A high-throughput screen for transcription activation domains reveals their 
sequence features and permits prediction by deep learning (Correction)

Erijman, A.; Kozlowski, L.; Sohrabi-Jahromi, S.; Fishburn, J.; Warfield, L.; Schreiber, J.; Noble, W. S.; Söding, J.; Hahn, S.

doi:10.1016/j.molcel.2020.08.013

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A high-throughput screen for transcription activation domains reveals their sequence features and permits prediction by deep learning (Correction)

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Sohrabi-Jahromi, S.
Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Söding, J.
Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society;

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Citation

Erijman, A., Kozlowski, L., Sohrabi-Jahromi, S., Fishburn, J., Warfield, L., Schreiber, J., et al. (2020). A high-throughput screen for transcription activation domains reveals their sequence features and permits prediction by deep learning (Correction). Molecular Cell, 79(6), 1066. doi:10.1016/j.molcel.2020.08.013.

Cite as: https://hdl.handle.net/21.11116/0000-0007-668F-6

Abstract

The authors discovered an error in Figure 7A of this article. In the lower panel, the blue and gray line colors were switched when making the plot. The figure has since been replaced online. The text and figure legend remain correct, and there are no changes to the conclusions of the article. The authors apologize for the error.