The human genome contains hundreds of thousands of enhancer sequences that switch our genes on and off when needed. Scientists have long tried to decipher the link between the enhancer sequence and its regulatory activity in the cell, with little success. The lab of Alexander Stark at the IMP has developed a deep learning model, DeepSTARR, which predicts enhancer activity from their DNA sequence with exceptional accuracy. The scientists extracted the rules learned by the model and used them to design synthetic enhancers with a desired level of activity. Their work is now published in the journal Nature Genetics.
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