PT  - JOURNAL ARTICLE
AU  - Sai Zhang
AU  - Hailin Hu
AU  - Jingtian Zhou
AU  - Xuan He
AU  - Tao Jiang
AU  - Jianyang Zeng
TI  - ROSE: a deep learning based framework for predicting ribosome stalling
AID  - 10.1101/067108
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 067108
4099  - http://biorxiv.org/content/early/2016/11/15/067108.short
4100  - http://biorxiv.org/content/early/2016/11/15/067108.full
AB  - 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.