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Absence of a simple code: how transcription factors read the genome
Open accessAbstract
Transcription factors (TFs) influence cell fate by interpreting the regulatory DNA within a genome. TFs recognize DNA in a specific manner; the mechanisms underlying this specificity have been identified for many TFs based on 3D structures of protein-DNA complexes. More recently, structural views have been complemented with data from high-throughput in vitro and in vivo explorations of the DNA-binding preferences of many TFs. Together, these approaches have greatly expanded our understanding of TF-DNA interactions. However, the mechanisms by which TFs select in vivo binding sites and alter gene expression remain unclear. Recent work has highlighted the many variables that influence TF-DNA binding, while demonstrating that a biophysical understanding of these many factors will be central to understanding TF function.
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Cites
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
Heinz, 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
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422
Fitting a mixture model by expectation maximization to discover motifs in biopolymers.
Bailey, T. L., & Elkan, C. (1994) · PubMed · Cited by 4,982
Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes
Whyte, W. A., Orlando, D. A., Hnisz, D., Abraham, B. J., Lin, C. Y., Kagey, M. H., Rahl, P. B., Lee, T. I., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.03.035 · Cited by 4,143
Super-enhancers in the control of cell identity and disease
Hnisz, D., Abraham, B. J., Lee, T. I., Lau, A., Saint-André, V., Sigova, A. A., Hoke, H. A., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.09.053 · Cited by 3,662
Genome-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
DNAase footprinting a simple method for the detection of protein-DNA binding specificity
Galas, D. J., & Schmitz, A. (1978) · Nucleic Acids Research · doi:10.1093/nar/5.9.3157 · Cited by 2,109
Garner, M. M., & Revzin, A. (1981) · Nucleic Acids Research · doi:10.1093/nar/9.13.3047 · Cited by 2,021
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Items 101–200 of 538
Kuzu, G., Kaye, E. G., Chery, J., Siggers, T., Yang, L., Dobson, J. R., Boor, S., Bliss, J. E., Liu, W., Jogl, G., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006120 · Cited by 56
Self-citationLópez‐Vidriero, I., Godoy, M., Grau, J., Peñuelas, M., Solano, R., & Franco‐Zorrilla, J. M. (2021) · Plant Communications · doi:10.1016/j.xplc.2021.100232 · Cited by 54
Levings, D. C., Wang, X., Kohlhase, D., Bell, D. A., & Slattery, M. G. (2018) · Redox Biology · doi:10.1016/j.redox.2018.07.026 · Cited by 53
Self-citationDeterminants of p53 DNA binding, gene regulation, and cell fate decisions
Fischer, M., & Sammons, M. A. (2024) · Cell Death and Differentiation · doi:10.1038/s41418-024-01326-1 · Cited by 59
Engineering altered protein–DNA recognition specificity
Bogdanove, A. J., Bohm, A., Miller, J. C., Morgan, R., & Stoddard, B. L. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky289 · Cited by 53
Madrigal, P., Deng, S., Feng, Y., Militi, S., Goh, K. J., Nibhani, R., Grandy, R. A., Osnato, A., Ortmann, D., Brown, S. E., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36116-9 · Cited by 48
TGIF1 homeodomain interacts with Smad MH1 domain and represses TGF-β signaling
Guca, E., Suñol, D., Ruiz, L., Konkol, A., Cordero, J., Torner, C., Aragón, E., Martín-Malpartida, P., Riéra, A., & Macías, M. J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky680 · Cited by 48
Barco, B., Kim, Y., & Clay, N. K. (2019) · Nature Communications · doi:10.1038/s41467-019-11406-3 · Cited by 48
Malik, F., & Guo, J. (2022) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26313 · Cited by 50
Tetrahedral DNA frameworks for biosensing and imaging analysis in living cells
Qi, Z., Wei, C., Zhang, F., Wang, Z., & Zuo, X. (2023) · Nano Today · doi:10.1016/j.nantod.2023.102127 · Cited by 50
Lesage, K., Huot, L., Mouveaux, T., Courjol, F., Saliou, J., & Gissot, M. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky373 · Cited by 49
Genome-wide determinants of sequence-specific DNA binding of general regulatory factorsPreprint
Rossi, M. J., Lai, W., & Pugh, B. F. (2018) · Genome Research · doi:10.1101/gr.229518.117 · Cited by 49
FoxP3 associates with enhancer-promoter loops to regulate Treg-specific gene expressionPreprint
Ramírez, R. N., Chowdhary, K., Léon, J., Mathis, D. J., & Benoist, C. O. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.12.468430 · Cited by 50
Genome access is transcription factor-specific and defined by nucleosome position
Grand, R. S., Pregnolato, M., Baumgartner, L., Hoerner, L., Burger, L., & Schübeler, D. (2024) · Molecular Cell · doi:10.1016/j.molcel.2024.08.009 · Cited by 49
Protein–DNA binding in high-resolution
Mahony, S., & Pugh, B. F. (2015) · Critical Reviews in Biochemistry and Molecular Biology · doi:10.3109/10409238.2015.1051505 · Cited by 48
Pahl, M. C., Doege, C. A., Hodge, K. M., Littleton, S. H., Leonard, M. E., Lu, S., Rausch, R., Pippin, J. A., Rosa, M. C. D., Basak, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27001-4 · Cited by 46
Zhang, Y., Wang, Z., Zeng, Y., Liu, Y., Xiong, S., Wang, M., Zhou, J., & Zou, Q. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab525 · Cited by 46
Aditham, A. K., Markin, C. J., Mokhtari, D. A., DelRosso, N. V., & Fordyce, P. M. (2020) · Cell Systems · doi:10.1016/j.cels.2020.11.012 · Cited by 45
ToxR Antagonizes H-NS Regulation of Horizontally Acquired Genes to Drive Host Colonization
Kazi, M. I., Conrado, A. R., Mey, A. R., Payne, S. M., & Davies, B. W. (2016) · PLoS Pathogens · doi:10.1371/journal.ppat.1005570 · Cited by 45
Identification of plant transcription factor target sequences
Franco‐Zorrilla, J. M., & Solano, R. (2016) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2016.05.001 · Cited by 46
Control of VEGF-A transcriptional programs by pausing and genomic compartmentalization
Kaikkonen, M. U., Niskanen, H., Romanoski, C. E., Kansanen, E., Kivelä, A. M., Laitalainen, J., Heinz, S., Benner, C. W., Glass, C. K., & Ylä‐Herttuala, S. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1036 · Cited by 46
Khoueiry, P., Girardot, C., Ciglar, L., Peng, P.-C., Gustafson, E. H., Sinha, S., & Furlong, E. E. (2017) · eLife · doi:10.7554/elife.28440 · Cited by 44
Alexandre, C. M., Urton, J. R., Jean-Baptiste, K., Huddleston, J., Dorrity, M. W., Cuperus, J. T., Sullivan, A. M., Bemm, F., Jolić, D., Arsovski, A. A., et al. (2017) · Molecular Biology and Evolution · doi:10.1093/molbev/msx326 · Cited by 44
Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data
Eggeling, R., Roos, T., Myllymäki, P. J., & Große, I. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0797-4 · Cited by 43
NextPBM: a platform to study cell-specific transcription factor binding and cooperativity
Mohaghegh, N., Bray, D., Keenan, J., Penvose, A., Andrilenas, K., Ramlall, V., & Siggers, T. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz020 · Cited by 30
Benaglio, P., D’Antonio‐Chronowska, A., Ma, W., Yang, F., Greenwald, W. W. Y., Donovan, M. K. R., DeBoever, C., Li, H., Drees, F., Singhal, S., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0499-3 · Cited by 42
Domain-adaptive neural networks improve cross-species prediction of transcription factor bindingPreprint
Cochran, K., Srivastava, D., Shrikumar, A., Balsubramani, A., Hardison, R. C., Kundaje, A. B., & Mahony, S. (2022) · Genome Research · doi:10.1101/gr.275394.121 · Cited by 42
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
Kuczynski, C., McCorkle, S., Keereetaweep, J., Shanklin, J., & Schwender, J. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.955589 · Cited by 42
Ding, P., Wang, Y., Zhang, X., Gao, X., Liu, G., & Yu, B. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad231 · Cited by 42
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Zhang, S., Li, M., Ji, H., & Fang, Z. (2018) · BMC Genomics · doi:10.1186/s12864-018-4828-1 · Cited by 40
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Chen, X. X., Yu, B., Carriero, N. J., Silva, C. T., & Bonneau, R. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx174 · Cited by 40
A Review About Transcription Factor Binding Sites Prediction Based on Deep Learning
Zeng, Y., Gong, M., Meng, L., Gao, D., & Zhang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3042903 · Cited by 42
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Albig, C., Tikhonova, E., Krause, S., Maksimenko, O. G., Regnard, C., & Becker, P. B. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1238 · Cited by 41
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Chiu, T.-P., Xin, B., Markarian, N., Wang, Y., & Rohs, R. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz970 · Cited by 41
Self-citationGandhi, M., Caudron‐Herger, M., & Diederichs, S. (2018) · Nature Structural & Molecular Biology · doi:10.1038/s41594-018-0155-0 · Cited by 39
Zhang, Y., Wang, Z., Zeng, Y., Zhou, J., & Zou, Q. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab273 · Cited by 40
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Galli, M., Feng, F., & Gallavotti, A. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.591194 · Cited by 41
Narasimhan, K., Pillay, S., Huang, Y.-H., Jayabal, S., Udayasuryan, B., Veerapandian, V., Kolatkar, P. R., Cojocaru, V., Pervushin, K. V., & Jauch, R. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1390 · Cited by 39
Steffens, E. K., Becker, K., Krevet, S., Teichert, I., & Kück, U. (2016) · Molecular Microbiology · doi:10.1111/mmi.13491 · Cited by 39
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Zeiske, T., Baburajendran, N., Kaczynska, A., Brasch, J., Palmer, A. G. I., Shapiro, L. S., Honig, B. H., & Mann, R. S. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.07.100 · Cited by 39
van der Burg, K. R. L., Lewis, J. J., Martin, A., Nijhout, H. F., Danko, C. G., & Reed, R. D. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.03.092 · Cited by 39
Zhang, L., Martini, G. D., Rube, H. T., Kribelbauer, J. F., Rastogi, C., FitzPatrick, V., Houtman, J. C. D., Bussemaker, H. J., & Pufall, M. A. (2017) · Genome Research · doi:10.1101/gr.222844.117 · Cited by 39
Gutiérrez, M. P., MacAlpine, H. K., & MacAlpine, D. M. (2019) · Genome Research · doi:10.1101/gr.243386.118 · Cited by 39
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Montes, R. A. C., Herrera‐Ubaldo, H., Serwatowska, J., & de Folter, S. (2015) · Current Plant Biology · doi:10.1016/j.cpb.2015.08.002 · Cited by 37
Guo, Y., & Gifford, D. K. (2017) · BMC Genomics · doi:10.1186/s12864-016-3434-3 · Cited by 39
Wang, K., Zeng, X., Zhou, J., Liu, F., Luan, X., & Wang, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae195 · Cited by 35
Andreassi, C., Crerar, H., & Riccio, A. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00304 · Cited by 32
Kribelbauer, J. F., Loker, R., Feng, S., Rastogi, C., Abe, N., Rube, H. T., Bussemaker, H. J., & Mann, R. S. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.01.027 · Cited by 36
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Barissi, S., Sala, A., Wieczór, M., Battistini, F., & Orozco, M. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac708 · Cited by 37
Al-Zyoud, W. A., Hynson, R. M. G., Ganuelas, L. A., Coster, A. C. F., Duff, A. P., Baker, M. A. B., Stewart, A. G., Giannoulatou, E., Ho, J. W. K., Gaus, K., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1466 · Cited by 36
Ghosh, R. P., Shi, Q., Yang, L., Reddick, M. P., Tv, N., Zhurkin, V. B., Fordyce, P. M., Stasevich, T. J., Chang, H. Y., Greenleaf, W. J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11118-8 · Cited by 40
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Arenas‐Mena, C. (2017) · Evolution & Development · doi:10.1111/ede.12217 · Cited by 39
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Martin, V., Zhao, J., Afek, A., Mielko, Z. E., & Gordân, R. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz363 · Cited by 35
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Kaplow, I. M., Schäffer, D. E., Wirthlin, M. E., Lawler, A. J., Brown, A. R., Kleyman, M., & Pfenning, A. R. (2022) · BMC Genomics · doi:10.1186/s12864-022-08450-7 · Cited by 37
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Dukatz, M., Dittrich, M., Stahl, E., Adam, S., de Mendoza, A., Bashtrykov, P., & Jeltsch, A. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.102462 · Cited by 35
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Lin, Q. X. X., Thieffry, D., Jha, S., & Benoukraf, T. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1088 · Cited by 33
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Tognon, M., Giugno, R., & Pinello, L. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad156 · Cited by 32
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Xie, Z., Chen, J. C., & Wu, Y. (2017) · Scientific Reports · doi:10.1038/srep46622 · Cited by 32
Farkaš, M., Hashimoto, H., Bi, Y., Davuluri, R. V., Resnick‐Silverman, L., Manfredi, J. J., Debler, E., & McMahon, S. B. (2021) · Nature Communications · doi:10.1038/s41467-020-20783-z · Cited by 31
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Glasscock, C. J., Pecoraro, R. J., McHugh, R., Doyle, L. A., Chen, W., Boivin, O., Lonnquist, B., Na, E., Politanska, Y., Haddox, H. K., et al. (2025) · Nature Structural & Molecular Biology · doi:10.1038/s41594-025-01669-4 · Cited by 58
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Roey, K. V., & Davey, N. E. (2015) · Cell Communication and Signaling · doi:10.1186/s12964-015-0123-9 · Cited by 29
Zhang, N., McHale, L. K., & Finer, J. J. (2018) · Plant Biotechnology Journal · doi:10.1111/pbi.13010 · Cited by 30
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Tsai, Z. T., Shiu, S., & Tsai, H. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004418 · Cited by 29
Hossain, M. A., Barrow, J. J., Shen, Y., Haq, M. I., & Bungert, J. (2015) · Journal of Cellular Biochemistry · doi:10.1002/jcb.25226 · Cited by 29
Azad, R. N., Zafiropoulos, D., Ober, D., Jiang, Y., Chiu, T.-P., Sagendorf, J. M., Rohs, R., & Tullius, T. D. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky033 · Cited by 29
Self-citationGene regulatory effects of disease-associated variation in the NRF2 network
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Self-citationDorman, C. J., & Dorman, M. J. (2017) · Environmental Microbiology · doi:10.1111/1462-2920.13838 · Cited by 28
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Poul, Y. L., Xin, Y., Ling, L., Mühling, B., Jaenichen, R., Hörl, D., Bunk, D., Harz, H., Leonhardt, H., Wang, Y., et al. (2020) · Science Advances · doi:10.1126/sciadv.abe2955 · Cited by 30
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Nowak, K., & Gaj, M. D. (2016) · Springer eBooks · doi:10.1007/978-3-319-33705-0_5 · Cited by 28
Guo, W.-L., & Huang, D.-S. (2017) · Molecular BioSystems · doi:10.1039/c7mb00155j · Cited by 27
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Hossain, K. A., Kogut, M., Słabońska, J., Sappati, S., Wieczór, M., & Czub, J. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2212501120 · Cited by 29
Neikes, H. K., Kliza, K. W., Gräwe, C., Wester, R. A., Jansen, P. W. T. C., Lamers, L. A., Baltissen, M. P. A., van Heeringen, S. J., Logie, C., Teichmann, S. A., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01715-w · Cited by 28
Andrilenas, K. K., Penvose, A. R., & Siggers, T. (2014) · Briefings in Functional Genomics · doi:10.1093/bfgp/elu046 · Cited by 27
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Zumajo‐Cardona, C., Ambrose, B. A., & Pabón‐Mora, N. (2017) · EvoDevo · doi:10.1186/s13227-017-0068-8 · Cited by 26
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
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Liu, L., Zhao, W., & Zhou, X. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1281 · Cited by 27
Can We Predict Gene Expression by Understanding Proximal Promoter Architecture?
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Multi-dimensional histone methylations for coordinated regulation of gene expression under hypoxia
Lee, S., Lee, J., Chae, S., Moon, Y., Lee, H., Park, B., Yang, E. G., Hwang, D., & Park, H. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx747 · Cited by 26
Zacksenhaus, E., Liu, J.-K., Jiang, Z., Yao, Y., Xia, L., Shrestha, M., & Ben‐David, Y. (2016) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2016.10.003 · Cited by 28
Käppel, S., Melzer, R., Rümpler, F., Gafert, C., & Theißen, G. (2018) · The Plant Journal · doi:10.1111/tpj.13954 · 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
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Metadata
- Authors
- Slattery, M., Zhou, T., Yang, L., Dantas Machado, A. C., Gordân, R., & Rohs, R.
- Affiliations
- University of Minnesota (US)
- University of Southern California (US)
- Duke University (US)
- Corresponding author
- Slattery, Matthew
- Gordân, Raluca
- Rohs, Remo
- Venue
- Trends in Biochemical Sciences
- Publisher
- Elsevier BV
- Published
- 2014-09
- Volume
- 39
- Issue
- 9
- Pages
- 381-399
- ISSN
- 0968-0004 (ISSN-L)
- PMC
- PMC4149858
- OpenAlex
- W2022736304
- Cited by
- 537
- References
- 254
- Citations by year
- 2026: 22 · 2025: 43 · 2024: 39 · 2023: 42 · 2022: 38 · 2021: 67 · 2020: 43 · 2019: 55 · 2018: 42 · 2017: 47 · 2016: 47 · 2015: 45 · 2014: 7
- FWCI
- 4.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Trends in Biochemical Sciences (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- National Institutes of Health
- National Human Genome Research Institute (R01HG003008)
- National Institute of General Medical Sciences (U01GM103804, R01GM106056)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Diffusion and Search Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- transcription factors, DNA binding specificity, protein-DNA complex, binding site selection, gene expression regulation, cell fate
- MeSH terms
- Animals, Binding Sites, DNA, Humans, Protein Binding, Transcription Factors, Genome, Computational Biology, Biophysical Phenomena
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