TITER: predicting translation initiation sites by deep learning

Bioinformatics
Sai ZhangJianyang Zeng

Abstract

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. 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. 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 sig...Continue Reading

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Citations

Sep 7, 2018·Nature Biotechnology·Michael WainbergBrendan J Frey
Oct 9, 2018·Bioinformatics·Hailin HuJianyang Zeng
May 14, 2019·Bioinformatics·Ashraful ArefeenTao Jiang
Jul 15, 2020·Biochemical Society Transactions·Shir Bahiri-Elitzur, Tamir Tuller
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Dec 5, 2020·Journal of Biomolecular Structure & Dynamics·Zhihua DuVladimir N Uversky
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Feb 3, 2021·Proceedings of the National Academy of Sciences of the United States of America·Peiyuan FengJianyang Zeng
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May 11, 2021·PLoS Computational Biology·Alexander KarollusJulien Gagneur
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Aug 4, 2021·Wiley Interdisciplinary Reviews. RNA·Tanvi SinhaAmaresh Chandra Panda

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