DeepShape: estimating isoform-level ribosome abundance and distribution with Ribo-seq data.BACKGROUND
 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 summary, we demonstrate that DeepShape can serve as a powerful tool for Ribo-seq data analysis.
DeepShape
DeepShape: estimating isoform-level ribosome abundance and distribution with Ribo-seq data.BACKGROUND
 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.  We applied DeepShape to a set of Ribo-seq data from PC3 human prostate cancer cells with and without PP242 treatment.  In summary, we demonstrate that DeepShape can serve as a powerful tool for Ribo-seq data analysis.
Ribo-seq
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. 
RNA-seq
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.  We found that the translation of several codons could be regulated by PP242 treatment.



PP242