The 4D nucleome project

An integrated encyclopedia of DNA elements in the human genome

The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072

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

Imaging Intracellular Fluorescent Proteins at Nanometer Resolution

Betzig, E., Patterson, G. H., Sougrat, R., Lindwasser, O. W., Olenych, S. G., Bonifacino, J. S., Davidson, M. W., Lippincott‐Schwartz, J., & Hess, H. F. (2006) · Science · doi:10.1126/science.1127344 · Cited by 8,941

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

Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)

Rust, M., Bates, M., & Zhuang, X. (2006) · Nature Methods · doi:10.1038/nmeth929 · Cited by 7,823

Topological domains in mammalian genomes identified by analysis of chromatin interactions

Dixon, J. R., Selvaraj, S., Yue, F., Kim, A., Li, Y., Shen, Y., Hu, M., Liu, J. S., & Ren, B. (2012) · Nature · doi:10.1038/nature11082 · Cited by 6,829

Integrative analysis of 111 reference human epigenomes

Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368

Systematic Localization of Common Disease-Associated Variation in Regulatory DNA

Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., et al. (2012) · Science · doi:10.1126/science.1222794 · Cited by 3,716

Ultra-High Resolution Imaging by Fluorescence Photoactivation Localization Microscopy

Hess, S. T., Girirajan, T. P. K., & Mason, M. D. (2006) · Biophysical Journal · doi:10.1529/biophysj.106.091116 · Cited by 3,684

Capturing Chromosome Conformation

Dekker, J., Rippe, K., Dekker, M., & Kleckner, N. (2002) · Science · doi:10.1126/science.1067799 · Cited by 3,652

Inference of the three-dimensional chromatin structure and its temporal behaviorPreprint

Cristescu, B.-C., Borsos, Z., Lygeros, J., Martínez, M. R., & Rapsomaniki, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.09619 · Cited by 7

L1 and B1 repeats blueprint the spatial organization of chromatinPreprint

Lu, J. Y., Chang, L., Li, T., Wang, T., Yin, Y., Zhan, G., Zhang, K., Percharde, M., Wang, L., Peng, Q., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/802173 · Cited by 7

Cell cycle dynamics of lamina associated DNAPreprint

van Schaik, T., Vos, M., Peric‐Hupkes, D., & van Steensel, B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.19.881979 · Cited by 7

Parkinson’s Disease Genetic Risk Evaluation in Microglia Highlights Autophagy and Lysosomal GenesPreprint

Booms, A., Pierce, S. E., & Coetzee, G. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.17.254276 · Cited by 7

Normalization and De-noising of Single-cell Hi-C Data with BandNorm and 3DVIPreprint

Zheng, Y., Shen, S., & Keleş, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.10.434870 · Cited by 7

3D chromatin structures associated with ncRNA roX2 for hyperactivation and coactivation across the entire X chromosome

Tian, S. Z., Yang, Y., Ning, D., Fang, K., Jing, K., Huang, G., Xu, Y., Yin, P., Huang, H., Chen, G., et al. (2024) · Science Advances · doi:10.1126/sciadv.ado5716 · Cited by 6

Structural basis of differential gene expression at eQTLs loci from high-resolution ensemble models of 3D single-cell chromatin conformations

Du, L., Farooq, H., Delafrouz, P., & Liang, J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf050 · Cited by 6

Tensor-FLAMINGO unravels the complexity of single-cell spatial architectures of genomes at high-resolution

Wang, H., Yang, J., Yu, X., Zhang, Y., Qian, J., & Wang, J. (2025) · Nature Communications · doi:10.1038/s41467-025-58674-w · Cited by 7

Foundation models in clinical oncology: Progresses and perspectives

Li, J., Fu, J., Geng, X., & Wang, H. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.218220 · Cited by 8

Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites

Chen, W., Zeng, Y. C., Achinger-Kawecka, J., Campbell, E. M., Jones, A. K., Stewart, A. G., Khoury, A., & Clark, S. J. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae530 · Cited by 8

Loss of structural specificity in 3D genome organization upon viral infection is predicted by polymer physics

Fontana, A., Bianco, S., Tafuri, F., Esposito, A., Abraham, A., Conte, M., Vercellone, F., Pierno, F. D., Kundu, S., Guha, S., et al. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0243454 · Cited by 6

Linking DNA-packing density distribution and TAD boundary locations

Meng, L., Sheong, F. K., & Luo, Q. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2418456122 · Cited by 6

Loop-closure kinetics reveal a stable, right-handed DNA intermediate in Cre recombination

Shoura, M. J., Giovan, S. M., Vetcher, A. A., Ziraldo, R., Hanke, A., & Levene, S. D. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa153 · Cited by 6

Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data

Miko, H., Qiu, Y., Gaertner, B., Sander, M., & Ohler, U. (2021) · BMC Genomics · doi:10.1186/s12864-021-07373-z · Cited by 6

Deciphering the Retinal Epigenome during Development, Disease and Reprogramming: Advancements, Challenges and Perspectives

Zibetti, C. (2022) · Cells · doi:10.3390/cells11050806 · Cited by 6

Dissecting the cosegregation probability from genome architecture mapping

Liu, L., Cao, X., Zhang, B., & Hyeon, C. (2022) · Biophysical Journal · doi:10.1016/j.bpj.2022.09.018 · Cited by 6

Exploring the Role of Enhancer-Mediated Transcriptional Regulation in Precision Biology

Wang, X., Liu, D., Luo, J., Kong, D., & Zhang, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241310843 · Cited by 7

Study of Cell-Type-Specific Chromatin Organization: In Situ Hi-C Library Preparation for Low-Input Plant Materials

Wang, N., & Liu, C. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0179-2_9 · Cited by 6

LILRB1 Intron 1 Has a Polymorphic Regulatory Region That Enhances Transcription in NK Cells and Recruits YY1

Yu, K., Davidson, C. E., & Burshtyn, D. N. (2020) · The Journal of Immunology · doi:10.4049/jimmunol.2000164 · Cited by 6

Estimation of three-dimensional chromatin morphology for nuclear classification and characterisation

Rana, P., Sowmya, A., Meijering, E. H. W., & Song, Y. (2021) · Scientific Reports · doi:10.1038/s41598-021-82985-9 · Cited by 6

Dynamics and Pathways of Chromosome Structural Organizations during Cell Transdifferentiation

Chu, X., & Wang, J. (2021) · JACS Au · doi:10.1021/jacsau.1c00416 · Cited by 6

Uncovering the Quantitative Relationships Among Chromosome Fluctuations, Epigenetics, and Gene Expressions of Transdifferentiation on Waddington Landscape

Chu, W., Chu, X., & Wang, J. (2022) · Advanced Science · doi:10.1002/advs.202103617 · Cited by 4

Spatially modulated illumination microscopy: application perspectives in nuclear nanostructure analysis

Cremer, C. G., & Birk, U. J. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0152 · Cited by 6

3DFAACTS-SNP: using regulatory T cell-specific epigenomics data to uncover candidate mechanisms of type 1 diabetes (T1D) risk

Liu, N., Sadlon, T. J., Wong, Y. Y., Pederson, S., Breen, J., & Barry, S. C. (2022) · Epigenetics & Chromatin · doi:10.1186/s13072-022-00456-5 · Cited by 6

Nuclear position modulates long-range chromatin interactions

Finn, E. H., & Misteli, T. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010451 · Cited by 6

Erratum: Corrigendum: The 4D nucleome project

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/nature24667 · Cited by 6

Micro-Meta App: an interactive software tool to facilitate the collection of microscopy metadata based on community-driven specificationsPreprint

Rigano, A., Ehmsen, S. R., Öztürk, S. U., Ryan, J., Balashov, A. K., Hammer, M., Kırlı, K., Bellve, K. D., Boehm, U., Brown, C. M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.31.446382 · Cited by 6

Population-based structure modeling reveals key roles of nuclear microenviroment in gene functionsPreprint

Yıldırım, A., Hua, N., Boninsegna, L., Polles, G., Gong, K. Q., Hao, S., Li, W., Zhou, X. J., & Alber, F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.11.451976 · Cited by 6

Widespread contribution of transposable elements to the rewiring of mammalian 3D genomes and gene regulationPreprint

Choudhary, M. N. K., Quaid, K., Xing, X., Schmidt, H. K., & Wang, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.01.475239 · Cited by 6

HiCHub: A Network-Based Approach to Identify Domains of Differential Interactions from 3D Genome DataPreprint

Li, X., Yuan, S., Zhu, S., Xue, H., & Peng, W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.16.488566 · Cited by 6

Two unrelated distal genes activated by a shared enhancer benefit from localizing inside the same small topological domainPreprint

Huang, Y., Verstegen, M. J. A. M., Tjalsma, S. J. D., Krijger, P. H. L., Gupta, K., Park, M., Boettiger, A. N., & de Laat, W. L. (2025) · Genes & Development · doi:10.1101/gad.352235.124 · Cited by 7

Bridging the variant-to-function gap in type 2 diabetes: advances and challenges

Maynard, A. G., Bhardwaj, R., Jones, T. R., & Claussnitzer, M. (2025) · Diabetologia · doi:10.1007/s00125-025-06600-6 · Cited by 7

Conformational Changes in Three-Dimensional Chromatin Structure in Paulownia fortunei After Phytoplasma Infection

Li, B., Lin, D., Zhai, X., Fan, G., Zhao, Z., Cao, X., Yang, H., Che, T., Yuan, Z., & Liu, T. (2021) · Phytopathology · doi:10.1094/phyto-01-21-0030-r · Cited by 5

TOAST: A novel method for identifying topologically associated domains based on graph auto-encoders and clustering

Gong, H., Zhang, D., & Zhang, X. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.09.019 · Cited by 5

A continuum of zinc finger transcription factor retention on native chromatin underlies dynamic genome organization

Hu, S., Liu, Y., Zhang, Q., Bai, J., & Xu, C. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00038-5 · Cited by 5

The CFDE Workbench: Integrating Metadata and Processed Data from Common Fund Programs

Evangelista, J. E., Clarke, D. J. B., Xie, Z., Olaiya, S., 김희수, Marino, G. B., Byrd, A. I., Srinivasan, S., Srinivasan, S., Maurya, M. R., et al. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169631 · Cited by 6

Genome Dashboards: Framework and Examples

Li, Z., Sun, R., & Bishop, T. C. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.02.018 · Cited by 5

The 3D organization of genome in the nucleus: from the nucleosome to the 4D nucleome

Li, G. (2020) · Science China Life Sciences · doi:10.1007/s11427-020-1723-y · Cited by 5

Dynamics of the host chromatin three-dimensional response to influenza virus infection

Shiimori, M., Nukiwa, R., & Imai, Y. (2021) · International Immunology · doi:10.1093/intimm/dxab043 · Cited by 5

Investigation of the Basic Steps in the Chromosome Conformation Capture Procedure

Bylino, O. V., Ibragimov, A. N., Pravednikova, A. E., & Shidlovskii, Y. V. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.733937 · Cited by 5

Hooked Up from a Distance: Charting Genome-Wide Long-Range Interaction Maps in Neural Cells Chromatin to Identify Novel Candidate Genes for Neurodevelopmental Disorders

Mercurio, S., Pozzolini, G., Baldi, R., Barilà, S. E., Pitasi, M., Catona, O., D’Aurizio, R., & Nicolis, S. K. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24021164 · Cited by 5

MINE is a method for detecting spatial density of regulatory chromatin interactions based on a multi-modal network

Gong, H., Li, M., Ji, M., Zhang, X., Yuan, Z., Zhang, S., Yang, Y., Li, C., & Chen, Y. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100386 · Cited by 5

The magic of unraveling genome architecture and function

Cosma, M. P., & Neguembor, M. V. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112361 · Cited by 7

HiCognition: a visual exploration and hypothesis testing tool for 3D genomics

Langer, C. C. H., Mitter, M., Stocsits, R. R., & Gerlich, D. W. (2023) · Genome biology · doi:10.1186/s13059-023-02996-9 · Cited by 3

HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM

Liu, T., & Wang, Z. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad263 · Cited by 6

SATB1, senescence and senescence‐related diseases

Qi, W., Bai, J., Wang, R., Zeng, X., & Zhang, L. (2024) · Journal of Cellular Physiology · doi:10.1002/jcp.31327 · Cited by 6

Misexpression of inactive genes in whole blood is associated with nearby rare structural variants

Vanderstichele, T., Burnham, K. L., de Klein, N., Tardáguila, M., Howell, B., Walter, K., Kundu, K., Koeppel, J., Lee, W., Tokolyi, A., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.06.017 · Cited by 5

Polymer Physics Models Reveal Structural Folding Features of Single-Molecule Gene Chromatin Conformations

Conte, M., Abraham, A., Esposito, A., Yang, L., Gibcus, J. H., Parsi, K. M., Vercellone, F., Fontana, A., Pierno, F. D., Dekker, J., et al. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms251810215 · Cited by 6

Evaluation of 3D Chromatin Interactions Using Hi-C

Hu, G. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0301-7_3 · Cited by 5

The corrected gene proximity map for analyzing the 3D genome organization using Hi-C data

Ye, C., Paccanaro, A., Gerstein, M., & Yan, K. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03545-y · Cited by 5

Suv4-20h2 protects against influenza virus infection by suppression of chromatin loop formation

Shiimori, M., Ichida, Y., Nukiwa, R., Sakuma, T., Abe, H., Kajitani, R., Fujino, Y., Kikuchi, A., Kawamura, T., Kodama, T., et al. (2021) · iScience · doi:10.1016/j.isci.2021.102660 · Cited by 5

Hi-C Analysis to Identify Genome-Wide Chromatin Structural Aberration in Cancer

Okabe, A., & Kaneda, A. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2433-3_15 · Cited by 5

Functional chromatin signatures premark future lineage-specific enhancers

Pulecio, J., Tayyebi, Z., Liu, D., Wong, W., Luo, R., Damodaran, J. R., Kaplan, S. J., Hu, N., Cho, H., Yan, J., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101189 · Cited by 5

Illuminating the structure and dynamics of chromatin by fluorescence labeling

Shao, S., Chang, L., Hou, Y., & Sun, Y. (2017) · Frontiers in Biology · doi:10.1007/s11515-017-1454-2 · Cited by 5

Meeting report – INDEPTH kick-off meeting

Parry, G., Probst, A. V., Baroux, C., & Tatout, C. (2018) · Journal of Cell Science · doi:10.1242/jcs.220558 · Cited by 5

Concert: Genome-wide prediction of sequence elements that modulate DNA replication timingPreprint

Yang, Y., Wang, Y., Zhang, Y., & Ma, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.21.488684 · Cited by 5

Genetically regulated eRNA expression predicts chromatin contact frequency and reveals genetic mechanisms at GWAS loci

Betti, M. J., Lin, P., Aldrich, M. C., & Gamazon, E. R. (2025) · Nature Communications · doi:10.1038/s41467-025-58023-x · Cited by 6

BRIGHT Enables High‐SNR Live‐Cell Imaging of Non‐Repetitive Sequences via Bivalent Fluorescent Nanobody‐Mediated Cascade‐Dependent Illumination

Feng, L., Huang, T., Han, Y., Meng, T., Wang, D., Mei, Z., Ma, Y., Ma, Y., Li, J., & Zhang, R. (2025) · Advanced Science · doi:10.1002/advs.202513014 · Cited by 4

EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles

Lin, J., Li, Z., Zhao, Y., Luo, R., & Pinello, L. (2026) · Nature Communications · doi:10.1038/s41467-026-70535-8 · Cited by 24

Tracking pairwise genomic loci by the ParB–ParS and Noc-NBS systems in living cells

He, X., Tan, Y., Feng, Y., Sun, Y., & Ma, H. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae134 · Cited by 5

Identifying topologically associating domains using differential kernels

Maisuradze, L., King, M. C., Surovtsev, I. V., Mochrie, S. G. J., Shattuck, M. D., & O’Hern, C. S. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012221 · Cited by 4

Transcribing RNA polymerases: Dynamics of twin supercoiled domains

Joyeux, M. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2024.10.002 · Cited by 4

Flexibility and rigidity index for chromosome packing, flexibility and dynamics analysis

Peng, J., Yang, J., Anand, D. V., Shang, X., & Xia, K. (2021) · Frontiers of Computer Science · doi:10.1007/s11704-021-0304-1 · Cited by 4

Visualizing the Nucleome Using the CRISPR–Cas9 System: From in vitro to in vivo

Maloshenok, L. G., Abushinova, G. A., Ryazanova, A. Y., Bruskin, S. A., & Zherdeva, V. V. (2023) · Biochemistry (Moscow) · doi:10.1134/s0006297923140080 · Cited by 4

Atlas of nascent RNA transcripts reveals tissue-specific enhancer to gene linkages

Sigauke, R. F., Sanford, L., Maas, Z. L., Jones, T., Stanley, J. T., Townsend, H. A., Allen, M. A., & Dowell, R. D. (2025) · BMC Genomics · doi:10.1186/s12864-025-11568-z · Cited by 9

Unfolding neural diversity: how dynamic three-dimensional genome architecture regulates brain function and disease

Logeman, B. L., Grieco, S. F., Holmes, T. C., & Xu, X. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03056-3 · Cited by 4

Computational Analysis of Hi-C Data

Forcato, M., & Bicciato, S. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0664-3_7 · Cited by 4

Chromophore‐Assisted Proximity Labeling of DNA Reveals Chromosomal Organization in Living Cells

Ding, T., Zhu, L., Fang, Y., Liu, Y., Tang, W., & Zou, P. (2020) · Angewandte Chemie · doi:10.1002/ange.202005486 · Cited by 4

Enhanced performance of gene expression predictive models with protein-mediated spatial chromatin interactions

Chiliński, M., Lipiński, J., Agarwal, A., Ruan, Y., & Plewczyński, D. (2023) · Scientific Reports · doi:10.1038/s41598-023-38865-5 · Cited by 4

DARIC, a Computational Framework for Identifying Quantitatively Differential Compartments from Hi-C Data

Yan, K., Liu, N., Orkin, S. H., & Yuan, G. (2022) · BMC Genomics · doi:10.1186/s12864-023-09675-w · Cited by 3

OpenNucleome for high-resolution nuclear structural and dynamical modeling

Lao, Z., Kamat, K., Jiang, Z., & Zhang, B. (2024) · eLife · doi:10.7554/elife.93223 · Cited by 12

A systematic analyses of different bioinformatics pipelines for genomic data and its impact on deep learning models for chromatin loop prediction

Halder, A. K., Agarwal, A., Jodkowska, K., & Plewczyński, D. (2024) · Briefings in Functional Genomics · doi:10.1093/bfgp/elae009 · Cited by 5

A mini-review of single-cell Hi-C embedding methods

Ma, R., Huang, J., Jiang, T., & Ma, W. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.11.002 · Cited by 6

Visual Pattern-Driven Exploration of Big Data

Behrisch, M., Krueger, R., Lekschas, F., Schreck, T., Gehlenborg, N., & Pfister, H. (2018) · Big Data Visual and Immersive Analytics · doi:10.1109/bdva.2018.8534028 · Cited by 4

5D superresolution imaging for a live cell nucleus

Chu, L., Chang, S., Tang, W., Tseng, Y., Chen, P., & Chen, B. (2020) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.11.005 · Cited by 5

Capturing cell type-specific chromatin structural patterns by applying topic modeling to single-cell Hi-C dataPreprint

Kim, H.-J., Yardımcı, G. G., Bonora, G., Ramani, V., Liu, J., Qiu, R., Lee, C., Hesson, J., Ware, C. B., Shendure, J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/534800 · Cited by 4

Mechanistic diversification of XIST regulatory network in mammalsPreprint

Rosspopoff, O., Huret, C., Collier, A. J., Casanova, M., Rugg‐Gunn, P. J., Ouimette, J., & Rougeulle, C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/689430 · Cited by 4

Cohesin depleted cells pass through mitosis and reconstitute a functional nuclear architecturePreprint

Cremer, M., Brandstetter, K., Maiser, A., Rao, S. S. P., Schmid, V. J., Mitra, N., Mamberti, S., Klein, K. N., Gilbert, D. M., Leonhardt, H., et al. (2019) · Open access LMU (Ludwid Maxmilian's Universitat Munchen) · doi:10.1101/816611 · Cited by 5

Capturing the complexity of topologically associating domains through multi-feature optimizationPreprint

Sauerwald, N., & Kingsford, C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.04.425264 · Cited by 4

dcHiC: differential compartment analysis of Hi-C datasetsPreprint

Chakraborty, A., Wang, J., & Ay, F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.02.429297 · Cited by 4

MethodsJ2: A Software Tool to Improve Microscopy Methods ReportingPreprint

Ryan, J., Pengo, T., Rigano, A., Llopis, P. D. L. M. M., Itano, M. S., Cameron, L., Marqués, G., Strambio‐De‐Castillia, C., Sanders, M. A., & Brown, C. M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.23.449674 · Cited by 4

Identification of Loci with High Transgene Expression in CHO Cells

Tasan, I., Eres, I. E., Wei, C., Bryant, E. E., Brisan, E., & Hubert, R. (2025) · ACS Synthetic Biology · doi:10.1021/acssynbio.4c00678 · Cited by 3

RobusTAD: reference panel based annotation of nested topologically associating domains

Zhang, Y., Dali, R., & Blanchette, M. (2025) · Genome biology · doi:10.1186/s13059-025-03568-9 · Cited by 3

Benchmarking peak calling methods for CUT&RUN

Nooranikhojasteh, A., Tavallaee, G., & Orouji, E. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf375 · Cited by 3

Geometrically Encoded Positioning of Introns, Intergenic Segments, and Exons in the Human Genome

Almassalha, L. M., MacQuarrie, K. L., Carignano, M. A., Dunton, C. L., Gong, R., Ibarra, J., Carter, L. M., Li, W. S., Nap, R. J., Dulai, P. S., et al. (2025) · Advanced Science · doi:10.1002/advs.202509964 · Cited by 4

Non-coding genome in nail-patella syndrome: Genetic diagnosis as a guide for personalized follow-up

Brunelle, P., Jourdain, A., Escande, F., Audebert‐Bellanger, S., Bouquillon, S., Cormier‐Daire, V., Desdoits, A., Lacombe, D., Molin, A., Gruchy, N., et al. (2026) · European Journal of Human Genetics · doi:10.1038/s41431-026-02062-5 · Cited by 4

X-chromosome inactivation draws L1 mutagenesis to the human X chromosome

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