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1/n Super proud of work published this week by @anzheng25 et al. in @NatMachIntell using #deeplearning to identify sequence context features predictive of transcription factor binding. rdcu.be/cdMmE Some key points:
2/n The main idea: TFs typically bind short motifs of 6-12bp. But only a small fraction of motifs in the genome are actually bound. How well can the question of “to bind or not to bind” be predicted by sequence context (1kb) around the motif using #DeepSea style CNNs?
3/n Pretty well! For most TFs we tried, we could predict whether its motif was bound based on ChIP-seq very well (mean auROC ~0.94) just from local sequence context
