TITER: predicting translation initiation sites by deep learning.

ABSTRACT:

Motivation

Translation initiation is a key step in the regulation of gene expression. In addition to the annotated translation initiation sites (TISs), the translation process may also start at multiple alternative TISs (including both AUG and non-AUG codons), which makes it challenging to predict TISs and study the underlying regulatory mechanisms. Meanwhile, the advent of several high-throughput sequencing techniques for profiling initiating ribosomes at single-nucleotide resolution, e.g. GTI-seq and QTI-seq, provides abundant data for systematically studying the general principles of translation initiation and the development of computational method for TIS identification.

Methods

We have developed a deep learning-based framework, named TITER, for accurately predicting TI

SUBMITTER: Zhang S 

PROVIDER: S-EPMC5870772 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature