., Li, J., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1518552112 · Cited by 1,730 · see citing works
An oestrogen-receptor-α-bound human chromatin interactome
Fullwood, M. J., Liu, M. H., Pan, Y. F., Liu, J., Han, X., Mohamed, Y. B., Orlov, Y. L., Velkov, S., Ho, A., Poh, H. M., et al. (2009) · Nature · doi:10.1038/nature08497 · Cited by 1,728 · see citing works
Self-citationThe Encyclopedia of DNA elements (ENCODE): data portal update
Davis, C. A., Hitz, B. C., Sloan, C. A., Chan, E. T., Davidson, J. M., Gabdank, I., Hilton, J. A., Jain, K., Baymuradov, U. K., Narayanan, A. K., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1081 · Cited by 1,717 · see citing works
Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites
Betel, D., Koppal, A., Agius, P., Sander, C., & Leslie, C. (2010) · Genome Biology · doi:10.1186/gb-2010-11-8-r90 · Cited by 1,592 · see citing works
Multiscale 3D Genome Rewiring during Mouse Neural Development
Bonev, B., Mendelson Cohen, N., Szabo, Q., Fritsch, L., Papadopoulos, G. L., Lubling, Y., Xu, X., Lv, X., Hugnot, J.-P., Tanay, A., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.043 · Cited by 1,338 · see citing works
Architectural protein subclasses shape 3D organization of genomes during lineage commitment
Phillips-Cremins, J. E., Sauria, M. E. G., Sanyal, A., Gerasimova, T. I., Lajoie, B. R., Bell, J. S. K., Ong, C.-T., Hookway, T. A., Guo, C., Sun, Y., et al. (2013) · Cell · doi:10.1016/j.cell.2013.04.053 · Cited by 1,187 · see citing works
HiChIP: efficient and sensitive analysis of protein-directed genome architecture
Mumbach, M. R., Rubin, A. J., Flynn, R. A., Dai, C., Khavari, P. A., Greenleaf, W. J., & Chang, H. Y. (2016) · Nature Methods · doi:10.1038/nmeth.3999 · Cited by 1,139 · see citing works
Long-range enhancer–promoter contacts in gene expression control
Schoenfelder, S., & Fraser, P. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0128-0 · Cited by 1,102 · see citing works
YY1 Is a Structural Regulator of Enhancer-Promoter Loops
Weintraub, A. S., Li, C. H., Zamudio, A. V., Sigova, A. A., Hannett, N. M., Day, D. S., Abraham, B. J., Cohen, M. A., Nabet, B., Buckley, D. L., et al. (2017) · Cell · doi:10.1016/j.cell.2017.11.008 · Cited by 992 · see citing works
Mapping long-range promoter contacts in human cells with high-resolution capture Hi-C
Mifsud, B., Tavares-Cadete, F., Young, A. N., Sugar, R., Schoenfelder, S., Ferreira, L., Wingett, S. W., Andrews, S., Grey, W., Ewels, P. A., et al. (2015) · Nature Genetics · doi:10.1038/ng.3286 · Cited by 990 · see citing works
CTCF-Mediated Human 3D Genome Architecture Reveals Chromatin Topology for Transcription
Tang, Z., Luo, O. J., Li, X., Zheng, M., Zhu, J. J., Szalaj, P., Trzaskoma, P., Magalska, A., Wlodarczyk, J., Ruszczycki, B., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.024 · Cited by 982 · see citing works
Self-citationHigher-Order Inter-chromosomal Hubs Shape 3D Genome Organization in the Nucleus
Quinodoz, S. A., Ollikainen, N., Tabak, B., Palla, A., Schmidt, J. M., Detmar, E., Lai, M. M., Shishkin, A. A., Bhat, P., Takei, Y., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.024 · Cited by 892 · see citing works
Mapping Nucleosome Resolution Chromosome Folding in Yeast by Micro-C
Hsieh, T.-H. S., Weiner, A., Lajoie, B., Dekker, J., Friedman, N., & Rando, O. J. (2015) · Cell · doi:10.1016/j.cell.2015.05.048 · Cited by 788 · see citing works
the 4D Nucleome Network, Dekker, J., Belmont, A. S., Guttman, M., Leshyk, V. O., Lis, J. T., Lomvardas, S., Mirny, L. A., O’Shea, C. C., Park, P. J., et al. (2017) · Nature · doi:10.1038/nature23884 · Cited by 732 · see citing works
Structural variation in the 3D genome
Spielmann, M., Lupiáñez, D. G., & Mundlos, S. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0007-0 · Cited by 719 · see citing works
Complex multi-enhancer contacts captured by genome architecture mapping
Beagrie, R. A., Scialdone, A., Schueler, M., Kraemer, D. C. A., Chotalia, M., Xie, S. Q., Barbieri, M., de Santiago, I., Lavitas, L.-M., Branco, M. R., et al. (2017) · Nature · doi:10.1038/nature21411 · Cited by 715 · see citing works
The Hierarchy of the 3D Genome
Gibcus, J. H., & Dekker, J. (2013) · Molecular Cell · doi:10.1016/j.molcel.2013.02.011 · Cited by 695 · see citing works
Ultrastructural Details of Mammalian Chromosome Architecture
Krietenstein, N., Abraham, S., Venev, S. V., Abdennur, N., Gibcus, J., Hsieh, T.-H. S., Parsi, K. M., Yang, L., Maehr, R., Mirny, L. A., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.03.003 · Cited by 622 · see citing works
Unsupervised pattern discovery in human chromatin structure through genomic segmentation
Hoffman, M. M., Buske, O. J., Wang, J., Weng, Z., Bilmes, J., & Noble, W. S. (2012) · Nature Methods · doi:10.1038/nmeth.1937 · Cited by 566 · see citing works
Mumbach, M. R., Satpathy, A. T., Boyle, E. A., Dai, C., Gowen, B. G., Cho, S. W., Nguyen, M. L., Rubin, A. J., Granja, J. M., Kazane, K. R., et al. (2017) · Nature Genetics · doi:10.1038/ng.3963 · Cited by 490 · see citing works
Statistical confidence estimation for Hi-C data reveals regulatory chromatin contactsPreprint
Ay, F., Bailey, T. L., & Noble, W. S. (2014) · Genome Research · doi:10.1101/gr.160374.113 · Cited by 479 · see citing works
Hughes, J. R., Roberts, N., McGowan, S., Hay, D., Giannoulatou, E., Lynch, M., De Gobbi, M., Taylor, S., Gibbons, R., & Higgs, D. R. (2014) · Nature Genetics · doi:10.1038/ng.2871 · Cited by 461 · see citing works
Integrative detection and analysis of structural variation in cancer genomes
Dixon, J. R., Xu, J., Dileep, V., Zhan, Y., Song, F., Le, V. T., Yardımcı, G. G., Chakraborty, A., Bann, D. V., Wang, Y., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0195-8 · Cited by 452 · see citing works
Self-citationCHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data
Cairns, J., Freire-Pritchett, P., Wingett, S. W., Várnai, C., Dimond, A., Plagnol, V., Zerbino, D., Schoenfelder, S., Javierre, B.-M., Osborne, C., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0992-2 · Cited by 417 · see citing works
Unsupervised pattern discovery in human chromatin structure through genomic segmentation
Hoffman, M. M., Buske, O. J., Wang, J., Weng, Z., Bilmes, J., & Noble, W. S. (2012) · eScholarship@UMassChan · Cited by 397 · see citing works
Mapping of long-range chromatin interactions by proximity ligation-assisted ChIP-seq
Fang, R., Yu, M., Li, G., Chee, S., Liu, T., Schmitt, A. D., & Ren, B. (2016) · Cell Research · doi:10.1038/cr.2016.137 · Cited by 344 · see citing works
ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing
Li, G., Fullwood, M. J., Han, X., Mulawadi, F. H., Velkov, S., Vega, V. B., Ariyaratne, P. N., Mohamed, Y. B., Ooi, H.-S., Tennakoon, C., et al. (2010) · Genome biology · doi:10.1186/gb-2010-11-2-r22 · Cited by 316 · see citing works
Self-citationComparison of computational methods for Hi-C data analysis
Forcato, M., Nicoletti, C., Pal, K., Livi, C. M., Ferrari, F., & Bicciato, S. (2017) · Nature Methods · doi:10.1038/nmeth.4325 · Cited by 304 · see citing works
Unsupervised pattern discovery in human chromatin structure through genomic segmentation
Hoffman, M. M., Buske, O. J., Wang, J., Weng, Z., Bilmes, J. A., & Noble, W. S. (2013) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2506583.2506701 · Cited by 288 · see citing works
Whole-genome haplotype reconstruction using proximity-ligation and shotgun sequencing
Selvaraj, S., Dixon, J. R., Bansal, V., & Ren, B. (2013) · Nature Biotechnology · doi:10.1038/nbt.2728 · Cited by 286 · see citing works
Genome-wide map of regulatory interactions in the human genome
Heidari, N., Phanstiel, D. H., He, C., Grubert, F., Jahanbani, F., Kasowski, M., Zhang, M. Q., & Snyder, M. P. (2014) · Genome Research · doi:10.1101/gr.176586.114 · Cited by 278 · see citing works
Xu, J., Ma, H., Jin, J., Uttam, S., Fu, R., Huang, Y., & Liu, Y. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.06.085 · Cited by 206 · see citing works
ZNF143 provides sequence specificity to secure chromatin interactions at gene promoters
Bailey, S. D., Zhang, X., Desai, K., Aid, M., Corradin, O., Cowper-Sal·lari, R., Akhtar-Zaidi, B., Scacheri, P. C., Haibe-Kains, B., & Lupien, M. (2015) · Nature Communications · doi:10.1038/ncomms7186 · Cited by 194 · see citing works
Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus
Zhang, Y., An, L., Xu, J., Zhang, B., Zheng, W. J., Hu, M., Tang, J., & Yue, F. (2018) · Nature Communications · doi:10.1038/s41467-018-03113-2 · Cited by 186 · see citing works
Self-citationPredicting enhancer‐promoter interaction from genomic sequence with deep neural networks
Singh, S. K., Yang, Y., Póczos, B., & Ma, J. (2019) · Quantitative Biology · doi:10.1007/s40484-019-0154-0 · Cited by 180 · see citing works
Polycomb-mediated chromatin loops revealed by a subkilobase-resolution chromatin interaction map
Eagen, K. P., Aiden, E. L., & Kornberg, R. D. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1701291114 · Cited by 178 · see citing works
CTCF-mediated topological boundaries during development foster appropriate gene regulationPreprint
Narendra, V. K., Bulajić, M., Dekker, J., Mazzoni, E. O., & Reinberg, D. (2016) · Genes & Development · doi:10.1101/gad.288324.116 · Cited by 172 · see citing works
Comparison of Hi-C results using in-solution versus in-nucleus ligation
Nagano, T., Várnai, C., Schoenfelder, S., Javierre, B.-M., Wingett, S. W., & Fraser, P. (2015) · Genome Biology · doi:10.1186/s13059-015-0753-7 · Cited by 166 · see citing works
Constructing 3D interaction maps from 1D epigenomes
Zhu, Y., Chen, Z., Zhang, K., Wang, M., Medovoy, D., Whitaker, J. W., Ding, B., Li, N., Zheng, L., & Wang, W. (2016) · Nature Communications · doi:10.1038/ncomms10812 · Cited by 139 · see citing works
MAPS: Model-based analysis of long-range chromatin interactions from PLAC-seq and HiChIP experiments
Juric, I., Yu, M., Abnousi, A., Raviram, R., Fang, R., Zhao, Y., Zhang, Y., Qiu, Y., Yang, Y., Li, Y., et al. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006982 · Cited by 121 · see citing works
Mango: a bias-correcting ChIA-PET analysis pipeline
Phanstiel, D. H., Boyle, A. P., Heidari, N., & Snyder, M. P. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv336 · Cited by 113 · see citing works
miniMDS: 3D structural inference from high-resolution Hi-C data
Rieber, L., & Mahony, S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx271 · Cited by 90 · see citing works
Trac-looping measures genome structure and chromatin accessibility
Lai, B., Tang, Q., Jin, W., Hu, G., Wangsa, D., Cui, K., Stanton, B. Z., Ren, G., Ding, Y., Zhao, M., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0107-y · Cited by 85 · see citing works
Reconstructing spatial organizations of chromosomes through manifold learning
Zhu, G., Deng, W., Hu, H., Ma, R., Zhang, S., Yang, J., Peng, J., Kaplan, T., & Zeng, J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky065 · Cited by 51 · see citing works
HIPMap: A High-Throughput Imaging Method for Mapping Spatial Gene PositionsPreprint
Shachar, S., Pegoraro, G., & Misteli, T. (2015) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.2015.80.027417 · Cited by 31 · see citing works
Zouari, Y. B., Molitor, A., Sikorska, N., Pancaldi, V., & Sexton, T. (2018) · Genome Biology · doi:10.1186/s13059-019-1706-3 · Cited by 27 · see citing works
Boost-HiC: computational enhancement of long-range contacts in chromosomal contact maps
Carron, L., Morlot, J. B., Matthys, V., Lesne, A., & Mozziconacci, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty1059 · Cited by 18 · see citing works
Narendra, V. K., Bulajić, M., Dekker, J., Mazzoni, E. O., & Reinberg, D. (2017) · Genes & Development · doi:10.1101/gad.306829.117 · Cited by 1 · see citing works
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Self-citationSubtype-specific 3D genome alteration in acute myeloid leukaemia
Xu, J., Song, F., Lyu, H., Kobayashi, M., Zhang, B., Zhao, Z., Hou, Y., Wang, X., Luan, Y., Jia, B., et al. (2022) · Nature · doi:10.1038/s41586-022-05365-x · Cited by 208 · see citing works
Self-citationWang, X., Xu, J., Zhang, B., Hou, Y., Song, F., Lyu, H., & Yue, F. (2021) · Nature Methods · doi:10.1038/s41592-021-01164-w · Cited by 152 · see citing works
Self-citationDeshpande, A. S., Ulahannan, N., Pendleton, M., Dai, X., Ly, L., Behr, J. M., Schwenk, S., Liao, W., Augello, M. A., Tyer, C., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01289-z · Cited by 155 · see citing works
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Ricker, E., Manni, M., Flores-Castro, D., Jenkins, D., Gupta, S. K., Rivera‐Correa, J., Meng, W., Rosenfeld, A. M., Pannellini, T., Bachu, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25102-8 · Cited by 110 · see citing works
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Wang, X., & Yue, F. (2024) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2024.102199 · Cited by 30 · see citing works
Self-citationBreakage fusion bridge cycles drive high oncogene number with moderate intratumoural heterogeneity
Dehkordi, S. R., Wong, I. T.-L., Ni, J., Luebeck, J., Zhu, K., Prasad, G., Krockenberger, L., Xu, G., Chowdhury, B., Rajkumar, U., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56670-8 · Cited by 29 · see citing works
Self-citationEnhancer Coamplification and Hijacking Promote Oncogene Expression in Liposarcoma
Liu, T., Wang, J., Yang, H., Jin, Q., Wang, X., Fu, Y., Luan, Y., Wang, Q., Youngblood, M. W., Lu, X., et al. (2023) · Cancer Research · doi:10.1158/0008-5472.can-22-1858 · Cited by 30 · see citing works
Self-citationRicher, S., Tian, Y., Schoenfelder, S., Hurst, L., Murrell, A., & Pisignano, G. (2022) · bioRxiv · doi:10.1186/s13059-023-02876-2 · Cited by 25 · see citing works
MATCHA: Probing Multi-way Chromatin Interaction with Hypergraph Representation Learning
Zhang, R., & Ma, J. (2020) · Cell Systems · doi:10.1016/j.cels.2020.04.004 · Cited by 27 · see citing works
Buyukcelebi, K., Chen, X., Abdula, F., Elkafas, H., Duval, A. J., Öztürk, H., Seker-Polat, F., Jin, Q., Yin, P., Yue, F., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39684-y · Cited by 26 · see citing works
Self-citationCheng, F., Li, H., Brooks, B. W., & You, J. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c00152 · Cited by 25 · see citing works
Xu, X., Khunsriraksakul, C., Eales, J., Rubin, S., Scannali, D., Saluja, S., Talavera, D., Markus, H., Wang, L., Drzal, M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46132-y · Cited by 24 · see citing works
Self-citationLiu, Y., Li, H., Czajkowsky, D. M., & Shao, Z. (2021) · Hereditas · doi:10.1186/s41065-021-00205-w · Cited by 24 · see citing works
Xue, Y., Liu, L., Zhang, Y., He, Y., Wang, J., Ma, Z.-C., Li, T.-J., Zhang, J.-Y., Huang, Y.-Y., & Gao, Y.-Q. (2024) · Molecular Cancer · doi:10.1186/s12943-024-02100-0 · Cited by 23 · see citing works
Li, L., Li, X., & Wu, H. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf289 · Cited by 23 · see citing works
A SWI/SNF-dependent transcriptional regulation mediated by POU2AF2/C11orf53 at enhancer
Szczepanski, A. P., Tsuboyama, N., Lyu, H., Wang, P., Beytullahoglu, O., Zhang, T., Singer, B. D., Yue, F., Zhao, Z., & Wang, L. (2024) · Nature Communications · doi:10.1038/s41467-024-46492-5 · Cited by 22 · see citing works
Self-citationComputational prediction and characterization of cell-type-specific and shared binding sites
Zhang, Q., Teng, P., Wang, S., He, Y., Cui, Z., Guo, Z., Liu, Y., Yuan, C., Liu, Q., & Huang, D.-S. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac798 · Cited by 21 · see citing works
Reference panel guided topological structure annotation of Hi-C data
Zhang, Y., & Blanchette, M. (2022) · Nature Communications · doi:10.1038/s41467-022-35231-3 · Cited by 21 · see citing works
Sun, Y., Xu, X., Lin, L., Xu, K., Zheng, Y., Ren, C., Tao, H., Wang, X., Zhao, H., Tu, W.-W., et al. (2023) · Genome biology · doi:10.1186/s13059-023-02916-x · Cited by 21 · see citing works
Self-citationGalan, S., Serra, F., & Martı́-Renom, M. A. (2022) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqac021 · Cited by 18 · see citing works
Machine and Deep Learning Methods for Predicting 3D Genome Organization
Wall, B. P. G., Nguyen, M., Harrell, J. C., & Dozmorov, M. G. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4136-1_22 · Cited by 19 · see citing works
preciseTAD: a transfer learning framework for 3D domain boundary prediction at base-pair resolution
Stilianoudakis, S. C., Marshall, M. A., & Dozmorov, M. G. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab743 · Cited by 18 · see citing works
Wang, S., Zhang, Q., He, Y., Cui, Z., Guo, Z., Han, K., & Huang, D.-S. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010572 · Cited by 16 · see citing works
Spatial 3D genome organization reveals intratumor heterogeneity in primary glioblastoma samples
Wang, Q., Wang, J., Mathur, R., Youngblood, M. W., Jin, Q., Hou, Y., Stasiak, L. A., Luan, Y., Zhao, H., Hilz, S., et al. (2025) · Science Advances · doi:10.1126/sciadv.adn2830 · Cited by 17 · see citing works
Self-citationXu, W., Cai, H., Zhang, Q., Wang, Z., Yang, J., Wu, X., Li, C., Cui, C., Liu, C., Jin, H., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03736-x · Cited by 16 · see citing works
Endogenous YAP/TAZ partitioning in TEAD condensates orchestrates the Hippo response.
Zhu, T., Li, H., Mao, S., Zhu, Y., Cao, H.-Y., Zhang, Q., Zhen, N., Zhang, H.-X., Tian, Y., Guo, Y., et al. (2025) · Molecules and Cells · doi:10.1016/j.molcel.2025.08.014 · Cited by 19 · see citing works
The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data
Raffo, A., & Paulsen, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad302 · Cited by 15 · see citing works
Reorganization of 3D chromatin architecture in doxorubicin-resistant breast cancer cells
Wang, X., Yan, J., Ye, Z., Zhang, Z., Wang, S., Hao, S., Shen, B., & Wei, G. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.974750 · Cited by 15 · see citing works
Self-citationComparative study on chromatin loop callers using Hi-C data reveals their effectiveness
Chowdhury, H. M. A. M., Boult, T. E., & Oluwadare, O. (2023) · bioRxiv · doi:10.1186/s12859-024-05713-w · Cited by 12 · see citing works
Interaction of methyl-CpG-binding protein 2 (MeCP2) with distinct enhancers in the mouse cortex
Mishra, G., Sun, E. X., Chin, T., Eckhardt, M., Greenberg, M. E., & Stroud, H. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01808-y · Cited by 13 · see citing works
A comprehensive review of bioinformatics tools for chromatin loop calling
Liu, L., Han, K.-Y., Sun, H., Han, L., Gao, D., Xi, Q., Zhang, L.-R., & Lin, H. (2023) · Briefings Bioinform. · doi:10.1093/bib/bbad072 · Cited by 12 · see citing works
Alinejad‐Rokny, H., Modegh, R. G., Rabiee, H. R., Sarbandi, E. R., Rezaie, N., Tam, K. T., & Forrest, A. R. R. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010241 · Cited by 19 · see citing works
GILoop: Robust chromatin loop calling across multiple sequencing depths on Hi-C data
Wang, F., Gao, T., Lin, J., Zheng, Z., Huang, L., Toseef, M., Li, X., & Wong, K. (2022) · iScience · doi:10.1016/j.isci.2022.105535 · Cited by 14 · see citing works
Wei, X., Xu, Y.-Y., Yang, D.-C., Kim, K., Yi, L., Luo, W.-Z., Lin, X., Xiang, Y., Williams, A. B., Wang, X.-T., et al. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03013-7 · Cited by 16 · see citing works
Self-citationDose-dependent sensitivity of human 3D chromatin to a heart disease-linked transcription factorPreprint
Grant, Z. L., Kuang, S., Zhang, S., Horrillo, A. J., Rao, K. S., Kameswaran, V., Joubran, C., Lau, P. K., Dong, K., Yang, B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.09.632202 · Cited by 13 · see citing works
Yu, C., Liu, T., Wang, Y., Guo, X., Chen, Y., Zhao, Y., Liu, X., Huang, W., Zhao, S., Mo, J., et al. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02468-5 · Cited by 14 · see citing works
Self-citationWang, X., & Yue, F. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad389 · Cited by 12 · see citing works
Self-citationZhou, T., Zhang, R., Jia, D., Doty, R. T., Munday, A. D., Gao, D., Xin, L., Abkowitz, J. L., Duan, Z., & Ma, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.20.549578 · Cited by 12 · see citing works
Wu, H., Liu, M., Zhang, P., & Zhang, H. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac057 · Cited by 11 · see citing works
Wu, X., Xiong, D., Liu, R., Lai, X., Tian, Y., Xie, Z., Chen, L., Hu, L., Duan, J., Gao, X., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58275-7 · Cited by 11 · see citing works
Genome structure mapping with high-resolution 3D genomics and deep learningPreprint
Hong, C. K., Feng, F., Ramanathan, V., Liu, J., & Hansen, A. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.06.650874 · Cited by 10 · see citing works
Fino, J., Marques, B., Dong, Z., & David, D. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.757170 · Cited by 9 · see citing works
Shen, Y., Zhong, Q., Tian, L., Wen, Z., Shen, W., & Li, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab602 · Cited by 10 · see citing works
Liu, E., Lyu, H., Peng, Q., Liu, Y., Wang, T., & Han, J. (2022) · Communications Biology · doi:10.1038/s42003-022-03546-y · Cited by 10 · see citing works
DeepChIA-PET: Accurately predicting ChIA-PET from Hi-C and ChIP-seq with deep dilated networks
Liu, T., & Wang, Z. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011307 · Cited by 10 · see citing works
Pioneer factor GATA6 promotes colorectal cancer through 3D genome regulation
Lyu, H., Chen, X., Cheng, Y., Zhang, T., Wang, P., Wong, J., Wang, J., Stasiak, L., Sun, L., Yang, G., et al. (2025) · Science Advances · doi:10.1126/sciadv.ads4985 · Cited by 8 · see citing works
Self-citationThakur, R., Xu, M., Sowards, H., Yon, J., Jessop, L., Myers, T., Zhang, T., Chari, R., Long, E., Rehling, T., et al. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.04.015 · Cited by 9 · see citing works
Metelova, M., Vigh, M. L., & Krietenstein, N. (2025) · Nature Communications · doi:10.1038/s41467-025-64350-w · Cited by 9 · see citing works
Yu, S., Fu, Y., Wong, J. H.-Y., Wang, J., Zhao, H., Zhao, J., & Yue, F. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1109 · Cited by 9 · see citing works
Self-citationBenton, M. L., Ruderfer, D. M., & Capra, J. A. (2023) · Genome Biology and Evolution · doi:10.1093/gbe/evad126 · Cited by 9 · see citing works
A Lightweight Framework For Chromatin Loop Detection at the Single‐Cell Level
Wang, F., Alinejad‐Rokny, H., Lin, J., Gao, T., Chen, X., Zheng, Z., Meng, L., Li, X., & Wong, K. (2023) · Advanced Science · doi:10.1002/advs.202303502 · Cited by 9 · see citing works
Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model
Fang, T., Liu, Y., Woicik, A., Lu, M., Jha, A., Wang, X., Li, G., Hristov, B. H., Liu, Z., Xu, H., et al. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae211 · Cited by 8 · see citing works
Self-citationRecipes and ingredients for deep learning models of 3D genome folding
Smaruj, P. N., Xiao, Y., & Fudenberg, G. (2025) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2024.102308 · Cited by 8 · see citing works
Inferring CTCF-binding patterns and anchored loops across human tissues and cell types
Xu, H., Yi, X., Fan, X., Wu, C., Wang, W., Chu, X., Zhang, S., Dong, X., Wang, Z., Wang, J., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100798 · Cited by 8 · see citing works
CD-Loop: a chromatin loop detection method based on the diffusion model
Shen, J., Wang, Y., & Luo, J. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1393406 · Cited by 8 · see citing works
Interrogating cell type-specific cooperation of transcriptional regulators in 3D chromatin
Yi, X., Zheng, Z., Xu, H., Zhou, Y., Huang, D., Wang, J., Feng, X., Zhao, K., Fan, X., Zhang, S., et al. (2021) · iScience · doi:10.1016/j.isci.2021.103468 · Cited by 8 · see citing works
The nuclear matrix stabilizes primed-specific genes in human pluripotent stem cells
Ma, G., Fu, X., Zhou, L. B., Babarinde, I. A., Shi, L., Yang, W., Chen, J., Xiao, Z., Qiao, Y., Ma, L., et al. (2025) · Nature Cell Biology · doi:10.1038/s41556-024-01595-5 · Cited by 9 · see citing works
Genome-Wide Analysis of Rare Haplotypes Associated with Breast Cancer Risk
Wang, F., Moon, W., Letsou, W., Sapkota, Y., Wang, Z., Im, C., Baedke, J. L., Robison, L. L., & Yasui, Y. (2022) · Cancer Research · doi:10.1158/0008-5472.can-22-1888 · Cited by 7 · see citing works
Wang, J., Cheng, K.-K., Yan, C.-K., Luo, H., & Luo, J.-W. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06092-6 · Cited by 6 · see citing works
The contribution of silencer variants to human diseases
Huang, D., & Ovcharenko, I. (2024) · Genome biology · doi:10.1186/s13059-024-03328-1 · Cited by 6 · see citing works
Be-1DCNN: a neural network model for chromatin loop prediction based on bagging ensemble learning
Wu, H., Zhou, B., Zhou, H., Zhang, P., & Wang, M. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elad015 · Cited by 6 · see citing works
EPIraction - an atlas of candidate enhancer-gene interactions in human tissues and cell linesPreprint
Nurtdinov, R., & Guigó, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.18.638885 · Cited by 6 · see citing works
A Bioconductor/R Workflow for the Detection and Visualization of Differential Chromatin Loops
Flores, J. P., Davis, E. S., Kramer, N., Love, M. I., & Phanstiel, D. H. (2024) · F1000Research · doi:10.12688/f1000research.153949.1 · Cited by 5 · see citing works
Fleck, K., Luria, V., Garag, N., Karger, A., Hunter, T., Marten, D., Phu, W., Nam, K.-M., Šestan, N., O’Donnell‐Luria, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.17.585403 · Cited by 5 · see citing works
CGLoop: a neural network framework for chromatin loop prediction
Wang, J., Wu, L., Wei, J., Yan, C., Luo, H., Luo, J., & Guo, F. (2025) · BMC Genomics · doi:10.1186/s12864-025-11531-y · Cited by 4 · see citing works
Maiti, A. K. (2024) · Immunogenetics · doi:10.1007/s00251-024-01348-6 · Cited by 4 · see citing works
Engineering Genomic Landscapes: Synthetic Biology Approaches in Genomic Rearrangement
Abdi, G., Patil, N., Tendulkar, R., Dhariwal, R., Mishra, P., Tariq, M., Tarighat, M. A., Jain, M., & Mudgal, G. (2024) · Springer eBooks · doi:10.1007/978-981-97-3169-5_12 · Cited by 5 · see citing works
Unveiling Multi‐Scale Architectural Features in Single‐Cell Hi‐C Data Using scCAFE
Wang, F., Lin, J., Alinejad‐Rokny, H., Ma, W., Meng, L., Huang, L., Yu, J., Chen, N., Wang, Y., Yao, Z., et al. (2025) · Advanced Science · doi:10.1002/advs.202416432 · Cited by 4 · see citing works
Halder, A. K., Agarwal, A., Jodkowska, K., & Plewczyński, D. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae009 · Cited by 5 · see citing works
Spectral-based detection of chromatin loops in multiplexed super-resolution FISH data
Liefsoens, M., Földes, T., & Barbi, M. (2023) · bioRxiv · doi:10.1038/s41467-024-51650-w · Cited by 4 · see citing works
Yang, H., Zhang, H., Luan, Y., Liu, T., Roberts, K. G., Qian, M., Zhang, B., Yang, W., Pérez-Andreu, V., Xu, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.23.961672 · Cited by 4 · see citing works
Self-citationDehkordi, S. R., Wong, I. T.-L., Ni, J., Luebeck, J., Zhu, K., Prasad, G., Krockenberger, L., Xu, G., Chowdhury, B., Rajkumar, U., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.12.571349 · Cited by 4 · see citing works
Self-citationSuper-silencers are crucial for development and carcinogenesis in B cells
Huang, D., Petrykowska, H. M., Kumar, D., Kardava, L., Moir, S. L., Afzali, B., Elnitski, L. L., & Ovcharenko, I. (2025) · Nature Communications · doi:10.1038/s41467-025-63329-x · Cited by 3 · see citing works
3D genetic architecture of schizophrenia risk across three neuronal subtypes
Powell, S. K., Liao, W., Ghorbani, S., Rigat, R., O’Shea, C. P., Kammourh, S., Elahi, R., Infante, D., Deans, P. M., Le, D. J., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03352-y · Cited by 4 · see citing works
Grant, Z. L., Kuang, S., Zhang, S., Horrillo, A., Chen, Z., Rao, K. S., Celen, C., Kameswaran, V., Joubran, C., Lau, P. K., et al. (2026) · Science · doi:10.1126/science.adv5434 · Cited by 3 · see citing works
Jajoo, A., Hirschi, O., Schulze, K., Guan, Y., & Hanchard, N. A. (2023) · Research Square · doi:10.21203/rs.3.rs-2852818/v1 · Cited by 3 · see citing works
A fast and adaptive detection framework for genome-wide chromatin loop mapping from Hi-C dataPreprint
Chen, S., Wang, J., Jung, I., Qiu, Z., Gao, X., & Li, Y. (2024) · Genome Research · doi:10.1101/gr.279274.124 · Cited by 3 · see citing works
Supervised Chromatin Loop Detection Using Peakachu Version 2
Wang, X. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4136-1_11 · Cited by 2 · see citing works
Self-citationUnraveling the three-dimensional genome structure using machine learning
Lee, J., Mo, H.-L., Ha, Y., Nam, D. Y., Lim, G., Park, J.-W., Park, S., Choi, W.-Y., Lee, H., & Rhee, J. (2025) · BMB Reports · doi:10.5483/bmbrep.2024-0020 · Cited by 2 · see citing works
Wei, J., Xue, Y., & Gao, Y. Q. (2025) · Advanced Science · doi:10.1002/advs.202504799 · Cited by 2 · see citing works
- Authors
- Salameh, T. J., Wang, X., Song, F., Zhang, B., Wright, S. M., Khunsriraksakul, C., Ruan, Y., & Yue, F.
- Affiliations
- Pennsylvania State University (US)
- Northwestern University (US)
- The Jackson Laboratory for Genomic Medicine (US)
- Corresponding author
- Wang, Xiaotao
- Yue, Feng
- ORCID
- Venue
- Nature Communications
- Published
- 2020-07-09
- Volume
- 11
- Issue
- 1
- Pages
- 3428-3428
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7347923
- OpenAlex
- W3041895828
- Cited by
- 165
- References
- 57
- Citations by year
- 2026: 26 · 2025: 33 · 2024: 34 · 2023: 32 · 2022: 18 · 2021: 18 · 2020: 4
- FWCI
- 6.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (R01HG009906)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35GM124820)
- U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (R24DK106766)
- U.S. Department of Health & Human Services | NIH | NCI | Division of Cancer Epidemiology and Genetics, National Cancer Institute (U01CA200060)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Chromosomal and Genetic Variations
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Hi-C contact maps, random forest classification, 3D genome organization, gene expression regulation, ChIA-PET, HiChIP, Capture Hi-C
- MeSH terms
- Supervised Machine Learning, Chromatin, Humans, Organ Specificity, Species Specificity, Genome, Sequence Analysis, DNA, K562 Cells, Databases, Genetic
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