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Deciphering cis-regulatory grammar with deep learning

A new study builds a novel deep-learning approach to unravel the syntax of transcription-factor binding from high-resolution ChIP–nexus data. In silico simulations lead to experimental validation of complex sequence-based predictions: helical periodicity and directional cooperativity between transcription factors.

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Fig. 1: BPNet simulations predict a soft TF-motif syntax in mouse embryonic stem cells.


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Correspondence to Matthew T. Weirauch.

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Miraldi, E.R., Chen, X. & Weirauch, M.T. Deciphering cis-regulatory grammar with deep learning. Nat Genet 53, 266–268 (2021).

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