Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors

A sequence motif representing the DNA-binding specificity of a transcription factor (TF) is commonly modelled with a positional weight matrix (PWM). Focusing on understudied human TFs, we processed results of 4,237 experiments for 394 TFs, assayed using five different experimental platforms. By human curation, we approved a subset of experiments that yielded consistent motifs across platforms and replicates, and evaluated quantitatively the cross-platform performance of PWMs obtained with ten motif discovery tools. Notably, nucleotide composition and information content are not correlated with motif performance and do not help in detecting underperformers, while motifs with low information content, in many cases, describe well the binding specificity assessed across different experimental platforms. By combining multiple PMWs into a random forest, we demonstrate the potential of accounting for multiple modes of TF binding. Finally, we present the Codebook Motif Explorer ( https://mex.autosome.org ), cataloguing motifs, benchmarking results, and the underlying experimental data.

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Publication Details

Journal
Communications Biology
Published
2025-11-07
DOI
https://doi.org/10.1038/s42003-025-08909-9
Citations
3
Primary Topic
Genomics and Chromatin Dynamics
Type
article
Field-Weighted Citation Impact
2.42

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article

Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors

Pavel Kravchenko, Marjan Barazandeh, Vsevolod J. Makeev, Jan Grau et al.
3 citations
Communications Biology
Genomics and Chromatin Dynamics
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article

Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors

Pavel Kravchenko, Marjan Barazandeh, Vsevolod J. Makeev, Jan Grau, Semyon Kolmykov, Antoni J. Gralak, Ilya E. Vorontsov, Zhenfeng Deng, G. A. Meshcheryakov, Ali Fathi, Ivo Große, Mikhail Salnikov, Aldo Hernández-Corchado, Rozita Razavi, Alexandr Boytsov, Isaac Yellan, Mihai Albu, Hamed Shateri Najafabadi, Quaid Morris, Arttu Jolma, Vladimir Nozdrin, Kaitlin U. Laverty, Samuel A. Lambert, Dmitry Penzar, Sara E. Pour, Kateřina Faltejsková, А П Власов, Sachi Inukai, Bart Deplancke, Ivan V. Kulakovskiy, Oriol Fornés, Timothy R. Hughes, Giovanna Ambrosini, Ivan Yevshin, Matthew T. Weirauch, Ally Yang, Arsenii Zinkevich, Alexander Brechalov, Fedor Kolpakov, Judith F. Kribelbauer, Sergey Abramov, Nikita Gryzunov, Mikhail Nikonov, Philipp Bücher, Chun Hu, Xiaoting Chen, Z. Patel, Ivan Kozin, Marie-Luise Plescher, Vasilii Kamenets
article en
3 citations

Abstract

A sequence motif representing the DNA-binding specificity of a transcription factor (TF) is commonly modelled with a positional weight matrix (PWM). Focusing on understudied human TFs, we processed results of 4,237 experiments for 394 TFs, assayed using five different experimental platforms. By human curation, we approved a subset of experiments that yielded consistent motifs across platforms and replicates, and evaluated quantitatively the cross-platform performance of PWMs obtained with ten motif discovery tools. Notably, nucleotide composition and information content are not correlated with motif performance and do not help in detecting underperformers, while motifs with low information content, in many cases, describe well the binding specificity assessed across different experimental platforms. By combining multiple PMWs into a random forest, we demonstrate the potential of accounting for multiple modes of TF binding. Finally, we present the Codebook Motif Explorer ( https://mex.autosome.org ), cataloguing motifs, benchmarking results, and the underlying experimental data.

Communications BiologyVol. 8(1)
SIB Swiss Institute of Bioinformatics (CH), Cincinnati Children's Hospital Medical Center (US), Memorial Sloan Kettering Cancer Center (US), University of British Columbia (CA), Lomonosov Moscow State University (RU), Czech Academy of Sciences (CZ), Charles University (CZ), Sunovion (United Kingdom) (GB), Amgen (Canada) (CA), Sirius University of Science and Technology (RU), Ufa Institute of Chemistry (RU), Occupational Cancer Research Centre (CA), Altius Institute for Biomedical Sciences (US), Institute of Protein Research (RU), Federal Research Center for Information and Computational Technologies (RU), Czech Academy of Sciences, Institute of Organic Chemistry and Biochemistry (CZ), Center for Systems Biology Dresden (DE), Vavilov Institute of General Genetics (RU), McGill University and Génome Québec Innovation Centre (CA), Max Planck Institute of Biochemistry (DE), BC Children's Hospital (CA), National Computing Centre (United Kingdom) (GB), École Polytechnique Fédérale de Lausanne (CH), Martin Luther University Halle-Wittenberg (DE), Novosibirsk State Technical University (RU), University of Lausanne (CH)
National Science Foundation, Deutsche Forschungsgemeinschaft, École Polytechnique Fédérale de Lausanne, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Canada Research Chairs, University of Toronto, Vetenskapsrådet, Russian Science Foundation, Alliance de recherche numérique du Canada, National Institutes of Health, Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 10%
Genomics and Chromatin Dynamics
2.42
DOIOpen Access PDFOpenAlex
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