TITER: predicting translation initiation sites by deep learning

• DOI: 10.1093/bioinformatics/btx247
• Publication Date: 2017-04-24T07:39:48Z
• Authors: Sai Zhang; Hailin Hu; Tao Jiang; Lei Zhang; Jianyang Zeng
• Keywords: 0301 basic medicine; 570; Neural Networks; Bioinformatics; 610; Codon, Initiator; Mathematical Sciences; Machine Learning; Computer; Mice; Open Reading Frames; 03 medical and health sciences; Genetic; Models; Information and Computing Sciences; Genetics; Animals; Humans; Codon; Peptide Chain Initiation, Translational; Models, Genetic; Human Genome; Translational; Initiator; Computational Biology; Biological Sciences; Ismb/Eccb 2017: The 25th Annual Conference Intelligent Systems for Molecular Biology Held Jointly with the 16th Annual European Conference on Computational Biology, Prague, Czech Republic, July 21–25, 2017; Peptide Chain Initiation; Neural Networks, Computer; Software; Biotechnology

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 TISs on a genome-wide scale based on QTI-seq data. TITER extracts the sequence features of translation initiation from the surrounding sequence contexts of TISs using a hybrid neural network and further integrates the prior preference of TIS codon composition into a unified prediction framework.

Results Extensive tests demonstrated that TITER can greatly outperform the state-of-the-art prediction methods in identifying TISs. In addition, TITER was able to identify important sequence signatures for individual types of TIS codons, including a Kozak-sequence-like motif for AUG start codon. Furthermore, the TITER prediction score can be related to the strength of translation initiation in various biological scenarios, including the repressive effect of the upstream open reading frames on gene expression and the mutational effects influencing translation initiation efficiency.

Availability and Implementation TITER is available as an open-source software and can be downloaded from https://github.com/zhangsaithu/titer.

Supplementary information Supplementary data are available at Bioinformatics online.