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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Baderna, V., Barzaghi, G., Kleinendorst, R., Chatsirisupachai, K., Moniot-Perron, L., Doyle, C., Schopp, M., Hochepied, T., Libert, C., Odom, D. T., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02703-x · Cited by 12
Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale
Mendelevich, A., Gupta, S., Pakharev, A., Teodosiadis, A., Mironov, A. A., & Gimelbrant, A. A. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad254 · Cited by 5
“AI analysis has been increasingly used for understanding regulatory variation (Pirinen et al. 2015; Moyerbrailean et al. 2016; GTEx_Consortium, 2017; Mohammadi et al. 2019; Uechi et al. 2020; Vierstra et al. 2020).”
Method
Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization
Reddy, A. J., Geng, X., Herschl, M. H., Kolli, S., Kumar, A., Hsu, P. D., Levine, S., & Ioannidis, N. M. (2024) · neural information processing systems · doi:10.52202/079017-2954 · Cited by 5
Transcription and genome integrity
Ljungman, M. E. (2022) · DNA repair · doi:10.1016/j.dnarep.2022.103373 · Cited by 5
The dynseq browser track shows context-specific features at nucleotide resolution
Nair, S., Barrett, A., Li, D., Raney, B. J., Lee, B. T., Kerpedjiev, P., Ramalingam, V., Pampari, A., Lekschas, F., Wang, T., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01194-w · Cited by 6
Single-fiber nucleosome density shapes the regulatory output of a mammalian chromatin remodeling enzymePreprint
Abdulhay, N. J., Hsieh, L. J., McNally, C. P., Ketavarapu, M., Kasinathan, S., Nanda, A. S., Ostrowski, M. S., Wu, K., Moore, C. M., Goodarzi, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.10.472156 · Cited by 5
“…chromatin fibers containing known gold-standard Ctcf bound sites in the mESC epigenome (Yue et al., 2014), as well as fibers harboring motif matches drawn randomly from the approximately 2.9E6 Ctcf sites found across the murine genome (i.e. unoccupied Ctcf sites) (Vierstra et al., 2020).”
Background
Systematic identification of disease-causing promoter and untranslated region variants in 8,040 undiagnosed individuals with rare diseasePreprint
Martin-Geary, A. C., Blakes, A. J. M., Dawes, R., Findlay, S. D., Lord, J., Walker, S., Talbot-Martin, J., Wieder, N., D’Souza, E. N., Fernandes, M., et al. (2023) · medRxiv · doi:10.1101/2023.09.12.23295416 · Cited by 5
“To account for this, as well as promoters that are not annotated at all in the ENCODE dataset, we calculated the average size of all ‘promoter-like’ cCREs that overlap with TSS of MANE transcripts.”
Influential
Multi-omics analysis reveals signatures of selection and loci associated with complex traits in pigsPreprint
Yi, G., Liu, L., Yao, Y., Liu, Y., Li, J., Yang, Y., Fang, L., Mo, D., Zhang, L., Liu, Y., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.19.558553 · Cited by 5
Targeting Interferon-Driven Inflammatory Memory Prevents Epigenetic Evolution of Cancer Immunotherapy ResistancePreprint
Qiu, J., Ye, D., Chen, X. E., Dangle, N. J., Yoshor, B., Zhang, T., Shao, Y., Nallamala, V. C., Wang, S., Ren, D., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.13.607862 · Cited by 5
The polygenic architecture of hidradenitis suppurativa reveals signaling mechanisms that implicate epithelial remodelingPreprint
Khan, A., Gould, P. A., Luo, Y., Prens, E. P., Wheless, L., Hung, A. M., Program, V. M. V., Drivas, T. G., Ritchie, M. D., Saeidian, A. H., et al. (2025) · medRxiv · doi:10.1101/2025.07.25.25332168 · Cited by 5
“Finally, we conducted genome-wide genetic correlation (r g ) analyses using LD score regression to examine shared susceptibility for paired traits ( Figure 4; Supplementary Data 17 ).”
Axes of biological variation in diffuse large B cell lymphoma
Wang, B., Wright, G. W., Enssle, J. C., Yu, X., Phelan, J. D., Yang, Y., Shaffer, A. L., Ruiz, A. E. B., Huang, D. W., Coulibaly, Z. A., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2025.12.015 · Cited by 4
HAMPLE: deciphering TF-DNA binding mechanism in different cellular environments by characterizing higher-order nucleotide dependency
Wang, Z., Xiong, S., Yu, Y., Zhou, J., & Zhang, Y. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad299 · Cited by 4
“Transcription factors (TFs) play a crucial role in the gene transcription regulation program by binding to degenerate DNA sequences at specific genomic locations (Vierstra et al. 2020).”
Background
The role of Immune cells in Alzheimer's disease: a bidirectional Mendelian randomization study
Zhang, E., Chen, T., Chen, Y., Long, C., Tao, L., Shen, X., & Dai, F. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1433691 · Cited by 6
Advance technologies for DNA-protein interactions and future research prospect
Qu, C., & Du, H. (2024) · Crop Design · doi:10.1016/j.cropd.2024.100082 · Cited by 4
Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning
Morrow, A., Hughes, J. W., Singh, J., Joseph, A. D., & Yosef, N. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab676 · Cited by 4
Causal role of immune cells in obstructive sleep apnea hypopnea syndrome: Mendelian randomization study
Zhao, H.-H., Ma, Z., & Guan, D.-S. (2024) · World Journal of Clinical Cases · doi:10.12998/wjcc.v12.i7.1227 · Cited by 4
Transcriptional synergy in human aortic endothelial cells is vulnerable to combination p300/CBP and BET bromodomain inhibition
Bracken, R. C., Davison, L. M., Buehler, D. P., Fulton, M. E., Carson, E. E., Sheng, Q., Stolze, L. K., Guillermier, C., Steinhauser, M. L., & Brown, J. D. (2024) · iScience · doi:10.1016/j.isci.2024.110011 · Cited by 4
KAS-ATAC reveals the genome-wide single-stranded accessible chromatin landscape of the human genomePreprint
Kim, S. H., Marinov, G. K., & Greenleaf, W. J. (2024) · Genome Research · doi:10.1101/gr.279621.124 · Cited by 4
Predictable Engineering of Signal-Dependent Cis-Regulatory ElementsPreprint
Cornwall-Scoones, J., Benzinger, D., Yu, T., Pezzotta, A., Sagner, A., Gerontogianni, L., Bernadet, S., Finnie, E., Boezio, G. L. M., Stuart, H. T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.07.642002 · Cited by 5
Genome-wide investigation of transcription factor footprints and dynamics using cFOOT-seq
Wang, H., Wu, A., Yang, M., Zhou, D., Chen, X., Shi, Z., Zhang, Y., Liu, Y.-X., Chen, K., Wang, X., et al. (2025) · Protein & Cell · doi:10.1093/procel/pwaf071 · Cited by 3
“S3A), which conceptionally adapted from TOBIAS and footprint-tools (Bentsen et al., 2020; Vierstra et al., 2020).”
Influential
TRsv: simultaneous detection of tandem repeat variations, structural variations, and short indels using long read sequencing data
Kosugi, S., & Terao, C. (2025) · Genome biology · doi:10.1186/s13059-025-03718-z · Cited by 3
PNA aptamer-based bioreceptors for cardiac biomarker (cTnI) detection: Insight into the structural and stability-related aspects
Basini, F., Hambli, A., Sahu, S., Bagale, R., Yesupatham, M. S., Ritzenthaler, C., Montaigne, D., Woitrain, E., Happy, H., Boukherroub, R., et al. (2025) · Nano Today · doi:10.1016/j.nantod.2025.102957 · Cited by 3
Long-term isolation and archaic introgression shape functional genetic variation in Near Oceania
Reilly, P. F., Rong, S., Tejada-Martínez, D., Miller, S. L., Tjahjadi, A., Liu, C., Akers, J. F., Pomer, A., Prentice, M. E., Merriwether, D. A., et al. (2026) · Science · doi:10.1126/science.adr6749 · Cited by 3
Decoding common and rare noncoding variant effects across cellular and developmental contexts
Marderstein, A. R., Kundu, S., Padhi, E. M., Deshpande, S., Wang, A., Robb, E., Sun, Y., Yun, C. M., Pomales-Matos, D. A., Xie, Y., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02619-6 · Cited by 18
Role of Genetic Variation in Transcriptional Regulatory Elements in Heart Rhythm
Jonker, T., Barnett, P., Boink, G. J. J., & Christoffels, V. M. (2023) · Cells · doi:10.3390/cells13010004 · Cited by 3
Identification of transcription factor co-binding patterns with non-negative matrix factorization
Rauluševičiūtė, I., Launay, T., Barzaghi, G., Nikumbh, S., Lenhard, B., Krebs, A. R., Castro-Mondragon, J. A., & Mathelier, A. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae743 · Cited by 5
“Importantly, TFs interacting with the DNA in open chromatin regions protect their TFBSs from the DNase cleavage, which leaves a footprint on the corresponding TF-BSs ( 50 ,64 ).”
Background
Exploring the transcriptional cooperation between RUNX2 and its associated elncRNA RAIN
Vitale, E., Manicardi, V., Gugnoni, M., Torricelli, F., Donati, B., Muccioli, S., Salviato, E., Rossi, T., Manzotti, G., Piana, S., et al. (2024) · Cell Death and Disease · doi:10.1038/s41419-024-07058-x · Cited by 5
“Central in these dynamics are Transcription Factors (TFs) that serve as primary docking sites for the entire transcriptional machinery [3, 6].”
Immune cells: Mediators in the metabolites and Alzheimer’s disease
Zhang, E., Dai, F., Tao, L., Chen, Y., Chen, T., & Shen, X. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877241313140 · Cited by 3
“44 The assessment of the instrumental variables ’ ef ﬁ cacy was anchored in the LD reference panel data from the 1000 Genomes Project, meticulously designed to represent the genetic tapestry of a European super-population, thereby providing a solid genetic substratum for our analysis.”
Uncovering the regulatory landscape of early human B cell lymphopoiesis and its implications in the pathogenesis of B-ALL
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., et al. (2025) · Science Advances · doi:10.1126/sciadv.adw3110 · Cited by 4
Decoding cnidarian cell type gene regulation
Elek, A., Iglesias, M., Mahieu, L., Zolotarov, G., Grau‐Bové, X., Aerts, S., & Sebé-Pedrós, A. (2025) · Nature Ecology & Evolution · doi:10.1038/s41559-025-02906-1 · Cited by 5
Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data
Vahab, N., Bonu, T., Kuhlmann, L., Ramialison, M., & Tyagi, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108068 · Cited by 3
Footprint-C reveals transcription factor modes in local clusters and long-range chromatin interactions
Liu, X., Wei, H., Zhang, Q., Zhang, N., Wu, Q., & Xu, C. (2024) · Nature Communications · doi:10.1038/s41467-024-55403-7 · Cited by 3
“In this study, we utilize Deoxyribonuclease I (DNase I), a nuclease previously used for mapping TF footprints 10 , and develop Footprint-C, which speci ﬁ cally captures chromatin interactions between TF footprints, and achieves higher ef ﬁ ciency in identifying chromatin structural features, such…”
Cardiovascular disease-associated non-coding variants disrupt GATA4-DNA binding and regulatory functions
Peña-Martínez, E. G., Messon-Bird, J. L., Rodríguez-Ríos, J. M., Velázquez-Roig, R., Pomales-Matos, D. A., Rivera-Madera, A., Sanabria-Alberto, L., Barreiro-Rosario, A. C., Figueroa-Rosado, J. A., Valle, J. R.-D., et al. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100415 · Cited by 3
“CVD-associated variants were intersected with known DGFs from multiple heart tissues 48 within putative cardiac enhancers that are active during heart development and the adult heart, 47 resulting in 1,535 CVD-associated variants within putative cardiac regulatory elements.”
CLIMB: High-dimensional association detection in large scale genomic data
Koch, H., Keller, C. A., Xiang, G., Giardine, B. M., Zhang, F., Wang, Y., Hardison, R. C., & Li, Q. (2022) · Nature Communications · doi:10.1038/s41467-022-34360-z · Cited by 3
Transcription factors organize into functional groups on the linear genome and in 3D chromatin
Vadnala, R. N., Hannenhalli, S. S., Narlikar, L., & Siddharthan, R. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e18211 · Cited by 4
ChromWave: Deciphering the DNA-encoded competition between transcription factors and nucleosomes with deep neural networksPreprint
Cakiroglu, S. A., Steinhauser, S., Smith, J., Wei, X., & Luscombe, N. M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.19.436198 · Cited by 3
Cell-intrinsic Aryl Hydrocarbon Receptor signalling is required for the resolution of injury-induced colonic stem cellsPreprint
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. (2021) · Research Square · doi:10.21203/rs.3.rs-827700/v1 · Cited by 3
Accurate estimation of intrinsic biases for improved analysis of bulk and single-cell chromatin accessibility sequencing data using SELMAPreprint
Hu, S. S., Liu, L. L., Li, Q., Ma, W., Guertin, M. J., Meyer, C. A., Deng, K., Zhang, T., & Zang, C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.22.465530 · Cited by 3
Identifying transcription factor-bound activators and silencers in the chromatin accessible human genome using ATAC-STARR-seqPreprint
Hansen, T. J., & Hodges, E. C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.25.485870 · Cited by 4
Interspecies regulatory landscapes and elements revealed by novel joint systematic integration of human and mouse blood cell epigenomesPreprint
Xiang, G., He, X., Giardine, B. M., Isaac, K. J., Taylor, D. J., McCoy, R. C., Jansen, C. S., Keller, C. A., Wixom, A. Q., Cockburn, A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.02.535219 · Cited by 3
CRISPR PERSIST-On enables heritable and fine-tunable human gene activationPreprint
Tak, Y. E., Hsu, J. Y., Shih, J., Schultz, H. T., Nguyen, I. T., Lam, K. C., Pinello, L., & Joung, J. K. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.26.590475 · Cited by 4
Pervasive and programmed nucleosome distortion patterns on single mammalian chromatin fibersPreprint
Yang, M. G., Richter, H. J., Wang, S., McNally, C. P., Harris, N., Dhillon, S., Maresca, M., de Wit, E., Willenbring, H., Maher, J. J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.17.633622 · Cited by 3
Cumulative TF binding and H3K27 Acetylation drive enhancer activation frequencyPreprint
Baderna, V., Barzaghi, G., Kleinendorst, R., Chatsirisupachai, K., Moniot-Perron, L., Schopp, M., Hochepied, T., Libert, C., Odom, D. T., Zaugg, J. B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.26.645413 · Cited by 3
Deep dynamical models of single-cell multiomic velocities predict loss-of-function and rescue perturbations in B cellsPreprint
Karbalayghareh, A., Pelzer, B., Chin, C. R., Melnick, A. M., Barišić, D., & Leslie, C. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.24.650458 · Cited by 3
AlphaGenome Atlas: in silico mutagenesis of the entire human genome improves prioritization and interpretation of non-coding variantsPreprint
authors:, L., Cheng, J., Taylor, K. R., Nicolaisen, L., Pan, J., Bycroft, C., Perino, M., Ward, T., Hawkes, G., Covill, L., et al. (2026) · medRxiv · doi:10.64898/2026.09.16.26363192 · Cited by 12
Researching the causal relationship between immune cells and frontotemporal Dementia: A Mendelian Randomization analysis
Cheng, Y.-M., He, X., Huang, X. J., Mao, F., Qin, F., & Wang, Y. (2025) · Brain Research · doi:10.1016/j.brainres.2025.149608 · Cited by 2
ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories
Avdeeva, M., Walker, S. K., van der Veeken, J., Rudensky, A. Y., & Pritykin, Y. (2026) · Nature Communications · doi:10.1038/s41467-026-74000-4 · Cited by 3
“64 FortheCD8Tcelldataanalysis,motif binding weights were generated using PWMs selected for T cells from Pritykin et al. 42 .”
Control of Gene Expression by Proteins That Bind Many Alternative Nucleic Acid Structures Through the Same Domain
Herbert, A. G. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms27010272 · Cited by 3
“The FOS and JUN proto-oncogenes dimerize through a bZIP domain that recognizes a TGASTCA (S = G/C) motif in response to growth factor/receptor tyrosine kinase signaling [75].”
The topography of nullomer-emerging mutations and their relevance to human disease
Chan, C. S. Y., Mouratidis, I., Montgomery, A., Tsiatsianis, G. C., Chantzi, N., Hemberg, M., Ahituv, N., & Georgakopoulos‐Soares, I. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.026 · Cited by 2
High-Dimensional Covariance Regression with Application to Co-Expression QTL Detection
Kim, R., & Zhang, J. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2520996 · Cited by 4
“These simultaneous sparsity assumptions are well supported by genetic studies (Gardner et al. 2003; Vierstra et al. 2020), and improve model interpretability when compared to using the group sparsity or element-wise sparsity alone.”
Machine learning tools for deciphering the regulatory logic of enhancers in health and disease
Foutadakis, S., Bourika, V., Styliara, I., Koufargyris, P., Safarika, A., & Karakike, E. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1603687 · Cited by 2
“…scale rely on their structural characteristics, such as chromatin accessibility (Buenrostro et al., 2013; Marinov et al., 2023; John et al., 2013; Vierstra et al., 2020), binding of transcription factors and cofactors (Robertson et al., 2007; Lambert et al., 2018), activating histone modi ﬁ…”
Mechanisms of enhanced or impaired DNA target selectivity driven by protein dimerization
Sang, M., Au, G., & Johnson, M. E. (2026) · PNAS Nexus · doi:10.1093/pnasnexus/pgag049 · Cited by 2
“Such isolated target motifs are common in the genome for proteins like GAF (64), and with a sufficiently transient protein – protein bond lifetime and moderate target binding, dimerizing proteins can occupy these isolated motifs more frequently but for short intervals (Fig.”
‘Enhancing’ red cell fate through epigenetic mechanisms
Rossmann, M. P., & Zon, L. I. (2021) · Current Opinion in Hematology · doi:10.1097/moh.0000000000000654 · Cited by 2
“A large-scale study, in which TF occupancy was mapped at nucleotide-resolution by DNase I footprinting in 243 different primary and transformed cell and tissue samples including primary CD34þ cells subjected to erythroid differentiation, revealed functional genetic variants to be preferentially localized with nucleoti…”
Background
Predicting the impact of sequence motifs on gene regulation using single-cell dataPreprint
Hepkema, J., Lee, N. K., Stewart, B. J., Ruangroengkulrith, S., Charoensawan, V., Clatworthy, M. R., & Hemberg, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.26.400218 · Cited by 2
Functional variants in hematopoietic transcription factor footprints and their roles in the risk of immune system diseasesPreprint
Kubota, N., & Suyama, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.22.436360 · Cited by 2
“CC (529) CT (126) TT (15) rs11748158 genotype -1.”
Candidate gene modifiers of dystrophinopathy identified by the uniform application of genome-wide datasets to novel GWAS-identified lociPreprint
Flanigan, K. M., Waldrop, M. A., Martin, P. T., Alles, R., Dunn, D. M., Alfano, L. N., Simmons, T. R., Moore‐Clingenpeel, M., Burian, J., Seok, S.-C., et al. (2021) · medRxiv · doi:10.1101/2021.11.03.21265899 · Cited by 2
Motif2Site: a Bioconductor package to detect accurate transcription factor binding sites from ChIP-seqPreprint
Zarrineh, P., Darieva, Z. A., & Bobola, N. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.22.509048 · Cited by 2
Functional characteristics and computational model of abundant hyperactive loci in the human genomePreprint
Hudaiberdiev, S., & Ovcharenko, I. (2024) · eLife · doi:10.7554/elife.95170 · Cited by 1
The pioneer transcription factor ELF2 remodels the nucleosome near transcription start sitesPreprint
Xiao, T., Crowe‐McAuliffe, C., Ochmann, M., Du, Y., Hou, T., Chen, Y., Zhu, F., Seyednasrollah, F. S., Osmala, M., Vanharanta, S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.24.595749 · Cited by 2
“Correlative evidence from DNase I footprinting indicates that many typical open chromatin regions contain five to six highly occupied TF motif positions ( 46 ).”
Learning sequence-based regulatory dynamics in single-cell genomicsPreprint
Ibarra, I. L., Schneeberger, J., Erdogan, E., Redl, L., Martens, L. D., Klein, D., Aliee, H., & Theis, F. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.07.605876 · Cited by 2
Decoding the gene regulatory landscape through multimodal learning of protein-DNA interactionsPreprint
Tan, J., Fu, X., Ling, X., Mo, S., Bai, J., Rabadán, R., Fenyö, D., Boeke, J. D., Tsirigos, A., & Xia, B. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.17.670761 · Cited by 5
EpiATLAS – a reference for human epigenomic researchPreprint
Manz, Q., Bilenky, M., Hecker, D., Aggarwal, N., Arcila-Galvis, J. E., Ashrafiyan, S., Baumgarten, N., Ardakani, F. B., Lins, P. R. B., Breeze, C. E., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.22.729579 · Cited by 2
Unraveling the complexity: Advanced methods in analyzing DNA, RNA, and protein interactions
Peixoto, M. L., & Madan, E. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.06.010 · Cited by 1
Epigenomic Sequencing Technologies
Chen, J., Keshavarzian, T., & Lupien, M. (2024) · Royal Society of Chemistry eBooks · doi:10.1039/9781837674916-00109 · Cited by 1
In vivo deletion of a GWAS-identified Myb distal enhancer acts on Myb expression, globin switching, and clinical erythroid parameters in β-thalassemia
Deleuze, V., Stephen, T., Salma, M., Orfeo, C., Jorna, R., Maas, A., Barroca, V., Arcangeli, M.-L., Lecellier, C., Andrieu‐Soler, C., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-94222-8 · Cited by 2
Causal Association Between Immune Cell Traits and Risk of Multiple Malignant and Nonmalignant CNS Diseases: A Mendelian Randomization and Single‐Cell Transcriptomic Analysis
Ke, S., Yan, J., Li, B., & Feng, X. (2025) · Brain and Behavior · doi:10.1002/brb3.70632 · Cited by 1
Gestational Melatonin Supplementation Attenuates Maternal Sleep Deprivation‐Induced Steatohepatitis Susceptibility in Offspring
Guo, F., Yang, Z., She, J., Chen, F., Hu, Y., Huang, H.-F., & Gao, L. (2025) · Cell Proliferation · doi:10.1111/cpr.70138 · Cited by 1
“Transcription factors regulate gene expression through specific binding to DNA sequences in gene promoters/enhancers via specialised domains [62].”
Enhancer dynamics and cellular architecture in the human spinal cord
Kandror, E. K., Wang, A., Carriere, M., Peterson, A., Liao, W., Tjärnberg, A., Fung, J. H., Mahbubani, K. T. A., Loper, J., Pangburn, W., et al. (2026) · Neuron · doi:10.1016/j.neuron.2025.12.035 · Cited by 2
How to build the regulatory genome: a constructionist guide to the cis-regulatory code
Bell, C. C., de Boer, C. G., Camellato, B. R., Delàs, M. J., Ibrahim, D. M., & Montgomery, M. T. (2026) · Development · doi:10.1242/dev.205516 · Cited by 2
NEXT-scASV: a Nextflow pipeline for allele-specific variant calling from single-cell RNA-seq data
Shevtsov, A., Buyan, A., Nozdrin, V., Akhtyamov, P., Stupnikov, A., Meshcheryakov, G., Kulakovskiy, I. V., & Medvedeva, Y. A. (2026) · GigaScience · doi:10.1093/gigascience/giag042
“A Python script [ 29 ] then parses these corrected BAM files to count the number of reads supporting the reference and alternative alleles for every variant in every sample × group combination, producing an extensive table of allelic read counts for statistical testing.”
Predictive design of tissue-specific mammalian enhancers that function in the mouse embryo
Chen, S., Loubière, V., Hollingsworth, E. W., Ken, M., Mandlburger, N., Jacinto, S. H., Dizehchi, A., Schreiber, J. M., Kvon, E. Z., & Stark, A. (2026) · Nature Genetics · doi:10.1038/s41588-026-02729-1 · Cited by 3
“Redundant PWMs were then filtered using the non-redundant motif clusters described in a previous publication 47 , selecting only the PWM with the highest z -score as the representative of each cluster.”
Influential
Computational analysis of congenital heart disease associated SNPs: unveiling their impact on the gene regulatory system
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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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