The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
Systematic dissection of genomic features determining transcription factor binding and enhancer function
Open accessAbstract
Significance: A central question in biology is how transcription factors (TFs) recognize specific binding sites in enhancers and regulate gene expression. In general, only a fraction of potential binding sites for TFs are occupied in a particular cell type. TF affinity for a motif site, local interactions among TFs, and larger-scale chromatin accessibility can influence binding, although the relative contributions of these factors is unclear. Moreover, little is known about how specific combinations of TFs control quantitative gene expression once bound. Here, we use large-scale synthetic biology approaches to explore the features that govern TF binding vs. enhancer activity. This approach provides a paradigm for systematic study of key regulatory sequences within enhancers and how they interact to influence gene expression.
Overview
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
Verification
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 cited source says what the claim attributes to it.
In the citing paper
“A major advantage of MPRAs is that sequences to be tested can simply be synthesized, enabling straightforward saturation mutagenesis [G] of enhancers67,184,185 as well as programming synthetic enhancers to inform modelling of their properties186,187.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [186]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multilineage potential of adult human mesenchymal stem cells
Pittenger, M. F., Mackay, A. M., Beck, S. C., Jaiswal, R. K., Douglas, R., Mosca, J. D., Moorman, M. A., Simonetti, D. W., Craig, S., & Marshak, D. R. (1999) · Science · doi:10.1126/science.284.5411.143 · Cited by 21,052
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Self-citationHeinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
Self-citationFIMO: scanning for occurrences of a given motif
Grant, C. E., Bailey, T. L., & Noble, W. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr064 · Cited by 4,247
Genome-wide maps of chromatin state in pluripotent and lineage-committed cells
Mikkelsen, T. S., Ku, M., Jaffe, D. B., Issac, B., Lieberman, E., Giannoukos, G., Alvarez, P., Brockman, W., Kim, T.-K., Koche, R. P., et al. (2007) · Nature · doi:10.1038/nature06008 · Cited by 4,129
Self-citationGenome-Wide Mapping of in Vivo Protein-DNA Interactions
Johnson, D. S., Mortazavi, A., Myers, R. M., & Wold, B. (2007) · Science · doi:10.1126/science.1141319 · Cited by 2,927
The accessible chromatin landscape of the human genome
Thurman, R. E., Rynes, E., Humbert, R., Vierstra, J., Maurano, M. T., Haugen, E., Sheffield, N. C., Stergachis, A. B., Wang, H., Vernot, B., et al. (2012) · Nature · doi:10.1038/nature11232 · Cited by 2,679
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
Kamei, Y., Xu, L., Heinzel, T., Torchia, J., Kurokawa, R., Gloss, B., Lin, S.-C., Heyman, R. A., Rose, D. W., Glass, C. K., et al. (1996) · Cell · doi:10.1016/s0092-8674(00)81118-6 · Cited by 2,150
Cited by
Sorted by citations
Determinants of enhancer and promoter activities of regulatory elements
Andersson, R., & Sandelin, A. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-019-0173-8 · Cited by 713
Linear models enable powerful differential activity analysis in massively parallel reporter assays
Myint, L., Avramopoulos, D., Goff, L., & Hansen, K. (2017) · BMC Genomics · doi:10.1186/s12864-019-5556-x · Cited by 140
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
Transcription factors: Bridge between cell signaling and gene regulation
Weidemüller, P., Kholmatov, M. M., Petsalaki, E., & Zaugg, J. B. (2021) · PROTEOMICS · doi:10.1002/pmic.202000034 · Cited by 295
DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of enhancers
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
Esposito, D., Weile, J., Shendure, J., Starita, L. M., Papenfuss, A. T., Roth, F. P., Fowler, D. M., & Rubin, A. F. (2019) · Genome biology · doi:10.1186/s13059-019-1845-6 · Cited by 260
Catarino, R. R., & Stark, A. (2018) · Genes & Development · doi:10.1101/gad.310367.117 · Cited by 215
Sequence determinants of human gene regulatory elements
Sahu, B., Hartonen, T., Pihlajamaa, P., Wei, B., Dave, K., Zhu, F., Kaasinen, E., Lidschreiber, K., Lidschreiber, M., Daub, C. O., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-01009-4 · Cited by 198
Generating specificity in genome regulation through transcription factor sensitivity to chromatin
Isbel, L., Grand, R. S., & Schübeler, D. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00512-6 · Cited by 152
Enhancer grammar in development, evolution, and disease: dependencies and interplay
Jindal, G. A., & Farley, E. K. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.02.016 · Cited by 152
Chromatin accessibility: methods, mechanisms, and biological insights
Mansisidor, A., & Risca, V. I. (2022) · Nucleus · doi:10.1080/19491034.2022.2143106 · Cited by 168
Massively parallel characterization of transcriptional regulatory elements
Agarwal, V., Inoue, F., Schubach, M., Penzar, D., Martin, B. K., Dash, P. M., Keukeleire, P., Zhang, Z., Sohota, A., Zhao, J., et al. (2025) · Nature · doi:10.1038/s41586-024-08430-9 · Cited by 146
Rabani, M., Pieper, L. M., Chew, G.-L., & Schier, A. F. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.11.014 · Cited by 127
Inoue, F., Kreimer, A., Ashuach, T., Ahituv, N., & Yosef, N. (2019) · Cell stem cell · doi:10.1016/j.stem.2019.09.010 · Cited by 109
NFIA co-localizes with PPARγ and transcriptionally controls the brown fat gene program
Hiraike, Y., Waki, H., Yu, J., Nakamura, M., Miyake, K., Nagano, G., Nakaki, R., Ken, S., Kobayashi, H., Yamamoto, S., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3590 · Cited by 102
Georgakopoulos-Soares, I., Deng, C., Agarwal, V., Chan, C. S. Y., Zhao, J., Inoue, F., & Ahituv, N. (2023) · Nature Communications · doi:10.1038/s41467-023-37960-5 · Cited by 82
The Cancer Epigenome: Exploiting Its Vulnerabilities for Immunotherapy
Yau, H. L., Ettayebi, I., & Carvalho, D. D. D. (2018) · Trends in Cell Biology · doi:10.1016/j.tcb.2018.07.006 · Cited by 99
Molecular and Cellular Insights into the Development of Uterine Fibroids
Machado-Lopez, A., Simón, C., & Mas, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22168483 · Cited by 103
Deep flanking sequence engineering for efficient promoter design using DeepSEED
Zhang, P., Wang, H., Xu, H., Wei, L., Liu, L., Hu, Z., & Wang, X. (2023) · Nature Communications · doi:10.1038/s41467-023-41899-y · Cited by 97
Positional specificity of different transcription factor classes within enhancers
Grossman, S. R., Engreitz, J., Ray, J. P., Nguyen, T. H., Hacohen, N., & Lander, E. S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804663115 · Cited by 91
Self-citationTargeted design of synthetic enhancers for selected tissues in the Drosophila embryo
de Almeida, B. P., Schaub, C., Pagani, M., Secchia, S., Furlong, E. E. M., & Stark, A. (2024) · Nature · doi:10.1038/s41586-023-06905-9 · Cited by 87
Maricque, B. B., Chaudhari, H. G., & Cohen, B. A. (2019) · Nature Biotechnology · doi:10.1038/nbt.4285 · Cited by 83
Wafer, R., Tandon, P., & Minchin, J. E. N. (2017) · Frontiers in Endocrinology · doi:10.3389/fendo.2017.00102 · Cited by 83
King, D., Hong, C. K., Shepherdson, J. L., Granas, D. M., Maricque, B. B., & Cohen, B. A. (2020) · eLife · doi:10.7554/elife.41279 · Cited by 76
Backenroth, D., He, Z., Kiryluk, K., Boeva, V., Petukhova, L., Khurana, E., Christiano, A. M., Buxbaum, J. D., & Ionita‐Laza, I. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.03.026 · Cited by 75
A transcription factor collective defines the HSN serotonergic neuron regulatory landscape
Lloret-Fernández, C., Maicas, M., Mora-Martínez, C., Artacho, A., Jimeno-Martín, Á., Chirivella, L., Weinberg, P., & Flames, N. (2018) · eLife · doi:10.7554/elife.32785 · Cited by 67
Challenges of Translating Gene Regulatory Information into Agronomic Improvements
Springer, N. M., de León, N., & Erich Grotewold, A. (2019) · Trends in Plant Science · doi:10.1016/j.tplants.2019.07.004 · Cited by 67
Mechanisms governing target search and binding dynamics of hypoxia-inducible factors
Chen, Y., Cattoglio, C., Dailey, G. M., Zhu, Q., Tjian, R., & Darzacq, X. (2022) · eLife · doi:10.7554/elife.75064 · Cited by 60
Yu, T. C., Liu, W. L., Brinck, M. S., Davis, J. E., Shek, J., Bower, G., Einav, T., Insigne, K. D., Phillips, R., Kosuri, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20094-3 · Cited by 59
Qiu, Y., & Köhler, C. (2020) · Biochemical Society Transactions · doi:10.1042/bst20190937 · Cited by 55
Singh, G., Mullany, S., Moorthy, S. D., Zhang, R., Mehdi, T., Tian, R., Duncan, A. G., Moses, A. M., & Mitchell, J. A. (2021) · Genome Research · doi:10.1101/gr.272468.120 · Cited by 60
Moyo, M., Parker, J. B., & Chakravarti, D. (2020) · Nature Communications · doi:10.1038/s41467-020-14701-6 · Cited by 53
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
Castaldi, P. J., Guo, F., Qiao, D., Du, F., Naing, Z. Z. C., Li, Y., Pham, B., Mikkelsen, T. S., Cho, M. H., Silverman, E. K., et al. (2018) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201802-0337oc · Cited by 51
Self-citationGlucocorticoid activation of anti-inflammatory macrophages protects against insulin resistance
Caratti, G., Stifel, U., Caratti, B., Jamil, A. J. M., Chung, K., Kiehntopf, M., Gräler, M. H., Blüher, M., Rauch, A., & Tuckermann, J. P. (2023) · Nature Communications · doi:10.1038/s41467-023-37831-z · Cited by 53
Rewriting regulatory DNA to dissect and reprogram gene expression
Martyn, G. E., Montgomery, M. T., Jones, H., Guo, K., Doughty, B. R., Linder, J., Bisht, D., Xia, F., Cai, X. S., Chen, Z., et al. (2023) · Cell · doi:10.1016/j.cell.2025.03.034 · Cited by 59
Self-citationKreimer, A., Ashuach, T., Inoue, F., Khodaverdian, A., Deng, C., Yosef, N., & Ahituv, N. (2022) · Nature Communications · doi:10.1038/s41467-022-28659-0 · Cited by 44
Designing Eukaryotic Gene Expression Regulation Using Machine Learning
de Jongh, R. P. H., van Dijk, A. D. J., Julsing, M. K., Schaap, P. J., & de Ridder, D. (2019) · Trends in biotechnology · doi:10.1016/j.tibtech.2019.07.007 · Cited by 46
Resolving mechanisms of immune‐mediated disease in primary CD4 T cells
Bourges, C., Groff, A. F., Burren, O. S., Gerhardinger, C., Mattioli, K., Hutchinson, A., Hu, T. X., Anand, T., Epping, M. W., Wallace, C., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.202012112 · Cited by 45
Hughes, A. E. O., Myers, C. A., & Corbo, J. C. (2018) · Genome Research · doi:10.1101/gr.231886.117 · Cited by 45
Mikl, M., Eletto, D., Nijim, M., Lee, M., Lafzi, A., Mhamedi, F., David, O., Sain, S. B., Handler, K., & Moor, A. E. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac806 · Cited by 41
Learning the Regulatory Code of Gene Expression
Zrimec, J., Buric, F., Kokina, M., García, V., & Zelezniak, A. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.673363 · Cited by 45
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-citationMulvey, B., Lagunas, T., & Dougherty, J. D. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.011 · Cited by 41
Adaptive Landscapes in the Age of Synthetic Biology
Yi, X., & Dean, A. M. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz004 · Cited by 38
Local sequence features that influence AP-1 cis-regulatory activityPreprint
Chaudhari, H. G., & Cohen, B. A. (2018) · Genome Research · doi:10.1101/gr.226530.117 · Cited by 36
Structure and function of an ectopic Polycomb chromatin domain
De, S., Cheng, Y., Sun, M.-A., Gehred, N., & Kassis, J. A. (2019) · Science Advances · doi:10.1126/sciadv.aau9739 · Cited by 34
Davis, J. E., Insigne, K. D., Jones, E. M., Hastings, Q. B., Boldridge, W. C., & Kosuri, S. (2020) · Cell Systems · doi:10.1016/j.cels.2020.05.011 · Cited by 34
Degirmenci, U., Li, J., Lim, Y. C., Siang, D. T. C., Lin, S., Liang, H., & Sun, L. (2019) · Scientific Reports · doi:10.1038/s41598-019-42162-5 · Cited by 32
PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids
Jourdon, A., Scuderi, S., Capauto, D., Abyzov, A. S., & Vaccarino, F. M. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0763-3 · Cited by 29
A structural mechanism of nuclear receptor biased agonism
Nemetchek, M. D., Chrisman, I. M., Rayl, M. L., Voss, A. H., & Hughes, T. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2215333119 · Cited by 33
Synthetic STARR-seq reveals how DNA shape and sequence modulate transcriptional output and noise
Schöne, S., Bothe, M., Einfeldt, E., Borschiwer, M., Benner, P., Vingron, M., Thomas‐Chollier, M., & Meijsing, S. H. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007793 · Cited by 27
CRUP: a comprehensive framework to predict condition-specific regulatory units
Ramisch, A., Heinrich, V., Glaser, L. V., Fuchs, A., Yang, X., Benner, P., Schöpflin, R., Li, N., Kinkley, S., Römer-Hillmann, A., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1860-7 · Cited by 29
High-Resolution Mapping of Multiway Enhancer-Promoter Interactions Regulating Pathogen Detection
Vangala, P., Murphy, R., Quinodoz, S. A., Gellatly, K. J., McDonel, P. E., Guttman, M., & Garber, M. G. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.09.005 · Cited by 29
Information content differentiates enhancers from silencers in mouse photoreceptors
Friedman, R. Z., Granas, D. M., Myers, C. A., Corbo, J. C., Cohen, B. A., & White, M. A. (2021) · eLife · doi:10.7554/elife.67403 · Cited by 28
Guo, W.-L., & Huang, D.-S. (2017) · Molecular BioSystems · doi:10.1039/c7mb00155j · Cited by 27
Ruas, F. A. D., Barboza, N. R., Castro‐Borges, W., & Guerra‐Sá, R. (2018) · Journal of Proteomics · doi:10.1016/j.jprot.2018.11.001 · Cited by 27
Massively Parallel Reporter Assays for High-Throughput In Vivo Analysis of Cis-Regulatory Elements
Zheng, Y., & VanDusen, N. J. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10040144 · Cited by 25
Lessons from eRNAs: understanding transcriptional regulation through the lens of nascent RNAs
Cardiello, J. F., Sanchez, G. J., Allen, M. A., & Dowell, R. D. (2019) · Transcription · doi:10.1080/21541264.2019.1704128 · Cited by 24
Shen, Z., Li, R. Z., Prohaska, T. A., Hoeksema, M. A., Spann, N. J., Tao, J., Fonseca, G. J., Le, T., Stolze, L. K., Sakai, M., et al. (2022) · eLife · doi:10.7554/elife.70878 · Cited by 25
Using synthetic biology to study gene regulatory evolution
Crocker, J., & Ilsley, G. R. (2017) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2017.09.001 · Cited by 25
Chromatin reprogramming in breast cancer
Swinstead, E. E., Paakinaho, V., & Hager, G. L. (2018) · Endocrine Related Cancer · doi:10.1530/erc-18-0033 · Cited by 24
Kreimer, A., Yan, Z., Ahituv, N., & Yosef, N. (2019) · Human Mutation · doi:10.1002/humu.23820 · Cited by 25
Clock-controlled rhythmic transcription: is the clock enough and how does it work?
Beytebiere, J. R., Greenwell, B. J., Sahasrabudhe, A., & Menet, J. S. (2019) · Transcription · doi:10.1080/21541264.2019.1673636 · Cited by 25
Multi-omics approaches in plant biology: decoding agronomic traits for sustainable agriculture
Younas, M. U., Zuo, S., Qasim, M., Ahmad, I., Feng, Z., Korai, S. K., Zulfiqar, U., Tukhtaboeva, F., Ibrokhim, I., & Malik, T. (2025) · Plant Stress · doi:10.1016/j.stress.2025.101118 · Cited by 25
Alizada, A., Khyzha, N., Wang, L., Antounians, L., Chen, X., Khor, M., Liang, M., Rathnakumar, K., Weirauch, M. T., Medina-Rivera, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20765-1 · Cited by 24
Self-citationLewis, E. M. A., Kaushik, K., Sandoval, L. A., Antony, I., Dietmann, S., & Kroll, K. L. (2021) · Neurochemistry International · doi:10.1016/j.neuint.2021.105039 · Cited by 25
Transcriptional regulation of neuronal identity
Sousa, E., & Flames, N. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15551 · Cited by 19
Bakoulis, S., Krautz, R., Alcaraz, N., Salvatore, M., & Andersson, R. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac088 · Cited by 23
Characterization of sequence determinants of enhancer function using natural genetic variation
Yang, M. G., Ling, E., Cowley, C. J., Greenberg, M. E., & Vierbuchen, T. (2022) · eLife · doi:10.7554/elife.76500 · Cited by 25
Hammelman, J., Krismer, K., Banerjee, B., Gifford, D. K., & Sherwood, R. I. (2020) · Genome Research · doi:10.1101/gr.263228.120 · Cited by 19
Catta-Preta, R., Lindtner, S., Ypsilanti, A. R., Seban, N., Price, J. D., Abnousi, A., Su-Feher, L., Wang, Y., Cichewicz, K., Boerma, S. A., et al. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.10.004 · Cited by 21
Active learning of enhancers and silencers in the developing neural retina
Friedman, R. Z., Ramu, A., Lichtarge, S., Wu, Y., Tripp, L., Lyon, D., Myers, C. A., Granas, D. M., Gause, M., Corbo, J. C., et al. (2025) · Cell Systems · doi:10.1016/j.cels.2024.12.004 · Cited by 22
Morgan, R. A., Ma, F., Unti, M. J., Brown, D., Ayoub, P. G., Tam, C., Lathrop, L. E., Aleshe, B., Kurita, R., Nakamura, Y., et al. (2020) · Molecular Therapy — Methods & Clinical Development · doi:10.1016/j.omtm.2020.04.006 · Cited by 19
Shen, Z., Hoeksema, M. A., Ouyang, Z., Benner, C. W., & Glass, C. K. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa476 · Cited by 16
He, A. Y., & Danko, C. G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.13.583868 · Cited by 24
Prediction of cooperative homeodomain DNA binding sites from high-throughput-SELEX data
Cain, B., Webb, J. A., Yuan, Z., Cheung, D., Lim, H., Kovall, R. A., Weirauch, M. T., & Gebelein, B. A. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad318 · Cited by 15
Systems-Genetics-Based Inference of a Core Regulatory Network Underlying White Fat Browning
Li, Y., Schwalie, P. C., Bast‐Habersbrunner, A., Mocek, S., Russeil, J., Fromme, T., Deplancke, B., & Klingenspor, M. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.053 · Cited by 16
Self-citationModeling and designing enhancers by introducing and harnessing transcription factor binding units
Li, J., Zhang, P., Xi, X., Liu, L., Wei, L., & Wang, X. (2025) · Nature Communications · doi:10.1038/s41467-025-56749-2 · Cited by 16
Organizing combinatorial transcription factor recruitment atcis-regulatory modules
Dubois‐Chevalier, J., Mazrooei, P., Lupien, M., Staels, B., Lefèbvre, P., & Eeckhoute, J. (2017) · Transcription · doi:10.1080/21541264.2017.1394424 · Cited by 16
Rampersaud, A., Lodato, N. J., Shin, A., & Waxman, D. J. (2019) · Toxicological Sciences · doi:10.1093/toxsci/kfz148 · Cited by 15
Transcription factor interactions explain the context-dependent activity of CRX binding sites
Loell, K., Friedman, R. Z., Myers, C. A., Corbo, J. C., Cohen, B. A., & White, M. A. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011802 · Cited by 17
A long-range flexible billboard model of gene activation
Vockley, C. M., McDowell, I. C., D'Ippolito, A. M., & Reddy, T. E. (2017) · Transcription · doi:10.1080/21541264.2017.1317694 · Cited by 16
Zhang, X., Ehrlich, K. C., Yu, F., Hu, X., Meng, X., Deng, H., Shen, H., & Ehrlich, M. (2020) · Epigenetics · doi:10.1080/15592294.2020.1716491 · Cited by 14
Self-citationDynamic neuro-immune regulation of psychiatric risk loci in human neurons
Townsley, K. G., Lee, S. H., Williams, S., Cartwright, S., Cohen, S., Sen, A., Jia, M., Young, H. E., Dobbyn, L., Deans, M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-77114-x · Cited by 14
Bakloushinskaya, I., & Matveevsky, S. N. (2018) · OBM Genetics · doi:10.21926/obm.genet.1803023 · Cited by 12
Smart Sustainable Agriculture Using Machine Learning and AI: A Review
Menaga, A., & Vasantha, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-7952-0_42 · Cited by 14
Genetic architecture of natural variation of cardiac performance from flies to humans
Saha, S., Spinelli, L., Mondragón, J. A. C., Kervadec, A., Lynott, M. R., Kremmer, L., Röder, L., Krifa, S., Torres, M., Brun, C., et al. (2022) · eLife · doi:10.7554/elife.82459 · Cited by 12
Deregulation of Transcriptional Enhancers in Cancer
Azad, F. M., & Atlasi, Y. (2021) · Cancers · doi:10.3390/cancers13143532 · Cited by 12
Permutational analysis of Saccharomyces cerevisiae regulatory elements
Dhillon, N., Shelansky, R. I., Townshend, B., Jain, M., Boeger, H., Endy, D., & Kamakaka, R. T. (2020) · Synthetic Biology · doi:10.1093/synbio/ysaa007 · Cited by 11
Proformer: a hybrid macaron transformer model predicts expression values from promoter sequences
Kwak, I., Kim, B.-C., Lee, J., Kang, T., Garry, D. J., Zhang, J. J., & Gong, W. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05645-5 · Cited by 10
Mutagenesis Analysis of ABCG2 Gene Promoter of Zebrafish (Danio Rerio)
YUSNI, N. Z. B. M., Lim, L. W. K., & Chung, H. H. (2020) · Trends in Undergraduate Research · doi:10.33736/tur.2501.2020 · Cited by 10
Multiplexed functional genomic assays to decipher the noncoding genome
Cooper, Y. A., Guo, Q., & Geschwind, D. H. (2022) · Human Molecular Genetics · doi:10.1093/hmg/ddac194 · Cited by 10
Jimeno-Martín, Á., Sousa, E., Brocal-Ruiz, R., Daroqui, N., Maicas, M., & Flames, N. (2022) · Genome Research · doi:10.1101/gr.275623.121 · Cited by 11
Biased signaling in drug discovery and precision medicine
Ji, R., & Tao, Y. (2025) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2025.108804 · Cited by 9
Motif-based models accurately predict cell type-specific distal regulatory elements
Cornejo‐Páramo, P., Zhang, X., Louis, L., Yang, Y., Li, Z., Humphreys, D., & Wong, E. S. W. (2025) · Nature Communications · doi:10.1038/s41467-025-65362-2 · Cited by 12
Self-citationLiu, J., Shively, C. A., & Mitra, R. D. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa141 · Cited by 9
Molecular competition can shape enhancer activity in the Drosophila embryo
Waymack, R., Gad, M., & Wunderlich, Z. (2021) · iScience · doi:10.1016/j.isci.2021.103034 · Cited by 7
Metadata
- Authors
- Grossman, S. R., Zhang, X., Wang, L., Engreitz, J., Melnikov, A., Rogov, P., Tewhey, R., Isakova, A., Deplancke, B., Bernstein, B. E., Mikkelsen, T. S., & Lander, E. S.
- Affiliations
- Broad Institute (US)
- SIB Swiss Institute of Bioinformatics (CH)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2017-01-30
- Volume
- 114
- Issue
- 7
- Pages
- E1291-E1300
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC5321001
- OpenAlex
- W2581468399
- Cited by
- 165
- References
- 101
- Citations by year
- 2026: 15 · 2025: 15 · 2024: 9 · 2023: 14 · 2022: 20 · 2021: 21 · 2020: 23 · 2019: 20 · 2018: 18 · 2017: 10
- FWCI
- 7.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- HHS | NIH | National Human Genome Research Institute (2U54HG003067-10)
- HHS | NIH | National Institute of General Medical Sciences (T32GM007753)
- Office of Extramural Research, National Institutes of Health (R01 HG006785)
- 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 › Peroxisome Proliferator-Activated Receptors
- Keywords
- enhancer function, transcription factor binding, PPARγ, transcription factor interaction, chromatin accessibility, binding site affinity, massively parallel reporter assay, synthetic enhancers, adipogenesis, gene expression regulation, human genetic variation
- MeSH terms
- Animals, Binding Sites, Enhancer Elements, Genetic, Gene Expression Regulation, Mutation, Protein Binding, Transcription Factors, Genomics, 3T3-L1 Cells, PPAR gamma, Mice, Nucleotide Motifs
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