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<p>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 (<a href="https://mex.autosome.org">https://mex.autosome.org</a>), cataloguing motifs, benchmarking results, and the underlying experimental data.</p>

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

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  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Ilya E. Vorontsov,
  • Ivan Kozin,
  • Sergey Abramov,
  • Alexandr Boytsov,
  • Arttu Jolma,
  • Mihai Albu,
  • Giovanna Ambrosini,
  • Katerina Faltejskova,
  • Antoni J. Gralak,
  • Nikita Gryzunov,
  • Sachi Inukai,
  • Semyon Kolmykov,
  • Pavel Kravchenko,
  • Judith F. Kribelbauer-Swietek,
  • Kaitlin U. Laverty,
  • Vladimir Nozdrin,
  • Zain M. Patel,
  • Dmitry Penzar,
  • Marie-Luise Plescher,
  • Sara E. Pour,
  • Rozita Razavi,
  • Ally W. H. Yang,
  • Ivan Yevshin,
  • Arsenii Zinkevich,
  • Matthew T. Weirauch,
  • Philipp Bucher,
  • Bart Deplancke,
  • Oriol Fornes,
  • Jan Grau,
  • Ivo Grosse,
  • Fedor A. Kolpakov,
  • Marjan Barazandeh,
  • Alexander Brechalov,
  • Zhenfeng Deng,
  • Ali Fathi,
  • Chun Hu,
  • Samuel A. Lambert,
  • Mikhail Salnikov,
  • Isaac Yellan,
  • Hong Zheng,
  • Georgy Meshcheryakov,
  • Mikhail Nikonov,
  • Vasilii Kamenets,
  • Anton Vlasov,
  • Aldo Hernandez-Corchado,
  • Hamed S. Najafabadi,
  • Quaid Morris,
  • Xiaoting Chen,
  • Vsevolod J. Makeev,
  • Timothy R. Hughes,
  • Ivan V. Kulakovskiy

摘要

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.