Deep Learning Model Reveals How Cells Know When to Stop DNA Transcription
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Researchers developed a deep learning model to understand how cells know when to stop copying DNA into RNA strands during transcription. Properly regulating this process is crucial for many cell functions.
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The model uses convolutional and recurrent neural networks to identify sequences in DNA that signal the polyadenylation (polyA) process to halt RNA transcription.
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The model revealed that reliable polyA requires certain genetic patterns in specific spacing in order to attract the proteins that control polyA.
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The high resolution of the model allowed the researchers to precisely capture details of the polyA process.
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The researchers plan to apply their model to identify disease-causing mutations and develop more targeted drugs, as well as study polyA in other organisms.