Cited by 382Genomics and Chromatin DynamicsGenomics and Phylogenetic StudiesGene Regulatory Network Analysis

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Evaluation of methods for modeling transcription factor sequence specificity

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DREAM5 Consortium, Weirauch, Matthew T, Cote, Atina, Norel, Raquel, Annala, Matti, Zhao, Yue, Riley, Todd R, Saez-Rodriguez, Julio, Cokelaer, Thomas, Vedenko, Anastasia, Talukder, Shaheynoor, Bussemaker, Harmen J, Morris, Quaid D, Bulyk, Martha L, Stolovitzky, Gustavo, Hughes, Timothy R·2013·Nature Biotechnology

DOI 10.1038/nbt.2486PMC PMC3687085

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Abstract

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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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: Weirauch, Matthew T
Norel, Raquel
Annala, Matti
Zhao, Yue
Cokelaer, Thomas
Bussemaker, Harmen J
Morris, Quaid D
Bulyk, Martha L
Stolovitzky, Gustavo
Hughes, Timothy R
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2013-01-27
Volume: 31
Issue: 2
Pages: 126-134
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/nbt.2486
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

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Evaluation of methods for modeling transcription factor sequence specificity

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