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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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Herring, C. A., Simmons, R. K., Freytag, S., Poppe, D., Moffet, J. J. D., Pflueger, J., Buckberry, S., Vargas-Landín, D. B., Clément, O., Goñi, E., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.039 · Cited by 242
Perspectives on ENCODE
Abascal, F., Acosta, R., Addleman, N. J., Adrian, J., Afzal, V., Aken, B. L., Ai, R., Akiyama, J. A., Jammal, O. A., Amrhein, H., et al. (2020) · Nature · doi:10.1038/s41586-020-2449-8 · Cited by 225
Chromatin accessibility profiling methods
Minnoye, L., Marinov, G. K., Krausgruber, T., Pan, L., Marand, A. P., Secchia, S., Greenleaf, W. J., Furlong, E. E. M., Zhao, K., Schmitz, R. J., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00008-9 · Cited by 206
Multiple causal variants underlie genetic associations in humans
Abell, N. S., DeGorter, M. K., Gloudemans, M. J., Greenwald, E., Smith, K. S., He, Z., & Montgomery, S. B. (2022) · Science · doi:10.1126/science.abj5117 · Cited by 180
scPerturb: harmonized single-cell perturbation data
Peidli, S., Green, T. D., Shen, C., Gross, T., Min, J., Garda, S., Yuan, B., Schumacher, L. J., Taylor-King, J. P., Marks, D. S., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02144-y · Cited by 194
“60; and marker-peaks per perturbation target, which quantifies the differential regulation of highly variable peaks for each type of perturbation.”
Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale
Zhou, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01065-4 · Cited by 177
Molecular Co-occupancy Identifies Transcription Factor Binding Cooperativity In Vivo
Sönmezer, C., Kleinendorst, R., Imanci, D., Barzaghi, G., Villacorta, L., Schübeler, D., Benes, V., Molina, N., & Krebs, A. R. (2021) · Molecular Cell · doi:10.1016/j.molcel.2020.11.015 · Cited by 162
High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer
Pierce, S. E., Granja, J. M., & Greenleaf, W. J. (2021) · Nature Communications · doi:10.1038/s41467-021-23213-w · Cited by 146
The ENCODE Uniform Analysis PipelinesPreprint
Hitz, B. C., Lee, J.-W., Jolanki, O., Kagda, M. S., Graham, K., Sud, P., Gabdank, I., Strattan, J. S., Sloan, C. A., Dreszer, T. R., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.04.535623 · Cited by 149
“For details on the statistics of the footprinting algorithm see the Supplementary Methods of Vierstra et al. 37 Alignment and trimming metrics are calculated by samtools and cutadapt, while other utilities measure the extent of read duplication and fragment size distribution.”
Method
ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variantsPreprint
Pampari, A., Shcherbina, A., Kvon, E. Z., Kosicki, M., Nair, S., Kundu, S., Kathiria, A. S., Risca, V. I., Kuningas, K., Alasoo, K., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.25.630221 · Cited by 183
Cardiac cell type–specific gene regulatory programs and disease risk association
Hocker, J. D., Poirion, O., Zhu, F., Buchanan, J., Zhang, K., Chiou, J., Wang, T., Zhang, Q., Hou, X., Li, Y. E., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf1444 · Cited by 136
Large-scale mapping and mutagenesis of human transcriptional effector domains
DelRosso, N., Tycko, J., Suzuki, P., Andrews, C., Aradhana, Mukund, A., Liongson, I., Ludwig, C., Spees, K., Fordyce, P., et al. (2023) · Nature · doi:10.1038/s41586-023-05906-y · Cited by 130
Core transcription programs controlling injury-induced neurodegeneration of retinal ganglion cells
Tian, F., Cheng, Y., Zhou, S., Wang, Q., Monavarfeshani, A., Gao, K., Jiang, W., Kawaguchi, R., Wang, Q., Tang, M., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.003 · Cited by 127
A foundation model of transcription across human cell types
(2025) · doi:10.1038/s41586-024-08391-z · Cited by 130
“As the availability of ATAC-seq and multiome data continues to grow, more comprehensive reference peak sets, such as the ENCODE DHS index 13 and cPeaks 65 , will further facilitate the adaptation of the GET model to an even broader range of cell types and experimental conditions.”
Influential
Mammalian evolution of human cis-regulatory elements and transcription factor binding sites
Andrews, G., Fan, K., Pratt, H. E., Phalke, N. N., Karlsson, E. K., Lindblad‐Toh, K., Gazal, S., Moore, J. E., Weng, Z., Andrews, G., et al. (2023) · Science · doi:10.1126/science.abn7930 · Cited by 110
Massively parallel discovery of human-specific substitutions that alter enhancer activity
Uebbing, S., Gockley, J., Reilly, S. K., Kocher, A. A., Geller, E. T., Gandotra, N., Scharfe, C., Cotney, J. L., & Noonan, J. P. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2007049118 · Cited by 113
“…corresponding factors in vivo, we repeated this analysis on TFBSs within regions of accessible chromatin in human neural stem cells and cortical organoids (assay for transposase-accessible chromatin using sequencing [ATAC-seq] data generated in this study and data from previous studies) (23, 26).”
Cancer cells resistant to immune checkpoint blockade acquire interferon-associated epigenetic memory to sustain T cell dysfunction
Qiu, J., Xu, B., Ye, D., Ren, D., Wang, S., Benci, J. L., Xu, Y., Ishwaran, H., Beltra, J., Wherry, E. J., et al. (2023) · Nature Cancer · doi:10.1038/s43018-022-00490-y · Cited by 117
Identification of constrained sequence elements across 239 primate genomes
Kuderna, L. F. K., Ulirsch, J. C., Rashid, S., Ameen, M., Sundaram, L., Hickey, G., Cox, A. J., Gao, H., Kumar, A., Aguet, F., et al. (2024) · Nature · doi:10.1038/s41586-023-06798-8 · Cited by 129
“After stratifying human genes by selective constraint quantified by loss-of-function observed/ expected upper bound fraction (LOEUF) scores 38 , we found that variants affecting the expression of highly constrained genes tended to be enriched at more deeply constrained DHS elements and footprints…”
Influential
Solid-phase capture and profiling of open chromatin by spatial ATAC
Llorens-Bobadilla, E., Zamboni, M., Marklund, M., Bhalla, N., Chen, X., Hartman, J., Frisén, J., & Ståhl, P. L. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01603-9 · Cited by 108
Hold out the genome: a roadmap to solving the cis-regulatory code
de Boer, C. G., & Taipale, J. (2024) · Nature · doi:10.1038/s41586-023-06661-w · Cited by 107
UniBind: maps of high-confidence direct TF-DNA interactions across nine species
Puig, R. R., Boddie, P., Khan, A., Castro-Mondragon, J. A., & Mathelier, A. (2021) · BMC Genomics · doi:10.1186/s12864-021-07760-6 · Cited by 102
Prime editing in mice reveals the essentiality of a single base in driving tissue-specific gene expression
Gao, P., Lyu, Q., Ghanam, A. R., Lazzarotto, C. R., Newby, G. A., Zhang, W., Choi, M., Slivano, O. J., Holden, K., Walker, J. A., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02304-3 · Cited by 96
A potent epigenetic editor targeting human PCSK9 for durable reduction of low-density lipoprotein cholesterol levels
Tremblay, F., Xiong, Q., Shah, S. S., Ko, C., Kelly, K., Morrison, M. S., Giancarlo, C., Ramírez, R. N., Hildebrand, E. M., Voytek, S. B., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03508-x · Cited by 116
ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination
Xu, Q., Γεωργίου, Γ., Frölich, S., van der Sande, M., Veenstra, G. J. C., Zhou, H., & van Heeringen, S. J. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab598 · Cited by 89
Cooperative binding between distant transcription factors is a hallmark of active enhancers
Rao, S., Ahmad, K., & Ramachandran, S. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.02.014 · Cited by 82
BANP opens chromatin and activates CpG-island-regulated genes
Grand, R. S., Burger, L., Gräwe, C., Michael, A. K., Isbel, L., Heß, D., Hoerner, L., Iešmantavičius, V., Durdu, S., Pregnolato, M., et al. (2021) · Nature · doi:10.1038/s41586-021-03689-8 · Cited by 95
DNA-guided transcription factor cooperativity shapes face and limb mesenchyme
Kim, S., Morgunova, E. Y., Naqvi, S., Goovaerts, S., Bader, M., Koska, M., Popov, A. N., Luong, C., Pogson, A. N., Swigut, T., et al. (2024) · Cell · doi:10.1016/j.cell.2023.12.032 · Cited by 83
“102 Motifs clusters from https://www.vierstra.org/ resources/motif_clustering 127 were used, with one manually added cluster for the Coordinator motif, into which the TWIST1 motifs from HOCOMOCO were moved.”
Method
RUNX2 promotes fibrosis via an alveolar-to-pathological fibroblast transition
Fang, Y., Chung, S. S. W., Xu, L., Xue, C., Liu, X., Jiang, D., Li, R., Korogi, Y., Yuan, K., Saqi, A., et al. (2025) · Nature · doi:10.1038/s41586-024-08542-2 · Cited by 96
Spatially mapped single-cell chromatin accessibility
Thornton, C. A., Mulqueen, R. M., Torkenczy, K. A., Nishida, A., Lowenstein, E. G., Fields, A. J., Steemers, F. J., Zhang, W. H., McConnell, H. L., Woltjer, R. L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21515-7 · Cited by 86
Evolutionary cell type mapping with single-cell genomics
Tanay, A., & Sebé-Pedrós, A. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.04.008 · Cited by 76
Transposable elements as tissue-specific enhancers in cancers of endodermal lineage
Karttunen, K., Patel, D., Xia, J., Fei, L., Palin, K., Aaltonen, L. A., & Sahu, B. (2023) · Nature Communications · doi:10.1038/s41467-023-41081-4 · Cited by 80
The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation
Kim, D. S., Risca, V. I., Reynolds, D. L., Chappell, J., Rubin, A. J., Jung, N., Donohue, L. K. H., Lopez-Pajares, V., Kathiria, A. S., Shi, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00947-3 · Cited by 80
Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation
Li, J., Jaiswal, M. K., Chien, J.-F., Kozlenkov, A., Jung, J., Zhou, P., Gardashli, M., Pregent, L., Engelberg-Cook, E., Dickson, D. W., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41033-y · Cited by 81
Stepwise activities of mSWI/SNF family chromatin remodeling complexes direct T cell activation and exhaustion
Battistello, E., Hixon, K., Comstock, D. E., Collings, C. K., Chen, X., Rodriguez-Hernaez, J., Lee, S., Cervantes, K. S., Hinkley, M. M., Ntatsoulis, K., et al. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.02.026 · Cited by 78
The functional and evolutionary impacts of human-specific deletions in conserved elements
Xue, J. R., Mackay-Smith, A., Mouri, K., García, M. F., Dong, M. X., Akers, J. F., Noble, M., Li, X., Lindblad‐Toh, K., Karlsson, E. K., et al. (2023) · Science · doi:10.1126/science.abn2253 · Cited by 81
“( 58 ).”
An atlas of transcribed human cardiac promoters and enhancers reveals an important role of regulatory elements in heart failure
Deviatiiarov, R. M., Gams, A., Kulakovskiy, I. V., Buyan, A., Meshcheryakov, G., Syunyaev, R. A., Singh, R., Shah, P. M., Tatarinova, T. V., Gusev, O., et al. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00182-x · Cited by 75
Predicting expression-altering promoter mutations with deep learning
Jaganathan, K., Ferraro, N. M., Novakovsky, G., Wang, Y., James, T., Schwartzentruber, J., Fiziev, P. P., Kassam, I., Cao, F., Hawe, J. S., et al. (2025) · Science · doi:10.1126/science.ads7373 · Cited by 87
VannoPortal: multiscale functional annotation of human genetic variants for interrogating molecular mechanism of traits and diseases
Huang, D., Zhou, Y., Yi, X., Fan, X., Wang, J., Yao, H., Sham, P. C., Hao, J., Chen, K., & Li, M. J. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab853 · Cited by 67
“We also incorporated allelic imbalance evidence of chromatin accessibility and TF binding from multiple studies (55,56).”
Method
Speed–Specificity Trade-Offs in the Transcription Factors Search for Their Genomic Binding Sites
Jana, T., Brodsky, S., & Barkai, N. (2021) · Trends in Genetics · doi:10.1016/j.tig.2020.12.001 · Cited by 64
“Such indirect cooperationmay explain the increased tendency of some motifs to co-occur in the same promoters [114], the localization of multiple TFs to the same enhancer regions [115–118], and the prevalence of multiple, single-TF footprints within DNase-hypersensitive regions [18].”
Background
ATAC-STARR-seq reveals transcription factor–bound activators and silencers within chromatin-accessible regions of the human genomePreprint
Hansen, T. J., & Hodges, E. C. (2022) · Genome Research · doi:10.1101/gr.276766.122 · Cited by 71
“For this reason, we refer to TFs using their ENCODE-defined “archetypes,” which reflects the group of TFs that share the same motif (Vierstra et al. 2020).”
Method
HeRA: an atlas of enhancer RNAs across human tissues
Zhang, Z., Hong, W., Ruan, H., Jing, Y., Li, S., Liu, Y., Wang, J., Li, W., Diao, L., & Han, L. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa940 · Cited by 63
“For those TFs with low percentage evidences, we speculated that this is due to the limited number of ChIP-seq experiments in ENCODE, that the majority of them are examined in ≤ 5 tissues / cell lines (34).”
Chromatin accessibility during human first-trimester neurodevelopment
Mannens, C. C. A., Hu, L., Lönnerberg, P., Schipper, M., Reagor, C. C., Li, X., He, X., Barker, R. A., Sundström, E., Posthuma, D., et al. (2024) · Nature · doi:10.1038/s41586-024-07234-1 · Cited by 69
“Additionally, all TFs were assigned to a family on the basis of their arche-motif 21 .”
Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers
Sundaram, L., Kumar, A., Zatzman, M., Salcedo, A., Ravindra, N. G., Shams, S., Louie, B. H., Bagdatli, S. T., Myers, M. A., Sarmashghi, S., et al. (2024) · Science · doi:10.1126/science.adk9217 · Cited by 66
“Weusedthesemodelsto predictwhichsequence-basedarchetypalmo-tifinstances( 33 )werelikelytobeactivewithin accessible putative distal regulatory elements in individual BRCA samples.”
BackgroundResultInfluential
Regulatory transposable elements in the encyclopedia of DNA elements
Du, A. Y., Chobirko, J. D., Zhuo, X., Feschotte, C., & Wang, T. (2024) · Nature Communications · doi:10.1038/s41467-024-51921-6 · Cited by 72
“Enriched motifs were grouped according to motif archetypes 47 .”
Background
“Stripe” transcription factors provide accessibility to co-binding partners in mammalian genomes
Zhao, Y., Vartak, S. V., Conte, A., Wang, X., Garcia, D. A., Stevens, E., Jung, S. K., Kieffer-Kwon, K.-R., Vian, L., Stodola, T. J., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.06.029 · Cited by 62
The correlation between CpG methylation and gene expression is driven by sequence variants
Stefánsson, Ó. A., Sigurpalsdottir, B. D., Rögnvaldsson, S., Halldorsson, G. H., Juliusson, K., Sveinbjörnsson, G., Gunnarsson, B., Beyter, D., Jónsson, H., Gudjonsson, S. A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01851-2 · Cited by 74
“DNA regulatory sequences, for example, promoters and enhancers, are frequently bound by transcription factors (TFs) 5,6 and depleted of CpG methylation 7,8 .”
Multiscale footprints reveal the organization of cis-regulatory elements
Yan, H., Horlbeck, M. A., Zhang, R., Ma, S., Shrestha, R., Kartha, V. K., Duarte, F. M., Hock, C., Savage, R. E., Labade, A. S., et al. (2025) · Nature · doi:10.1038/s41586-024-08443-4 · Cited by 93
Cell-intrinsic Aryl Hydrocarbon Receptor signalling is required for the resolution of injury-induced colonic stem cells
Shah, K., Maradana, M. R., Delàs, M. J., Metidji, A., Graelmann, F. J., Llorian, M., Chakravarty, P., Li, Y., Tolaini, M., Shapiro, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29098-7 · Cited by 58
Single-nucleotide-level mapping of DNA regulatory elements that control fetal hemoglobin expression
Cheng, L., Li, Y., Qi, Q., Xu, P., Feng, R., Palmer, L. E., Chen, J., Wu, R., Yee, T., Zhang, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00861-8 · Cited by 59
Decoding Non-coding Variants: Recent Approaches to Studying Their Role in Gene Regulation and Human Diseases
Peña-Martínez, E. G., & Rodríguez‐Martínez, J. A. (2024) · Frontiers in Bioscience-Scholar · doi:10.31083/j.fbs1601004 · Cited by 63
“Enhancers are one of the most abundant CREs responsible for enhancing transcription and regulating the spatial and temporal expression of genes in a tissue-specific manner [14].”
Background
A mutation rate model at the basepair resolution identifies the mutagenic effect of polymerase III transcription
Seplyarskiy, V. B., Koch, E. M., Lee, D. J., Lichtman, J. S., Luan, H. H., & Sunyaev, S. R. (2023) · Nature Genetics · doi:10.1038/s41588-023-01562-0 · Cited by 54
“This analysis finds the following two such processes: transcription by polymerase III (Pol III) increases mutation rate by an order of magnitude and binding of transcription factor (TF) in testis increases T > G rate (~1.6-fold).”
BackgroundInfluential
Functional dissection of human cardiac enhancers and noncoding de novo variants in congenital heart disease
Xiao, F., Zhang, X., Morton, S. U., Kim, S. W., Fan, Y., Gorham, J., Zhang, H., Berkson, P., Mazumdar, N., Cao, Y., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01669-y · Cited by 62
“Enriched motifs were identified using Homer (v.4.11.1) 38 and a previously described nonredundant motif database 50 .”
Influential
The origin of human mutation in light of genomic data
Seplyarskiy, V. B., & Sunyaev, S. R. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00376-2 · Cited by 58
Mechanisms underlying divergent responses of genetically distinct macrophages to IL-4
Hoeksema, M. A., Shen, Z., Holtman, I. R., Zheng, A., Spann, N. J., Cobo, I., Gymrek, M., & Glass, C. K. (2021) · Science Advances · doi:10.1126/sciadv.abf9808 · Cited by 51
EWS-FLI1 regulates and cooperates with core regulatory circuitry in Ewing sarcoma
Shi, X., Zheng, Y., Jiang, L., Zhou, B. O., Yang, W., Li, L., Ding, L., Huang, M., Gery, S., Lin, D., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa901 · Cited by 54
“The spacing of TF binding sites can provide insights into the regulatory organization and cooperativity between TFs (38).”
Background
Denisovan introgression has shaped the immune system of present-day Papuans
Vespasiani, D. M., Jacobs, G. S., Cook, L. E., Brucato, N., Leavesley, M., Kinipi, C., Ricaut, F.-X., Cox, M. P., & Gallego Romero, I. (2022) · PLOS Genetics · doi:10.1371/journal.pgen.1010470 · Cited by 46
“[31], which clusters HOCOCOMO and Jaspar PWMs into 286 distinct clusters on the basis of sequence similarity, and considered only those a/naSNPs that disrupted PWMs included in these clusters (n = 3,028 Denisovan, 2,457 Neanderthal and 21,104 modern human variants).”
Developmental cell fate choice in neural tube progenitors employs two distinct cis-regulatory strategies
Delàs, M. J., Kalaitzis, C. M., Fawzi, T., Demuth, M., Zhang, I., Stuart, H. T., Costantini, E., Ivanovitch, K., Tanaka, E. M., & Briscoe, J. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.11.016 · Cited by 42
“We used TOBIAS(33) with motifs from three databases, grouping motifs into published archetypes based on position weight matrix clustering of motifs.(37) The analysis revealed homeodomain (HD) and PAXmotifs among those most differentially scoring between pMN, p2, and p0-1 at day 5 and day 6 (Figure 3D).”
Method
Longitudinal immune dynamics of mild COVID-19 define signatures of recovery and persistencePreprint
Talla, A., Vasaikar, S. V., Lemos, M. P., Moodie, Z., Pebworth, M.-P., Henderson, K. E., Cohen, K. W., Czartoski, J., Lai, L., Suthar, M. S., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.26.442666 · Cited by 46
“, 2009), ENCODE reference accessible regions (ENCODE file ID ENCFF503GCK; (Vierstra et al., 2020)), and TSS regions (TSS ±2kb from Ensembl v93; (Yates et al.”
Background
SMARCE1 deficiency generates a targetable mSWI/SNF dependency in clear cell meningioma
Pierre, R. S., Collings, C. K., Guerra, D. D. S., Widmer, C. J., Bolonduro, O., Mashtalir, N., Sankar, A., Liang, Y., Bi, W. L., Gerkes, E. H., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01077-0 · Cited by 45
Extrusion fountains are hallmarks of chromosome organization emerging upon zygotic genome activation
Galitsyna, A. A., Ulianov, S. V., Bazarevich, M., Bykov, N. S., Veil, M., Gao, M., Perevoschikova, K., Gelfand, M. S., Razin, S. V., Mirny, L. A., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69105-9 · Cited by 42
“…the site of facilitated cohesin loading as a 1-Kb region (one simulation monomer), consistent with recent characterizations of regulatory elements 49 and the average length of ATAC-seq peaks at fountain bases (606 bp) 41 , which corresponds to the narrow (~10 Kb) bases of the fountains we…”
Parallel functional testing identifies enhancers active in early postnatal mouse brain
Lambert, J. T., Su-Feher, L., Cichewicz, K., Warren, T. L., Zdilar, I., Wang, Y., Lim, K. J., Haigh, J., Morse, S. J., Canales, C. P., et al. (2021) · eLife · doi:10.7554/elife.69479 · Cited by 39
A GO catalogue of human DNA-binding transcription factors
Lovering, R. C., Gaudet, P., Acencio, M. L., Ignatchenko, A., Jolma, A., Fornés, O., Kuiper, M. T. R., Kulakovskiy, I. V., Lægreid, A., Martin, M. J., et al. (2021) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2021.194765 · Cited by 40
Single-molecule states link transcription factor binding to gene expression
Doughty, B. R., Hinks, M. M., Schaepe, J. M., Marinov, G. K., Thurm, A. R., Rios-Martinez, C., Parks, B. E., Tan, Y., Marklund, E., Dubocanin, D., et al. (2024) · Nature · doi:10.1038/s41586-024-08219-w · Cited by 42
In Vivo Dissection of Chamber-Selective Enhancers Reveals Estrogen-Related Receptor as a Regulator of Ventricular Cardiomyocyte Identity
Cao, Y., Zhang, X., Akerberg, B. N., Yuan, H., Sakamoto, T., Xiao, F., VanDusen, N. J., Zhou, P., Sweat, M., Wang, Y., et al. (2023) · Circulation · doi:10.1161/circulationaha.122.061955 · Cited by 41
Evolving cell states and oncogenic drivers during the progression of IDH-mutant gliomas
Wu, J., Castro, L. N. G., Battaglia, S., Farran, C. A. E., D’Antonio, J. P., Miller, T. E., Suvà, M. L., & Bernstein, B. (2024) · Nature Cancer · doi:10.1038/s43018-024-00865-3 · Cited by 48
“Motifs with similar sequences were collapsed into one motif through motif clustering 65 .”
Functional dissection of complex trait variants at single-nucleotide resolution
Siraj, L., Castro, R. I., Dewey, H. B., Kales, S., Butts, J. C., Nguyen, T. T. L., Kanai, M., Berenzy, D., Mouri, K., Wang, Q. S., et al. (2026) · Nature · doi:10.1038/s41586-026-10121-6 · Cited by 56
Self-citation
Assigning function to SNPs: Considerations when interpreting genetic variation
Fadason, T., Farrow, S. L., Gokuladhas, S., Golovina, E., Nyaga, D. M., O'Sullivan, J. M., & Schierding, W. S. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.08.008 · Cited by 44
Constructing gene regulatory networks using epigenetic data
Sonawane, A. R., DeMeo, D. L., Quackenbush, J. F., & Glass, K. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00208-3 · Cited by 45
Multimodal CRISPR perturbations of GWAS loci associated with coronary artery disease in vascular endothelial cells
Wünnemann, F., Tadjo, T. F., Beaudoin, M., Lalonde, S., Lo, K. S., Kleinstiver, B. P., & Lettre, G. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010680 · Cited by 40
Deep learning the cis-regulatory code of chromatin dynamics during cellular reprogrammingPreprint
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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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