Abstract | BACKGROUND: 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. RESULTS: 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. CONCLUSION: In summary, we demonstrate that DeepShape can serve as a powerful tool for Ribo-seq data analysis.
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Authors | Hongfei Cui, Hailin Hu, Jianyang Zeng, Ting Chen |
Journal | BMC bioinformatics
(BMC Bioinformatics)
Vol. 20
Issue Suppl 24
Pg. 678
(Dec 20 2019)
ISSN: 1471-2105 [Electronic] England |
PMID | 31861979
(Publication Type: Journal Article)
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Chemical References |
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Topics |
- Cell Line, Tumor
- Codon
(genetics, metabolism)
- Humans
- Protein Isoforms
(genetics)
- Ribosomes
(metabolism)
- Sequence Analysis, RNA
(methods)
- Software
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