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Base-resolution models of transcription-factor binding reveal soft motif syntax
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Abstract
The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)-nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using clustered regularly interspaced short palindromic repeat (CRISPR)-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.
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“The BPNet architecture has been used widely in genomics and represents a very strong default architecture for modeling small-sized datasets from scratch through supervised learning.”
From Nucleotide Transformer: building and evaluating robust foundation models for human genomics (2025) · cites this paper as [9]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Machine-learning models trained to predict function from DNA sequences have been successful at characterizing regulatory syntax and interpreting genetic variant effects.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [7]
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“Thus far, such models have focused on assays in which measured activity is proportional to local sequencing read counts.”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [7]
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“Finer resolution has potential benefits when the data support it15, but would extend the sequence length entering the quadratic complexity MHA and make the model engineering intractable on currently available hardware.”
From Effective gene expression prediction from sequence by integrating long-range interactions (2021) · cites this paper as [15]
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“Additionally, a deep learning algorithm applied to binding of pluripotency TFs at base resolution in mouse embryonic stem cells (ESCs) suggested a “soft motif syntax” whereby TFs help each other to bind directly to the enhancer with a lenient distance-dependency between binding sites [98].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [98]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889
Kimura, M. (1980) · Journal of Molecular Evolution · doi:10.1007/bf01731581 · Cited by 29,778
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 21,341
Research works printed but not linked: 1
- Shrikumar, A. et al. TF-MoDISco v0.4.2.2-alpha: technical note. Preprint at arXiv https://arxiv.org/abs/1811.00416 (2018). · arXiv
Cited by
Sorted by citations
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“Benefiting from these model 52 explainers, computational biologists discovered the syntax of transcription factor (TF) binding motifs by 53 interpreting models trained to predict chromatin accessibility [19, 20]; and screened for cancer marker 54”
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“To illustrate a typical use case, we trained separate BPNet neural networks ( Ž .”
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Aerts, S. (2022) · Nature Methods · doi:10.1038/s41592-022-01570-8
Liu, C., & Wang, W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.19.504467 · Cited by 1
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BackgroundZhao, N., Wang, S., Huang, Q., Dong, S., & Boyle, A. P. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.23.525250
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Nayak, D. S. K., Das, J., & Swarnkar, T. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815079005123050011
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Marinov, G. K., Ramalingam, V., Greenleaf, W. J., & Kundaje, A. B. (2025) · PLoS ONE · doi:10.1371/journal.pone.0318796
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Spirov, A. V. (2025) · Biosystems · doi:10.1016/j.biosystems.2025.105629
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Ramstein, G. P., & Buckler, E. S. (2022) · Figshare · doi:10.6084/m9.figshare.20786966
Karollus, A., Mauermeier, T., & Gagneur, J. (2023) · Figshare · doi:10.6084/m9.figshare.22626518
Self-citationKarollus, A., Mauermeier, T., & Gagneur, J. (2023) · Figshare · doi:10.6084/m9.figshare.22626521
Self-citationKhodursky, S., Zheng, E. B., Svetec, N., Durkin, S. M., Benjamin, S., Gadau, A., Wu, X., & Zhao, L. (2024) · Figshare · doi:10.6084/m9.figshare.26627413
Additional file 3 of Predicting RNA splicing from DNA sequence using Pangolin
Zeng, T., & Li, Y. (2022) · Figshare · doi:10.6084/m9.figshare.19635056
Kaplow, I. M., Banerjee, A., & Foo, C. S. (2022) · Figshare · doi:10.6084/m9.figshare.19582248
Ji, X., Wang, C. H., Zhang, H., Gong, X., Zhu, C., Zhao, Y., Liu, Y., Han, Z., & Hang, H. E. (2025) · Theoretical and Applied Genetics · doi:10.1007/s00122-025-05109-1
“This framework has been validated: (Shri-kumar et al. 2018)showed TF-MoDISco accurately recovered known motifs from ChIP-seq data; (Avsec et al. 2021) demonstrated > 90% overlap between extracted motifs and experimental TF footprints; (Gosai et al. 2024) successfully designed functional CREs using…”
Fallon, C., Ling, L., Cooper, B. H., Ceolin, S., Chiu, T.-P., Mitra, R., Mühling, B., Srinivasan, V., Durmaz, A. D., Veselinovic, D., et al. (2026) · Science Advances · doi:10.1126/sciadv.adz5902
“Numerous studies us-ing machine learning have shown that this is within reach ( 14 , 56 , 61 ) and that models that predict enhancer activity from a sequence are available.”
Manoj, M., Bhagavathi, K., Fathima, S. A. N., Pillai, S. S., Malarvizhi, M., Shenbagam, M., Velayuthaprabhu, S., & Anand, A. V. (2026) · CABI biotechnology series. · doi:10.1079/9781800628649.0001
inMOTIFin: a lightweight end-to-end simulation software for regulatory sequences
Ferenc, K., Martini, L., Rauluševičiūtė, I., Sandve, G. K., & Mathelier, A. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag026
TPCAV: Interpreting deep learning genomics models via concept attributionPreprint
Yang, J., & Mahony, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.20.700723 · Cited by 1
“A prominent class of these models – sequence-to-function models – typically take one-hot-encoded DNA sequences as input to predict diverse genomic features, including transcription factor (TF) binding 1,2 , regulatory signal tracks 3,4 , and RNA expression levels 5 .”
EpiExpr: Predicting gene expression using epigenetic data and chromatin interactionsPreprint
Bhattacharyya, S., & Ay, F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.06.704509
“Transformer-based 22 foundation models trained on a large number of DNA sequence datasets are used together with explainable-AI techniques 23,24 to decode the contribution of regulatory sequence motifs 14 , or regulatory regions in transcriptional regulation 25 .”
Modeling nascent transcription from chromatin landscape and structure with CLASTER
Avelli, M. P., Sigurdsson, A. I., Ollé-López, J., Narita, T., Krietenstein, N., Choudhary, C., & Rasmussen, S. (2026) · Genome biology · doi:10.1186/s13059-026-03992-5
Metadata
- Authors
- Avsec, Ž., Weilert, M., Shrikumar, A., Krueger, S., Alexandari, A., Dalal, K., Fropf, R., McAnany, C., Gagneur, J., Kundaje, A., & Zeitlinger, J.
- Affiliations
- Google DeepMind (United Kingdom) (GB)
- Stowers Institute for Medical Research (US)
- Stanford University (US)
- Technical University of Munich (DE)
- Corresponding author
- Kundaje, Anshul
- Zeitlinger, Julia
- ORCID
- Venue
- Nature Genetics
- Published
- 2021-02-18
- Volume
- 53
- Issue
- 3
- Pages
- 354-366
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC8812996
- OpenAlex
- W3130819465
- Cited by
- 610
- References
- 163
- Citations by year
- 2026: 118 · 2025: 108 · 2024: 124 · 2023: 102 · 2022: 90 · 2021: 49
- FWCI
- 29.88
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (1R01HG010211, 1U01HG009431, 1R01HG009674)
- Howard Hughes Medical Institute (International Student Research Fellowship)
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (1DP2GM123485)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- transcription factor binding, pluripotency transcription factors, Nanog, helical periodicity, explainable deep learning
- MeSH terms
- Mouse Embryonic Stem Cells, Nanog Homeobox Protein, Deep Learning, Animals, Binding Sites, Transcription Factors, Reproducibility of Results, Neural Networks, Computer, Computational Biology, Chromatin Immunoprecipitation, Octamer Transcription Factor-3, Mice, SOXB1 Transcription Factors, Nucleotide Motifs, 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.
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