Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Deep learning of the regulatory grammar of yeast 5’ untranslated regions from 500,000 random sequences

View ORCID ProfileJosh Cuperus, Benjamin Groves, Anna Kuchina, Alexander B. Rosenberg, Nebojsa Jojic, Stanley Fields, Georg Seelig
doi: https://doi.org/10.1101/137547
Josh Cuperus
1Department of Genome Sciences, University of Washington
6Howard Hughes Medical Institute, University of Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Josh Cuperus
Benjamin Groves
2Department of Electrical Engineering, University of Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anna Kuchina
2Department of Electrical Engineering, University of Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alexander B. Rosenberg
2Department of Electrical Engineering, University of Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nebojsa Jojic
3Microsoft Research
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stanley Fields
1Department of Genome Sciences, University of Washington
5Department of Medicine, University of Washington
6Howard Hughes Medical Institute, University of Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: gseelig@uw.edu fields@uw.edu
Georg Seelig
2Department of Electrical Engineering, University of Washington
4Department of Computer Science & Engineering, University of Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: gseelig@uw.edu fields@uw.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the translational efficiency of the 5’ untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide-long random 5’ UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on translation of Kozak sequence composition, upstream open reading frames (uORFs) and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the translational efficiency of both a held-out set of the random 5’ UTRs as well as native S. cerevisiae 5’ UTRs. The model additionally was used to computationally evolve highly translating 5’ UTRs. We confirmed experimentally that the great majority of the evolved sequences lead to higher translation rates than the starting sequences, demonstrating the predictive power of this model.

Copyright 
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-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted May 19, 2017.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Deep learning of the regulatory grammar of yeast 5’ untranslated regions from 500,000 random sequences
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Deep learning of the regulatory grammar of yeast 5’ untranslated regions from 500,000 random sequences
Josh Cuperus, Benjamin Groves, Anna Kuchina, Alexander B. Rosenberg, Nebojsa Jojic, Stanley Fields, Georg Seelig
bioRxiv 137547; doi: https://doi.org/10.1101/137547
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Deep learning of the regulatory grammar of yeast 5’ untranslated regions from 500,000 random sequences
Josh Cuperus, Benjamin Groves, Anna Kuchina, Alexander B. Rosenberg, Nebojsa Jojic, Stanley Fields, Georg Seelig
bioRxiv 137547; doi: https://doi.org/10.1101/137547

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Synthetic Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (3502)
  • Biochemistry (7343)
  • Bioengineering (5319)
  • Bioinformatics (20258)
  • Biophysics (10008)
  • Cancer Biology (7735)
  • Cell Biology (11293)
  • Clinical Trials (138)
  • Developmental Biology (6434)
  • Ecology (9947)
  • Epidemiology (2065)
  • Evolutionary Biology (13315)
  • Genetics (9359)
  • Genomics (12579)
  • Immunology (7696)
  • Microbiology (19008)
  • Molecular Biology (7437)
  • Neuroscience (41011)
  • Paleontology (300)
  • Pathology (1228)
  • Pharmacology and Toxicology (2134)
  • Physiology (3155)
  • Plant Biology (6858)
  • Scientific Communication and Education (1272)
  • Synthetic Biology (1895)
  • Systems Biology (5311)
  • Zoology (1087)