DeepShape Estimating isoform-level ribosome abundance and distribution with Ribo-seq data


Ribosome profiling brings insight to the process of translation. A basic step in profile construction at transcript level is to map Ribo-seq data to transcripts, and then assign a huge number of multiple-mapped reads to similar isoforms. Existing methods either discard the multiple mapped-reads, or allocate them randomly, or assign them proportionally according to transcript abundance estimated from RNA-seq data. Here we present DeepShape , an RNA-seq free computational method to estimate ribosome abundance of isoforms, and simultaneously compute their ribosome profiles using a deep learning model. Our simulation results demonstrate that DeepShape can provide more accurate estimations on both ribosome abundance and profiles when compared to state-of-the-art methods. We applied DeepShape to a set of Ribo-seq data from PC3 human prostate cancer cells with and without PP242 treatment. In the four cell invasion/metastasis genes that are translationally regulated by PP242 treatment, different isoforms show very different characteristics of translational efficiency and regulation patterns. Transcript level ribosome distributions were analyzed by “ Codon Residence Index (CRI) ” proposed in this study to investigate the relative speed that a ribosome moves on a codon compared to its synonymous codons. We observe consistent CRI patterns in PC3 cells. We found that the translation of several codons could be regulated by PP242 treatment.


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  1. Hongfei Cui

    School of Economics and Management, University of Science and Technology Beijing, China

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Tool TypeToolkit
CategoryRibo-seq analysis
TechnologiesPython2, Python3
User InterfaceTerminal Command Line
Input DataFASTA
Latest ReleaseDeepShape1.0 (May 31, 2021)
Download Count2322
Submitted ByHongfei Cui