Cited by 394Genomics and Chromatin DynamicsGenetic Associations and EpidemiologyChromatin Remodeling and Cancer

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Global reference mapping of human transcription factor footprints

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Vierstra, Jeff, Lazar, John, Sandstrom, Richard, Halow, Jessica, Lee, Kristen, Bates, Daniel, Diegel, Morgan, Dunn, Douglas, Neri, Fidencio, Haugen, Eric, Rynes, Eric, Reynolds, Alex, Nelson, Jemma, Johnson, Audra, Frerker, Mark, Buckley, Michael, Kaul, Rajinder, Meuleman, Wouter, Stamatoyannopoulos, John A.·2020·Nature

DOI 10.1038/s41586-020-2528-xPMC PMC7410829

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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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Fig. 1 A nucleotide-resolution atlas of TF occupancy on the human genome. a, DNase I cleavage patterns (RELB locus in CD8+ T cells). Top, windowed DNase I cleavage density. Below, per-nucleotide cleavage and footprint posterior probabilities within two DHSs. b, Heat map of footprint posterior probabilities integrating 243 biosamples. Rows are individual biosamples grouped by tissue or organ systems; columns are individual nucleotides. Black fills to right of heat maps indicate overlapping DHSs in biosample. Below, DHS sequence scaled by footprint prevalence. Grey boxes, consensus footprints in one or more cell or tissue types (footprint posterior >0.99). c, Consensus map of TF occupancy derived from 243 biosamples covering 1.6 million DHSs providing expansive nucleotide-resolution annotation of regulatory DNA. d, Proportion of DHSs with footprints at given sequencing depth. Dashed red lines and dot show read depth (tags per 250 million uniquely mapped reads) at which footprint is detected in 90% of DHSs. e, Histogram of footprint location relative to DHS peak summit. Dashed red lines represent average size of a DHS peak (203 bp).CC BY · PubMed Central

Fig. 2 Footprints encapsulate topological structures of individual TF–DNA interactions. a, Structure of CTCF zinc fingers 3–11 bound to cognate DNA recognition sequence (Protein Data Bank (PDB) codes: 5YEF and 5YEL)21. DNA coloration shows mean observed versus expected cleavage at footprinted CTCF motifs (in T regulatory cells). b, Heat map of relative cleavage at each of 25,852 footprinted CTCF motifs (posterior probability >0.99). Below, aggregate (summed) nuclease cleavage relative to footprinted motifs. Right, nuclease cleavage (observed and expected) at three footprints randomly selected across genome. c, Footprint width is tightly correlated with the width of the TF recognition sequence (Spearman’s ρ = 0.9, P = 0.001).CC BY · PubMed Central

Fig. 3 Modes of TF occupancy within regulatory DNA. a, Overlap and spatial enrichment of TF recognition sequences within footprints binned by width. Left, density heat map of motif occurrences around footprints binned by width. Right, proportion of footprints uniquely overlapped by 0, 1 or 2 or more recognition sequences. b, Percentage of footprints representing occupancy of single TF (≤30 bp) or multiple TFs (>30 bp). c, d, Footprint density and spacing (edge-to-edge) plotted against mean normalized cleavage density (tags per 150 bp per million reads) within promoter-proximal and distal DHSs. Solid lines and shaded regions indicate median and middle 50th percentile, respectively. e, A typical DHS contains about five or six directly bound TFs spaced roughly 20 bp apart.CC BY · PubMed Central

Fig. 4 Functional genetic variation localizes in TF footprints. a, Allelic imbalance assessed at 1.65 million variants discovered from 147 unique individuals represented in 243 biosamples. b, Percentage of variants imbalanced in footprinted and non-footprinted segments of DHS peaks. c, Variant rs10171498 (C→G; C allele ancestral) creates a de novo NFIX footprint. Top, allelically resolved per-nucleotide DNase cleavage aggregated from 56 heterozygotes. Middle, DNase cleavage in two samples homozygous for reference or alternative alleles. Bottom, mean differential per-nucleotide cleavage (log2) between homozygous reference (n = 74) and alternative allele samples (n = 12). Colour indicates statistical significance (–log10 P) of per-nucleotide differential test (Methods). Variant and differentially footprinted nucleotides precisely colocalize at the NFIX element. d, Histogram of allelic ratios for variants that overlap the footprinted NFIX recognition sequence. Grey, all variants tested (n = 7,110). Blue, significantly imbalanced variants (n = 1,889). Prop., proportion. e, Scatter plot of allelic imbalance in heterozygous individuals (x-axis) against relative difference in footprint depth between homozygous individuals at variants overlapping an NFIX footprint. Each point shows an individual SNV within the footprinted NFIX binding site that is both imbalanced (q < 0.2) in heterozygotes and differentially footprinted (nominal P < 0.05) in homozygotes. Grey line, fitted linear model. f, Allelic imbalance versus predicted energetic effects of variants within NFIX footprints. Shown is median log-odds score (reference versus alternate allele) of all tested variants within footprinted motifs binned by allelic ratio. Error bars show 5th and 95th percentiles of log-odds motif scores in each bin.CC BY · PubMed Central

Fig. 5 Footprints are highly polymorphic in human populations. a, Histogram of mean phyloP scores within consensus footprints. Dashed red line, phyloP = 1. b, Nucleotide diversity (π) within footprints (mean ± 95% confidence interval of mean). Yellow bar, 95% confidence interval of mean π computed at fourfold degenerate coding sites. c, Mean nucleotide diversity versus distance from consensus footprints. Shaded bar represents average footprint width (14 bp). d, Density of observed and expected rare variation within footprints. Top, variants with minor allele frequencies (maf) <0.0001. Bottom, expected rare variation computed using 7-mer sequence context mutation rate model42.CC BY · PubMed Central

Fig. 6 Trait-associated variation is concentrated in consensus footprints. a, Enrichment of GWAS variants within or outside consensus footprints versus randomly sampled 1,000 Genomes Project (1KGP) variants, after expanding both with variants in perfect LD (r2 = 1.0, central European population). Centre lines, median; boxes, interquartile range (IQR); whiskers, 5th and 95th percentile (of enrichments from 1,000 sampling iterations). Statistical significance determined by a normal distribution fitted to sampled data (Supplementary Methods). b, c, Enrichment of SNP-based trait heritability using LD-score regression for UK BioBank (UKBB) GWAS on lymphocyte count (b) and red blood cell count (c). Pr(h2), proportion of trait narrow-sense heritability explained by SNPs overlapping DHSs or footprints. Pr(SNPs), proportion of total SNPs within each annotation. Asterisk, enrichment P < 0.01.CC BY · PubMed Central

Extended Data Fig. 1 Statistical modelling of DNase I cleavage variation and footprint detection within a single dataset. a, A negative binomial model was fit from the distribution of observed cleavage counts for each predicted cleavage rate. Shown are histograms of observed cleavage counts in CD19+ B cells at all genomic sites with 5, 25, or 60 expected cleavages. Red, the negative binomial distribution fit to the observed data using maximum likelihood estimation (Supplementary Methods). Blue, Poisson distribution with λ set to the corresponding expected cleavage rate. Lower right panel, means of fitted negative binomial distributions vs means of observed cleavage rates. Dashed grey line indicates y = x for reference. b, Estimated power of empirical cleavage dispersion model. Computed P values for different cleavage rate effect sizes with respect to expected cleavage rates in CD19+ B cells. Coloured lines represent the modelled effect size (depletion of cleavages) relative to the expected rate corresponding to a hexamer sequence model. c, Example of footprint detection within promoters for TMEM143 and SYNGR4 in CD19+ B cells. Expected cleavages were generated by reassigning observed cleavages according to a hexamer cleavage model (Supplementary Methods). The significance of difference between the observed and expected cleavages was evaluated per nucleotide using the negative binomial dispersion model. Individual P values are combined in 7-bp windows using Stouffer’s Z-score method. Per-nucleotide false discovery rates were computed by sampling from the expected null distributions. d, Autocorrelation of P values sampled from the expected negative binomial distribution. e, f, Histogram of windowed P values for observed (e) and sampled (f) data. g, Observed and sampled P values compared to empirically determine and calibrate false-positive rates (FPR).CC BY · PubMed Central

Extended Data Fig. 2 Genomic footprints are reproducible, overlap evolutionarily constrained nucleotides, and are enriched for TF recognition sequences. a, Motif density is associated with footprint strength. Plotted is the overlap of motif recognition sequence matches (P < 0.0001) with nucleotides ranked by footprint P value from CD19+ B cells. b, As in a, but for per-nucleotide evolutionary conservation (phyloP). c, Scatter plot of per-nucleotide footprint P values for replicate experiments from the same cell line (NAMALWA Burkitt’s lymphoma cells). All individual nucleotides within FDR 1% footprints in either replicate were considered for correlation analysis. d, As in c, but for replicates of the same primary cell (CD8+ T cells) between two distinct individuals. e, Pearson’s correlation between replicates pairs grouped by whether they were derived from the same cell and individual (n = 43) or were the same primary cell or tissue from different individuals (n = 111). Boxes indicate median and inner quartile-range (IQR). Whiskers, 5th and 95th percentile.CC BY · PubMed Central

Extended Data Fig. 3 Characteristics of consensus derived genomic footprints. a, Histogram of footprint discovery concordance (Jaccard similarity) between replicates (same cell type, same individual) using footprints called either independently (FDR 1%) (light orange) or with the empirical Bayes approach (posterior probability >0.99). b, Genomic distribution of consensus footprints stratified by DNase I signal of the encompassing DHS. c, Enrichment of footprint overlap in gene annotations vs. the distribution of DHSs. d, Effect of sequencing depth on footprint detection in CD8+ T cells. Sequencing tags were randomly subsampled (90% down to 10%) from the complete library. Footprints were called independently on each subsampled set of tags. Plotted is the number of FDR 1% footprints detected vs subsampled sequencing depth. Red dashed line, linear model fit. e, Same as d for CD4+ T cells. f, g, Contribution of individual samples to the consensus footprint index. Datasets were ordered randomly, and the collective number of footprints was computed after the footprints present within each additional dataset was considered. f, Mean total number of consensus footprints detected vs. number of datasets included after 100 iterations of random dataset orderings. Red dashed line shows a logarithmic curve fit (excluding first 50 samples). Grey dashed line indicates number of datasets that recapitulate 50% of consensus footprints. g, Mean number of novel footprints detected after the sequential addition of each sample.CC BY · PubMed Central

Extended Data Fig. 4 Clustering motifs by similarity. a, Outline of motif clustering approach. Motif models (n = 2,174) from ref. 11, JASPAR12 (2018), and HOCOMOCO13 were clustered by motif similarity. b, Hierarchically clustered heat map of the pairwise similarity scores between motifs. The cluster dendrogram was cut at height 0.7 to create non-redundant archetypal clusters of motifs. c–f, Exemplar clusters of similar TF recognition sequences corresponding to KLF/SP (C2H2 family), EGR (C2H2 family), MEF2 (MADS) and E-box/CATATG (bHLH).CC BY · PubMed Central

Extended Data Fig. 5 Classification of ChIP–seq data by genomic footprinting. a, Precision-recall (PR) curve for predictions of CTCF motif occupancy (that is, overlap CTCF ChIP–seq peak) based on footprint posterior probabilities in CD20+ B cells. Black dot indicates precision and recall at posterior footprint probability threshold of >0.99. Blue, PR curve computed after shuffling ChIP–seq peak labels. b, Area under precision-recall curve (AUPR) computed for 21 ENCODE cell types and/or replicates (n = 33 total datasets). c, Distribution of MOODS scores stratified by motif overlap with a ChIP–seq peak and/or a genomic footprint in CD20+ B cells. d, ChIP–seq signal intensity at peaks overlapping a footptrinted CTCF motif vs. non-footprinted motifs in CD20+ B cells. e, Relative ChIP-seq signal at footprinted and non-footprinted CTCF peaks containing a motif across 21 cell types (n = 32 total datasets). f–m, PR curves and relative ChIP–seq intensities for ATF3 (f, g), GATA1 (h, i), GABPA (j, k) and NFE2 (l, m) in K562 cells. For all TFs analysed, only motifs overlapping DHSs were considered. DHS, ChIP-seq and motif models are described in Supplementary Table 3. Boxes indicate median and IQR. Whiskers, 5th and 95th percentiles.CC BY · PubMed Central

Extended Data Fig. 6 Aggregate DNase I cleavage profiles for diverse TFs. a, Physical structure of the paired-box TF PAX6 bound to its cognate recognition element (PDB: 6PAX)24. b–e, Per-nucleotide DNase I cleavage patterns surrounding instances of motifs within genomic footprints for PAX6 (fetal eye) (b); EBF1 (differentiated bipolar neuron) (c); SOX3 (differentiated neuronal cell) (d); and MYF6 (fetal tongue) (e). For each, top left shows a randomly ordered heat map of the per-nucleotide relative DNase I cleavage protection (observed/expected) for each footprinted motif instance (posterior footprint probability >0.99). Below, aggregate DNase I protection averaged over all footprinted sites. Right, DNase I cleavage at individual motif instances (blue, observed cleavage; yellow, expected cleavage).CC BY · PubMed Central

Extended Data Fig. 7 Cell-selective occupancy of TF recognition sequences. a, Hierarchically clustered heat map of TF recognition sequence enrichment (–log10 q values; Supplementary Methods) overlapping consensus footprints. Rows correspond to motifs and columns correspond to individual samples. b, Clustered heat maps of posterior probabilities for footprints (left) overlapping an E-box/CAGCTG (MYF6_bHLH_1 motif model) and their corresponding DNase I density (right) in each sample. Rows and columns are ordered using K-means (k = 6) and hierarchical clustering, respectively. c, d, Same as b, for footprints overlapping an E-box/CATATG (c, Neurog1_MA0623.1 motif model) or MEIS (d, MEIS1_MEIS_1 motif model) recognition sequence.CC BY · PubMed Central

Extended Data Fig. 8 Comparative footprinting identifies cell-selective TF occupancy at nucleotide resolution. a, b, Comparative footprinting within the SCAMP5 (a) and UCP2 (b) promoters identifies footprints that are differentially occupied in nervous cell and tissue types. Top, DNase I cleavage in two exemplar nervous and non-nervous cell types. Bottom, mean differential per nucleotide cleavage (log2 observed/expected) between nervous system-derived (SCAMP5: n = 26; UCP2: n = 28 out of 31) and non-nervous samples (SCAMP5: n = 151; UCP2: n = 189 out of 212) in which region is DNase I hypersensitive (Supplementary Methods). The colour of each bar indicates the statistical significance (–log10 p) of the per-nucleotide differential test.CC BY · PubMed Central

Extended Data Fig. 9 Differentially occupied nucleotides reflect aggregate DNase I cleavage profiles. a, Differential footprint testing within thousands of accessible DHSs between nervous and non-nervous related biosamples. The vast majority of tested DHSs encode a single TF binding topology. Top, percentage of the DHSs tested that containing one or more differentially occupied element. Bottom left, distribution of differentially footprinted elements per DHS. Bottom right, selected TF recognition sequences significantly enriched in differentially occupied footprints (binomial test P < 0.01). Indicated in parenthesis is the fold-enrichment vs expected (based on prevalence of footprinted motif in tested regions). b, Density histograms of relative footprint occupancy between nervous-system derived and non-nervous-system derived samples for the TF recognition sequences of REST, NFIB, ZIC1 and EBF1. Grey indicates distribution of all motif instances tested. Black indicates differentially footprinted. c, Per-nucleotide aggregate plots of the mean relative DNase I protection (top) and differential test P value (–log10, bottom) around differential occupied motifs. d, Cell- and tissue-specific expression of genes nearby differentially occupied REST footprints. Enrichment was performed using Enrichr27. Shown are cell and tissues with an adjusted Fisher exact test P value <0.01.CC BY · PubMed Central

Extended Data Fig. 10 Detection of chromatin-altering variants. a, Scatter plot of allelic ratios at 100 randomly selected high-confidence SNVs (Supplementary Methods) computed after aggregating reads from different samples (x-axis) against the distribution of allelic ratios at the same SNVs in each sample (y-axis; mean ± s.d.). The average s.d. indicated in the top left corner was used to tune the parameters of a beta-binomial distribution. b, Simulation of allelic ratios from the observed total read depth at high-confidence SNVs assuming a binomial distribution (P = 0.5) or a beta-binomial distribution. Grey indicates the observed allelic ratios at the same variants. c, Density histogram of allelic ratios for all tested SNVs (grey line) and significantly imbalanced SNVs (blue line). d, Proportion of SNVs imbalanced with respect to read depth for variants within (blue) or outside (orange) consensus footprints (posterior probability >0.99).CC BY · PubMed Central

Extended Data Fig. 11 Enrichment of imbalanced variants within footprinted TF recognition sequences. a–d, Distribution of SNVs around the recognition sequences for CREB/ATF, NFI, TEAD and TFAP2 TFs. For each TF, shown are the total SNVs tested for imbalance (top), imbalanced variants (middle), and the proportion of variants imbalanced stratified overlap with a consensus footprint. e, log2 enrichment of imbalanced variants residing within TF recognition sequences relative to non-imbalanced SNVs for both variants within footprinted (blue) and non-footprinted motifs (orange). Motifs are grouped into clusters, where each point represents an individual motif model (Extended Data Fig. 4, Supplementary Table 2 and Supplementary Methods). Black bars indicate mean enrichment across all motifs in each cluster and footprint overlap. Only motifs with significant (q < 0.05) enrichment of imbalanced SNVs with a footprinted recognition sequence are shown.CC BY · PubMed Central

Extended Data Fig. 12 Allelic imbalance parallels the predicted energetic effect of genetic variation. a, Histogram of allelic ratios for variants overlapping footprinted CREB1 (CREB/ATF), TEAD1 (TEAD) and TFAP2C (TFAP2) recognition sequence. Grey line, all variants tested for imbalance. Blue line, all variants significantly imbalanced. b, Median log-odds score (reference versus alternate allele) of all tested variants within footprinted motifs binned by allelic ratio. Error bars show 5th and 95th percentiles of log-odds motif scores in each bin.CC BY · PubMed Central

Extended Data Fig. 13 Nucleotide-resolved patterns of genetic variation within TF binding sites. a–c, Per-nucleotide profiles of phyloP scores (top) and human nucleotide diversity (π) (bottom) within footprinted motifs for ETS1 (a), JDP2 (b), and CTCF (c) motifs. Black box in the motif consensus logo annotates CpG dinucleotides. Asterisk indicates the position of the CpG dinucleotide in the profiles below. d–f, Ancient and recent constraints at the TF recognition sequences with respect to proximity to DHSs and consensus footprints. TF recognition sequences are grouped by those residing within ±5 kb of DHS peaks but not inside (outside DHS), inside DHSs but not a footprint (outside footprint) and those overlapping a consensus footprint. Top, percentage of TF recognition sequence under elevated evolutionary constraint (mean phyloP score >1) in each group. Bottom, mean nucleotide diversity within the footprinted motifs additionally stratified by evolutionary constraint. Boxes indicate median and IQR of enrichments from 1,000 bootstrap samples. Whiskers, 5th and 95th percentile.CC BY · PubMed Central

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Planell, N., Martinez‐de‐Morentin, X., Mouzo, D., Lara‐Astiaso, D., Vilas‐Zornoza, A., Martín‐Úriz, P. S., Alignani, D., Paiva, B., Maillo, A., Kurowska, A. K., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.01.547234
Prioritizing Cardiovascular Disease-Associated Variants Altering NKX2-5 Binding through an Integrative Computational ApproachPreprint
Peña-Martínez, E. G., Pomales-Matos, D. A., Rivera-Madera, A., Messon-Bird, J. L., Medina-Feliciano, J. G., Sanabria-Alberto, L., Barreiro-Rosario, A. C., Rodríguez-Ríos, J. M., & Rodríguez‐Martínez, J. A. (2023) · medRxiv · doi:10.1101/2023.09.01.23294951
ProA and ProB repeat sequences shape 3D genome organization in eukaryotesPreprint
Bonnet, K., Hulo, N., Mourad, R., Ewing, A. D., Croce, O., Naville, M., Vassetzky, N. S., Gilson, É., Picard, D., & Fourel, G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.27.564043
Causal Effects of 1400 Blood Metabolites And Sleep Disorders: A Mendelian Randomization StudyPreprint
Ma, Z., min, Z., Zhao, H., & Qu, N. (2024) · Research Square · doi:10.21203/rs.3.rs-3914147/v1
Additional file 6 of Prime editing in mice reveals the essentiality of a single base in driving tissue-specific gene expression
Gao, P., Lyu, Q., Ghanam, A. R., Lazzarotto, C. R., Newby, G. A., Zhang, W., Choi, M., Slivano, O. J., Holden, K., Walker, J. A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14220663
Additional file 1 of Jdp2 is a spatiotemporal transcriptional activator of the AhR via the Nrf2 gene battery
Wuputra, K., Tsai, M.-H., Kato, K., Ku, C., Pan, J.-B., Yang, Y.-H., Saito, S., Wu, C., Lin, Y., Cheng, K., et al. (2023) · Figshare · doi:10.6084/m9.figshare.23993127
Additional file 8 of Prime editing in mice reveals the essentiality of a single base in driving tissue-specific gene expression
Gao, P., Lyu, Q., Ghanam, A. R., Lazzarotto, C. R., Newby, G. A., Zhang, W., Choi, M., Slivano, O. J., Holden, K., Walker, J. A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14220669
Additional file 7 of Prime editing in mice reveals the essentiality of a single base in driving tissue-specific gene expression
Gao, P., Lyu, Q., Ghanam, A. R., Lazzarotto, C. R., Newby, G. A., Zhang, W., Choi, M., Slivano, O. J., Holden, K., Walker, J. A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14220666
Additional file 5 of Prime editing in mice reveals the essentiality of a single base in driving tissue-specific gene expression
Gao, P., Lyu, Q., Ghanam, A. R., Lazzarotto, C. R., Newby, G. A., Zhang, W., Choi, M., Slivano, O. J., Holden, K., Walker, J. A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14220660
Additional file 2 of Prime editing in mice reveals the essentiality of a single base in driving tissue-specific gene expression
Gao, P., Lyu, Q., Ghanam, A. R., Lazzarotto, C. R., Newby, G. A., Zhang, W., Choi, M., Slivano, O. J., Holden, K., Walker, J. A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14220651
Additional file 4 of Prime editing in mice reveals the essentiality of a single base in driving tissue-specific gene expression
Gao, P., Lyu, Q., Ghanam, A. R., Lazzarotto, C. R., Newby, G. A., Zhang, W., Choi, M., Slivano, O. J., Holden, K., Walker, J. A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14220657
Additional file 3 of Prime editing in mice reveals the essentiality of a single base in driving tissue-specific gene expression
Gao, P., Lyu, Q., Ghanam, A. R., Lazzarotto, C. R., Newby, G. A., Zhang, W., Choi, M., Slivano, O. J., Holden, K., Walker, J. A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14220654
Sequence characteristics and an accurate model of abundant hyperactive loci in the human genomePreprint
Hudaiberdiev, S., & Ovcharenko, I. (2024) · eLife · doi:10.7554/elife.95170.1
The insertion/deletion polymorphism rs201494641 at ITGA9 influences blood CD34+ cell levels by altering ZNF384 binding
Cafaro, C., Ali, Z., Arrizabalaga, A. L., Bello, D. T. D., Duran-Lozano, L., Ekdahl, L., Thodberg, M., Pertesi, M., Portilla, A. L. D. L., & Nilsson, B. (2024) · Haematologica · doi:10.3324/haematol.2024.285363
“2c ) 10 .”
Isoform and pathway-specific regulation of post-transcriptional RNA processing in human cellsPreprint
Bedi, K., Magnuson, B., Narayanan, I. V., McShane, A., Ashaka, M., Paulsen, M. T., Wilson, T. E., & Ljungman, M. E. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.12.598705 · Cited by 1
Common rules of the organization and architecture of cis-regulatory modules in the human and mouse genomesPreprint
Yuan, S., Ni, P., & Su, Z. (2024) · Research Square · doi:10.21203/rs.3.rs-4510188/v1
The metaplastic precursor state to oesophageal adenocarcinoma represents reversion to a transient epithelial cell state in the developing oesophagusPreprint
Baker, S. M., Mullan, A. F., Jennings, R. E., Hanley, K. P., Ang, Y. S., Palles, C., Hanley, N. A., & Sharrocks, A. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.25.605105
Causal role of immune cells in uveitis: a Mendelian randomization study
Pu, J., Zhao, Z., Duan, Y., Lü, J., Yao, Y., Wen, Y., Li, Y., Zhang, Y., & Ye, F. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1445775
“Summary statistics for all immunological features were acquired from the GWAS catalog (ranging from registration number GCST0001391 to GCST0002121) (20).”
Background
Emerging Technologies for Epigenetic Drug Discovery
(2024) · Royal Society of Chemistry eBooks · doi:10.1039/9781837674916-part2
A DNA base-specific sequence interposed between CRX and NRL contributes to RHODOPSIN expression
Maritato, R., Medugno, A., D’Andretta, E., Riso, G. D., Lupo, M., Botta, S., Marrocco, E., Renda, M., Sofia, M., Mussolino, C., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-76664-8
“TFBSs are short degenerate DNA sequences (also known as motifs) that occur as monomeric 11,12 , dimeric, and overlapping sequences embedded in cis -regulatory elements, so that the DNA context surrounding them can also influence how they are bound by TFs 13–18 .”
Background
Comparative atlas of genome-wide chromatin-associated protein co-occupancyPreprint
White, S. M., Moyers, B. A., Wang, T., Mackiewicz, M., Weimer, A. K. K., Grubert, F., Ramalingam, V., Luo, J. X. J., Jiang, L., Shi, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.17.628199 · Cited by 1
C.La.P.: Enhancing transformer-based genomic signal modeling by integrating DNA sequences and chromatin accessibility dataPreprint
Firbas, P., Gonçalves, C. D. S. T., & de Polavieja, G. G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.19.638643 · Cited by 1
Functional interrogation of candidate cis -regulatory elements at the LDLR locusPreprint
Leix, K., Serrano-Zayas, C., Graham, S. E., & Emmer, B. T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.20.644385
Transcription factor occupancy is affected by genetic variation in the white adipose tissue of C57BL6/j and 129S1/SvImJ micePreprint
Mononen, J., Taipale, M., Malinen, M., Levonen, A., Ruotsalainen, A., Norton, L., & Heikkinen, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.08.652810
“A recent study by Viestra et al. highlighted the usefulness of footprinting in identifying candidate rSNPs by showing that TF footprints were enriched for genetic variation compared to the rest of the non-coding genome [18].”
Complex causal association between immunophenotypes and adenomyosis: Univariable, bidirectional, and multivariable Mendelian randomization
Wu, Q., Shao, W., Jia, Z., Li, Z., Zhao, Z., & Shi, W. (2025) · Journal of Reproductive Immunology · doi:10.1016/j.jri.2025.104551
Analysis of chromatin accessibility associated with azacitidine response in higher-risk myelodysplastic neoplasms
Kim, D., Park, S., Kwon, Y.-R., Yoon, H., Cho, B., Baek, I., Kang, J. H., Park, T.-E., Corces, M., Kim, H.-J., et al. (2025) · iScience · doi:10.1016/j.isci.2025.113297 · Cited by 1
“…CD8 + T cells of responders and healthy con-trols—but not from non-responders—exhibited significant enrichment of TBX/EOMES-binding motifs (AC0489 42 ), indicating that TBX21-and EOMES-regulated pathways in CD8 + T cells may play a critical role in shaping the AZA response (Figure 2D),…”
p53motifDB: integration of genomic information and tumour suppressor p53 binding motifs
Baniulyte, G., Hicks, S. M., & Sammons, M. A. (2025) · Database · doi:10.1093/database/baaf053 · Cited by 1
“These data have been successfully integrated with TF binding information and other data sources, such as genetic variation and evolutionary conservation, to provide insight into the biological function and mechanisms of transcriptional regulatory regions [ 6 , 14 ].”
Individuals with intellectual disability have an increased burden of de novo variants in cis -regulatory elements of glutamatergic neuronsPreprint
Sosa, E., O’Shea, D., Rosean, S., Yoon, C., Stauber, J., Castillo, P. E., Seoighe, C., Fazzari, M. J., & Greally, J. M. (2025) · medRxiv · doi:10.1101/2025.09.23.25336266
Evolution of chromatin accessibility associated with traits of cichlid phenotypic diversityPreprint
Mehta, T. K., Man, A., Etherington, G., Smith, A. M., Indermaur, A., Salzburger, W., Joyce, D. A., Palma, F. D., & Haerty, W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.09.681187
Mapping transcription factor binding sites by learning UV damage fingerprints
Wilson, H. E., Stevison, S., Lamprey, L., & Wyrick, J. J. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1014
“…highly specific (i.e. “ChIP-grade”) antibodies, and typically can only measure the binding profile of a single protein at a time, highlighting the need for alternative methods to map cellular TFBS. TFBS have also been characterized by an orthogonal approach known as DNA footprinting [ 15 , 16 ].”
Robust footprinting with sample-specific Tn5 bias correction for bulk and single cell ATAC-seq
Lin, Y., Wang, H., Wilson, P. C., & Zhang, N. R. (2026) · Nature Communications · doi:10.1038/s41467-026-73164-3
“We further analyzed the composition of predicted footprints by measuring the proportion that overlapped with Decima attribution peaks and with DNase-seq footprints 30 (p-value = 0.05).”
Influential
The transcriptional regulatory circuit as a driver and therapeutic target in CML blast crisisPreprint
Jiang, L., Lou, E., Fang, J., Jiang, L., Peng, G., Chen, J., Liu, B., Lu, J., Meng, Y., Zhang, H., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.02.686161
Chromatin Profiling Reveals Distinct Male and Female Trajectories for Developmental Learning Potential
Kunzelman, G. W., Batistuzzo, A., & London, S. E. (2025) · Developmental Neurobiology · doi:10.1002/dneu.23017 · Cited by 1
Clustering of Direct and Indirect DNA Binding Motifs of Human and Mouse Transcription Factors: X-TFBS Compendium from ChIP-seqPreprint
Sharov, A. A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.09.687492
Evo2HiC: a multimodal foundation model for integrative analysis of genome sequence and architecturePreprint
Fang, T., Li, Y., Xiao, Z., Xiao, Z., Hang, S., Murtaza, G., Yang, J., Xu, H., Jha, A., Noble, W. S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.18.689171 · Cited by 1
“We next obtained a precomputed, genome-wide catalog of motif matches [44].”
The architecture of allele-specific regulatory variant effects across five human genomesPreprint
Dura, K., Siklenka, K., Strouse, K., Morrow, S., Majoros, W., & Reddy, T. E. (2025) · bioRxiv · doi:10.1101/2025.11.22.689315
“Clustering together overlapping instances of highly similar motifs 22 , we arrived at a final test set of 207,556 instances of 168 motif clusters.”
Melody: Decoding the Sequence Determinants of Locus Specific DNA Methylation Across Human TissuesPreprint
Jin, J., Wang, D., Qiao, J., Gao, W., Liu, Y., Chen, S., Zou, Q., Wu, S., Su, R., & Wei, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.23.689975
Flipons: Biological roles of alternative nucleic acid conformations
Herbert, A. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-323-99507-8.00022-6
Global Evaluation of Congenital Heart Disease-Associated Non-Coding VariantsPreprint
Peña-Martínez, E. G., Peña-Martínez, E., Sharma, S., Medina-Feliciano, J., Root, E., Parks, L., Granitto, M., Pomales-Matos, D., Bird, J. M., Barreiro-Rosario, A., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-8429365/v1
Additional file 6 of Global properties of regulatory sequences are predicted by transcription factor recognition mechanisms
Patel, Z. M., & Hughes, T. R. (2021) · Figshare · doi:10.6084/m9.figshare.16767622
Additional file 1 of 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) · Figshare · doi:10.6084/m9.figshare.14853540
Additional file 2 of Predicting the impact of sequence motifs on gene regulation using single-cell data
Hepkema, J., Lee, N. K., Stewart, B. J., Ruangroengkulrith, S., Charoensawan, V., Clatworthy, M. R., & Hemberg, M. (2023) · Figshare · doi:10.6084/m9.figshare.23962000
Additional file 1 of Prime editing in mice reveals the essentiality of a single base in driving tissue-specific gene expression
Gao, P., Lyu, Q., Ghanam, A. R., Lazzarotto, C. R., Newby, G. A., Zhang, W., Choi, M., Slivano, O. J., Holden, K., Walker, J. A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14220648
Additional file 2 of Global properties of regulatory sequences are predicted by transcription factor recognition mechanisms
Patel, Z. M., & Hughes, T. R. (2021) · Figshare · doi:10.6084/m9.figshare.16767610
Additional file 9 of Prime editing in mice reveals the essentiality of a single base in driving tissue-specific gene expression
Gao, P., Lyu, Q., Ghanam, A. R., Lazzarotto, C. R., Newby, G. A., Zhang, W., Choi, M., Slivano, O. J., Holden, K., Walker, J. A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14220672
Additional file 1 of Comprehensive multi-omics integration identifies differentially active enhancers during human brain development with clinical relevance
Yousefi, S., Deng, R., Lanko, K., Salsench, E. M., Nikoncuk, A., van der Linde, H. C., Perenthaler, E., van Ham, T. J., Mulugeta, E., & Barakat, T. S. (2021) · Figshare · doi:10.6084/m9.figshare.16829134
Additional file 3 of Predicting the impact of sequence motifs on gene regulation using single-cell data
Hepkema, J., Lee, N. K., Stewart, B. J., Ruangroengkulrith, S., Charoensawan, V., Clatworthy, M. R., & Hemberg, M. (2023) · Figshare · doi:10.6084/m9.figshare.23962003
Additional file 1 of Predicting the impact of sequence motifs on gene regulation using single-cell data
Hepkema, J., Lee, N. K., Stewart, B. J., Ruangroengkulrith, S., Charoensawan, V., Clatworthy, M. R., & Hemberg, M. (2023) · Figshare · doi:10.6084/m9.figshare.23961997
Additional file 2 of DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants
Biddie, S. C., Weykopf, G., Hird, E. F., Friman, E. T., & Bickmore, W. A. (2024) · Figshare · doi:10.6084/m9.figshare.26758288
Additional file 1 of DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants
Biddie, S. C., Weykopf, G., Hird, E. F., Friman, E. T., & Bickmore, W. A. (2024) · Figshare · doi:10.6084/m9.figshare.26758285
Additional file 1 of Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin
Kaplow, I. M., Schäffer, D. E., Wirthlin, M. E., Lawler, A. J., Brown, A. R., Kleyman, M., & Pfenning, A. R. (2022) · Figshare · doi:10.6084/m9.figshare.19582522
GenoME: a MoE-based generative model for individualized, multimodal prediction and perturbation of genomic profiles
Wei, J., Xue, Y., Chai, H., & Gao, Y. Q. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag902
“The corresponding sequence was scanned against the hg38 reference genome, using 2179 TF motif position weight matrices previously compiled in a global reference map [ 55 ].”
Activator–promoter compatibility in mammals: a CpG-Island-specific co-activator directly bridges transcription factors to TFIIDPreprint
Nemčko, F., Sabath, K., Pagani, M., Kleinfercher, S., Plaschka, C., & Stark, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.28.696747 · Cited by 1
Identification of novel DNA sequence motifs that modulate transcription in T cells
Knoetze, N., Yung, E., Bayega, A., Brown, S. D., & Holt, R. A. (2026) · BMC Genomics · doi:10.1186/s12864-025-12425-9
“To avoid redundancy, we ensured that no two selected motifs belonged to the same DNA-binding archetype as defined by Vierstra et al. [3] ( h t t p s : / / r e s o u r c e s . a l t i u s . o r g / ~ j v i e r s t r a / p r o j e c t s / m o t i f - c l u s t e r i n g - v 2 .”
Genetic variation shapes the chromatin accessibility landscape and transcriptional responses in mouse adipose tissue
Mononen, J., Taipale, M., Malinen, M., Levonen, A., Ruotsalainen, A., Norton, L., & Heikkinen, S. (2026) · PLoS Genetics · doi:10.1371/journal.pgen.1011716
“…cell type specific binding, which is still reflected on chromatin accessibility, is less likely to present with strong for footprint analysis or even DAR analysis with bulk ATAC-seq where the signals from several different cell types combine, which could be solved using single cell approaches [24].”
Influential
TPCAV: Interpreting deep learning genomics models via concept attributionPreprint
Yang, J., & Mahony, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.20.700723 · Cited by 1
“Specifically, we created a library of DNA motif concepts by embedding instances of known human TF binding motifs into randomly sampled sequences from the human genome, taking the non-redundant clustered motif collection from the Vierstra lab as our source of known motifs 13 (see Methods ).”
eScreen: a deep learning framework for functionally decoding the regulatory genome at single-nucleotide resolutionPreprint
Luo, S., Lin, L., Zhang, H., Yang, Y., Liu, Y., Han, Y., Chen, F., Liang, R., Niang, X., Dai, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.02.703403
An Integrative Single-Cell Epigenomic Atlas Annotates the Regulatory Genome of the Adult Mouse BrainPreprint
Wang, Z., Zu, S., Armand, E. J., Loe, T. H., Rink, J. A., Wu, W., Xie, Y., Chang, L., Zhu, C., Johnson, N. D., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.07.704075 · Cited by 1
Distinct DNA-binding syntax and chromatin remodeling capacities of bHLH transcription factors in cell differentiation, reprogramming and cancerPreprint
de Martin, X., Martínez-Miralles, J., Beletskaya, S., Navarro, A., Izquierdo, J., Oliva, B., & Santpere, G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.10.705010
Distinguishing causal from tagging enhancers using single-cell multiome dataPreprint
Dorans, E., & Price, A. L. (2026) · medRxiv · doi:10.64898/2026.02.15.26346353
“We determined that this approach did not outperform using all candidate peak-gene pairs in fine-mapping ( Supplementary Figure 49 ).”
A Cytosine Deaminase–Based Genomic Footprinting Assay (cFOOT-seq) for Detecting Transcription Factor Occupancy
Yang, M., Wu, A., Wang, H., Liu, X., Shi, J., Gao, S., & Zhang, J. (2026) · BIO-PROTOCOL · doi:10.21769/bioprotoc.5637
Genome-wide maps of transcription factor footprints identify noncoding variants rewiring gene regulatory networksPreprint
Lin, J., Dong, W., Zhang, J., Xie, C., Jing, X., Zhao, J., Ma, K., Kang, H., Jiang, Y., Xie, X., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.03.708783
TFBSpedia: a comprehensive human and mouse transcription factor binding sites databasePreprint
Li, S., Chou, E., Wang, K., Boyle, A. P., & Sartor, M. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.04.709638
Functional interrogation of candidate cis-regulatory elements at the LDLR locus
Leix, K., Serrano-Zayas, C., Vyas, H., Graham, S. E., & Emmer, B. T. (2026) · PLoS Genetics · doi:10.1371/journal.pgen.1012082 · Cited by 1
“In brief, regions of open chromatin in hepatocytes were identified by colocalization with peaks for ATAC-seq or DHS-seq from human liver tissue and/or HepG2 cells [17,18].”
Étude des éléments cis-régulateurs à différentes échelles : approches fondamentales à appliquer
Hammal, F. (2023) · theses.fr (ABES) · doi:10.70675/90dfd1eczcfebz4ccfz9115z46efa92c2877
Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning
Magnitov, M. D., van der Weide, R. H., Witvliet, A. F., Hernández-Quiles, M., Martinovic, M., Teunissen, H., Braccioli, L., Vermeulen, M., & de Wit, E. (2026) · Nature Communications · doi:10.1038/s41467-026-72392-x
“To reduce redundancy and retain motifs consistently identi ﬁ ed across multiple individuals, we adapted the published framework to perform motif clustering 111 (see “ Meth-ods ” ), and then matched the resulting clusters to known TF motif instances from the JASPAR database 112,113 (Fig.”
Epigenetic conditioning improves sequence-based modeling of gene regulation across cell types and allelesPreprint
Dixon-Luinenburg, O., Bajwa, A., Vollger, M. R., Stergachis, A., Streets, A., & Ioannidis, N. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.02.729723 · Cited by 1
“Genome-wide motif matches were downloaded from Non-redundant archetype motif clusters (version 1.0) [68].”
Influential
ARID1A orchestrates the activity of FOXA1 and AP-2 transcription factors in lobular breast cancer cellsPreprint
Eastwood, N., Clarke, R. B., Sharrocks, A. D., & Nagarajan, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.08.730919
Disentangling covariate effects on single-cell-resolved epigenomes with DeepDive
Møller, A. F., & Madsen, J. G. S. (2026) · Patterns · doi:10.1016/j.patter.2026.101589
“For motif activity analysis, pyChromVAR 11 was used to estimate motif activity of clustered non-redundant motifs 20 on re-constructed profiles and T2D-to-ND counterfactuals, adjusting for the covariates cell type, disease, sex, race, age, BMI, and HbA1c, with default parameters.”
Identifying causal genetic variants for high-altitude adaptation through blood eQTL analysis in plateau populations
Zhao, C., Guan, J., Liu, J., Zhang, Z., Zhu, M., Fu, L., Dai, A., Lin, K., Zhang, L., Wang, W., et al. (2026) · Communications Biology · doi:10.1038/s42003-026-10840-6
Tissue-derived extracellular matrix hydrogels instruct epigenetic adaptation in metastatic colonization
Yoon, H., Kim, D., Jeong, H.-J., Han, H., Kim, S. J., Jang, J., Jung, D., Myung, S., Yu, C. S., Song, I. H., et al. (2026) · Bioactive Materials · doi:10.1016/j.bioactmat.2026.08.031
“Next, to identify enriched transcription factor binding motifs within peak clusters, we utilized non-redundant transcription factor motifs [97].”
Oriented binding of transcription factors to nucleosomes remodels chromatin at human promoters
Xiao, T., Crowe‐McAuliffe, C., Ochmann, M., Warrier, T., Du, Y., Hou, T., Zou, S., Chen, Y., Zhu, F., Zhang, D., et al. (2026) · Molecular Cell · doi:10.1016/j.molcel.2026.08.005
A single-nucleus multiomic and spatial atlas of gene regulation in human spermatogenesisPreprint
Bhaskaran, J., Ing‐Simmons, E., Balsells, I. B., Persio, S. D., Chan, M., Politz, M., Cremers, J., Rotte, N., Kliesch, S., Friedrich, C., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.04.749409
The devil is in the enhancer: decoding regulatory variation at the ZNF827 locus in SCAD
Soler, É. (2026) · Clinical Science · doi:10.1042/cs20261108
Genome-Wide Evolution of Lineage-Specific Chromatin Accessibility Links Regulatory Changes to Cichlid Phenotypic Diversity
Mehta, T. K., Man, A., Etherington, G., Smith, A. M., Indermaur, A., Salzburger, W., Joyce, D. A., Palma, F. D., & Haerty, W. (2026) · Genome Biology and Evolution · doi:10.1093/gbe/evag247
Integration by parts: The multi-layered mechanisms of cis-regulatory control
Cornwall-Scoones, J., Briscoe, J., & Delàs, M. J. (2026) · Molecular Cell · doi:10.1016/j.molcel.2026.09.016
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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: Vierstra, Jeff
Lazar, John
Sandstrom, Richard
Lee, Kristen
Bates, Daniel
Haugen, Eric
Rynes, Eric
Reynolds, Alex
Nelson, Jemma
Frerker, Mark
Buckley, Michael
Kaul, Rajinder
Meuleman, Wouter
Stamatoyannopoulos, John A.
Venue: Nature
Publisher: Springer Science and Business Media LLC
Published: 2020-07-29
Volume: 583
Issue: 7818
Pages: 729-736
ISSN: 0028-0836 (ISSN-L)
1476-4687
DOI: 10.1038/s41586-020-2528-x
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
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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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