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ROSE: a deep learning based framework for predicting ribosome stalling

Sai Zhang, Hailin Hu, Jingtian Zhou, Xuan He, Tao Jiang, Jianyang Zeng
doi: https://doi.org/10.1101/067108
Sai Zhang
1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
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Hailin Hu
2School of Pharmaceutical Sciences, Tsinghua University, Beijing, China.
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Jingtian Zhou
2School of Pharmaceutical Sciences, Tsinghua University, Beijing, China.
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Xuan He
1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
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Tao Jiang
3Department of Computer Science and Engineering, University of California, Riverside, CA, USA.
4MOE Key Lab of Bioinformatics and Bioinformatics Division, TNLIST/Department of Computer Science and Technology, Tsinghua University, Beijing, China.
5Institute of Integrative Genome Biology, University of California, Riverside, CA, USA.
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Jianyang Zeng
1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.
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  • For correspondence: zengjy321@tsinghua.edu.cn
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Abstract

We present a deep learning based framework, called ROSE, to accurately predict ribosome stalling events in translation elongation from coding sequences based on high-throughput ribosome profiling data. Our validation results demonstrate the superior performance of ROSE over conventional prediction models. ROSE provides an effective index to estimate the likelihood of translational pausing at codon resolution and understand diverse putative regulatory factors of ribosome stalling. Also, the ribosome stalling landscape computed by ROSE can recover the functional interplay between ribosome stalling and cotranslational events in protein biogenesis, including protein targeting by the signal recognition particle (SRP) and protein secondary structure formation.

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Posted November 15, 2016.
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ROSE: a deep learning based framework for predicting ribosome stalling
Sai Zhang, Hailin Hu, Jingtian Zhou, Xuan He, Tao Jiang, Jianyang Zeng
bioRxiv 067108; doi: https://doi.org/10.1101/067108
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ROSE: a deep learning based framework for predicting ribosome stalling
Sai Zhang, Hailin Hu, Jingtian Zhou, Xuan He, Tao Jiang, Jianyang Zeng
bioRxiv 067108; doi: https://doi.org/10.1101/067108

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