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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Salma, M., Andrieu‐Soler, C., Deleuze, V., & Soler, É. (2023) · Blood Cells Molecules and Diseases · doi:10.1016/j.bcmd.2023.102745 · Cited by 19
Statistical framework for calling allelic imbalance in high-throughput sequencing data
Buyan, A., Meshcheryakov, G., Safronov, V., Abramov, S., Boytsov, A., Nozdrin, V., Baulin, E. F., Kolmykov, S. K., Vierstra, J. D., Kolpakov, F., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55513-2 · Cited by 23
“…or direct variant impact on allele-speciﬁc transcription factor binding (AS-binding, ASB) using ChIP-Seq data 7–11 , allele-speciﬁc chromatin accessibility (ASA) using e.g. DNase-Seq or ATAC-Seq 8,12–16 , allele-speciﬁc DNA methylation 17,18 , and allele-speciﬁc gene expression per se 9,19–29 .”
Self-citation
Discovering unknown human and mouse transcription factor binding sites and their characteristics from ChIP-seq data
Yu, C.-P., Kuo, C.-H., Nelson, C. W., Chen, C., Soh, Z. T., Lin, J.-J., Hsiao, R.-X., Chang, C.-Y., & Li, W. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2026754118 · Cited by 20
“Recently, Vierstra et al. (14) found that CTCF has a canonical and a secondary motif that is similar to the first motif above.”
Background
Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data
Osorio, D., Capasso, A., Eckhardt, S. G., Giri, U. P., Somma, A. J., Pitts, T. M., Lieu, C. H., Messersmith, W. A., Bagby, S. M., Singh, H., et al. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00597-5 · Cited by 25
“40.”
Background
Human gene regulatory evolution is driven by the divergence of regulatory element function in both cis and trans
Hansen, T. J., Fong, S., Day, J. K., Capra, J. A., & Hodges, E. C. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100536 · Cited by 20
Predicting genotype-specific gene regulatory networksPreprint
Weighill, D. A., Guebila, M. B., Glass, K., Quackenbush, J. F., & Platig, J. (2022) · Genome Research · doi:10.1101/gr.275107.120 · Cited by 22
Fine-tuning sequence-to-expression models on personal genome and transcriptome data
Rastogi, R., Reddy, A. J., Chung, R., & Ioannidis, N. M. (2026) · Genome biology · doi:10.1186/s13059-026-04091-1 · Cited by 22
Thermodynamic principles link in vitro transcription factor affinities to single-molecule chromatin states in cells
Schaepe, J. M., Fries, T., Doughty, B. R., Ramalingam, V., Liu, B. B., Crocker, O. J., Marinov, G. K., Hinks, M. M., Marklund, E., & Greenleaf, W. J. (2025) · Cell · doi:10.1016/j.cell.2025.11.008 · Cited by 23
Detection of trait-associated structural variations using short-read sequencing
Kosugi, S., Kamatani, Y., Harada, K., Tomizuka, K., Momozawa, Y., Morisaki, T., & Terao, C. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100328 · Cited by 19
“To infer the causality of the GWAS variants on the traits, we used the transcription factor (TF)-binding footprints responsible for gene regulation in noncoding regions.(51) Of the 41 top-ranked GWAS SVs, 13 DELs (59% of the 22 top-ranked DELs) overlappedwith the TF-binding footprints, most of whichwere footprints fro…”
Method
High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes
Duan, Y.-Y., Chen, X., Zhu, R., Jia, Y., Huang, X., Zhang, M., Yang, N., Dong, S., Zeng, M., Feng, Z., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.07.002 · Cited by 18
The architecture of binding cooperativity between densely bound transcription factors
Lupo, O., Kumar, D. K., Livne, R., Chappleboim, M., Levy, I., & Barkai, N. (2023) · Cell Systems · doi:10.1016/j.cels.2023.06.010 · Cited by 19
Exposure to sevoflurane results in changes of transcription factor occupancy in sperm and inheritance of autism†
Wang, H. V., Forestier, S., & Corces, V. G. (2021) · Biology of Reproduction · doi:10.1093/biolre/ioab097 · Cited by 19
A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity
Li, Z., Zhang, Y., Peng, B., Qin, S., Zhang, Q., Chen, Y., Chen, C., Bao, Y., Zhu, Y. S., Hong, Y., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae912 · Cited by 22
Preferential CEBP binding to T:G mismatches and increased C-to-T human somatic mutations
Yang, J., Horton, J., Akdemir, K., Li, J., Huang, Y., Kumar, J., Blumenthal, R., Zhang, X., & Cheng, X. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab276 · Cited by 16
“These ENCODE footprints represent TF occupancy, at nucleotide resolution, from hundreds of human cell and tissue types (14).”
Background
Master lineage transcription factors anchor trans mega transcriptional complexes at highly accessible enhancer sites to promote long-range chromatin clustering and transcription of distal target genes
White, S. M., Snyder, M. P., & Yi, C. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1105 · Cited by 17
“01) for footprints present in the ENCODE consensus footprint dataset, a high-confidence dataset identified by analyzing DNase-seq profiles across 243 human cell and tissue types (42).”
Background
Of mice and human-specific long noncoding RNAs
Ghanam, A. R., Bryant, W. B., & Miano, J. M. (2022) · Mammalian Genome · doi:10.1007/s00335-022-09943-2 · Cited by 17
“The disruption of regulatory cassettes is of particular concern given widespread transcription of the genome and the presence of millions of predicted TFBS (Jensen et al. 2013; Vierstra et al. 2020).”
BackgroundInfluential
The cis-regulatory logic integrating spatial and temporal patterning in the vertebrate neural tube
Zhang, I., Boezio, G. L. M., Cornwall-Scoones, J., Frith, T. J. R., Finnie, E., Luo, J., Jiang, M., Howell, M. T., Lovell-Badge, R. H., Sagner, A., et al. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.06.029 · Cited by 17
Chromatin occupancy and target genes of the haematopoietic master transcription factor MYB
Lemma, R. B., Ledsaak, M., Fuglerud, B. M., Sandve, G. K., Eskeland, R., & Gabrielsen, O. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-88516-w · Cited by 17
Single-molecule chromatin configurations link transcription factor binding to expression in human cellsPreprint
Doughty, B. R., Hinks, M. M., Schaepe, J. M., Marinov, G., Thurm, A. R., Rios-Martinez, C., Parks, B. E., Tan, Y.-X., Marklund, E., Dubocanin, D., et al. (2024) · bioRxiv · doi:10.1101/2024.02.02.578660 · Cited by 14
“On average, endogenous enhancers contain binding sites for 5-6 TFs 1 , and in many tractable experimental systems, increasing the number of sites leads to strongly nonlinear effects on gene expression 2,3 .”
Background
The Thioesterase ACOT1 as a Regulator of Lipid Metabolism in Type 2 Diabetes Detected in a Multi-Omics Study of Human Liver
Cavalli, M., Diamanti, K., Dang, Y., Xing, P., Pan, G., Chen, X., & Wadelius, C. (2021) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2021.0093 · Cited by 16
“Within each cluster motifs are aligned to each other to generate an archetypal consensus motif (Vierstra et al., 2020).”
BackgroundResult
Kinetic networks identify TWIST2 as a key regulatory node in adipogenesisPreprint
Dutta, A. B., Lank, D. S., Przanowska, R. K., Przanowski, P., Wang, L., Nguyen, B., Walavalkar, N. M., Duarte, F. M., & Guertin, M. J. (2023) · Genome Research · doi:10.1101/gr.277559.122 · Cited by 16
“Combining accessibility data with TF motif analyses can accurately infer TF binding without the need for factor- and species-specific antibodies (Wu et al. 1979; Vierstra et al. 2020).”
Background
Massively parallel reporter assays and mouse transgenic assays provide correlated and complementary information about neuronal enhancer activity
Kosicki, M., Cintrón, D. L., Keukeleire, P., Schubach, M., Page, N. F., Georgakopoulos‐Soares, I., Akiyama, J. A., Plajzer-Frick, I., Novak, C. S., Kato, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-60064-1 · Cited by 27
“Motifs affected by variants tested in the transgenic assay were detected using motifbreakR 2.16.0 50 with ﬁ lterp=T, threshold=1e-4 and pwmList from Viestra 2020 68 .”
Influential
Multiomics and deep learning dissect regulatory syntax in human development
Liu, B. B., Jessa, S., Kim, S. H., Ng, Y. T., Higashino, S. I., Marinov, G. K., Chen, D. C., Parks, B. E., Li, L., Nguyen, T. C., et al. (2026) · Nature · doi:10.1038/s41586-026-10326-9 · Cited by 36
“The lexicon also recovered variants of canonical motifs, including both the core and upstream CTCF motifs, reflecting DNA contacts by distinct CTCF zinc-fingers 5,32 (Extended Data Fig.”
Integrative dissection of gene regulatory elements at base resolution
Chen, Z., Javed, N. M., Moore, M. M., Wu, J., Sun, G., Vinyard, M. E., Collins, A., Pinello, L., Najm, F. J., & Bernstein, B. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100318 · Cited by 17
“To avoid motif redundancy, we obtained non-redundant motif cluster definitions and the corresponding PWMs from https://resources.altius.org/~jvierstra/projects/motif-clustering-v2.0beta/ (Vierstra et al., 2020).”
Method
Are non‐protein coding RNAs junk or treasure?
Walter, N. G. (2024) · BioEssays · doi:10.1002/bies.202300201 · Cited by 19
Massively parallel binding assay (MPBA) reveals limited transcription factor binding cooperativity, challenging models of specificity
Jana, T., Brodsky, S., Manadre, W., Vidavski, M., Valinsky, G., Mindel, V., Ilan, G., Carmi, M., Jonas, F., & Barkai, N. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae846 · Cited by 17
Base-resolution UV footprinting by sequencing reveals distinctive damage signatures for DNA-binding proteins
Elliott, K., Singh, V. K., Boström, M., & Larsson, E. (2023) · Nature Communications · doi:10.1038/s41467-023-38266-2 · Cited by 15
“DNase I footprints from Vierstra, et al.31 are indicated as green lines in b–d. Source data are provided as a Source Data file.”
BackgroundMethodInfluential
High Resolution Single Cell Maps Reveals Distinct Cell Organization and Function Across Different Regions of the Human IntestinePreprint
Hickey, J. W., Becker, W. R., Nevins, S. A., Horning, A. M., Espín-Pérez, A., Chiu, R., Chen, D. C., Cotter, D. J., Esplin, E. D., Weimer, A. K. K., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.25.469203 · Cited by 16
“We next identified the TFs with the highest correlation between their gene expression and the chromatin accessibility activity level of their putative DNA binding motifs 44 to nominate TFs directly driving accessibility changes.”
Background
A multi-modal transformer for cell type-agnostic regulatory predictions
Javed, N. M., Weingarten, T., Sehanobish, A., Roberts, A., Dubey, A., Choromański, K., & Bernstein, B. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100762 · Cited by 22
“We then selected 50k random peaks for each dataset and computed their motif enrichments relative to the cell type agnostic background using the SEA 44 tool from the MEME suite and 693 consensus motif models deﬁned in Vierstra et al. 2020.”
Cell-type-specific cis-regulatory divergence in gene expression and chromatin accessibility revealed by human-chimpanzee hybrid cells
Wang, B., Starr, A. L., & Fraser, H. B. (2023) · eLife · doi:10.7554/elife.89594 · Cited by 30
Transcription factor interactions explain the context-dependent activity of CRX binding sites
Loell, K., Friedman, R. Z., Myers, C. A., Corbo, J. C., Cohen, B. A., & White, M. A. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011802 · Cited by 17
Evolutionary Innovations in Conserved Regulatory Elements Associate With Developmental Genes in Mammals
Uebbing, S., Kocher, A. A., Baumgartner, M., Ji, Y., Bai, S., Xing, X., Nottoli, T., & Noonan, J. P. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae199 · Cited by 15
Perspective on recent developments and challenges in regulatory and systems genomics
Zeitlinger, J., Roy, S., Ay, F., Mathelier, A., Medina-Rivera, A., Mahony, S., Sinha, S. K., & Ernst, J. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbaf106 · Cited by 18
“…to DNase digestion (Burch and Weintraub 1983), and DNase-seq provides comprehensive and quantitative information on chromatin accessibility genome-wide, while also revealing TF binding footprints with deeper sequencing (Song and Crawford 2010, Thurman et al. 2012, Vierstra et al. 2020).”
Metacode: One code to rule them all
Paredes, O., Morales, J. A., Mendizabal-Ruiz, A. P., & Romo‐Vázquez, R. (2021) · Biosystems · doi:10.1016/j.biosystems.2021.104486 · Cited by 14
Tissue context determines the penetrance of regulatory DNA variation
Halow, J. M., Byron, R., Hogan, M. S., Ordóñez, R., Groudine, M. T., Bender, M. A., Stamatoyannopoulos, J. A., & Maurano, M. T. (2021) · Nature Communications · doi:10.1038/s41467-021-23139-3 · Cited by 15
“…the challenge of obtaining TF-specific occupancy data for all factors expressed in a given cell type, we expect that improved recognition of in vivo occupied TF binding sites from DNase I footprinting data 26,27 will be the most fruitful way to obtain further improvements in prediction performance.”
Background
Differences in nanoscale organization of regulatory active and inactive human chromatin
Brandstetter, K., Zülske, T., Ragoczy, T., Hörl, D., Guirao‐Ortiz, M., Steinek, C., Barnes, T., Stumberger, G., Schwach, J., Haugen, E., et al. (2022) · Biophysical Journal · doi:10.1016/j.bpj.2022.02.009 · Cited by 15
Large-scale mapping and systematic mutagenesis of human transcriptional effector domainsPreprint
DelRosso, N. V., Tycko, J., Suzuki, P., Andrews, C. J., Aradhana, Mukund, A. X., Liongson, I., Ludwig, C., Spees, K. K., Fordyce, P. M., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.26.505496 · Cited by 14
Regulatory grammar in human promoters uncovered by MPRA-based deep learning
Barbadilla-Martínez, L., Klaassen, N. H. M., Franceschini-Santos, V. H., Breda, J., Yücel, H., Hernández-Quiles, M., van Lieshout, T., Urzúa-Traslaviña, C. G., Boi, M. C. L., Akbarzadeh, M., et al. (2026) · Nature · doi:10.1038/s41586-025-10093-z · Cited by 33
“To cluster motifs separately for the Hocomoco (v.11) human database and for the unknown motifs, we followed previously described meth-ods 81 .”
Epigenetics and tissue immunity—Translating environmental cues into functional adaptations*
Tuong, Z. K., Stewart, B. J., Guo, S. A., & Clatworthy, M. R. (2021) · Immunological Reviews · doi:10.1111/imr.13036 · Cited by 13
RUNX1 loss renders hematopoietic and leukemic cells dependent on IL-3 and sensitive to JAK inhibition
Fan, A. C., Nakauchi, Y., Bai, L., Azizi, A., Nuno, K. A., Zhao, F., Köhnke, T., Karigane, D., Hernandez, D. C., Reinisch, A., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci167053 · Cited by 16
“Transcription factor motif enrichment was performed using ChromVAR (63) as previously described using JASPAR 2018 motif clusters (64) to reduce redundancy.”
Method
Strategies for effectively modelling promoter-driven gene expression using transfer learningPreprint
Reddy, A. J., Herschl, M. H., Geng, X., Kolli, S., Lu, A. X., Kumar, A., Hsu, P. D., Levine, S., & Ioannidis, N. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.24.529941 · Cited by 14
“First, we obtain a list of clustered TF-binding motifs (Vierstra et al., 2020) 1 .”
Systematic identification of disease-causing promoter and untranslated region variants in 8040 undiagnosed individuals with rare disease
Martin-Geary, A. C., Blakes, A. J. M., Dawes, R., Findlay, S. D., Lord, J., Dong, S., Walker, S., Talbot-Martin, J., Wieder, N., D’Souza, E. N., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01464-2 · Cited by 17
Cap analysis of gene expression (CAGE) and noncoding regulatory elements
Guerrini, M. M., Oguchi, A., Suzuki, A., & Murakawa, Y. (2021) · Seminars in Immunopathology · doi:10.1007/s00281-021-00886-5 · Cited by 12
Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA
Hu, S. S., Liu, L., Li, Q., Ma, W., Guertin, M. J., Meyer, C. A., Deng, K., Zhang, T., & Zang, C. (2022) · Nature Communications · doi:10.1038/s41467-022-33194-z · Cited by 12
“DNase/ATAC-seq footprint detection has been implicated as an effective approach for identifying accurate TF binding sites at base-pair resolution 6,7 .”
BackgroundMethodInfluential
A genome-wide association analysis of loss of ambulation in dystrophinopathy patients suggests multiple candidate modifiers of disease severity
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. (2023) · European Journal of Human Genetics · doi:10.1038/s41431-023-01329-5 · Cited by 12
“We used ENCODE transcription factor (TF) footprints [29] to assess whether rs2061566 disrupted a regulatory protein binding site within the E1929501 enhancer.”
BackgroundMethod
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) · Inflammation and Regeneration · doi:10.1186/s41232-023-00290-6 · Cited by 13
“In addition, the GST–Jdp2 protein bound to both DRE and ARE sites, probably through the CG-rich sequences [52].”
Background
A SWI/SNF-specific Ig-like domain, SWIFT, is a transcription factor binding platform
Jain, S. U., Williamson, K. E., Ying, A. W., Turner, A. M., Jiang, R. J., Raval, S., So, K., Allison, M. J., Sankar, A., Sáme Guerra, D. D., et al. (2026) · Science · doi:10.1126/science.aeb3627 · Cited by 11
Sperm chromatin accessibility’s involvement in the intergenerational effects of stress hormone receptor activation
Fischer, V., Kretschmer, M., Germain, P., Kaur, J., Barrenechea, S. M., Pelczar, P., Schürmann, D., Schär, P., & Gapp, K. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02684-z · Cited by 13
“Motifs were obtained from the HOCOMOCO v11 core motif database, and genome-wide hits for motif archetypes were downloaded from a previously published study [81].”
Method
Adaptive Selection of Cis-regulatory Elements in the Han Chinese
Liu, S., Luo, H., Zhang, P., Li, Y., Hao, D., Zhang, S., Song, T., Xu, T., & He, S. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae034 · Cited by 10
“The π is a prior probability of selection, and in this work, we integrated the evolution conservation and functional genomic features of sequences, where l represents the LINSIGHT scores (Huang et al. 2017) indicating sequence conservation, and the latter six factors represent the assumed prior probability for six fun…”
MethodInfluential
A relic of design: against proper functions in biology
Ratti, E., & Germain, P. (2022) · Biology & Philosophy · doi:10.1007/s10539-022-09856-z · Cited by 11
CSC software corrects off-target mediated gRNA depletion in CRISPR-Cas9 essentiality screens
Perez, A. R., Sala, L., Perez, R. K., & Vidigal, J. A. (2021) · Nature Communications · doi:10.1038/s41467-021-26722-w · Cited by 11
Prioritization and functional analysis of GWAS risk loci for Barrett’s esophagus and esophageal adenocarcinoma
Chen, J., Ali, M. W., Yan, L., Dighe, S. G., Dai, J. Y., Vaughan, T. L., Casey, G., & Buas, M. F. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab259 · Cited by 10
Review of gene expression using microarray and RNA-seq
Villaseñor-Altamirano, A. B., Balderas-Martínez, Y. I., & Medina-Rivera, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-817218-6.00008-5 · Cited by 10
Pioneering new enhancers by GATA3: role of facilitating transcription factors and chromatin remodeling
Orlando, K. A., Grimm, S. A., & Wade, P. A. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf473 · Cited by 12
Functional annotation of breast cancer risk loci: current progress and future directions
Cardoso, S. R., Gillespie, A., Haider, S. A., & Fletcher, O. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01612-6 · Cited by 10
“have generated a high-resolution genome-wide consensus TF-footprint index in 243 human cell and tissue types, including a breast cancer cell line (T-47D), a normal mammary epithelial cell line (MCF10a) and normal mammary fibroblasts [38].”
Background
Expanded ENCODE delivers invaluable genomic encyclopedia
Hon, C.-C., & Carninci, P. (2020) · Nature · doi:10.1038/d41586-020-02139-1 · Cited by 10
Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model
Han, S. K., Muto, Y., Wilson, P. C., Humphreys, B. D., Sampson, M. G., Chakravarti, A., & Lee, D. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2212810119 · Cited by 10
DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural NetworkPreprint
Li, Y., Wen, J., Li, G., Chen, J. C., Sun, Q., Liu, W., Guan, W., Lai, B., Szatkiewicz, J., He, X., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2399024/v1 · Cited by 11
“The candidate cis-regulatory elements (cCREs) from ENCODE3 were collected based on DHS (DNase I-hypersensitive sites), H3K27ac, H3K4me3, CTCF and transcription factors (TF) 48,49 and were downloaded from https://www.vierstra.org/resources/dgf.”
Method
Dissecting regulatory syntax in human development with scalable multiomics and deep learningPreprint
Liu, B. B., Jessa, S., Kim, S. H., Ng, Y. T., Higashino, S. I., Marinov, G. K., Chen, D. C., Parks, B. E., Li, L., Nguyen, T. C., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.30.651381 · Cited by 10
ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning
Saelens, W., Pushkarev, O., & Deplancke, B. (2025) · Nature Communications · doi:10.1038/s41467-024-55447-9 · Cited by 8
“8a), an observation further con ﬁ rmed on DNase I hypersensitivity datasets 56 , which typically have a higher sensitivity to detect footprints (Supplementary Fig.”
The epigenetic landscape shapes smoking-induced mutagenesis by modulating DNA damage susceptibility and repair efficiency
Heilbrun, E. E., Tseitline, D., Wasserman, H., Kirshenbaum, A., Cohen, Y., Gordân, R., & Adar, S. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf048 · Cited by 9
Cancer cell type-specific derepression of transposable elements by inhibition of chromatin modifier enzymes
Patel, D., Tiusanen, V., Karttunen, K., Pihlajamaa, P., & Sahu, B. (2025) · Communications Biology · doi:10.1038/s42003-025-08413-0 · Cited by 9
Coupling CRISPR scanning with targeted chromatin accessibility profiling using a double-stranded DNA deaminase
Roh, H., Shen, S. P., Yan, H., Kwok, H. S., Siegenfeld, A. P., Lee, C., Zepeda, M. L., Guo, C., Roseman, S. A., Comenho, C., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02811-2 · Cited by 12
“The aggregate TF footprint scores correlate with DNase-seq footprint scores 28 , particularly for TFs known to exhibit strong footprinting in DNase-seq and ATAC-seq (Extended Data Fig.”
Conservation of cis-regulatory codes over half a billion years of evolution
Ogawa, Y., Liu, Y., Myers, C. A., Morshedian, A., Fain, G., Sampath, A. P., & Corbo, J. C. (2025) · Science Advances · doi:10.1126/sciadv.adw7681 · Cited by 9
“The motif clustering approach was adapted from a prior study ( 68 ).”
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) · Genome biology · doi:10.1186/s13059-023-03021-9 · Cited by 12
“To organize the rd motifs, they are assigned, based on their most significant motif alignment, to pre-specified motif clusters published else-where [34] (Fig.”
BackgroundInfluential
The regulatory grammar of human promoters uncovered by MPRA-trained deep learningPreprint
Barbadilla-Martínez, L., Klaassen, N. H. M., Franceschini-Santos, V. H., Breda, J., Hernández-Quiles, M., van Lieshout, T., Urzúa-Traslaviña, C. G., Yücel, H., Boi, M. C. L., Hermana-Garcia-Agullo, C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.09.602649 · Cited by 9
Continuous cell type diversification throughout the embryonic and postnatal mouse visual cortex developmentPreprint
Gao, Y., van Velthoven, C. T. J., Lee, C., Thomas, E. D., Bertagnolli, D., Carey, D., Casper, T., Chakka, A. B., Chakrabarty, R., Clark, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.02.616246 · Cited by 9
Codebook: sequence specificity and genomic binding of poorly-characterized human transcription factorsPreprint
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“…expression and chromatin accessibility analysis was performed using a non-redundant, multi-model motif reference database curated by Vierstra et al. 71 The integrated approach allowed us to discriminate between specific TFs within TF families, and the curated motif reference provided information…”
Influential
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Self-citation
Epigenetic control of metabolic identity across cell types
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“We accessed the human reference genome-wide motif occurrences of 280 non-redundant archetypal TF motif modules [30].”

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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Global reference mapping of human transcription factor footprints

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