TITER: predicting translation initiation sites by deep learning.MOTIVATION
 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 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.


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



TITER
TITER: predicting translation initiation sites by deep learning.MOTIVATION
 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 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.


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



TITER
TITER: predicting translation initiation sites by deep learning.MOTIVATION
 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 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.


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



TITER
TITER: predicting translation initiation sites by deep learning.MOTIVATION
 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 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.


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



TITER
TITER: predicting translation initiation sites by deep learning.MOTIVATION
 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 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.


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



TITER