Global reference mapping of human transcription factor footprints
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Abstract
Abstract Combinatorial binding of transcription factors to regulatory DNA underpins gene regulation in all organisms. Genetic variation in regulatory regions has been connected with diseases and diverse phenotypic traits 1 , yet it remains challenging to distinguish variants that impact regulatory function 2 . Genomic DNase I footprinting enables quantitative, nucleotide-resolution delineation of sites of transcription factor occupancy within native chromatin 3–5 . However, to date only a small fraction of such sites have been precisely resolved on the human genome sequence 5 . To enable comprehensive mapping of transcription factor footprints, we produced high-density DNase I cleavage maps from 243 human cell and tissue types and states and integrated these data to delineate at nucleotide resolution ~4.5 million compact genomic elements encoding transcription factor occupancy. We map the fine-scale structure of ~1.6 million DHS and show that the overwhelming majority is populated by well-spaced sites of single transcription factor:DNA interaction. Cell context-dependent cis-regulation is chiefly executed by wholesale actuation of accessibility at regulatory DNA versus by differential transcription factor occupancy within accessible elements. We show further that the well-described enrichment of disease- and phenotypic trait-associated genetic variants in regulatory regions 1,6 is almost entirely attributable to variants localizing within footprints, and that functional variants impacting transcription factor occupancy are nearly evenly partitioned between loss- and gain-of-function alleles. Unexpectedly, we find that the global density of human genetic variation is markedly increased within transcription factor footprints, revealing an unappreciated driver of cis-regulatory evolution. Our results provide a new framework for both global and nucleotide-precision analyses of gene regulatory mechanisms and functional genetic variation.
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In some cases, activity of enhancers with Gfi1 sites was correlated with expression of Gfi1b, with weak evidence for stronger correlations with other factors from the same motif family.
“In some cases, we found statistically weak evidence for stronger correlations with other factors from the same motif family,45 e.g., the activity of enhancers with Gfi1 sites was correlated with the expression of Gfi1b (Figure S3C).”
From Design principles of cell-state-specific enhancers in hematopoiesis (2025) · cites this paper as [45]
The ‘Assigning TFs to footprints’ and ‘Cell-selective TF occupancy landscapes’ sections (page not available) say that closely related transcription factors can share recognition sequences, supporting the same-motif-family premise. They do not report enhancer-activity correlations with factor expression or evidence that those correlations are stronger for other family members. The Gfi1/Gfi1b example is presented as the citing authors’ own finding, with a citation to their Figure S3C. (LLM, conf=0.76)
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“We next identified the TFs with the highest correlation between their gene expression and the chromatin-accessibility activity level of their putative DNA-binding motifs 43 to nominate TFs that directly drive accessibility changes.”
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“Interestingly, DNase I footprinting data from a related colon cancer cell line (RKO 82 ) suggest that the AP-1 instance with low importance was not bound endogenously, in contrast to the three important AP-1 instances (Fig.”
BackgroundEvolutionary constraint and innovation across hundreds of placental mammals
Christmas, M. J., Kaplow, I. M., Genereux, D. P., Dong, M. X., Hughes, G. M., Li, X., Sullivan, P. F., Hindle, A. G., Andrews, G., Armstrong, J. C., et al. (2023) · Science · doi:10.1126/science.abn3943 · Cited by 257 · see citing works
Functional inference of gene regulation using single-cell multi-omics
Kartha, V. K., Duarte, F. M., Hu, Y., Ma, S., Chew, J. G., Lareau, C. A., Earl, A., Burkett, Z. D., Kohlway, A. S., Lebofsky, R., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100166 · Cited by 224 · see citing works
“Higher-coverage multi-omics measurements may enable use of TF footprinting, 74,75 providing a direct measurement of TF-peak interactions.”
MethodHerring, C. A., Simmons, R. K., Freytag, S., Poppe, D., Moffet, J. J. D., Pflueger, J., Buckberry, S., Vargas-Landín, D. B., Clément, O., Goñi, E., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.039 · Cited by 226 · see citing works
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Chromatin accessibility profiling methods
Minnoye, L., Marinov, G. K., Krausgruber, T., Pan, L., Marand, A. P., Secchia, S., Greenleaf, W. J., Furlong, E. E. M., Zhao, K., Schmitz, R. J., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00008-9 · Cited by 206 · see citing works
Multiple causal variants underlie genetic associations in humans
Abell, N. S., DeGorter, M. K., Gloudemans, M. J., Greenwald, E., Smith, K. S., He, Z., & Montgomery, S. B. (2022) · Science · doi:10.1126/science.abj5117 · Cited by 186 · see citing works
scPerturb: harmonized single-cell perturbation data
Peidli, S., Green, T. D., Shen, C., Gross, T., Min, J., Garda, S., Yuan, B., Schumacher, L. J., Taylor-King, J. P., Marks, D. S., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02144-y · Cited by 194 · see citing works
“60; and marker-peaks per perturbation target, which quantifies the differential regulation of highly variable peaks for each type of perturbation.”
Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale
Zhou, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01065-4 · Cited by 177 · see citing works
Molecular Co-occupancy Identifies Transcription Factor Binding Cooperativity In Vivo
Sönmezer, C., Kleinendorst, R., Imanci, D., Barzaghi, G., Villacorta, L., Schübeler, D., Benes, V., Molina, N., & Krebs, A. R. (2021) · Molecular Cell · doi:10.1016/j.molcel.2020.11.015 · Cited by 162 · see citing works
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The ENCODE Uniform Analysis PipelinesPreprint
Hitz, B. C., Lee, J.-W., Jolanki, O., Kagda, M. S., Graham, K., Sud, P., Gabdank, I., Strattan, J. S., Sloan, C. A., Dreszer, T. R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.04.535623 · Cited by 149 · see citing works
“For details on the statistics of the footprinting algorithm see the Supplementary Methods of Vierstra et al. 37 Alignment and trimming metrics are calculated by samtools and cutadapt, while other utilities measure the extent of read duplication and fragment size distribution.”
MethodPampari, A., Shcherbina, A., Kvon, E. Z., Kosicki, M., Nair, S., Kundu, S., Kathiria, A. S., Risca, V. I., Kuningas, K., Alasoo, K., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.25.630221 · Cited by 161 · see citing works
Cardiac cell type–specific gene regulatory programs and disease risk association
Hocker, J. D., Poirion, O., Zhu, F., Buchanan, J., Zhang, K., Chiou, J., Wang, T., Zhang, Q., Hou, X., Li, Y. E., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf1444 · Cited by 131 · see citing works
Large-scale mapping and mutagenesis of human transcriptional effector domains
DelRosso, N., Tycko, J., Suzuki, P., Andrews, C., Aradhana, Mukund, A., Liongson, I., Ludwig, C., Spees, K., Fordyce, P., et al. (2023) · Nature · doi:10.1038/s41586-023-05906-y · Cited by 130 · see citing works
Core transcription programs controlling injury-induced neurodegeneration of retinal ganglion cells
Tian, F., Cheng, Y., Zhou, S., Wang, Q., Monavarfeshani, A., Gao, K., Jiang, W., Kawaguchi, R., Wang, Q., Tang, M., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.003 · Cited by 125 · see citing works
A foundation model of transcription across human cell types
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“As the availability of ATAC-seq and multiome data continues to grow, more comprehensive reference peak sets, such as the ENCODE DHS index 13 and cPeaks 65 , will further facilitate the adaptation of the GET model to an even broader range of cell types and experimental conditions.”
InfluentialMammalian evolution of human cis-regulatory elements and transcription factor binding sites
Andrews, G., Fan, K., Pratt, H. E., Phalke, N. N., Karlsson, E. K., Lindblad‐Toh, K., Gazal, S., Moore, J. E., Weng, Z., Andrews, G., et al. (2023) · Science · doi:10.1126/science.abn7930 · Cited by 117 · see citing works
Massively parallel discovery of human-specific substitutions that alter enhancer activity
Uebbing, S., Gockley, J., Reilly, S. K., Kocher, A. A., Geller, E. T., Gandotra, N., Scharfe, C., Cotney, J. L., & Noonan, J. P. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2007049118 · Cited by 111 · see citing works
“…corresponding factors in vivo, we repeated this analysis on TFBSs within regions of accessible chromatin in human neural stem cells and cortical organoids (assay for transposase-accessible chromatin using sequencing [ATAC-seq] data generated in this study and data from previous studies) (23, 26).”
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Identification of constrained sequence elements across 239 primate genomes
Kuderna, L. F. K., Ulirsch, J. C., Rashid, S., Ameen, M., Sundaram, L., Hickey, G., Cox, A. J., Gao, H., Kumar, A., Aguet, F., et al. (2023) · Nature · doi:10.1038/s41586-023-06798-8 · Cited by 103 · see citing works
“After stratifying human genes by selective constraint quantified by loss-of-function observed/ expected upper bound fraction (LOEUF) scores 38 , we found that variants affecting the expression of highly constrained genes tended to be enriched at more deeply constrained DHS elements and footprints…”
InfluentialSolid-phase capture and profiling of open chromatin by spatial ATAC
Llorens-Bobadilla, E., Zamboni, M., Marklund, M., Bhalla, N., Chen, X.-S., Hartman, J., Frisén, J., & Ståhl, P. L. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01603-9 · Cited by 94 · see citing works
Hold out the genome: a roadmap to solving the cis-regulatory code
de Boer, C. G., & Taipale, J. (2024) · Nature · doi:10.1038/s41586-023-06661-w · Cited by 107 · see citing works
UniBind: maps of high-confidence direct TF-DNA interactions across nine species
Puig, R. R., Boddie, P., Khan, A., Castro-Mondragon, J. A., & Mathelier, A. (2021) · BMC Genomics · doi:10.1186/s12864-021-07760-6 · Cited by 102 · see citing works
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Cooperative binding between distant transcription factors is a hallmark of active enhancers
Rao, S., Ahmad, K., & Ramachandran, S. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.02.014 · Cited by 82 · see citing works
BANP opens chromatin and activates CpG-island-regulated genes
Grand, R. S., Burger, L., Gräwe, C., Michael, A. K., Isbel, L., Heß, D., Hoerner, L., Iešmantavičius, V., Durdu, S., Pregnolato, M., et al. (2021) · Nature · doi:10.1038/s41586-021-03689-8 · Cited by 91 · see citing works
DNA-guided transcription factor cooperativity shapes face and limb mesenchyme
Kim, S., Morgunova, E. Y., Naqvi, S., Goovaerts, S., Bader, M., Koska, M., Popov, A. N., Luong, C., Pogson, A. N., Swigut, T., et al. (2024) · Cell · doi:10.1016/j.cell.2023.12.032 · Cited by 81 · see citing works
“102 Motifs clusters from https://www.vierstra.org/ resources/motif_clustering 127 were used, with one manually added cluster for the Coordinator motif, into which the TWIST1 motifs from HOCOMOCO were moved.”
MethodRUNX2 promotes fibrosis via an alveolar-to-pathological fibroblast transition
Fang, Y., Chung, S. S. W., Xu, L., Xue, C., Liu, X., Jiang, D., Li, R., Korogi, Y., Yuan, K., Saqi, A., et al. (2025) · Nature · doi:10.1038/s41586-024-08542-2 · Cited by 95 · see citing works
Spatially mapped single-cell chromatin accessibility
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Evolutionary cell type mapping with single-cell genomics
Tanay, A., & Sebé-Pedrós, A. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.04.008 · Cited by 75 · see citing works
Transposable elements as tissue-specific enhancers in cancers of endodermal lineage
Karttunen, K., Patel, D., Xia, J., Fei, L., Palin, K., Aaltonen, L., & Sahu, B. (2022) · bioRxiv · doi:10.1038/s41467-023-41081-4 · Cited by 66 · see citing works
The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation
Kim, D. S., Risca, V. I., Reynolds, D., Chappell, J., Rubin, A. J., Jung, N., Donohue, L., Lopez-Pajares, V., Kathiria, A. S., Shi, M.-Y., et al. (2020) · Nature Genetics · doi:10.1038/s41588-021-00947-3 · Cited by 79 · see citing works
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The functional and evolutionary impacts of human-specific deletions in conserved elements
Xue, J. R., Mackay-Smith, A., Mouri, K., García, M. F., Dong, M. X., Akers, J. F., Noble, M., Li, X., Lindblad‐Toh, K., Karlsson, E. K., et al. (2023) · Science · doi:10.1126/science.abn2253 · Cited by 74 · see citing works
“( 58 ).”
Deviatiiarov, R. M., Gams, A., Kulakovskiy, I. V., Buyan, A., Meshcheryakov, G., Syunyaev, R. A., Singh, R., Shah, P. M., Tatarinova, T. V., Gusev, O., et al. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00182-x · Cited by 74 · see citing works
Predicting expression-altering promoter mutations with deep learning
Jaganathan, K., Ferraro, N. M., Novakovsky, G., Wang, Y., James, T., Schwartzentruber, J., Fiziev, P. P., Kassam, I., Cao, F., Hawe, J. S., et al. (2025) · Science · doi:10.1126/science.ads7373 · Cited by 69 · see citing works
Huang, D., Zhou, Y., Yi, X., Fan, X., Wang, J., Yao, H., Sham, P. C., Hao, J., Chen, K., & Li, M. J. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab853 · Cited by 66 · see citing works
“We also incorporated allelic imbalance evidence of chromatin accessibility and TF binding from multiple studies (55,56).”
MethodSpeed–Specificity Trade-Offs in the Transcription Factors Search for Their Genomic Binding Sites
Jana, T., Brodsky, S., & Barkai, N. (2021) · Trends in Genetics · doi:10.1016/j.tig.2020.12.001 · Cited by 63 · see citing works
“Such indirect cooperationmay explain the increased tendency of some motifs to co-occur in the same promoters [114], the localization of multiple TFs to the same enhancer regions [115–118], and the prevalence of multiple, single-TF footprints within DNase-hypersensitive regions [18].”
BackgroundHansen, T. J., & Hodges, E. C. (2022) · Genome Research · doi:10.1101/gr.276766.122 · Cited by 69 · see citing works
“For this reason, we refer to TFs using their ENCODE-defined “archetypes,” which reflects the group of TFs that share the same motif (Vierstra et al. 2020).”
MethodHeRA: an atlas of enhancer RNAs across human tissues
Zhang, Z., Hong, W., Ruan, H., Jing, Y., Li, S., Liu, Y., Wang, J., Li, W., Diao, L., & Han, L. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa940 · Cited by 62 · see citing works
“For those TFs with low percentage evidences, we speculated that this is due to the limited number of ChIP-seq experiments in ENCODE, that the majority of them are examined in ≤ 5 tissues / cell lines (34).”
Chromatin accessibility during human first-trimester neurodevelopment
Mannens, C. C. A., Hu, L., Lönnerberg, P., Schipper, M., Reagor, C. C., Li, X., He, X., Barker, R. A., Sundström, E., Posthuma, D., et al. (2024) · Nature · doi:10.1038/s41586-024-07234-1 · Cited by 67 · see citing works
“Additionally, all TFs were assigned to a family on the basis of their arche-motif 21 .”
Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers
Sundaram, L., Kumar, A., Zatzman, M., Salcedo, A., Ravindra, N. G., Shams, S., Louie, B. H., Bagdatli, S. T., Myers, M. A., Sarmashghi, S., et al. (2024) · Science · doi:10.1126/science.adk9217 · Cited by 66 · see citing works
“Weusedthesemodelsto predictwhichsequence-basedarchetypalmo-tifinstances( 33 )werelikelytobeactivewithin accessible putative distal regulatory elements in individual BRCA samples.”
BackgroundResultInfluentialRegulatory transposable elements in the encyclopedia of DNA elements
Du, A. Y., Chobirko, J. D., Zhuo, X., Feschotte, C., & Wang, T. (2024) · Nature Communications · doi:10.1038/s41467-024-51921-6 · Cited by 68 · see citing works
“Enriched motifs were grouped according to motif archetypes 47 .”
Background“Stripe” transcription factors provide accessibility to co-binding partners in mammalian genomes
Zhao, Y., Vartak, S. V., Conte, A., Wang, X., Garcia, D. A., Stevens, E., Jung, S. K., Kieffer-Kwon, K.-R., Vian, L., Stodola, T. J., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.06.029 · Cited by 62 · see citing works
The correlation between CpG methylation and gene expression is driven by sequence variants
Stefánsson, Ó. A., Sigurpalsdottir, B. D., Rögnvaldsson, S., Halldorsson, G. H., Juliusson, K., Sveinbjörnsson, G., Gunnarsson, B., Beyter, D., Jónsson, H., Gudjonsson, S. A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01851-2 · Cited by 67 · see citing works
“DNA regulatory sequences, for example, promoters and enhancers, are frequently bound by transcription factors (TFs) 5,6 and depleted of CpG methylation 7,8 .”
Multiscale footprints reveal the organization of cis-regulatory elements
Yan, H., Horlbeck, M. A., Zhang, R., Ma, S., Shrestha, R., Kartha, V. K., Duarte, F. M., Hock, C., Savage, R. E., Labade, A. S., et al. (2025) · Nature · doi:10.1038/s41586-024-08443-4 · Cited by 80 · see citing works
Shah, K., Maradana, M. R., Delàs, M. J., Metidji, A., Graelmann, F. J., Llorian, M., Chakravarty, P., Li, Y., Tolaini, M., Shapiro, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29098-7 · Cited by 57 · see citing works
Single-nucleotide-level mapping of DNA regulatory elements that control fetal hemoglobin expression
Cheng, L., Li, Y., Qi, Q., Xu, P., Feng, R., Palmer, L. E., Chen, J., Wu, R., Yee, T., Zhang, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00861-8 · Cited by 56 · see citing works
Peña-Martínez, E. G., & Rodríguez‐Martínez, J. A. (2024) · Frontiers in Bioscience-Scholar · doi:10.31083/j.fbs1601004 · Cited by 61 · see citing works
“Enhancers are one of the most abundant CREs responsible for enhancing transcription and regulating the spatial and temporal expression of genes in a tissue-specific manner [14].”
BackgroundSeplyarskiy, V. B., Koch, E. M., Lee, D. J., Lichtman, J. S., Luan, H. H., & Sunyaev, S. R. (2023) · Nature Genetics · doi:10.1038/s41588-023-01562-0 · Cited by 51 · see citing works
“This analysis finds the following two such processes: transcription by polymerase III (Pol III) increases mutation rate by an order of magnitude and binding of transcription factor (TF) in testis increases T > G rate (~1.6-fold).”
BackgroundInfluentialXiao, F., Zhang, X., Morton, S. U., Kim, S. W., Fan, Y., Gorham, J., Zhang, H., Berkson, P., Mazumdar, N., Cao, Y., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01669-y · Cited by 56 · see citing works
“Enriched motifs were identified using Homer (v.4.11.1) 38 and a previously described nonredundant motif database 50 .”
InfluentialThe origin of human mutation in light of genomic data
Seplyarskiy, V. B., & Sunyaev, S. R. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00376-2 · Cited by 54 · see citing works
Mechanisms underlying divergent responses of genetically distinct macrophages to IL-4
Hoeksema, M. A., Shen, Z., Holtman, I. R., Zheng, A., Spann, N. J., Cobo, I., Gymrek, M., & Glass, C. K. (2021) · Science Advances · doi:10.1126/sciadv.abf9808 · Cited by 50 · see citing works
EWS-FLI1 regulates and cooperates with core regulatory circuitry in Ewing sarcoma
Shi, X., Zheng, Y., Jiang, L., Zhou, B. O., Yang, W., Li, L., Ding, L., Huang, M., Gery, S., Lin, D., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa901 · Cited by 54 · see citing works
“The spacing of TF binding sites can provide insights into the regulatory organization and cooperativity between TFs (38).”
BackgroundDenisovan introgression has shaped the immune system of present-day Papuans
Vespasiani, D. M., Jacobs, G. S., Cook, L. E., Brucato, N., Leavesley, M., Kinipi, C., Ricaut, F.-X., Cox, M. P., & Gallego Romero, I. (2022) · PLOS Genetics · doi:10.1371/journal.pgen.1010470 · Cited by 46 · see citing works
“[31], which clusters HOCOCOMO and Jaspar PWMs into 286 distinct clusters on the basis of sequence similarity, and considered only those a/naSNPs that disrupted PWMs included in these clusters (n = 3,028 Denisovan, 2,457 Neanderthal and 21,104 modern human variants).”
Delás, M., Kalaitzis, C. M., Fawzi, T., Demuth, M., Zhang, I., Stuart, H., Costantini, E., Ivanovitch, K., Tanaka, E. M., & Briscoe, J. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.11.016 · Cited by 31 · see citing works
“We used TOBIAS(33) with motifs from three databases, grouping motifs into published archetypes based on position weight matrix clustering of motifs.(37) The analysis revealed homeodomain (HD) and PAXmotifs among those most differentially scoring between pMN, p2, and p0-1 at day 5 and day 6 (Figure 3D).”
MethodLongitudinal immune dynamics of mild COVID-19 define signatures of recovery and persistencePreprint
Talla, A., Vasaikar, S. V., Lemos, M. P., Moodie, Z., Pebworth, M.-P., Henderson, K. E., Cohen, K. W., Czartoski, J., Lai, L., Suthar, M. S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.26.442666 · Cited by 45 · see citing works
“, 2009), ENCODE reference accessible regions (ENCODE file ID ENCFF503GCK; (Vierstra et al., 2020)), and TSS regions (TSS ±2kb from Ensembl v93; (Yates et al.”
BackgroundSMARCE1 deficiency generates a targetable mSWI/SNF dependency in clear cell meningioma
Pierre, R. S., Collings, C. K., Guerra, D. D. S., Widmer, C. J., Bolonduro, O., Mashtalir, N., Sankar, A., Liang, Y., Bi, W. L., Gerkes, E. H., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01077-0 · Cited by 45 · see citing works
Extrusion fountains are hallmarks of chromosome organization emerging upon zygotic genome activation
Galitsyna, A. A., Ulianov, S. V., Bazarevich, M., Bykov, N. S., Veil, M., Gao, M., Perevoschikova, K., Gelfand, M. S., Razin, S. V., Mirny, L. A., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69105-9 · Cited by 40 · see citing works
“…the site of facilitated cohesin loading as a 1-Kb region (one simulation monomer), consistent with recent characterizations of regulatory elements 49 and the average length of ATAC-seq peaks at fountain bases (606 bp) 41 , which corresponds to the narrow (~10 Kb) bases of the fountains we…”
Parallel functional testing identifies enhancers active in early postnatal mouse brain
Lambert, J. T., Su-Feher, L., Cichewicz, K., Warren, T. L., Zdilar, I., Wang, Y., Lim, K. J., Haigh, J., Morse, S. J., Canales, C. P., et al. (2021) · eLife · doi:10.7554/elife.69479 · Cited by 39 · see citing works
A GO catalogue of human DNA-binding transcription factors
Lovering, R. C., Gaudet, P., Acencio, M. L., Ignatchenko, A., Jolma, A., Fornés, O., Kuiper, M. T. R., Kulakovskiy, I. V., Lægreid, A., Martin, M. J., et al. (2021) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2021.194765 · Cited by 40 · see citing works
Single-molecule states link transcription factor binding to gene expression
Doughty, B. R., Hinks, M. M., Schaepe, J. M., Marinov, G. K., Thurm, A. R., Rios-Martinez, C., Parks, B. E., Tan, Y., Marklund, E., Dubocanin, D., et al. (2024) · Nature · doi:10.1038/s41586-024-08219-w · Cited by 42 · see citing works
Cao, Y.-P., Zhang, X.-R., Akerberg, B. N., Yuan, H., Sakamoto, T., Xiao, F., VanDusen, N. J., Zhou, P., Sweat, M., Wang, Y., et al. (2023) · Circulation · doi:10.1161/circulationaha.122.061955 · Cited by 35 · see citing works
Evolving cell states and oncogenic drivers during the progression of IDH-mutant gliomas
Wu, J., Castro, L. N. G., Battaglia, S., Farran, C. A. E., D’Antonio, J. P., Miller, T. E., Suvà, M. L., & Bernstein, B. (2024) · Nature Cancer · doi:10.1038/s43018-024-00865-3 · Cited by 41 · see citing works
“Motifs with similar sequences were collapsed into one motif through motif clustering 65 .”
Functional dissection of complex trait variants at single-nucleotide resolution
Siraj, L., Castro, R. I., Dewey, H. B., Kales, S., Butts, J. C., Nguyen, T. T. L., Kanai, M., Berenzy, D., Mouri, K., Wang, Q. S., et al. (2026) · Nature · doi:10.1038/s41586-026-10121-6 · Cited by 51 · see citing works
Self-citationAssigning function to SNPs: Considerations when interpreting genetic variation
Fadason, T., Farrow, S. L., Gokuladhas, S., Golovina, E., Nyaga, D. M., O'Sullivan, J. M., & Schierding, W. S. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.08.008 · Cited by 42 · see citing works
Constructing gene regulatory networks using epigenetic data
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“This intronic variant demonstrates significant allelic imbalance of open chromatin in CD14+ monocytes (32) and obtains a high genome-scale pathogenicity prediction score [e.”
BackgroundDe novo human brain enhancers created by single-nucleotide mutations
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BackgroundPioneer factors in development and cancer
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- Authors
- Vierstra, J., Lazar, J., Sandstrom, R., Halow, J., Lee, K., Bates, D., Diegel, M., Dunn, D., Neri, F., Haugen, E., Rynes, E., Reynolds, A., Nelson, J., Johnson, A., Frerker, M., Buckley, M., Kaul, R., Meuleman, W., & Stamatoyannopoulos, J. A.
- Affiliations
- Altius Institute for Biomedical Sciences (US)
- University of Washington (US)
- Corresponding author
- Vierstra, Jeff
- Stamatoyannopoulos, John A.
- ORCID
- Venue
- Nature
- Published
- 2020-07-29
- Volume
- 583
- Issue
- 7818
- Pages
- 729-736
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC7410829
- OpenAlex
- W3046196569
- Cited by
- 386
- References
- 62
- Citations by year
- 2026: 48 · 2025: 84 · 2024: 75 · 2023: 58 · 2022: 39 · 2021: 67 · 2020: 15
- FWCI
- 15.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- GlaxoSmithKline
- National Institutes of Health
- National Human Genome Research Institute (U54HG007010, UM1HG009444, R35HG011317)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- transcription factor footprints, DNase I footprinting, DNase I hypersensitive sites, transcription factor occupancy, chromatin accessibility, cis-regulatory evolution, loss-of-function alleles, gain-of-function alleles, gene expression regulation, functional genetic variation
- MeSH terms
- Deoxyribonuclease I, DNA, Genetics, Population, Humans, Models, Molecular, Regulatory Sequences, Nucleic Acid, Transcription Factors, Genome, Human, Consensus Sequence, DNA Footprinting, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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