Probing DNA shape and methylation state on a genomic scale with DNase I

DNA methylation patterns and epigenetic memory

Bird, A. (2002) · Genes & Development · doi:10.1101/gad.947102 · Cited by 7,124

Human DNA methylomes at base resolution show widespread epigenomic differences

Lister, R., Pelizzola, M., Dowen, R. H., Hawkins, R. D., Hon, G., Tonti-Filippini, J., Nery, J. R., Lee, L., Ye, Z., Ngo, Q.-M., et al. (2009) · Nature · doi:10.1038/nature08514 · Cited by 4,673

DNA methylation landscapes: Provocative insights from epigenomics

Suzuki, M. M., & Bird, A. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2341 · Cited by 3,039

Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome

Weber, M., Hellmann, I., Stadler, M. B., Ramos, L., Pääbo, S., Rebhan, M., & Schübeler, D. (2007) · Nature Genetics · doi:10.1038/ng1990 · Cited by 2,297

DNAase footprinting a simple method for the detection of protein-DNA binding specificity

Galas, D. J., & Schmitz, A. (1978) · Nucleic Acids Research · doi:10.1093/nar/5.9.3157 · Cited by 2,109

Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells

Lister, R., Pelizzola, M., Kida, Y. S., Hawkins, R. D., Nery, J. R., Hon, G. C., Antosiewicz‐Bourget, J., O’Malley, R. C., Castanon, R., Klugman, S. A., et al. (2011) · Nature · doi:10.1038/nature09798 · Cited by 1,595

DNA methylation profiling of human chromosomes 6, 20 and 22

Eckhardt, F., Lewin, J., Cortese, R., Rakyan, V. K., Attwood, J., Burger, M., Burton, J. H., Cox, T. V., Davies, R., Down, T. A., et al. (2006) · Nature Genetics · doi:10.1038/ng1909 · Cited by 1,378

The role of DNA shape in protein–DNA recognition

Rohs, R., West, S. M., Sosinsky, A., Liu, P., Mann, R. S., & Honig, B. (2009) · Nature · doi:10.1038/nature08473 · Cited by 1,096

Increased methylation variation in epigenetic domains across cancer types

Hansen, K. D., Timp, W., Bravo, H. C., Sabunciyan, S., Langmead, B., McDonald, O. G., Wen, B., Wu, H., Liu, Y., Diep, D. H., et al. (2011) · Nature Genetics · doi:10.1038/ng.865 · Cited by 1,062

Chromatin accessibility pre-determines glucocorticoid receptor binding patterns

John, S., Sabo, P. J., Thurman, R. E., Sung, M.-H., Biddie, S. C., Johnson, T. A., Hager, G. L., & Stamatoyannopoulos, J. A. (2011) · Nature Genetics · doi:10.1038/ng.759 · Cited by 954

Role of Shape Deformation of DNA-Binding Sites in Regulating the Efficiency and Specificity in Their Recognition by DNA-Binding Proteins

Sangeeta, Mishra, S. K., & Bhattacherjee, A. (2024) · JACS Au · doi:10.1021/jacsau.4c00393 · Cited by 12

Deciphering the dynamic code: DNA recognition by transcription factors in the ever-changing genome

Yao, Y. M., Miodownik, I., O’Hagan, M., Jbara, M., & Afek, A. (2024) · Transcription · doi:10.1080/21541264.2024.2379161 · Cited by 12

Unmethylated CpG motifs inToxoplasma gondiiDNA induce TLR9- and IFN-β-dependent expression ofα-defensin-5 in intestinal epithelial cells

Santamaría, M. H., Caballero, E. P., & Corral, R. S. (2015) · Parasitology · doi:10.1017/s0031182015001456 · Cited by 12

Discovering human transcription factor physical interactions with genetic variants, novel DNA motifs, and repetitive elements using enhanced yeast one-hybrid assaysPreprint

Shrestha, S., Sewell, J. A., Santoso, C., Forchielli, E., Pro, S. C., Martinez, M., & Bass, J. I. F. (2019) · Genome Research · doi:10.1101/gr.248823.119 · Cited by 12

STARRPeaker: Uniform processing and accurate identification of STARR-seq active regionsPreprint

Lee, D., Shi, M., Moran, J., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/694869 · Cited by 11

Topology- and size-dependent binding of DNA nanostructures to the DNase I

Xu, Y., Yan, Z., Ma, Y., & Ding, H. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.128703 · Cited by 9

Reading cytosine modifications within chromatin

Mahé, E. A., Madigou, T., & Salbert, G. (2018) · Transcription · doi:10.1080/21541264.2017.1406435 · Cited by 11

Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery

Jyoti, Ritu, R., Gupta, S., & Shankar, R. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e39140 · Cited by 9

DNA methylation trajectories during innate and adaptive immune responses of human B lymphocytes

Jhuang, K. F., Hsu, M. L., Chen, Y., Chang, J., & Zouali, M. (2023) · Immunology · doi:10.1111/imm.13632 · Cited by 7

Structural Features of DNA in tRNA Genes and Their Upstream Sequences

Savina, E. A., Shumilina, T. G., Porolo, V. A., Lebedev, G. S., Orlov, Y. L., Anashkina, A. A., & Il’icheva, I. A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252111758 · Cited by 7

Abnormal methylation in the NDUFA13 gene promoter of breast cancer cells breaks the cooperative DNA recognition by transcription factors

Hörberg, J., Hallbäck, B., Moreau, K., & Reymer, A. (2022) · QRB Discovery · doi:10.1017/qrd.2022.21 · Cited by 7

Decoding the conformation‐linked functional properties of nucleic acids by the use of computational tools

Iacovelli, F., & Falconi, M. (2015) · FEBS Journal · doi:10.1111/febs.13315 · Cited by 7

Analysis of nucleoid‐associated protein‐binding regions reveals DNA structural features influencing genome organization in Mycobacterium tuberculosis

Sarkar, S., Dey, U., Trust Boitumelo Khohliwe, Yella, V. R., & Kumar, A. A. (2021) · FEBS Letters · doi:10.1002/1873-3468.14178 · Cited by 7

Symphony of the DNA flexibility and sequence environment orchestrates p53 binding to its responsive elements

Vanaja, A., Mallick, S. P., Kulandaivelu, U., Kumar, A. A., & Yella, V. R. (2021) · Gene · doi:10.1016/j.gene.2021.145892 · Cited by 7

Deep Characterization of Paired Chromatin and Transcriptomes in Four Immune Cell Types from Multiple Sclerosis Patients

Fernandes, S. J., Ericsson, M., Khademi, M., Jagodic, M., Olsson, T. P., Gómez-Cabrero, D., Kockum, I. S., & Tegner, J. N. (2021) · Epigenomics · doi:10.2217/epi-2021-0205 · Cited by 7

Mechanistic Insights into Crossover-Dependent Stability and Exceptional Resistance of PX/JX Nanostructures to DNase I using Enhanced SamplingPreprint

Mandal, S., Chandrasekaran, A. R., & Maiti, P. K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.17.649409 · Cited by 7

DNA methylation shapes transcription factor binding beyond canonical CpG contexts

Miodownik, I., Solozabal, R., O’Hagan, M., Albeck, S., Peleg, Y., Takáč, M., & Afek, A. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2520814122 · Cited by 6

Effects of parental age and polymer composition on short tandem repeat de novo mutation rates

Goldberg, M. E., Noyes, M. D., Eichler, E. E., Quinlan, A. R., & Harris, K. (2024) · Genetics · doi:10.1093/genetics/iyae013 · Cited by 8

Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model

Batmanov, K., & Wang, J. (2017) · Genes · doi:10.3390/genes8090233 · Cited by 6

Core Promoter Regions of Antisense and Long Intergenic Non-Coding RNAs

Savina, E. A., Shumilina, T. G., Tumanyan, V. G., Anashkina, A. A., & Il’icheva, I. A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24098199 · Cited by 6

MeinteR: A framework to prioritize DNA methylation aberrations based on conformational and cis-regulatory element enrichment

Malousi, A., Kouidou, S., Tsagiopoulou, M., Papakonstantinou, N., Bouras, E., Georgiou, E., Tzimagiorgis, G., & Σταματόπουλος, Κ. (2019) · Scientific Reports · doi:10.1038/s41598-019-55453-8 · Cited by 6

Tight DNA-protein complexes isolated from barley seedlings are rich in potential guanine quadruplex sequences

Sjakste, T., Ļeonova, E., Petrovs, R., Ilva, T., Röder, M. S., & Sjakste, N. (2020) · PeerJ · doi:10.7717/peerj.8569 · Cited by 5

Unique and assay specific features of NOMe-, ATAC- and DNase I-seq dataPreprint

Nordström, K. J. V., Schmidt, F., Gasparoni, N., Salhab, A., Gasparoni, G., Kattler, K., Müller, F., Ebert, P., Costa, I. G., Pfeifer, N., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/547596 · Cited by 5

The Pattern and Evolution of Looped Gene Bendability

Dai, Z., Xiong, Y., & Dai, X. (2013) · Molecular Biology and Evolution · doi:10.1093/molbev/mst188 · Cited by 4

Intrinsic DNA topology as a prioritization metric in genomic fine-mapping studies

Ainsworth, H. C., Howard, T. D., & Langefeld, C. D. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa877 · Cited by 3

Maternal Prenatal Stress and the Developmental Origins of Mental Health

O’Donnell, K. J., & Glover, V. A. S. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801383-0.00007-4 · Cited by 4

Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding modelsPreprint

Grau, J., Schmidt, F., & Schulz, M. H. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.21.348193 · Cited by 4

DeepTFBS: Transkripsiyon Faktörü Bağlanma Bölgeleri Tahmini İçin Derin Öğrenme Yöntemleri Kullanan Hibrit Bir Model

Hatipoğlu, A., & Altuntaş, V. (2024) · Journal of Polytechnic · doi:10.2339/politeknik.1509329 · Cited by 3

H2A.Z facilitates Sox2-nucleosome interaction by promoting DNA and histone H3 tail mobility

Moos, H. K., Patel, R., Flaherty, S. K., Loverde, S. M., & Nikolova, E. N. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag371 · Cited by 3

Epigenetics in infectious disease

Kanyal, A., Nahata, S., & Karmodiya, K. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814259-2.00008-x · Cited by 2

Genome-wide transcription factor activities are explained by intrinsic conformational dynamics of binding-sites and distal flanking-regionsPreprint

Andrabi, M., Hutchins, A. P., Miranda‐Saavedra, D., Kono, H., Nussinov, R., Mizuguchi, K., & Ahmad, S. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/020602 · Cited by 2

MADS specificity : Unravelling the dual function of the MADS domain protein FRUITFULL

van Mourik, H. (2017) · doi:10.18174/421563 · Cited by 2

Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets via protocol-specific bias modelingPreprint

Calviello, A. K., Hirsekorn, A., Wurmus, R., Yusuf, D., & Ohler, U. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/284364 · Cited by 2

Correction of transposase sequence bias in ATAC-seq data with rule ensemble modelingPreprint

Wolpe, J. B., Martins, A. L., & Guertin, M. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.08.519600 · Cited by 2

Functional Characterization of Accessible Chromatin in Common Wheat

Zheng, D., Lin, K., Yang, X., Zhang, W., & Cheng, X. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25179384 · Cited by 2

ISDH ‐seq: a robust methodology for profiling and characterization of open chromatin

Li, Z., Run, Y., Yang, Y., Shi, Y., Tavakoli, M., Peng, Y., She, L., Борисович, Б. Г., & Zhang, W. (2025) · Plant Biotechnology Journal · doi:10.1111/pbi.70069 · Cited by 1

Mechanistic insights into crossover-dependent nuclease resistance of PX vs. dsDNA using enhanced sampling

Mandal, S., Chandrasekaran, A. R., & Maiti, P. K. (2026) · Nanoscale · doi:10.1039/d5nr04538j · Cited by 9

Constitutive activation of a hybrid two-component regulator reveals cross-regulation of polysaccharide utilization genes in Bacteroides

Gao, R., Sillick, B., Thomas, L. M., Wu, C., Wu, T., & Stock, A. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.113195 · Cited by 1

DNase-capture reveals differential transcription factor binding modalities

Kang, D. D., Sherwood, R. I., Barkal, A. A., Hashimoto, T., Engstrom, L., & Gifford, D. K. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187046 · Cited by 1

Discovering human transcription factor interactions with genetic variants, novel DNA motifs, and repetitive elements using enhanced yeast one-hybrid assaysPreprint

Shrestha, S., Sewell, J. A., Santoso, C., Forchielli, E., Pro, S. C., Martinez, M., & Bass, J. I. F. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/459305 · Cited by 1

NHLBI support of systems biology

Qasba, P., & Larkin, J. E. (2013) · Frontiers in Physiology · doi:10.3389/fphys.2013.00299

METHOD Open Access

Maretty, L., Sibbesen, J. A., & Krogh, A. (2014)

Exploiting Chromatin Biology to Understand Immunology

Johnson, J. L., & Vahedi, G. (2016) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2016.03.016

Effect of DNA methylation on the interaction with tetrahydrocortisol–apolipoprotein A-I complex

Kuznetsov, P. A., Зайцев, Б. Н., Гимаутдинова, О. И., Demchenko, E. N., & Panin, L. E. (2016) · Nanotechnologies in Russia · doi:10.1134/s1995078016030101

LR-DNase: Predicting TF binding from DNase-seq dataPreprint

van der Velde, A., Purcaro, M. J., Noble, W. S., & Weng, Z. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/082594

Processing and analysis of DNA methylation and statistical integration with genetic and clinical data

Pan, H. (2017) · doi:10.32657/10356/69957

Prediction of Transcription Factor Binding Sites Using Deep Learning Combined with DNA Sequences and Shape Feature Data

Li, Y., Liu, J., & Liu, H. (2021) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3469877.3497696

Methods for studying nucleic acid structure

Neidle, S., & Sanderson, M. R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819677-9.00008-1

Methylation in NDUFA13 gene promoter disrupts communication between collaborative transcription factors – potential mechanism for onset of breast cancerPreprint

Hörberg, J., Hallbäck, B., Kevin, M., & Reymer, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.01.494372

Recommendation: Abnormal methylation in the NDUFA13 gene promoter of breast cancer cells breaks the cooperative DNA recognition by transcription factors — R2/PR4

(2022) · QRB Discovery · doi:10.1017/qrd.2022.21.pr4

Recommendation: Abnormal methylation in the NDUFA13 gene promoter of breast cancer cells breaks the cooperative DNA recognition by transcription factors — R1/PR3

(2022) · QRB Discovery · doi:10.1017/qrd.2022.21.pr3

Recommendation: Abnormal methylation in the NDUFA13 gene promoter of breast cancer cells breaks the cooperative DNA recognition by transcription factors — R0/PR2

(2022) · QRB Discovery · doi:10.1017/qrd.2022.21.pr2

Additional file 4 of STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

Lee, D., Shi, M., Moran, J. R., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352114

Additional file 8 of STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

Lee, D., Shi, M., Moran, J. R., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352129

Additional file 7 of STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

Lee, D., Shi, M., Moran, J. R., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352126

Additional file 3 of STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

Lee, D., Shi, M., Moran, J. R., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352111

Additional file 5 of STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

Lee, D., Shi, M., Moran, J. R., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352120

Additional file 6 of STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

Lee, D., Shi, M., Moran, J. R., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352123

Additional file 9 of STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

Lee, D., Shi, M., Moran, J. R., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352132

Charting epimutation dynamics in human hematopoietic differentiation

Qin, X., Lu, J., Wu, P., Zhang, C., Shi, L., & Zhu, P. (2024) · Blood Science · doi:10.1097/bs9.0000000000000197

XL-DNase-Seq: Footprinting Analysis of Dynamic Transcription Factors

Oh, K., Aqdas, M., & Sung, M. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4071-5_15

Helix α1 of human ACE2 mimics dsDNA and Spike of SARS-CoV-2 binds DNA

Bangaru, M. L. Y., & Raghavendra, N. K. (2025) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2025.153020

Tracking Transcription Factors on the Genome by their DNase-seq Footprints

Yardımcı, G. G. (2014) · DukeSpace (Duke University)

Additional file 1 of STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

Lee, D., Shi, M., Moran, J., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352102

Additional file 10 of STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

Lee, D., Shi, M., Moran, J., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352099

Additional file 2 of STARRPeaker: uniform processing and accurate identification of STARR-seq active regions

Lee, D., Shi, M., Moran, J., Wall, M., Zhang, J., Liu, J. J., Fitzgerald, D., Kyono, Y., Ma, L., White, K. P., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13352105

Single-Molecule Visualization of DNase I-Mediated DNA Cleavage by High-Speed Atomic Force Microscopy

Elessawy, N., Sinan, S., Zhang, H., Yang, Z., Lu, Y., Finkelstein, I. J., & Lin, Y. (2026) · ACS Chemical Biology · doi:10.1021/acschembio.6c00195

Replication-uncoupled MET1/DNMT1 activity shapes inheritance of DNA methylation

Edera, A., Larue, M., Leduque, B., Michaud, C., Selles, B., Boutserin, S., Biocanin, I., Serret, J., Leblanc, O., Jullien, P. E., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117702

Structural Features of DNA in TATA-Containing and TATA-Less Core Promoters of RNA Polymerase II Differ

Savina, E. A., Soldatenkova, O. D., Anashkina, A. A., & Il’icheva, I. A. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27177735

103 Probing DNA shape and methylation state on a genomic scale with DNase I

Lazarovici, A., Zhou, T., Shafer, A., Machado, A. C. D., Sandstrom, R. S., Sabo, P. J., Lü, Y., Rohs, R., Stamatoyannopoulos, J. A., & Bussemaker, H. J. (2013) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2013.786345 · Cited by 5

Systematic prediction of DNA shape changes due to CpG methylation explains epigenetic effects on protein–DNA binding

Rao, S., Chiu, T.-P., Kribelbauer, J. F., Mann, R. S., Bussemaker, H. J., & Rohs, R. (2018) · Epigenetics & Chromatin · doi:10.1186/s13072-018-0174-4 · Cited by 97

MOESM9 of Systematic prediction of DNA shape changes due to CpG methylation explains epigenetic effects on proteinâ DNA binding

Rao, S., Chiu, T.-P., Kribelbauer, J., Mann, R., Bussemaker, H. J., & Rohs, R. (2018) · Figshare · doi:10.6084/m9.figshare.5862939

MOESM10 of Systematic prediction of DNA shape changes due to CpG methylation explains epigenetic effects on proteinâ DNA binding

Rao, S., Chiu, T.-P., Kribelbauer, J., Mann, R., Bussemaker, H. J., & Rohs, R. (2018) · Figshare · doi:10.6084/m9.figshare.5862819

MOESM1 of Systematic prediction of DNA shape changes due to CpG methylation explains epigenetic effects on proteinâ DNA binding

Rao, S., Chiu, T.-P., Kribelbauer, J., Mann, R., Bussemaker, H. J., & Rohs, R. (2018) · Figshare · doi:10.6084/m9.figshare.5862843