Single-cell Hi-C reveals cell-to-cell variability in chromosome structure

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

Topological domains in mammalian genomes identified by analysis of chromatin interactions

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Capturing Chromosome Conformation

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

Spatial partitioning of the regulatory landscape of the X-inactivation centre

Nora, E. P., Lajoie, B. R., Schulz, E. G., Giorgetti, L., Okamoto, I., Servant, N., Piolot, T., van Berkum, N. L., Meisig, J., Sedat, J., et al. (2012) · Nature · doi:10.1038/nature11049 · Cited by 2,895

Chromosome territories, nuclear architecture and gene regulation in mammalian cells

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Three-dimensional folding and functional organization principles of the Drosophila genome

Sexton, T., Yaffe, E., Kenigsberg, E., Bantignies, F., Leblanc, B., Hoichman, M., Parrinello, H., Tanay, A., & Cavalli, G. (2012) · Cell · doi:10.1016/j.cell.2012.01.010 · Cited by 1,983

Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture–on-chip (4C)

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Chromosome Conformation Capture Carbon Copy (5C): A massively parallel solution for mapping interactions between genomic elementsPreprint

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Beyond the Sequence: Cellular Organization of Genome Function

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Active genes dynamically colocalize to shared sites of ongoing transcription

Osborne, C. S., Chakalova, L., Brown, K. E., Carter, D., Horton, A., Debrand, E., Goyenechea, B., Mitchell, J. A., Lopes, S. M. C. D. S., Reik, W., et al. (2004) · Nature Genetics · doi:10.1038/ng1423 · Cited by 1,080

Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review)

Ren, L., Huang, D., Liu, H., Lin, N., Cai, P., Yu, X., Zhang, Y., Luo, N., Lin, H., Su, J., et al. (2024) · Oncology Letters · doi:10.3892/ol.2024.14285 · Cited by 28

Epigenetic regulation of cardiovascular diseases induced by behavioral and environmental risk factors: Mechanistic, diagnostic, and therapeutic insights

Bi, F., Gao, C., & Guo, H. (2024) · FASEB BioAdvances · doi:10.1096/fba.2024-00080 · Cited by 27

Plasticity in the transcriptional and epigenetic circuits regulating dendritic cell lineage specification and function

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Deconvolution of Ensemble Chromatin Interaction Data Reveals the Latent Mixing Structures in Cell Subpopulations

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Blank spots on the map: some current questions on nuclear organization and genome architecture

Adriaens, C., Serebryannyy, L., Feric, M., Schibler, A., Meaburn, K. J., Kubben, N., Trzaskoma, P., Shachar, S., Vidak, S., Finn, E. H., et al. (2018) · Histochemistry and Cell Biology · doi:10.1007/s00418-018-1726-1 · Cited by 30

Single-cell landscape of nuclear configuration and gene expression during stem cell differentiation and X inactivation

Bonora, G., Ramani, V., Singh, R., Fang, H., Jackson, D. L., Srivatsan, S. R., Qiu, R., Lee, C., Trapnell, C., Shendure, J., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02432-w · Cited by 26

Reconstruct high-resolution 3D genome structures for diverse cell-types using FLAMINGO

Wang, H., Yang, J., Zhang, Y., Qian, J., & Wang, J. (2022) · Nature Communications · doi:10.1038/s41467-022-30270-2 · Cited by 28

Single-Cell Analysis of the Transcriptome and Epigenome

Mazan-Mamczarz, K., Ha, J., De, S., & Sen, P. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1831-8_3 · Cited by 27

Differences in Nuclear DNA Organization Between Lymphocytes, Hodgkin and Reed–Sternberg Cells Revealed by Structured Illumination Microscopy

Righolt, C. H., Guffei, A., Knecht, H., Young, I. T., Stallinga, S., A, L. J. V. V., & Mai, S. (2014) · Journal of Cellular Biochemistry · doi:10.1002/jcb.24800 · Cited by 26

Structural aspects of the inactive X chromosome

Bonora, G., & Distèche, C. M. (2017) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2016.0357 · Cited by 26

Conformational state switching and pathways of chromosome dynamics in cell cycle

Chu, X., & Wang, J. (2020) · Applied Physics Reviews · doi:10.1063/5.0007316 · Cited by 24

Organization of nuclear architecture during adipocyte differentiation

Charó, N. L., Ceschan, M. I. R., Galigniana, N. M., Toneatto, J., & Piwien‐Pilipuk, G. (2016) · Nucleus · doi:10.1080/19491034.2016.1197442 · Cited by 27

DiffDomain enables identification of structurally reorganized topologically associating domains

Hua, D., Gu, M., Zhang, X., Du, Y., Xie, H., Qi, L., Du, X., Bai, Z., Zhu, X., & Tian, D. (2024) · Nature Communications · doi:10.1038/s41467-024-44782-6 · Cited by 24

Are we there yet? Exploring astrocyte heterogeneity one cell at a time

O’Dea, M. R., & Hasel, P. (2024) · Glia · doi:10.1002/glia.24621 · Cited by 27

Finding Friends in the Crowd: Three-Dimensional Cliques of Topological Genomic Domains

Collas, P., Ali, T. M. L., Brunet, A., & Germier, T. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00602 · Cited by 24

Advances in technologies for 3D genomics research

Zhang, Y., & Li, G. (2020) · Science China Life Sciences · doi:10.1007/s11427-019-1704-2 · Cited by 27

Si-C is a method for inferring super-resolution intact genome structure from single-cell Hi-C data

Meng, L., Wang, C., Shi, Y. Y., & Luo, Q. (2021) · Nature Communications · doi:10.1038/s41467-021-24662-z · Cited by 23

Noncoding loci without epigenomic signals can be essential for maintaining global chromatin organization and cell viability

Ding, B., Liu, Y., Liu, Z., Zheng, L., Xu, P., Zhao, C., Wu, P., Zhao, Y., Pan, Q., Guo, Y., et al. (2021) · Science Advances · doi:10.1126/sciadv.abi6020 · Cited by 22

Uncovering the statistical physics of 3D chromosomal organization using data-driven modeling

Contessoto, V. G., Cheng, R. R., & Onuchic, J. N. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102418 · Cited by 23

Life at the mesoscale: the self-organised cytoplasm and nucleoplasm

Sear, R. P., Pagonabarraga, I., & Flaus, A. (2015) · BMC Biophysics · doi:10.1186/s13628-015-0018-6 · Cited by 25

Impact of data resolution on three-dimensional structure inference methods

Park, J., & Lin, S. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0894-z · Cited by 25

One protein to rule them all: The role of CCCTC-binding factor in shaping human genome in health and disease

Łaźniewski, M., Dawson, W., Rusek, A. M., & Plewczyński, D. (2018) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2018.08.003 · Cited by 24

Diverse Genome Topologies Characterize Dosage Compensation across Species

Jordan, W. T., Rieder, L. E., & Larschan, E. (2019) · Trends in Genetics · doi:10.1016/j.tig.2019.02.001 · Cited by 23

Applications of single-cell technologies in drug discovery for tumor treatment

Liu, B., Hu, S., & Wang, X. (2024) · iScience · doi:10.1016/j.isci.2024.110486 · Cited by 24

3D Chromosomal Landscapes in Hematopoiesis and Immunity

Kloetgen, A., Thandapani, P., Tsirigos, A., & Aifantis, I. (2019) · Trends in Immunology · doi:10.1016/j.it.2019.07.003 · Cited by 25

Three-dimensional genome: developmental technologies and applications in precision medicine

Li, Y., Tao, T., Du, L., & Zhu, X. M. (2020) · Journal of Human Genetics · doi:10.1038/s10038-020-0737-7 · Cited by 24

Enhancing the plasticity, proper function and efficient use of energy of the Sun, genes and microtubules using variability

Ilan, Y. (2022) · Clinical and Translational Discovery · doi:10.1002/ctd2.103 · Cited by 24

Every gene everywhere all at once: High-precision measurement of 3D chromosome architecture with single-cell Hi-C

Chi, Y., Shi, J., Xing, D., & Tan, L. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.959688 · Cited by 25

SMC complexes link gene expression and genome architecture

Dowen, J. M., & Young, R. A. (2014) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2013.11.009 · Cited by 25

Reconstruction of 3D genome architecture via a two-stage algorithm

Segal, M. R., & Bengtsson, H. L. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0799-2 · Cited by 23

Three-dimensional regulation of transcription

Cao, J., Luo, Z., Cheng, Q., Xu, Q., Zhang, Y., Wang, F., Wu, Y., & Song, X. (2015) · Protein & Cell · doi:10.1007/s13238-015-0135-7 · Cited by 26

The Capacity to Act in Trans Varies Among Drosophila Enhancers

Blick, A. J., Mayer-Hirshfeld, I., Malibiran, B. R., Cooper, M. A., Martino, P. A., Johnson, J. E., & Bateman, J. R. (2016) · Genetics · doi:10.1534/genetics.115.185645 · Cited by 24

4D nucleomes in single cells: what can computational modeling reveal about spatial chromatin conformation?

Sekelja, M., Paulsen, J., & Collas, P. (2016) · Genome biology · doi:10.1186/s13059-016-0923-2 · Cited by 26

Nanog Expression in Embryonic Stem Cells – An Ideal Model System to Dissect Enhancer Function

Blinka, S. M., & Rao, S. (2017) · BioEssays · doi:10.1002/bies.201700086 · Cited by 24

CTCF, Cohesin, and Chromatin in Human Cancer

Song, S., & Kim, T. (2017) · Genomics & Informatics · doi:10.5808/gi.2017.15.4.114 · Cited by 28

Calculation of 3D genome structures for comparison of chromosome conformation capture experiments with microscopy: An evaluation of single-cell Hi-C protocols

Lando, D., Stevens, T. J., Basu, S., & Laue, E. D. (2018) · Nucleus · doi:10.1080/19491034.2018.1438799 · Cited by 25

Organization of the Pluripotent GenomePreprint

Lim, P. S. L., & Meshorer, E. (2020) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a040204 · Cited by 22

Nanoscale 3D DNA tracing in single human cells visualizes loop extrusion directly in situPreprint

Beckwith, K. S., Ødegård-Fougner, Ø., Morero, N. R., Barton, C., Schueder, F., Tang, W., Alexander, S., Peters, J., Jungmann, R., Birney, E., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.12.439407 · Cited by 24

Application of single-cell multi-omics approaches in horticulture research

Zhang, J., Ahmad, M., & Gao, H. (2023) · Molecular Horticulture · doi:10.1186/s43897-023-00067-y · Cited by 25

A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles

Wang, Y., Kong, S., Zhou, C., Zhang, Y., Zhang, Y., Fang, Y., & Li, G. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae651 · Cited by 25

The International Nucleome Consortium

Tashiro, S., & Lanctôt, C. (2015) · Nucleus · doi:10.1080/19491034.2015.1022703 · Cited by 23

On the demultiplexing of chromosome capture conformation data

Junier, I., Spill, Y. G., Martı́-Renom, M. A., Beato, M., & Dily, F. L. (2015) · FEBS Letters · doi:10.1016/j.febslet.2015.05.049 · Cited by 21

Reproducibility of 3D chromatin configuration reconstructions

Segal, M. R., Xiong, H., Capurso, D., Vázquez, M., & Arsuaga, J. (2014) · Biostatistics · doi:10.1093/biostatistics/kxu003 · Cited by 24

Functional genomic approaches to elucidate the role of enhancers during development

Ryan, G. E., & Farley, E. K. (2019) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1467 · Cited by 22

Use of Single-Cell -Omic Technologies to Study the Gastrointestinal Tract and Diseases, From Single Cell Identities to Patient Features

Islam, M., Chen, B., Spraggins, J. M., Kelly, R., & Lau, K. S. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.04.073 · Cited by 23

Non-backtracking walks reveal compartments in sparse chromatin interaction networks

Polovnikov, K., Gorsky, A., Nechaev, S., Razin, S. V., & Ulianov, S. V. (2020) · Scientific Reports · doi:10.1038/s41598-020-68182-0 · Cited by 25

Chrom-Lasso: a lasso regression-based model to detect functional interactions using Hi-C data

Lu, J., Wang, X., Sun, K., & Lan, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab181 · Cited by 24

Extracting multi-way chromatin contacts from Hi-C data

Liu, L., Zhang, B., & Hyeon, C. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009669 · Cited by 27

Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants

Zhong, W., Liu, W., Chen, J. C., Sun, Q., Hu, M., & Li, Y. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.957292 · Cited by 24

The 3D genome and its impacts on human health and disease

Wang, S., Luo, Z., Liu, W., Hu, T., Zhao, Z., Rosenfeld, M. G., & Song, X. (2023) · Life Medicine · doi:10.1093/lifemedi/lnad012 · Cited by 22

Live-cell imaging of chromatin contacts opens a new window into chromatin dynamics

van Staalduinen, J., van Staveren, T., Grosveld, F. G., & Wendt, K. S. (2023) · Epigenetics & Chromatin · doi:10.1186/s13072-023-00503-9 · Cited by 26

Large-scale probabilistic 3D organization of human chromosome territories

Sehgal, N., Fritz, A. J., Večeřová, J., Ding, H., Chen, Z., Stojkovic, B., Bhattacharya, S., Xu, J., & Berezney, R. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv479 · Cited by 22

Cellular identity at the single-cell level

Coskun, A. F., Eser, U., & Islam, S. (2016) · Molecular BioSystems · doi:10.1039/c6mb00388e · Cited by 24

Microscopic Chromosomal Structural and Dynamical Origin of Cell Differentiation and Reprogramming

Chu, X., & Wang, J. (2020) · Advanced Science · doi:10.1002/advs.202001572 · Cited by 21

Chromatin Dynamics in Lineage Commitment and Cellular Reprogramming

Shchuka, V. M., Malek-Gilani, N., Singh, G., Langroudi, L., Dhaliwal, N. K., Moorthy, S. D., Davidson, S., Macpherson, N. N., & Mitchell, J. A. (2015) · Genes · doi:10.3390/genes6030641 · Cited by 23

Perspectives in Super-Resolved Fluorescence Microscopy: What Comes Next?

Cremer, C. G., & Birk, U. J. (2016) · Frontiers in Physics · doi:10.3389/fphy.2016.00011 · Cited by 22

Quantitative differences in TAD border strength underly the TAD hierarchy in Drosophila chromosomes

Luzhin, A. V., Flyamer, I. M., Khrameeva, E. E., Ulianov, S. V., Razin, S. V., & Gavrilov, A. A. (2018) · Journal of Cellular Biochemistry · doi:10.1002/jcb.27737 · Cited by 22

Network Inference from Single-Cell Transcriptomic Data

Todorov, H., Cannoodt, R., Saelens, W., & Saeys, Y. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8882-2_10 · Cited by 24

From Loops to Looks: Transcription Factors and Chromatin Organization Shaping Terminal B Cell Differentiation

Azagra, A., Marina-Zárate, E., Ramiro, A. R., Javierre, B. M., & Parra, M. (2019) · Trends in Immunology · doi:10.1016/j.it.2019.11.006 · Cited by 25

A perspective on the molecular simulation of DNA from structural and functional aspects

Mondal, M., Yang, L., Cai, Z., Patra, P., & Gao, Y. Q. (2021) · Chemical Science · doi:10.1039/d0sc05329e · Cited by 22

A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data

Wang, Y., Lian, B., Zhang, H., Zhong, Y., He, J. Y., Wu, F., Reinert, K., Shang, X., Yang, H., & Hu, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad005 · Cited by 24

Applications of Single-Cell Sequencing for Multiomics

Xu, Y., & Zhou, X. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7717-8_19 · Cited by 22

5C-ID: Increased resolution Chromosome-Conformation-Capture-Carbon-Copy with in situ 3C and double alternating primer design

Kim, J. H., Titus, K. R., Gong, W., Beagan, J. A., Cao, Z., & Phillips‐Cremins, J. E. (2018) · Methods · doi:10.1016/j.ymeth.2018.05.005 · Cited by 21

Functional Consequences of Chromosomal Rearrangements on Gene Expression: Not So Deleterious After All?

Ghavi-Helm, Y. (2019) · Journal of Molecular Biology · doi:10.1016/j.jmb.2019.09.010 · Cited by 20

Protein/DNA interactions in complex DNA topologies: expect the unexpected

Noy, A., Sutthibutpong, T., & Harris, S. A. (2016) · Biophysical Reviews · doi:10.1007/s12551-016-0241-7 · Cited by 21

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

Wu, F., Olson, B. G., & Yao, J. (2016) · Journal of Visualized Experiments · doi:10.3791/53620 · Cited by 22

Uncovering the Principles of Genome Folding by 3D Chromatin ModelingPreprint

Yıldırım, A., Boninsegna, L., Zhan, Y., & Alber, F. (2021) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a039693 · Cited by 25

Recollections of a scientific journey published in human genetics: from chromosome territories to interphase cytogenetics and comparative genome hybridization

Cremer, T. J., Cremer, C. G., & Lichter, P. (2014) · Human Genetics · doi:10.1007/s00439-014-1425-5 · Cited by 22

Higher order assembly: folding the chromosome

Sewitz, S. A., Fahmi, Z., & Lipkow, K. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.02.004 · Cited by 22

The Dynamic 3D Genome in Gametogenesis and Early Embryonic Development

Li, F., An, Z., & Zhang, Z. (2019) · Cells · doi:10.3390/cells8080788 · Cited by 21

Retinoic Acid-Regulated Target Genes During Development: Integrative Genomics Analysis

Rochette‐Egly, C. (2020) · Sub-cellular biochemistry/Subcellular biochemistry · doi:10.1007/978-3-030-42282-0_3 · Cited by 21

Cancer systems immunology

Reticker-Flynn, N. E., & Engleman, E. G. (2020) · eLife · doi:10.7554/elife.53839 · Cited by 23

Deep generative modeling and clustering of single cell Hi-C data

Liu, Q., Zeng, W., Zhang, W., Wang, S., Chen, H., Jiang, R., Zhou, M., & Zhang, S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac494 · Cited by 21

Evidence for low nanocompaction of heterochromatin in living embryonic stem cells

Dupont, C., Chahar, D., Trullo, A., Gostan, T., Surcis, C., Grimaud, C., Fisher, D., Feil, R., & Llères, D. (2023) · The EMBO Journal · doi:10.15252/embj.2021110286 · Cited by 18

New discoveries in the field of metabolism by applying single-cell and spatial omics

Xie, B., Gao, D., Zhou, B., Chen, S., & Wang, L. (2023) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2023.06.002 · Cited by 21

Ising-model description of long-range correlations in DNA sequences

Colliva, A., Pellegrini, R., Testori, A., & Caselle, M. (2015) · Physical Review E · doi:10.1103/physreve.91.052703 · Cited by 21

From association to mechanism in complex disease genetics: the role of the 3D genome

Fu, Y., Tessneer, K. L., Li, C., & Gaffney, P. M. (2018) · Arthritis Research & Therapy · doi:10.1186/s13075-018-1721-x · Cited by 21

Bayesian inference of chromatin structure ensembles from population-averaged contact data

Carstens, S., Nilges, M., & Habeck, M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910364117 · Cited by 21

Robust and efficient single-cell Hi-C clustering with approximate k-nearest neighbor graphs

Wolff, J., Backofen, R., & Grüning, B. A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab394 · Cited by 22

Noncommutative Biology: Sequential Regulation of Complex Networks

Letsou, W., & Cai, L. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005089 · Cited by 21

A modern challenge of polymer physics: Novel ways to study, interpret, and reconstruct chromatin structure

Fiorillo, L., Bianco, S., Esposito, A., Conte, M., Sciarretta, R., Musella, F., & Chiariello, A. M. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1454 · Cited by 24

Chromatin arranges in chains of mesoscale domains with nanoscale functional topography independent of cohesinPreprint

Miron, E., Oldenkamp, R., Brown, J. M., Pinto, D. M. S., Xu, C. S., Faria, A. R., Shaban, H. A., Rhodes, J. D. P., Innocent, C., de Ornellas, S., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/566638 · Cited by 21

Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome dataPreprint

Uzun, Y., Wu, H., & Tan, K. (2020) · Genome Research · doi:10.1101/gr.267047.120 · Cited by 22

MomicPred: A Cell Cycle Prediction Framework Based on Dual-Branch Multi-Modal Feature Fusion for Single-Cell Multi-Omics Data

Shi, Z., Cong, L., & Wu, H. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3595904 · Cited by 22

A matter of scale: how emerging technologies are redefining our view of chromosome architecture

Cattoni, D. I., Valeri, A., Gall, A. L., & Nöllmann, M. (2015) · Trends in Genetics · doi:10.1016/j.tig.2015.05.011 · Cited by 20

Genetics and immunity in the era of single-cell genomics

Braga, F. A. V., Teichmann, S. A., & Chen, X. X. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw192 · Cited by 20

Exploring chromatin hierarchical organization via Markov State Modelling

Tan, Z. W., Guarnera, E., & Berezovsky, I. N. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006686 · Cited by 18

Hi-C implementation of genome structure for in silico models of radiation-induced DNA damage

Ingram, S., Henthorn, N. T., Warmenhoven, J.-W., Kirkby, N. F., Mackay, R. I., Kirkby, K. J., & Merchant, M. J. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008476 · Cited by 22

Chromatin Profiling Techniques: Exploring the Chromatin Environment and Its Contributions to Complex Traits

Chawla, A., Nagy, C., & Turecki, G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22147612 · Cited by 22

Perspectives for the reconstruction of 3D chromatin conformation using single cell Hi-C data

Kos, P. I., Galitsyna, A. A., Ulianov, S. V., Gelfand, M. S., Razin, S. V., & Chertovich, A. V. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009546 · Cited by 21

A Comparison of Topologically Associating Domain Callers Based on Hi-C Data

Liu, K., Li, H.-D., Li, Y., Wang, J., & Wang, J. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3147805 · Cited by 22

SnapHiC2: A computationally efficient loop caller for single cell Hi-C data

Li, X., Lee, L., Abnousi, A., Yu, M., Liu, W., Huang, L., Li, Y., & Hu, M. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.046 · Cited by 20

Integrating Micro and Nano Technologies for Cell Engineering and Analysis: Toward the Next Generation of Cell Therapy Workflows

Mukherjee, P., Park, S. H., Pathak, N., Patino, C. A., Bao, G., & Espinosa, H. D. (2022) · ACS Nano · doi:10.1021/acsnano.2c05494 · Cited by 20

Reassessment of weak parent-of-origin expression bias shows it rarely exists outside of known imprinted regions

Edwards, C., Watkinson, W. M. D., Telerman, S., Hülsmann, L. C., Hamilton, R., & Ferguson-Smith, A. (2023) · eLife · doi:10.7554/elife.83364 · Cited by 12

Three invariant Hi-C interaction patterns: Applications to genome assembly

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Single Cell Imaging of Nuclear Architecture Changes

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