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  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., 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.

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 Creators:
Erijman, A., Author
Kozlowski, L., Author
Sohrabi-Jahromi, S.1, Author              
Fishburn, J., Author
Warfield, L., Author
Schreiber, J., Author
Noble, W. S., Author
Söding, J.1, Author              
Hahn, S., Author
Affiliations:
1Research Group of Computational Biology, MPI for Biophysical Chemistry, Max Planck Society, ou_1933286              

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 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.

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Language(s): eng - English
 Dates: 2020-09-17
 Publication Status: Published online
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 Table of Contents: Correction to: Molecular Cell. Vol. 78 (5), 4 June 2020, Pages 890-902.e6
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.molcel.2020.08.013
 Degree: -

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Title: Molecular Cell
Source Genre: Journal
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Pages: - Volume / Issue: 79 (6) Sequence Number: - Start / End Page: 1066 Identifier: -