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GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors

Nature Methods Bioinformatics AI Arttu Jolma, Aldo Hernandez-Corchado, Ally W. H. Yang, Ali Fathi, Kaitlin U. Laverty, Alexander Brechalov, Rozita Razavi, Mihai Albu, Hong Zheng, Philipp Bucher, Bart Deplancke, Oriol Fornes, Jan Grau, Ivo Grosse, Fedor A. Kolpakov, Vsevolod J. Makeev, Marjan Barazandeh, Zhenfeng Deng, Chun Hu, Samuel A. Lambert, Zain M. Patel, Sara E. Pour, Mikhail Salnikov, Isaac Yellan, Georgy Meshcheryakov, Giovanna Ambrosini, Antoni J. Gralak, Sachi Inukai, Judith F. Kribelbauer-Swietek, Marie-Luise Plescher, Semyon Kolmykov, Ivan Yevshin, Nikita Gryzunov, Ivan Kozin, Mikhail Nikonov, Vladimir Nozdrin, Arsenii Zinkevich, Katerina Faltejskova, Pavel Kravchenko, Sergey Abramov, Alexandr Boytsov, Vasilii Kamenets, Dmitry Penzar, Anton Vlasov, Ilya E. Vorontsov, Quaid Morris, Xiaoting Chen, Matthew T. Weirauch, Ivan V. Kulakovskiy, Hamed S. Najafabadi, Timothy R. Hughes 2026-08-05

TL;DR - A Nature Methods paper introducing GHT-SELEX, a high-throughput SELEX variant that uses fragmented genomic DNA instead of random oligos to profile human transcription factor (TF) binding. It matters because it shows TF binding is far more sequence-specific and structurally complex than standard motif models assume, which has direct implications for regulatory-genomics datasets and the models trained on them.

GHT-SELEX performs in vitro selection over fragmented genomic DNA, so the measured binding preferences are anchored to real genomic loci rather than synthetic random sequence libraries.
Result: many human TFs display unexpectedly high intrinsic sequence specificity — they discriminate strongly among candidate genomic sites, rather than binding degenerately as simple PWM-style motifs would predict.
C2H2 zinc finger proteins show mode-switching: the same protein engages different subsets of its zinc fingers at different binding sites, implying a single consensus motif per TF is an inadequate representation.
Practical implication: benchmark and training data for sequence-to-function / regulatory models may need multi-mode binding representations; note this summary is based only on the published abstract blurb, so quantitative results (TF counts, effect sizes) are not available here.

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