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

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 protein expression 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 protein expression 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 pe

SUBMITTER: Cuperus JT 

PROVIDER: S-EPMC5741052 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature