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The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission." /> <meta name="DC.AccessRights" content="restricted" /> <meta name="DC.Description" content="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." /> <meta name="DC.Contributor" content="Anvita Gupta" /> <meta name="DC.Contributor" content="Anshul Kundaje" /> <meta name="article:published_time" content="2019-07-28" /> <meta name="article:section" content="New Results" /> <meta name="citation_title" content="Targeted optimization of regulatory DNA sequences with neural editing architectures" /> <meta name="citation_abstract" lang="en" content="<h3>Abstract</h3> <p>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.</p><p>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.</p>" /> <meta name="citation_journal_title" content="bioRxiv" /> <meta name="citation_publisher" content="Cold Spring Harbor Laboratory" /> <meta name="citation_publication_date" content="2019/01/01" /> <meta name="citation_mjid" content="biorxiv;714402v2" /> <meta name="citation_id" content="714402v2" /> <meta name="citation_public_url" content="https://www.biorxiv.org/content/10.1101/714402v2" /> <meta name="citation_abstract_html_url" content="https://www.biorxiv.org/content/10.1101/714402v2.abstract" /> <meta name="citation_full_html_url" content="https://www.biorxiv.org/content/10.1101/714402v2.full" /> <meta name="citation_pdf_url" content="https://www.biorxiv.org/content/biorxiv/early/2019/07/27/714402.full.pdf" /> <meta name="citation_doi" content="10.1101/714402" /> <meta name="citation_num_pages" content="7" /> <meta name="citation_article_type" content="Article" /> <meta name="citation_section" content="New Results" /> <meta name="citation_firstpage" content="714402" /> <meta name="citation_author" content="Anvita Gupta" /> <meta name="citation_author_institution" content="Department of Computer Science, Stanford University" /> <meta name="citation_author_email" content="avgupta@stanford.edu" /> <meta name="citation_author" content="Anshul Kundaje" /> <meta name="citation_author_institution" content="Department of Computer Science, Stanford University" /> <meta name="citation_author_institution" content="Department of Genetics, Stanford University" /> <meta name="citation_author_orcid" content="http://orcid.org/0000-0003-3084-2287" /> <meta name="citation_reference" content="E. P. Consortium et al. An integrated encyclopedia of dna elements in the human genome. Nature, 489(7414):57, 2012." /> <meta name="citation_reference" content="J. E. Davis, K. D. Insigne, E. M. Jones, Q. B. Hastings, and S. Kosuri. Multiplexed dissection of a model human transcription factor binding site architecture. bioRxiv, 2019. doi: 10.1101/625434. URL https://www.biorxiv.org/content/early/2019/05/02/625434." /> <meta name="citation_reference" content="M. Ghandi, D. Lee, M. Mohammad-Noori, and M. A. Beer. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS computational biology, 10(7):e1003711, 2014." /> <meta name="citation_reference" content="I. J. Goodfellow, J. Shlens, and C. Szegedy. Explaining and harnessing adversarial examples. stat, 1050:20, 2015." /> <meta name="citation_reference" content="A. Gupta and J. Zou. Feedback gan for dna optimizes protein functions. Nature Machine Intelligence, 1(2):105, 2019." /> <meta name="citation_reference" content="A. Gupta, A. T. Müller, B. J. Huisman, J. A. Fuchs, P. Schneider, and G. Schneider. Generative recurrent networks for de novo drug design. Molecular informatics, 37(1-2):1700111, 2018." /> <meta name="citation_reference" content="K. Guu, T. B. Hashimoto, Y. Oren, and P. Liang. Generating sentences by editing prototypes. Transactions of the Association of Computational Linguistics, 6:437–450, 2018." /> <meta name="citation_reference" content="S. Heinz, C. Benner, N. Spann, E. Bertolino, Y. C. Lin, P. Laslo, J. X. Cheng, C. Murre, H. Singh, and C. K. Glass. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and b cell identities. Molecular cell, 38(4):576–589, 2010." /> <meta name="twitter:title" content="Targeted optimization of regulatory DNA sequences with neural editing architectures" /> <meta name="twitter:site" content="@biorxivpreprint" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:image" content="https://www.biorxiv.org/sites/default/files/images/biorxiv_logo_homepage7-5-small.png" /> <meta name="twitter:description" content="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." /> <meta name="og-title" property="og:title" content="Targeted optimization of regulatory DNA sequences with neural editing architectures" /> <meta name="og-url" property="og:url" content="https://www.biorxiv.org/content/10.1101/714402v2" /> <meta name="og-site-name" property="og:site_name" content="bioRxiv" /> <meta name="og-description" property="og:description" content="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." /> <meta name="og-type" property="og:type" content="article" /> <meta name="og-image" property="og:image" content="https://www.biorxiv.org/sites/default/files/images/biorxiv_logo_homepage7-5-small.png" /> <meta name="citation_date" content="2019-07-28" /> <link rel="alternate" type="application/vnd.ms-powerpoint" title="Powerpoint" href="/content/10.1101/714402v2.ppt" /> <meta name="description" content="bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution" /> <meta name="generator" content="Drupal 7 (http://drupal.org)" /> <link rel="canonical" href="https://www.biorxiv.org/content/10.1101/714402v2" /> <link rel="shortlink" href="https://www.biorxiv.org/node/843498" /> <title>Targeted 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class="highwire-article-citation highwire-citation-type-highwire-article node843498" data-node-nid="843498" id="node-843498--2943974629" data-pisa="biorxiv;714402v2" data-pisa-master="biorxiv;714402" data-apath="/biorxiv/early/2019/07/27/714402.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-biorxiv-article-top clearfix has-author-tooltip" > <span class="biorxiv-article-type"> New Results </span> <h1 class="highwire-cite-title" id="page-title">Targeted optimization of regulatory DNA sequences with neural editing architectures</h1> <div class="highwire-cite-authors" ><span class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">Anvita</span> <span class="nlm-surname">Gupta</span></span>, <span class="highwire-citation-author hw-author-orcid-logo-wrapper" data-delta="1"><a href="http://orcid.org/0000-0003-3084-2287" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Anshul</span> <span class="nlm-surname">Kundaje</span></span></span></div> <div class="highwire-cite-metadata" ><span class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">doi:</span> https://doi.org/10.1101/714402 </span></div> </div> <div id="hw-article-author-popups-node-843498--2943974629" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Anvita Gupta </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Department of Computer Science, Stanford University</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Anvita%2BGupta%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Gupta%20A&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AAnvita%2BGupta%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-corresp-email-link last"><span>For correspondence: <a href="mailto:avgupta@stanford.edu" class="" data-icon-position="" data-hide-link-title="0">avgupta@stanford.edu</a></span></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Anshul Kundaje </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Department of Computer Science, Stanford University</span></div><div class='author-affiliation'><span class='nlm-sup'>2</span><span class='nlm-institution'>Department of Genetics, Stanford University</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Anshul%2BKundaje%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Kundaje%20A&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3AAnshul%2BKundaje%2B" rel="nofollow" class="" data-icon-position="" 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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.</p><p id="p-4">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.</p></div><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-biorxiv-copyright" > <div class="pane-content"> <div class="field field-name-field-highwire-copyright field-type-text field-label-inline clearfix"><div class="field-label">Copyright </div><div class="field-items"><div class="field-item even">The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.<span class="license-type-none"> All rights reserved. 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