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Targeted optimization of regulatory DNA sequences with neural editing architectures

Anvita Gupta, View ORCID ProfileAnshul Kundaje
doi: https://doi.org/10.1101/714402
Anvita Gupta
1Department of Computer Science, Stanford University
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  • For correspondence: avgupta@stanford.edu
Anshul Kundaje
1Department of Computer Science, Stanford University
2Department of Genetics, Stanford University
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Abstract

Targeted optimizing of existing DNA sequences for useful properties, has the potential to enable several synthetic biology applications from modifying DNA to treat genetic disorders to designing regulatory elements to fine tune context-specific gene expression. Current approaches for targeted genome editing are largely based on prior biological knowledge or ad-hoc rules. Few if any machine learning approaches exist for targeted optimization of regulatory DNA sequences.

Here, we propose a novel generative neural network architecture for targeted DNA sequence editing – the EDA architecture – consisting of an encoder, decoder, and analyzer. We showcase the use of EDA to optimize regulatory DNA sequences to bind to the transcription factor SPI1. Compared to other state-of-the-art approaches such as a textual variational autoencoder and rule-based editing, EDA significantly improves predicted binding of SPI1 of genomic sequences with the minimal set of edits. We also use EDA to design regulatory elements with optimized grammars of CREB1 binding sites that can tune reporter expression levels as measured by massively parallel reporter assays (MPRA). We analyze the properties of the binding sites in the edited sequences and find patterns that are consistent with previously reported grammatical rules which tie gene expression to CRE binding site density, spacing and affinity.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 28, 2019.
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Targeted optimization of regulatory DNA sequences with neural editing architectures
Anvita Gupta, Anshul Kundaje
bioRxiv 714402; doi: https://doi.org/10.1101/714402
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Targeted optimization of regulatory DNA sequences with neural editing architectures
Anvita Gupta, Anshul Kundaje
bioRxiv 714402; doi: https://doi.org/10.1101/714402

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