DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of enhancers

Bernardo P. de Almeida; Franziska Reiter; Michaela Pagani; Alexander Stark

doi:10.1101/2021.10.05.463203

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DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of enhancers

View ORCID ProfileBernardo P. de Almeida, Franziska Reiter, Michaela Pagani, View ORCID ProfileAlexander Stark

doi: https://doi.org/10.1101/2021.10.05.463203

Bernardo P. de Almeida

1Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria

2Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, A-1030, Vienna, Austria

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ORCID record for Bernardo P. de Almeida

Franziska Reiter

1Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria

2Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, A-1030, Vienna, Austria

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Michaela Pagani

1Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria

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Alexander Stark

1Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, Vienna, Austria

3Medical University of Vienna, Vienna BioCenter (VBC), Vienna, Austria

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ORCID record for Alexander Stark
For correspondence: stark@starklab.org

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https://github.com/bernardo-de-almeida/DeepSTARR
https://doi.org/10.5281/zenodo.5502060

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Posted October 07, 2021.

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DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of enhancers

Bernardo P. de Almeida, Franziska Reiter, Michaela Pagani, Alexander Stark

bioRxiv 2021.10.05.463203; doi: https://doi.org/10.1101/2021.10.05.463203

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DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of enhancers

Bernardo P. de Almeida, Franziska Reiter, Michaela Pagani, Alexander Stark

bioRxiv 2021.10.05.463203; doi: https://doi.org/10.1101/2021.10.05.463203

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