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Deciphering eukaryotic cis-regulatory logic with 100 million random promoters

Carl G. de Boer, Eeshit Dhaval Vaishnav, Ronen Sadeh, Esteban Luis Abeyta, Nir Friedman, Aviv Regev
doi: https://doi.org/10.1101/224907
Carl G. de Boer
1Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Eeshit Dhaval Vaishnav
1Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
2Howard Hughes Medical Institute and Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02140 USA
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Ronen Sadeh
3School of Computer Science and Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
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Esteban Luis Abeyta
4Initiative for Maximizing Student Development Program, University of New Mexico, Albuquerque, NM, USA
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Nir Friedman
1Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
3School of Computer Science and Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
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Aviv Regev
1Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
2Howard Hughes Medical Institute and Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02140 USA
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  • For correspondence: aregev@broadinstitute.org
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Abstract

Deciphering cis-regulation, the code by which transcription factors (TFs) interpret regulatory DNA sequence to control gene expression levels, is a long-standing challenge. Previous studies of native or engineered sequences have remained limited in scale. Here, we use random sequences as an alternative, allowing us to measure the expression output of over 100 million synthetic yeast promoters. Random sequences yield a broad range of reproducible expression levels, indicating that the fortuitous binding sites in random DNA are functional. From these data we learn models of transcriptional regulation that predict over 94% of the expression driven from independent test data and nearly 89% from sequences from yeast promoters. These models allow us to characterize the activity of TFs and their interactions with chromatin, and help refine cis-regulatory motifs. We find that strand, position, and helical face preferences of TFs are widespread and depend on interactions with neighboring chromatin. Such massive-throughput regulatory assays of random DNA provide the diverse examples necessary to learn complex models of cis-regulatory logic.

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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. It is made available under a CC-BY-ND 4.0 International license.
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Posted September 19, 2018.
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Deciphering eukaryotic cis-regulatory logic with 100 million random promoters
Carl G. de Boer, Eeshit Dhaval Vaishnav, Ronen Sadeh, Esteban Luis Abeyta, Nir Friedman, Aviv Regev
bioRxiv 224907; doi: https://doi.org/10.1101/224907
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Deciphering eukaryotic cis-regulatory logic with 100 million random promoters
Carl G. de Boer, Eeshit Dhaval Vaishnav, Ronen Sadeh, Esteban Luis Abeyta, Nir Friedman, Aviv Regev
bioRxiv 224907; doi: https://doi.org/10.1101/224907

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