Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations
Open accessAbstract
Enhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases 1-4. Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer-gene connections across cell types 5,6. We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome, and we applied it to test >3,500 potential enhancer-gene connections for 30 genes. We found that a simple activity-by-contact model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This activity-by-contact model allows us to construct genome-wide maps of enhancer-gene connections in a given cell type, on the basis of chromatin state measurements. Together, CRISPRi-FlowFISH and the activity-by-contact model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The probability of loop formation is an important biophysical quantity that can be used to inform quantitative models of chromatin looping and its downstream effects.
“Thus, the probability of loop formation represents an important biophysical quantity, which can be used to inform quantitative models of chromatin looping and its downstream effects.27–34”
From Genome-wide absolute quantification of chromatin looping (2026) · cites this paper as [30]
The ABC-model discussion (section labeled Abstract; page unavailable) uses enhancer–promoter contact frequency to model an enhancer’s quantitative effect on gene expression. It does not identify that frequency as the probability of loop formation or use it to model chromatin looping itself, so those parts of the claim are not supported. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThe ABC score relies on experimental data, such as a HiC-based interaction frequency and H3K27ac as input, whereas Enformer uses only DNA sequence as input and was never trained to explicitly locate enhancers.
“This is remarkable because the ABC score relies on experimental data, such as a HiC-based interaction frequency and H3K27ac as input (Fig. 2b, blue versus green, and Extended Data Fig. 7a), whereas Enformer uses only DNA sequence as input and was never trained to explicitly locate enhancers.”
From Effective gene expression prediction from sequence by integrating long-range interactions (2021) · cites this paper as [13]
The ABC model description states that the score uses H3K27ac ChIP-seq data and, in the described implementation, Hi-C contact frequency. Our copy does not address Enformer, so it cannot support the claims about Enformer's inputs or training. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThis fixed and broader window yielded better performance compared to the variable window size of ~500 bp as used in the original ABC score.
“This fixed and broader window yielded better performance compared to the variable window size of ~500 bp as used in the original ABC score (Extended Data Fig. 4a).”
From Effective gene expression prediction from sequence by integrating long-range interactions (2021) · cites this paper as Fulco et al. [13]
The Methods (Activity by Contact model; page not provided) say the original ABC model used 500-bp candidate elements and merged overlapping regions, producing variable lengths (576 bp on average in K562). Our copy does not report that a fixed, broader window performed better; the citing sentence points to its own Extended Data Fig. 4a for that comparison. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMachine learning strategies to predict enhancer-gene links on the basis of 1D and 3D biochemical annotations are already advancing beyond the simple 'nearest gene' approach.
“For example, machine learning strategies to predict enhancer–gene links on the basis of 1D and 3D biochemical annotations are already advancing beyond the simple ‘nearest gene’ approach134,209,222.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [209]
The paper’s model description and performance results (section labeled Abstract; page unavailable) show that Activity-by-Contact combines biochemical activity with 3D contact and predicts enhancer–gene links better than genomic distance or assignment to the closest gene. But the paper describes ABC as a simple biochemical model, not a machine-learning strategy, and reports that the prior machine-learning approaches it tested were unsuccessful. That part of the attribution is not supported. (LLM, conf=0.87)
Experimental — accuracy not yet measuredEven in interphase nuclei, genomic distance continues to be a reasonable predictor of functional enhancer-promoter contacts, particularly in the short to medium range (<100 kb).
“even in interphase nuclei, genomic distance continues to be a reasonable predictor of functional enhancer–promoter contacts, particularly in the short to medium range (< 100 kb), as evidenced by high-throughput enhancer perturbation data [183, 184].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [183]
The paper’s results (indexed under Abstract; page unavailable) report that 84% of regulatory distal elements were within 100 kb of their target promoter, supporting the distance-range part of the claim. But the same passage says distance-threshold predictions performed poorly: only 13% of distal elements within 100 kb of an expressed promoter had a regulatory effect. It does not establish the claim specifically for interphase nuclei. (LLM, conf=0.86)
Experimental — accuracy not yet measured
The ABC model showed good performance in predicting transcriptional effects of CRISPRi-mediated enhancer perturbations in dozens of loci in human cell lines, particularly for more proximal enhancers.
“The ABC model showed a good performance in predicting the transcriptional effects of CRISPRi-mediated enhancer perturbations (one-by-one) in dozens of loci in human cell lines, particularly for the more proximal enhancers [183, 184].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [183]
The paper’s main-text passage labeled Abstract (page unavailable) reports that ABC scores correlated with measured effects of CRISPR perturbations on gene expression and outperformed alternative predictors. The Methods report CRISPRi-FlowFISH screens for 30 genes in K562 cells. Although the paper says most regulatory enhancers were within 100 kb of their target promoters, the passages do not establish that ABC performed better for proximal than for more distant enhancers. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMinimal regulatory and structural complexities introduce deviations from this behaviour and might therefore confound its detection outside a highly controlled genomic environment, notably when studying regulatory sequences in their endogenous context.
“Minimal regulatory and structural complexities introduce deviations from this behaviour and might therefore confound its detection outside a highly controlled genomic environment, notably when studying regulatory sequences in their endogenous context23.”
From Nonlinear control of transcription through enhancer–promoter interactions (2022) · cites this paper as [23]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe activity-by-contact model predicts that rs6024870 also contacts multiple other genes within the same TAD, including CASS4, CSTF1, AURKA, FAM210B, and TFAP2C in THP1 cells.
“the activity-by-contact model predicts that this SNP also contacts multiple other genes within the same TAD, including CASS4, CSTF1, AURKA, FAM210B, and TFAP2C in THP1 cells (an immortalized human monocyte cell line derived from an acute monocytic leukemia patient)72,73.”
From Variant-to-function mapping of late-onset Alzheimer’s disease GWAS signals in human microglial cell models implicates <i>RTFDC1</i> at the <i>CASS4</i> locus (2024) · cites this paper as [73]
The paper describes the activity-by-contact model as a predictor of enhancer–gene connections (Methods, “Activity by Contact (ABC) model”). But its Data Availability Statement lists predictions for six cell types, not THP1, and our copy does not report a prediction linking rs6024870 to CASS4, CSTF1, AURKA, FAM210B, or TFAP2C within a TAD. (LLM, conf=0.86)
Experimental — accuracy not yet measuredWhen comparing Borzoi, Enformer and a distance-to-TSS baseline on classifying measured positive from negative enhancer-gene interactions, Borzoi has superior AUPRC and AUROC at all distances.
“When comparing Borzoi, Enformer and a distance-to-TSS baseline on their ability to classify measured positive from negative enhancer–gene interactions in data from previous works60–65, we find that Borzoi has superior average precision (AUPRC) and area under the receiver operating characteristic curve (AUROC) at all distances (Fig. 4c and Extended Data Fig. 5a).”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [60]
The performance of Enformer was comparable to, and in some cases even better than, the ABC score, a state-of-the-art method recently proposed specifically for enhancer prioritization.
“The performance of Enformer was comparable to, and in some cases even better than, the ABC score13, a state-of-the-art method recently proposed specifically for enhancer prioritization.”
From Effective gene expression prediction from sequence by integrating long-range interactions (2021) · cites this paper as [13]
CRISPRi has been used in multiple single-gene screens of regulatory elements.
“CRISPRi has subsequently been used in multiple single-gene screens of regulatory elements105,208,209.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [209]
In the ABC model, each enhancer is considered separately and the dose of activation it contributes to a promoter depends on its individual activity in cis and the frequency of the respective enhancer-promoter contact.
“In ABC, each enhancer is considered separately and the “dose” of activation it contributes to a promoter depends on its individual activity in cis and the frequency of the respective enhancer–promoter contact [183, 184].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [183]
Combining functional methods with complex readouts can generate empirical truth data that supports in silico models predicting causal variants, effector transcripts, and cellular effects.
“Combined with complex readouts–including high-content imaging128 and single-cell transcriptomics and epigenomics129,130–these methods can generate empirical ‘truth’ data, supporting the development of in silico models to predict causal variants, effector transcripts126 and cellular effects.”
From A brief history of human disease genetics (2020) · cites this paper as [126]
Variations on functional genomics methods allow evaluation of genes, regulatory elements, and genetic variants at increasing scale and resolution.
“Variations on these methods enable the functional evaluation of genes (via knockout screens124), regulatory elements (using CRISPR interference and CRISPR activation screens125,126), and genetic variants (base editors127) at increasing scale and resolution29.”
From A brief history of human disease genetics (2020) · cites this paper as [126]
In the ABC model, the multiplication of enhancer activity and 3D interaction frequency was the best predictor of enhancer-target gene pairs.
“In the ABC model, the multiplication of enhancer activity and 3D interaction frequency was the best predictor of enhancer–target gene pairs37.”
From Three-dimensional genome restructuring across timescales of activity-induced neuronal gene expression (2020) · cites this paper as [37]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072 · see citing works
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R., Carey, V., Bates, D., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome Biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 13,133 · see citing works
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723 · see citing works
A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping
Rao, S. S. P., Huntley, M. H., Durand, N. C., Stamenova, E. K., Bochkov, I. D., Robinson, J. T., Sanborn, A. L., Machol, I., Omer, A. D., Lander, E. S., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.021 · Cited by 9,212 · see citing works
Self-citationComprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome
Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B. R., Sabo, P. J., Dorschner, M. O., et al. (2009) · Science · doi:10.1126/science.1181369 · Cited by 8,898 · see citing works
Self-citationAligning sequence reads, clone sequences and assembly con*gs with BWA-MEMPreprint
Li, H. (2014) · arXiv (Cornell University) · doi:10.6084/m9.figshare.963153.v1 · Cited by 5,150 · see citing works
DNA targeting specificity of RNA-guided Cas9 nucleases
Hsu, P. D., Scott, D. A., Weinstein, J. A., Ran, F. A., Konermann, S., Agarwala, V., Li, Y., Fine, E. J., Wu, X., Shalem, O., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2647 · Cited by 4,741 · see citing works
Juicer Provides a One-Click System for Analyzing Loop-Resolution Hi-C Experiments
Durand, N. C., Shamim, M. S., Machol, I., Rao, S. S. P., Huntley, M. H., Lander, E. S., & Aiden, E. L. (2016) · Cell Systems · doi:10.1016/j.cels.2016.07.002 · Cited by 3,724 · see citing works
Self-citationSystematic Localization of Common Disease-Associated Variation in Regulatory DNA
Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., et al. (2012) · Science · doi:10.1126/science.1222794 · Cited by 3,716 · see citing works
CRISPR-Mediated Modular RNA-Guided Regulation of Transcription in Eukaryotes
Gilbert, L. A., Larson, M. H., Morsut, L., Liu, Z., Brar, G. A., Torres, S. E., Stern-Ginossar, N., Brandman, O., Whitehead, E. H., Doudna, J. A., et al. (2013) · Cell · doi:10.1016/j.cell.2013.06.044 · Cited by 3,696 · see citing works
ATAC-seq: a method for assaying chromatin accessibility genome-wide
Buenrostro, J. D., Wu, B., Chang, H. Y., & Greenleaf, W. J. (2015) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb2129s109 · Cited by 3,289 · see citing works
Chen, X., Han, X., Yuan, P., Fang, F., Huss, M., Vega, V. B., Wong, E., Orlov, Y. L., Zhang, W., Jiang, J., et al. (2008) · Cell · doi:10.1016/j.cell.2008.04.043 · Cited by 2,474 · see citing works
A unique chromatin signature uncovers early developmental enhancers in humans
Rada-Iglesias, A., Bajpai, R., Swigut, T., Brugmann, S. A., Flynn, R. A., & Wysocka, J. (2011) · Nature · doi:10.1038/nature09692 · Cited by 2,233 · see citing works
Transcription factors: from enhancer binding to developmental control
Spitz, F., & Furlong, E. E. M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3207 · Cited by 2,227 · see citing works
A promoter-level mammalian expression atlas
The FANTOM Consortium and the RIKEN PMI and CLST (DGT) (2014) · Nature · doi:10.1038/nature13182 · Cited by 1,982 · see citing works
The ENCODE Blacklist: Identification of Problematic Regions of the Genome
Amemiya, H. M., Kundaje, A., & Boyle, A. P. (2019) · Scientific Reports · doi:10.1038/s41598-019-45839-z · Cited by 1,831 · see citing works
Identifying ChIP-seq enrichment using MACS
Feng, J., Liu, T., Qin, B., Zhang, Y., & Liu, X. S. (2012) · Nature Protocols · doi:10.1038/nprot.2012.101 · Cited by 1,805 · see citing works
Sanborn, A. L., Rao, S. S. P., Huang, S.-C., Durand, N. C., Huntley, M. H., Jewett, A. I., Bochkov, I. D., Chinnappan, D., Cutkosky, A., Li, J., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1518552112 · Cited by 1,730 · see citing works
Self-citationChIP-seq accurately predicts tissue-specific activity of enhancers
Visel, A., Blow, M. J., Li, Z., Zhang, T., Akiyama, J. A., Holt, A., Plajzer-Frick, I., Shoukry, M., Wright, C., Chen, F., et al. (2009) · Nature · doi:10.1038/nature07730 · Cited by 1,639 · see citing works
CTCF: Master Weaver of the Genome
Phillips‐Cremins, J. E., & Corces, V. G. (2009) · Cell · doi:10.1016/j.cell.2009.06.001 · Cited by 1,540 · see citing works
Transcriptional enhancers: from properties to genome-wide predictions
Shlyueva, D., Stampfel, G., & Stark, A. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3682 · Cited by 1,356 · see citing works
Local regulation of gene expression by lncRNA promoters, transcription and splicing
Engreitz, J. M., Haines, J. E., Perez, E. M., Munson, G., Chen, J., Kane, M., McDonel, P. E., Guttman, M., & Lander, E. S. (2016) · Nature · doi:10.1038/nature20149 · Cited by 1,253 · see citing works
Self-citationHuman housekeeping genes, revisited
Eisenberg, E., & Levanon, E. Y. (2013) · Trends in Genetics · doi:10.1016/j.tig.2013.05.010 · Cited by 1,249 · see citing works
NUCLEASE HYPERSENSITIVE SITES IN CHROMATIN
Gross, D. S., & Garrard, W. T. (1988) · Annual Review of Biochemistry · doi:10.1146/annurev.bi.57.070188.001111 · Cited by 1,233 · see citing works
Li, G., Ruan, X., Auerbach, R. K., Sandhu, K. S., Zheng, M., Wang, P., Poh, H. M., Goh, Y., Lim, J., Zhang, J., et al. (2012) · Cell · doi:10.1016/j.cell.2011.12.014 · Cited by 1,232 · see citing works
Corces, M. R., Buenrostro, J. D., Wu, B., Greenside, P. G., Chan, S. M., Koenig, J. L., Snyder, M. P., Pritchard, J. K., Kundaje, A., Greenleaf, W. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3646 · Cited by 1,151 · see citing works
From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus
Musunuru, K., Strong, A., Frank-Kamenetsky, M., Lee, N. E., Ahfeldt, T., Sachs, K. V., Li, X., Li, H., Kuperwasser, N., Ruda, V. M., et al. (2010) · Nature · doi:10.1038/nature09266 · Cited by 1,140 · see citing works
HiChIP: efficient and sensitive analysis of protein-directed genome architecture
Mumbach, M. R., Rubin, A. J., Flynn, R. A., Dai, C., Khavari, P. A., Greenleaf, W. J., & Chang, H. Y. (2016) · Nature Methods · doi:10.1038/nmeth.3999 · Cited by 1,139 · see citing works
Thakore, P. I., D'Ippolito, A. M., Song, L., Safi, A., Shivakumar, N. K., Kabadi, A. M., Reddy, T. E., Crawford, G. E., & Gersbach, C. A. (2015) · Nature Methods · doi:10.1038/nmeth.3630 · Cited by 1,012 · see citing works
BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis
Canver, M. C., Smith, E. C., Sher, F., Pinello, L., Sanjana, N. E., Shalem, O., Chen, D. D., Schupp, P. G., Vinjamur, D. S., Garcia, S. P., et al. (2015) · Nature · doi:10.1038/nature15521 · Cited by 886 · see citing works
Functional and Mechanistic Diversity of Distal Transcription Enhancers
Bulger, M., & Groudine, M. (2011) · Cell · doi:10.1016/j.cell.2011.01.024 · Cited by 803 · see citing works
Histone modification levels are predictive for gene expression
Karlić, R., Chung, H., Lasserre, J., Vlahoviček, K., & Vingron, M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0909344107 · Cited by 794 · see citing works
Eukaryotic core promoters and the functional basis of transcription initiation
Haberle, V., & Stark, A. (2018) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-018-0028-8 · Cited by 737 · see citing works
CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells
Liu, S. J., Horlbeck, M. A., Cho, S. W., Birk, H. S., Malatesta, M., He, D. X., Attenello, F. J., Villalta, J. E., Cho, M. Y., Chen, Y., et al. (2016) · Science · doi:10.1126/science.aah7111 · Cited by 735 · see citing works
Core, L. J., Martins, A. L., Danko, C. G., Waters, C. T., Siepel, A., & Lis, J. T. (2014) · Nature Genetics · doi:10.1038/ng.3142 · Cited by 693 · see citing works
Systematic mapping of functional enhancer–promoter connections with CRISPR interference
Fulco, C. P., Munschauer, M., Anyoha, R., Munson, G., Grossman, S. R., Perez, E. M., Kane, M., Cleary, B., Lander, E. S., & Engreitz, J. M. (2016) · Science · doi:10.1126/science.aag2445 · Cited by 643 · see citing works
Self-citationA Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens
Gasperini, M., Hill, A. J., McFaline-Figueroa, J. L., Martin, B., Kim, S., Zhang, M. D., Jackson, D., Leith, A., Schreiber, J., Noble, W. S., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.029 · Cited by 613 · see citing works
Genomic views of distant-acting enhancers
Visel, A., Rubin, E. M., & Pennacchio, L. A. (2009) · Nature · doi:10.1038/nature08451 · Cited by 610 · see citing works
Transcriptional interference – a crash course
Shearwin, K. E., Callen, B. P., & Egan, J. B. (2005) · Trends in Genetics · doi:10.1016/j.tig.2005.04.009 · Cited by 565 · see citing works
Mumbach, M. R., Satpathy, A. T., Boyle, E. A., Dai, C., Gowen, B. G., Cho, S. W., Nguyen, M. L., Rubin, A. J., Granja, J. M., Kazane, K. R., et al. (2017) · Nature Genetics · doi:10.1038/ng.3963 · Cited by 490 · cited 1× · see citing works
“…20 ; promoters in same Hi-C contact domain (“D”); and promoters at the opposite anchors of Hi-C “loops” (“L”), RNA Polymerase II ChIA-PET loops (“P”) 40 , or H3K27ac HiChIP “loops” (“H”) 30 ; or assigning each expressed gene to the closest DE (“E”). b, Calculation of the ABC score (see Methods).”
Self-citationTewhey, R., Kotliar, D., Park, D. S., Liu, B., Winnicki, S., Reilly, S. K., Andersen, K. G., Mikkelsen, T. S., Lander, E. S., Schaffner, S. F., et al. (2016) · Cell · doi:10.1016/j.cell.2016.04.027 · Cited by 480 · see citing works
Promoter of lncRNA Gene PVT1 Is a Tumor-Suppressor DNA Boundary Element
Cho, S. W., Xu, J., Sun, R., Mumbach, M. R., Carter, A. C., Chen, Y. G., Yost, K. E., Kim, J., He, J., Nevins, S. A., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.068 · Cited by 459 · see citing works
Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin
Whalen, S., Truty, R. M., & Pollard, K. S. (2016) · Nature Genetics · doi:10.1038/ng.3539 · Cited by 456 · see citing works
Klann, T. S., Black, J. B., Chellappan, M., Safi, A., Song, L., Hilton, I. B., Crawford, G. E., Reddy, T. E., & Gersbach, C. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3853 · Cited by 447 · see citing works
Groner, A. C., Meylan, S., Ciuffi, A., Zangger, N., Ambrosini, G., Dénervaud, N., Bucher, P., & Trono, D. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000869 · Cited by 364 · see citing works
Multiplexed Engineering and Analysis of Combinatorial Enhancer Activity in Single Cells
Xie, S., Duan, J., Li, B., Zhou, P., & Hon, G. C. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.03.007 · Cited by 334 · see citing works
Systematic Functional Dissection of Common Genetic Variation Affecting Red Blood Cell Traits
Ulirsch, J. C., Nandakumar, S. K., Wang, L., Giani, F. C., Zhang, X., Rogov, P., Melnikov, A., McDonel, P., Do, R., Mikkelsen, T. S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.04.048 · Cited by 328 · see citing works
Sripathy, S. P., Stevens, J., & Schultz, D. C. (2006) · Molecular and Cellular Biology · doi:10.1128/mcb.00487-06 · Cited by 323 · see citing works
Genome-wide characterization of mammalian promoters with distal enhancer functions
Dao, L. T. M., Galindo-Albarrán, A. O., Castro-Mondragon, J. A., Andrieu-Soler, C., Medina-Rivera, A., Souaid, C., Charbonnier, G., Griffon, A., Vanhille, L., Stephen, T., et al. (2017) · Nature Genetics · doi:10.1038/ng.3884 · Cited by 281 · see citing works
High-resolution interrogation of functional elements in the noncoding genome
Sanjana, N. E., Wright, J. B., Zheng, K., Shalem, O., Fontanillas, P., Joung, J., Cheng, C. S., Regev, A., & Zhang, F. (2016) · Science · doi:10.1126/science.aaf7613 · Cited by 280 · see citing works
A tiling-deletion-based genetic screen for cis-regulatory element identification in mammalian cells
Diao, Y., Fang, R., Li, B., Meng, Z., Yu, J., Qiu, Y., Lin, K. C., Huang, H., Liu, T., Marina, R. J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4264 · Cited by 259 · see citing works
Unlinking an lncRNA from Its Associated cis Element
Paralkar, V. R., Taborda, C., Huang, P., Yao, Y., Kossenkov, A. V., Prasad, R., Luan, J. Y., Davies, J. O. J., Hughes, J. R., Hardison, R. C., et al. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.02.029 · Cited by 253 · see citing works
Dissecting super-enhancer hierarchy based on chromatin interactions
Huang, J., Li, K., Cai, W., Liu, X., Zhang, Y., Orkin, S. H., Xu, J., & Yuan, G.-C. (2018) · Nature Communications · doi:10.1038/s41467-018-03279-9 · Cited by 241 · see citing works
High-throughput mapping of regulatory DNA
Rajagopal, N., Srinivasan, S., Kooshesh, K., Guo, Y., Edwards, M. D., Banerjee, B., Syed, T., Emons, B. J. M., Gifford, D. K., & Sherwood, R. I. (2016) · Nature Biotechnology · doi:10.1038/nbt.3468 · Cited by 238 · see citing works
Cao, Q., Anyansi, C., Hu, X. S., Xu, L., Xiong, L., Tang, W., Mok, M. T. S., Cheng, C., Fan, X., Gerstein, M., et al. (2017) · Nature Genetics · doi:10.1038/ng.3950 · Cited by 216 · see citing works
Alpern, D., Gardeux, V., Russeil, J., Mangeat, B., Meireles-Filho, A. C. A., Breysse, R., Hacker, D., & Deplancke, B. (2019) · Genome Biology · doi:10.1186/s13059-019-1671-x · Cited by 216 · see citing works
CRISPR Reveals a Distal Super-Enhancer Required for Sox2 Expression in Mouse Embryonic Stem Cells
Li, Y., Rivera, C. M., Ishii, H., Jin, F., Selvaraj, S., Lee, A. Y., Dixon, J. R., & Ren, B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114485 · Cited by 212 · see citing works
GATA-1-Mediated Proliferation Arrest during Erythroid Maturation
Rylski, M., Welch, J. J., Chen, Y., Letting, D. L., Diehl, J. A., Chodosh, L. A., Blobel, G. A., & Weiss, M. J. (2003) · Molecular and Cellular Biology · doi:10.1128/mcb.23.14.5031-5042.2003 · Cited by 195 · see citing works
A <i>Sox2</i> distal enhancer cluster regulates embryonic stem cell differentiation potential
Zhou, H. Y., Katsman, Y., Dhaliwal, N. K., Davidson, S., Macpherson, N. N., Sakthidevi, M., Collura, F., & Mitchell, J. A. (2014) · Genes & Development · doi:10.1101/gad.248526.114 · Cited by 192 · see citing works
Fuentes, D., Swigut, T., & Wysocka, J. (2018) · eLife · doi:10.7554/elife.35989 · Cited by 191 · see citing works
Moorthy, S. D., Davidson, S., Shchuka, V. M., Singh, G., Malek-Gilani, N., Langroudi, L., Martchenko, A., So, V., Macpherson, N. N., & Mitchell, J. A. (2016) · Genome Research · doi:10.1101/gr.210930.116 · Cited by 190 · see citing works
Comprehensive Functional Annotation of 77 Prostate Cancer Risk Loci
Hazelett, D. J., Rhie, S. K., Gaddis, M., Yan, C., Lakeland, D. L., Coetzee, S. G., Henderson, B. E., Noushmehr, H., Cozen, W., Kote‐Jarai, Z., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004102 · Cited by 188 · see citing works
A Comprehensive Mouse Transcriptomic BodyMap across 17 Tissues by RNA-seq
Li, B., Qing, T., Zhu, J., Wen, Z., Yu, Y., Fukumura, R., Zheng, Y., Gondo, Y., & Shi, L. (2017) · Scientific Reports · doi:10.1038/s41598-017-04520-z · Cited by 184 · see citing works
Human globin locus activation region (LAR): role in temporal control
Townes, T. M., & Behringer, R. R. (1990) · Trends in Genetics · doi:10.1016/0168-9525(90)90182-6 · Cited by 180 · see citing works
hichipper: a preprocessing pipeline for calling DNA loops from HiChIP data
Lareau, C. A., & Aryee, M. J. (2018) · Nature Methods · doi:10.1038/nmeth.4583 · Cited by 178 · see citing works
Self-citationIn search of the determinants of enhancer–promoter interaction specificity
van Arensbergen, J., van Steensel, B., & Bussemaker, H. J. (2014) · Trends in Cell Biology · doi:10.1016/j.tcb.2014.07.004 · Cited by 172 · see citing works
Measuring the reproducibility and quality of Hi-C data
Yardımcı, G. G., Özadam, H., Sauria, M. E. G., Ursu, O., Yan, K., Yang, T., Chakraborty, A., Kaul, A., Lajoie, B. R., Song, F., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1658-7 · Cited by 171 · see citing works
Xu, J., Shao, Z., Li, D., Xie, H., Kim, W., Huang, J., Taylor, J. E., Pinello, L., Glass, K., Jaffe, J. D., et al. (2015) · Molecular Cell · doi:10.1016/j.molcel.2014.12.009 · Cited by 150 · see citing works
How many human genes can be defined as housekeeping with current expression data?
Zhu, J., He, F., Song, S., Wang, J., & Yu, J. M. (2008) · BMC Genomics · doi:10.1186/1471-2164-9-172 · Cited by 149 · see citing works
Gasperini, M., Findlay, G. M., McKenna, A., Milbank, J. H., Lee, C., Zhang, M. D., Cusanovich, D. A., & Shendure, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.010 · Cited by 143 · see citing works
Regulation of β-globin gene expression: straightening out the locus
Martin, D. I. K., Fiering, S., & Groudine, M. T. (1996) · Current Opinion in Genetics & Development · doi:10.1016/s0959-437x(96)80072-4 · Cited by 141 · see citing works
Genome-wide mapping of promoter-anchored interactions with close to single-enhancer resolution
Sahlén, P., Abdullayev, I., Ramsköld, D., Matskova, L. V., Rilakovic, N., Lötstedt, B., Albert, T. J., Lundeberg, J., & Sandberg, R. (2015) · Genome biology · doi:10.1186/s13059-015-0727-9 · Cited by 131 · see citing works
Super-Enhancers at the Nanog Locus Differentially Regulate Neighboring Pluripotency-Associated Genes
Blinka, S. M., Reimer, M. H., Pulakanti, K., & Rao, S. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.09.002 · Cited by 128 · see citing works
Carleton, J. B., Berrett, K. C., & Gertz, J. (2017) · Cell Systems · doi:10.1016/j.cels.2017.08.011 · Cited by 113 · see citing works
Canver, M. C., Lessard, S., Pinello, L., Wu, Y., Ilboudo, Y., Stern, E. N., Needleman, A. J., Galactéros, F., Brugnara, C., Kutlar, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3793 · Cited by 105 · see citing works
Epigenome editing strategies for the functional annotation of CTCF insulators
Tarjan, D. R., Flavahan, W. A., & Bernstein, B. E. (2019) · Nature Communications · doi:10.1038/s41467-019-12166-w · Cited by 88 · see citing works
Spisák, S., Lawrenson, K., Fu, Y., Csabai, I., Cottman, R. T., Seo, J.-H., Haiman, C. A., Han, Y., Lenci, R., Li, Q., et al. (2015) · Nature Medicine · doi:10.1038/nm.3975 · Cited by 88 · see citing works
Chromatin state maps: new technologies, new insights
Mendenhall, E. M., & Bernstein, B. (2008) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2008.01.010 · Cited by 84 · see citing works
Epigenetic modulation of a hardwired 3D chromatin landscape in two naive states of pluripotency
Atlasi, Y., Megchelenbrink, W., Peng, T., Habibi, E., Joshi, O., Wang, S.-Y., Wang, C., Logie, C., Poser, I., Marks, H., et al. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0310-9 · Cited by 66 · see citing works
Wakabayashi, A., Ulirsch, J. C., Ludwig, L. S., Fiorini, C., Yasuda, M., Choudhuri, A., McDonel, P. E., Zon, L. I., & Sankaran, V. G. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1521754113 · Cited by 59 · see citing works
Gong, Q., & Dean, A. (1993) · Molecular and Cellular Biology · doi:10.1128/mcb.13.2.911-917.1993 · Cited by 58 · see citing works
Kim, Y. W., Kim, S., Kim, C. G., & Kim, A. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr253 · Cited by 48 · see citing works
Dissecting super-enhancer hierarchy based on chromatin interactions
(2017) · doi:10.1101/149583 · Cited by 32 · see citing works
Wang, X., Raghavan, A., Peters, D., Pashos, E. E., Rader, D. J., & Musunuru, K. (2017) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.117.310103 · Cited by 31 · see citing works
Measuring the reproducibility and quality of Hi-C dataPreprint
Yardımcı, G. G., Özadam, H., Sauria, M. E. G., Ursu, O., Yan, K., Yang, T., Chakraborty, A., Kaul, A., Lajoie, B. R., Song, F., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/188755 · Cited by 28 · see citing works
High-resolution interrogation of functional elements in the noncoding genomePreprint
Sanjana, N. E., Wright, J. B., Zheng, K., Shalem, O., Fontanillas, P., Joung, J., Cheng, C. S., Regev, A., & Zhang, F. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/049130 · Cited by 27 · see citing works
Tissue-specific Gene Expression Prediction Associates Vitiligo with SUOX through an Active EnhancerPreprint
Qi, Z., Xie, S., Chen, R., Aisa, H. A., Hon, G. C., & Guan, Y. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/337196 · Cited by 10 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Effective gene expression prediction from sequence by integrating long-range interactions
Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021) · Nature Methods · doi:10.1038/s41592-021-01252-x · Cited by 1,326 · see citing works
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
Bravo González-Blas, C., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01938-4 · Cited by 778 · see citing works
A brief history of human disease genetics
Claussnitzer, M., Cho, J. H., Collins, R., Cox, N. J., Dermitzakis, E. T., Hurles, M. E., Kathiresan, S., Kenny, E. E., Lindgren, C. M., MacArthur, D. G., et al. (2020) · Nature · doi:10.1038/s41586-019-1879-7 · Cited by 697 · see citing works
Inherited causes of clonal haematopoiesis in 97,691 whole genomes
Bick, A. G., Weinstock, J. S., Nandakumar, S. K., Fulco, C. P., Bao, E. L., Zekavat, S. M., Szeto, M. D., Liao, X., Leventhal, M. J., Nasser, J., et al. (2020) · Nature · doi:10.1038/s41586-020-2819-2 · Cited by 733 · see citing works
Self-citationMimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529 · see citing works
Self-citationRoles of transposable elements in the regulation of mammalian transcription
Fueyo, R., Judd, J., Feschotte, C., & Wysocka, J. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-022-00457-y · Cited by 451 · see citing works
Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues
Bartosovic, M., Kabbe, M., & Castelo-Branco, G. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00869-9 · Cited by 483 · see citing works
Nonlinear control of transcription through enhancer–promoter interactions
Zuin, J., Roth, G., Zhan, Y., Cramard, J., Redolfi, J., Piskadlo, E., Mach, P., Kryzhanovska, M., Tihanyi, G., Kohler, H., et al. (2022) · Nature · doi:10.1038/s41586-022-04570-y · Cited by 415 · see citing works
Towards a comprehensive catalogue of validated and target-linked human enhancers
Gasperini, M., Tome, J. M., & Shendure, J. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0209-0 · Cited by 396 · see citing works
AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model
Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025) · doi:10.1101/2025.06.25.661532 · Cited by 395 · see citing works
Advancing regulatory variant effect prediction with AlphaGenome
Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2026) · Nature · doi:10.1038/s41586-025-10014-0 · Cited by 373 · see citing works
Corces, M. R., Shcherbina, A., Kundu, S., Gloudemans, M. J., Frésard, L., Granja, J. M., Louie, B. H., Eulalio, T., Shams, S., Bagdatli, S. T., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00721-x · Cited by 358 · see citing works
Systematic differences in discovery of genetic effects on gene expression and complex traits
Mostafavi, H., Spence, J. P., Naqvi, S., & Pritchard, J. K. (2023) · Nature Genetics · doi:10.1038/s41588-023-01529-1 · Cited by 336 · see citing works
A new era in functional genomics screens
Przybyla, L., & Gilbert, L. A. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00409-w · Cited by 281 · see citing works
de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2021) · doi:10.1101/2021.10.05.463203 · Cited by 284
de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2022) · Nature Genetics · doi:10.1038/s41588-022-01048-5 · Cited by 283 · see citing works
Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation
Linder, J., Srivastava, D., Yuan, H., Agarwal, V., & Kelley, D. R. (2025) · Nature Genetics · doi:10.1038/s41588-024-02053-6 · Cited by 265 · see citing works
Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2
Kaul, A., Bhattacharyya, S., & Ay, F. (2020) · Nature Protocols · doi:10.1038/s41596-019-0273-0 · Cited by 226 · see citing works
Combining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity
Gazal, S., Weissbrod, O., Hormozdiari, F., Dey, K. K., Nasser, J., Jagadeesh, K. A., Weiner, D. J., Shi, H., Fulco, C. P., O’Connor, L. J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01087-y · Cited by 181 · see citing works
Self-citationEpigenomic dissection of Alzheimer’s disease pinpoints causal variants and reveals epigenome erosion
Xiong, X., James, B. T., Boix, C. A., Park, Y. P., Galani, K., Victor, M. B., Sun, N., Hou, L., Ho, L.-L., Mantero, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.040 · Cited by 205 · see citing works
Spatially resolved epigenomic profiling of single cells in complex tissues
Lu, T., Ang, C. E., & Zhuang, X. (2022) · Cell · doi:10.1016/j.cell.2022.09.035 · Cited by 197 · see citing works
Enhancer selectivity in space and time: from enhancer–promoter interactions to promoter activation
Yang, J. H., & Hansen, A. S. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00710-6 · Cited by 191 · see citing works
Genetics of the human microglia regulome refines Alzheimer’s disease risk loci
Kosoy, R., Fullard, J. F., Zeng, B., Bendl, J., Dong, P., Rahman, S., Kleopoulos, S. P., Shao, Z., Girdhar, K., Humphrey, J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01149-1 · Cited by 168 · see citing works
Precise modulation of transcription factor levels identifies features underlying dosage sensitivity
Naqvi, S., Kim, S., Hoskens, H., Matthews, H. S., Spritz, R. A., Klein, O. D., Hallgrímsson, B., Swigut, T., Claes, P., Pritchard, J. K., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01366-2 · Cited by 132 · see citing works
Beagan, J. A., Pastuzyn, E. D., Fernandez, L. R., Guo, M. H., Feng, K., Titus, K. R., Chandrashekar, H., Shepherd, J. D., & Phillips-Cremins, J. E. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0634-6 · Cited by 143 · see citing works
Rood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139 · see citing works
Scalable genetic screening for regulatory circuits using compressed Perturb-seq
Yao, D., Binan, L., Bezney, J., Simonton, B., Freedman, J., Frangieh, C. J., Dey, K., Geiger-Schuller, K., Eraslan, B., Gusev, A., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01964-9 · Cited by 101 · see citing works
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
González-Blas, C. B., De Winter, S., Hulselmans, G., Hecker, N., Matetovici, I., Christiaens, V., Poovathingal, S., Wouters, J., Aibar, S., & Aerts, S. (2022) · doi:10.1101/2022.08.19.504505 · Cited by 778 · see citing works
Epigenomic priming of immune genes implicates oligodendroglia in multiple sclerosis susceptibility
Meijer, M., Agirre, E., Kabbe, M., van Tuijn, C. A., Heskol, A., Zheng, C., Mendanha Falcão, A., Bartosovic, M., Kirby, L., Calini, D., et al. (2022) · Neuron · doi:10.1016/j.neuron.2021.12.034 · Cited by 98 · see citing works
Anderson, A. G., Rogers, B. B., Loupe, J. M., Rodriguez-Nunez, I., Roberts, S. C., White, L. M., Brazell, J. N., Bunney, W. E., Bunney, B. G., Watson, S. J., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100263 · Cited by 82 · see citing works
Transcriptional enhancers and their communication with gene promoters
Ray-Jones, H., & Spivakov, M. (2021) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-021-03903-w · Cited by 81 · see citing works
The cis-regulatory effects of modern human-specific variants
Weiss, C. V., Harshman, L., Inoue, F., Fraser, H. B., Petrov, D. A., Ahituv, N., & Gokhman, D. (2021) · eLife · doi:10.7554/elife.63713 · Cited by 70 · see citing works
The three-dimensional landscape of cortical chromatin accessibility in Alzheimer’s disease
Bendl, J., Hauberg, M. E., Girdhar, K., Im, E., Vicari, J. M., Rahman, S., Fernando, M. B., Townsley, K. G., Dong, P., Misir, R., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01166-7 · Cited by 65 · see citing works
Nonlinear control of transcription through enhancer-promoter interactions
Zuin, J., Roth, G., Zhan, Y., Cramard, J., Redolfi, J., Piskadlo, E., Mach, P., Kryzhanovska, M., Tihanyi, G., Kohler, H., et al. (2021) · doi:10.1101/2021.04.22.440891 · Cited by 67 · see citing works
Weng, C., Xi, J., Li, H., Cui, J., Gu, A., Lai, S., Leskov, K., Ke, L., Jin, F., & Li, Y. (2020) · Nature Metabolism · doi:10.1038/s42255-020-00314-2 · Cited by 65 · see citing works
Kim, S. S., Truong, B., Jagadeesh, K., Dey, K. K., Shen, A. Z., Raychaudhuri, S., Kellis, M., & Price, A. L. (2024) · Nature Communications · doi:10.1038/s41467-024-44742-0 · Cited by 56 · see citing works
Long-range regulation of transcription scales with genomic distance in a gene-specific manner
Jensen, C. L., Chen, L.-F., Swigut, T., Crocker, O. J., Yao, D., Bassik, M. C., Ferrell, J. E., Boettiger, A. N., & Wysocka, J. (2025) · Molecular Cell · doi:10.1016/j.molcel.2024.10.021 · Cited by 39 · see citing works
Single cell epigenomic atlas of the developing human brain and organoids
Ziffra, R. S., Kim, C. N., Wilfert, A., Turner, T. N., Haeussler, M., Casella, A. M., Przytycki, P. F., Kreimer, A., Pollard, K. S., Ament, S. A., et al. (2019) · doi:10.1101/2019.12.30.891549 · Cited by 33 · see citing works
Spatial promoter–enhancer hubs in cancer: organization, regulation, and function
Zhao, J., & Faryabi, R. B. (2023) · Trends in Cancer · doi:10.1016/j.trecan.2023.07.017 · Cited by 28 · see citing works
Single-cell multi-omics map of human fetal blood in Down syndrome
Marderstein, A. R., De Zuani, M., Moeller, R., Bezney, J., Padhi, E. M., Wong, S., Coorens, T. H. H., Xie, Y., Xue, H., Montgomery, S. B., et al. (2024) · Nature · doi:10.1038/s41586-024-07946-4 · Cited by 26 · see citing works
Aherrahrou, R., Reinberger, T., Hashmi, S., & Erdmann, J. (2024) · Cardiovascular Research · doi:10.1093/cvr/cvae161 · Cited by 25 · see citing works
Genome-wide absolute quantification of chromatin looping
Jusuf, J. M., Yang, J. H., Toppen, J., Grosse-Holz, S., Gabriele, M., Mach, P., Flyamer, I. M., Zechner, C., Giorgetti, L., Mirny, L. A., et al. (2026) · Nature Structural & Molecular Biology · doi:10.1038/s41594-026-01819-2 · Cited by 22 · see citing works
Lu, Z., Chen, C., Zhang, H., Li, B., Liu, Y., Guo, J., Xu, R., Shi, K., Ma, Q., Zhang, M., et al. (2025) · Nature Cancer · doi:10.1038/s43018-025-01031-z · Cited by 4 · see citing works
Burton, E. A., Argenziano, M., Cook, K., Ridler, M., Lu, S., Su, C., Manduchi, E., Littleton, S. H., Leonard, M. E., Hodge, K. M., et al. (2024) · doi:10.1101/2024.08.22.609230 · Cited by 3 · see citing works
Live-cell imaging of enhancer-promoter dynamics reveals transient contact-driven gene activation
Yang, J. H., Pinholt, H. D., Toppen, J., Huseyin, M. K., Jusuf, J. M., Katsifis, C. C., Kæstel-Hansen, J., Mirny, L. A., Zechner, C., & Hansen, A. S. (2026) · doi:10.64898/2026.06.21.733446 · Cited by 3 · see citing works
Choppavarapu, L., Nosirov, B., Kahn, M., & Jin, V. X. (2026) · Cancers · doi:10.3390/cancers18213416
- Authors
- Fulco, C. P., Nasser, J., Jones, T. R., Munson, G., Bergman, D. T., Subramanian, V., Grossman, S. R., Anyoha, R., Doughty, B. R., Patwardhan, T. A., Nguyen, T. H., Kane, M., Perez, E. M., Durand, N. C., Lareau, C. A., Stamenova, E. K., Aiden, E. L., Lander, E. S., & Engreitz, J. M.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Lander, Eric S.
- Engreitz, Jesse M.
- ORCID
- Venue
- Nature Genetics
- Published
- 2019-11-29
- Volume
- 51
- Issue
- 12
- Pages
- 1664-1669
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC6886585
- OpenAlex
- W2991283206
- Cited by
- 1,111
- References
- 88
- Citations by year
- 2026: 192 · 2025: 207 · 2024: 199 · 2023: 174 · 2022: 139 · 2021: 115 · 2020: 77 · 2019: 6 · 2012: 2
- FWCI
- 27.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (1K99HG009917-01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Keywords
- chromatin state, non-coding variants, disease risk variants, gene expression regulation
- MeSH terms
- Histone Deacetylase 6, RNA, Guide, CRISPR-Cas Systems, Animals, Enhancer Elements, Genetic, Gene Expression Regulation, Humans, Models, Genetic, Promoter Regions, Genetic, In Situ Hybridization, Fluorescence, K562 Cells, GATA1 Transcription Factor, Mice, Clustered Regularly Interspaced Short Palindromic Repeats
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
