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Evaluation of methods for modeling transcription factor sequence specificity
Open accessAbstract
Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein's DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro-derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10% of the TFs examined here). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.
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“and is instead consistent with recent observations that inhibiting p300 catalytic activity actually stabilizes interactions with chromatin51,52.”
From Single-molecule states link transcription factor binding to gene expression (2024) · cites this paper as [52]
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Metadata
- Authors
- DREAM5 Consortium, Weirauch, M. T., Cote, A., Norel, R., Annala, M., Zhao, Y., Riley, T. R., Saez-Rodriguez, J., Cokelaer, T., Vedenko, A., Talukder, S., Bussemaker, H. J., Morris, Q. D., Bulyk, M. L., Stolovitzky, G., & Hughes, T. R.
- Affiliations
- University of Toronto (CA)
- IBM (United States) (US)
- Tampere University of Technology (FI)
- University of Pennsylvania (US)
- Columbia University Irving Medical Center (US)
- European Bioinformatics Institute (GB)
- Brigham and Women's Hospital (US)
- Corresponding author
- Hughes, Timothy R
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2013-01-27
- Volume
- 31
- Issue
- 2
- Pages
- 126-134
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3687085
- OpenAlex
- W2159324718
- Cited by
- 381
- References
- 59
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 18 · 2023: 25 · 2022: 18 · 2021: 26 · 2020: 23 · 2019: 34 · 2018: 32 · 2017: 44 · 2016: 36 · 2015: 40 · 2014: 31 · 2013: 21
- FWCI
- 19.10
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- National University of Singapore (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Funders
- National Science Foundation (R01HG003008, 0957573, 1022140)
- March of Dimes Foundation
- John Simon Guggenheim Memorial Foundation
- Canadian Institute for Advanced Research
- Genome Canada
- Ontario Genomics
- Ontario Genomics Institute
- European Commission (223575)
- Academy of Finland (260403)
- Israel Science Foundation (802/08)
- Tel Aviv University
- National Institutes of Health (U54-CA121852)
- Canadian Institutes of Health Research (MOP-77721)
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory
- National Human Genome Research Institute (R01HG003985, R01HG003008)
- National Cancer Institute (U54CA121852)
- National Institute on Minority Health and Health Disparities (G12MD007591)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- transcription factor binding sites, DNA binding specificity, position weight matrices, protein binding microarrays, information content
- MeSH terms
- Algorithms, Animals, DNA-Binding Proteins, Transcription Factors, Genome, Computational Biology, Protein Array Analysis, Mice, Position-Specific Scoring Matrices, 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.
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