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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The Human Transcription Factors
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Predicting effects of noncoding variants with deep learning–based sequence model
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Opportunities and obstacles for deep learning in biology and medicine
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JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles
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JASPAR 2020: update of the open-access database of transcription factor binding profiles
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Ever-Changing Landscapes: Transcriptional Enhancers in Development and Evolution
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Base-resolution models of transcription-factor binding reveal soft motif syntax
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DNA-dependent formation of transcription factor pairs alters their binding specificity
Jolma, A., Yin, Y., Nitta, K. R., Dave, K., Popov, A. N., Taipale, M., Enge, M., Kivioja, T., Morgunova, E. Y., & Taipale, J. (2015) · Nature · doi:10.1038/nature15518 · Cited by 555 · see citing works
Theißen, G., Melzer, R., & Rümpler, F. (2016) · Development · doi:10.1242/dev.134080 · Cited by 523 · see citing works
Competition between DNA methylation and transcription factors determines binding of NRF1
Domcke, S., Bardet, A. F., Adrian Ginno, P., Hartl, D., Burger, L., & Schübeler, D. (2015) · Nature · doi:10.1038/nature16462 · Cited by 508 · see citing works
The Genetics of Transcription Factor DNA Binding Variation
Deplancke, B., Alpern, D., & Gardeux, V. (2016) · Cell · doi:10.1016/j.cell.2016.07.012 · Cited by 390 · see citing works
Enhancer Features that Drive Formation of Transcriptional Condensates
Shrinivas, K., Sabari, B. R., Coffey, E. L., Klein, I. A., Boija, A., Zamudio, A. V., Schuijers, J., Hannett, N. M., Sharp, P. A., Young, R. A., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.07.009 · Cited by 407 · see citing works
Transcription factor–DNA binding: beyond binding site motifs
Inukai, S., Kock, K. H., & Bulyk, M. L. (2017) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2017.02.007 · Cited by 387 · see citing works
Sensitivity of transcription factors to DNA methylation
Héberlé, É., & Bardet, A. F. (2019) · Essays in Biochemistry · doi:10.1042/ebc20190033 · Cited by 347 · see citing works
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 282 · see citing works
Specificity and nonspecificity in RNA–protein interactions
Jankowsky, E., & Harris, M. E. (2015) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm4032 · Cited by 277 · see citing works
Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets
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G-quadruplexes are transcription factor binding hubs in human chromatin
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Quantitative modeling of transcription factor binding specificities using DNA shape
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Self-citationLow-Affinity Binding Sites and the Transcription Factor Specificity Paradox in Eukaryotes
Kribelbauer, J. F., Rastogi, C., Bussemaker, H. J., & Mann, R. S. (2019) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100617-062719 · Cited by 216 · see citing works
Transcriptional events defining plant immune responses
Birkenbihl, R. P., Liu, S., & Somssich, I. E. (2017) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2017.04.004 · Cited by 216 · see citing works
Transcription Factor Activities Enhance Markers of Drug Sensitivity in Cancer
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Short tandem repeats bind transcription factors to tune eukaryotic gene expression
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Self-citationDeconvolving the Recognition of DNA Shape from Sequence
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Self-citationMultiple causal variants underlie genetic associations in humans
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Deep conservation of the enhancer regulatory code in animals
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Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure
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Li, R. Y., Di Felice, R., Rohs, R., & Lidar, D. A. (2018) · npj Quantum Information · doi:10.1038/s41534-018-0060-8 · Cited by 202 · see citing works
Self-citationGenerating specificity in genome regulation through transcription factor sensitivity to chromatin
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Self-citationUnraveling determinants of transcription factor binding outside the core binding sitePreprint
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Self-citationDNA Shape Features Improve Transcription Factor Binding Site Predictions In Vivo
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Self-citationAydın, B., Kakumanu, A., Rossillo, M., Moreno‐Estellés, M., Garipler, G., Ringstad, N., Flames, N., Mahony, S., & Mazzoni, E. O. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0399-y · Cited by 134 · see citing works
To Be Specific or Not: The Critical Relationship Between Hox And TALE Proteins
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Transcription factor family-specific DNA shape readout revealed by quantitative specificity models
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Self-citationCross-Family Transcription Factor Interactions: An Additional Layer of Gene Regulation
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Evolving insights on how cytosine methylation affects protein-DNA binding
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Self-citationDeepSite: bidirectional LSTM and CNN models for predicting DNA–protein binding
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Identification of a functional antioxidant response element at the HIF1A locus
Lacher, S. E., Levings, D. C., Freeman, S., & Slattery, M. G. (2018) · Redox Biology · doi:10.1016/j.redox.2018.08.014 · Cited by 117 · see citing works
Self-citationAn ancient protein-DNA interaction underlying metazoan sex determination
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Building Transcription Factor Binding Site Models to Understand Gene Regulation in Plants
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Self-citationTranscription factor control of virulence in phytopathogenic fungi
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Supervised enhancer prediction with epigenetic pattern recognition and targeted validation
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Identification of altered cis-regulatory elements in human disease
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Sequential actions of EOMES and T-BET promote stepwise maturation of natural killer cells
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Self-citationA map of direct TF–DNA interactions in the human genome
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Low overlap of transcription factor DNA binding and regulatory targets
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A SAM oligomerization domain shapes the genomic binding landscape of the LEAFY transcription factor
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Controlling gene expression with deep generative design of regulatory DNA
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Deep flanking sequence engineering for efficient promoter design using DeepSEED
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The LEAFY floral regulator displays pioneer transcription factor properties
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Self-citationmiR-497 expression, function and clinical application in cancer
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Self-citationBeyond antioxidant genes in the ancient Nrf2 regulatory network
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Self-citationDMRT proteins and coordination of mammalian spermatogenesis
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DNAproDB: an expanded database and web-based tool for structural analysis of DNA–protein complexes
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Self-citationNew insights into protein–DNA binding specificity from hydrogen bond based comparative study
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Geometric deep learning of protein–DNA binding specificity
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Synthetic epigenetics—towards intelligent control of epigenetic states and cell identity
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Living Organisms Author Their Read-Write Genomes in Evolution
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Signaling by Steroid Hormones in the 3D Nuclear Space
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A Novel AT-Rich DNA Recognition Mechanism for Bacterial Xenogeneic Silencer MvaT
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Facilitated Diffusion Mechanisms in DNA Base Excision Repair and Transcriptional Activation
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Self-citationZinc Finger Readers of Methylated DNA
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DNA helix: the importance of being AT-rich
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Floral Organogenesis: When Knowing Your ABCs Is Not Enough
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- 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
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 ›
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