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ROSE: a deep learning based framework for predicting ribosome stalling
[article]
2016
bioRxiv
pre-print
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
doi:10.1101/067108
fatcat:a2rjx5sem5hppfoe3x337gscfu