Abstract
A DNA sequence pattern, or “motif”, is an essential representation of DNA-binding specificity of a transcription factor (TF). Any particular motif model has potential flaws due to shortcomings of the underlying experimental data and computational motif discovery algorithm. As a part of the Codebook/GRECO-BIT initiative, here we evaluated at large scale the cross-platform recognition performance of positional weight matrices (PWMs), which remain popular motif models in many practical applications. We applied ten different DNA motif discovery tools to generate PWMs from the “Codebook” data comprised of 4,237 experiments from five different platforms profiling the DNA-binding specificity of 394 human proteins, focusing on understudied transcription factors of different structural families. For many of the proteins, there was no prior knowledge of a genuine motif. By benchmarking-supported human curation, we constructed an approved subset of experiments comprising about 30% of all experiments and 50% of tested TFs which displayed consistent motifs across platforms and replicates. We present the Codebook Motif Explorer (https://mex.autosome.org), a detailed online catalog of DNA motifs, including the top-ranked PWMs, and the underlying source and benchmarking data. We demonstrate that in the case of high-quality experimental data, most of the popular motif discovery tools detect valid motifs and generate PWMs, which perform well both on genomic and synthetic data. Yet, for each of the algorithms, there were problematic combinations of proteins and platforms, and the basic motif properties such as nucleotide composition and information content offered little help in detecting such pitfalls. By combining multiple PMWs in decision trees, we demonstrate how our setup can be readily adapted to train and test binding specificity models more complex than PWMs. Overall, our study provides a rich motif catalog as a solid baseline for advanced models and highlights the power of the multi-platform multi-tool approach for reliable mapping of DNA binding specificities.
Principal investigators (steering committee) Philipp Bucher, Bart Deplancke, Oriol Fornes, Jan Grau, Ivo Grosse, Timothy R. Hughes, Arttu Jolma, Fedor A. Kolpakov, Ivan V. Kulakovskiy, Vsevolod J. Makeev
Analysis Centers University of Toronto (Data production and analysis): Mihai Albu, Marjan Barazandeh, Alexander Brechalov, Zhenfeng Deng, Ali Fathi, Arttu Jolma, Chun Hu, Timothy R. Hughes, Samuel A. Lambert, Kaitlin U. Laverty, Zain M. Patel, Sara E. Pour, Rozita Razavi, Mikhail Salnikov, Ally W.H. Yang, Isaac Yellan, Hong Zheng
Institute of Protein Research (Data analysis): Ivan V. Kulakovskiy, Georgy Meshcheryakov
EPFL, École polytechnique fédérale de Lausanne (Data production and analysis):
Giovanna Ambrosini, Bart Deplancke, Antoni J. Gralak, Sachi Inukai, Judith F. Kribelbauer-Swietek
Martin Luther University Halle-Wittenberg (Data analysis): Jan Grau, Ivo Grosse, Marie-Luise Plescher
Sirius University of Science and Technology (Data analysis): Semyon Kolmykov, Fedor Kolpakov
Biosoft.Ru (Data analysis): Ivan Yevshin
Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University (Data analysis): Nikita Gryzunov, Ivan Kozin, Mikhail Nikonov, Vladimir Nozdrin, Arsenii Zinkevich
Institute of Organic Chemistry and Biochemistry (Data analysis): Katerina Faltejskova
Max Planck Institute of Biochemistry (Data analysis): Pavel Kravchenko
Swiss Institute for Bioinformatics (Data analysis): Philipp Bucher
University of British Columbia (Data analysis): Oriol Fornes
Vavilov Institute of General Genetics (Data analysis): Sergey Abramov, Alexandr Boytsov, Vasilii Kamenets, Vsevolod J. Makeev, Dmitry Penzar, Anton Vlasov, Ilya E. Vorontsov
McGill University (Data analysis): Aldo Hernandez-Corchado, Hamed S. Najafabadi
Memorial Sloan Kettering (Data production and analysis): Kaitlin U. Laverty, Quaid Morris
Cincinnati Children’s Hospital (Data analysis): Xiaoting Chen, Matthew T. Weirauch
Competing Interest Statement
O.F. is employed by Roche.
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