PT  - JOURNAL ARTICLE
AU  - Sai Zhang
AU  - Hailin Hu
AU  - Tao Jiang
AU  - Lei Zhang
AU  - Jianyang Zeng
TI  - TITER: predicting translation initiation sites by deep learning
AID  - 10.1101/103374
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 103374
4099  - http://biorxiv.org/content/early/2017/05/12/103374.short
4100  - http://biorxiv.org/content/early/2017/05/12/103374.full
AB  - 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 (uORFs) on gene expression and the mutational effects influencing translation initiation efficiency.Availability TITER is available as an open-source software and can be downloaded from https://github.com/zhangsaithu/titerContact lzhang20{at}mail.tsinghua.edu.cn and zengjy321{at}tsinghua.edu.cn