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

Hongfei Cui,Ting Chen,Hailin Hu,Jianyang Zeng

doi:10.1186/s12859-019-3244-0

Abstract

BackgroundRibosome 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.ResultsHere 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.ConclusionIn summary, we demonstrate that DeepShape can serve as a powerful tool for Ribo-seq data analysis.

Highlights

Ribosome profiling brings insight to the process of translation
The sites where Ribo-seq reads are piled up suggest ribosome stalling or translation slow-down, which could be caused by factors such as
Our results show a better performance (0.57~0.63 Pearson correlation coefficients (PCC)) without any smoothing procedure than that of RiboShape on smoothed V7 space (0.46~0.52 PCC), and even on very smoothed V4 space (0.49~0.62 PCC) (Table 1)

Summary

Introduction

A basic step in profile construction at transcript level is to map Ribo-seq data to transcripts, and 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. Multiple factors can influence ribosome binding and ribosome distribution at Ribosome sequencing (Ribo-seq) is an important approach to obtain ribosome distributions by sequencing “ribosome-protected fragments” (RPFs) [9]. Computational analysis involves mapping sequencing Ribo-seq reads into transcripts and counting number of reads at each position to obtain distributions of ribosomes along the whole transcriptome. The sites where Ribo-seq reads are piled up suggest ribosome stalling or translation slow-down, which could be caused by factors such as

Results

Discussion

Conclusion