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Full-length ribosome density prediction by a multi-input and multi-output model

Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eukaryotes. Although recent advance in ribosome profiling techniques has enabled one to capture the genome-wide ribosome footprints along transcripts at codon resolution, the regulatory codes of elongat...

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Autores principales: Tian, Tingzhong, Li, Shuya, Lang, Peng, Zhao, Dan, Zeng, Jianyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026034/
https://www.ncbi.nlm.nih.gov/pubmed/33770074
http://dx.doi.org/10.1371/journal.pcbi.1008842
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author Tian, Tingzhong
Li, Shuya
Lang, Peng
Zhao, Dan
Zeng, Jianyang
author_facet Tian, Tingzhong
Li, Shuya
Lang, Peng
Zhao, Dan
Zeng, Jianyang
author_sort Tian, Tingzhong
collection PubMed
description Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eukaryotes. Although recent advance in ribosome profiling techniques has enabled one to capture the genome-wide ribosome footprints along transcripts at codon resolution, the regulatory codes of elongation dynamics are still not fully understood. Most of the existing computational approaches for modeling translation elongation from ribosome profiling data mainly focus on local contextual patterns, while ignoring the continuity of the elongation process and relations between ribosome densities of remote codons. Modeling the translation elongation process in full-length coding sequence (CDS) level has not been studied to the best of our knowledge. In this paper, we developed a deep learning based approach with a multi-input and multi-output framework, named RiboMIMO, for modeling the ribosome density distributions of full-length mRNA CDS regions. Through considering the underlying correlations in translation efficiency among neighboring and remote codons and extracting hidden features from the input full-length coding sequence, RiboMIMO can greatly outperform the state-of-the-art baseline approaches and accurately predict the ribosome density distributions along the whole mRNA CDS regions. In addition, RiboMIMO explores the contributions of individual input codons to the predictions of output ribosome densities, which thus can help reveal important biological factors influencing the translation elongation process. The analyses, based on our interpretable metric named codon impact score, not only identified several patterns consistent with the previously-published literatures, but also for the first time (to the best of our knowledge) revealed that the codons located at a long distance from the ribosomal A site may also have an association on the translation elongation rate. This finding of long-range impact on translation elongation velocity may shed new light on the regulatory mechanisms of protein synthesis. Overall, these results indicated that RiboMIMO can provide a useful tool for studying the regulation of translation elongation in the range of full-length CDS.
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spelling pubmed-80260342021-04-15 Full-length ribosome density prediction by a multi-input and multi-output model Tian, Tingzhong Li, Shuya Lang, Peng Zhao, Dan Zeng, Jianyang PLoS Comput Biol Research Article Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eukaryotes. Although recent advance in ribosome profiling techniques has enabled one to capture the genome-wide ribosome footprints along transcripts at codon resolution, the regulatory codes of elongation dynamics are still not fully understood. Most of the existing computational approaches for modeling translation elongation from ribosome profiling data mainly focus on local contextual patterns, while ignoring the continuity of the elongation process and relations between ribosome densities of remote codons. Modeling the translation elongation process in full-length coding sequence (CDS) level has not been studied to the best of our knowledge. In this paper, we developed a deep learning based approach with a multi-input and multi-output framework, named RiboMIMO, for modeling the ribosome density distributions of full-length mRNA CDS regions. Through considering the underlying correlations in translation efficiency among neighboring and remote codons and extracting hidden features from the input full-length coding sequence, RiboMIMO can greatly outperform the state-of-the-art baseline approaches and accurately predict the ribosome density distributions along the whole mRNA CDS regions. In addition, RiboMIMO explores the contributions of individual input codons to the predictions of output ribosome densities, which thus can help reveal important biological factors influencing the translation elongation process. The analyses, based on our interpretable metric named codon impact score, not only identified several patterns consistent with the previously-published literatures, but also for the first time (to the best of our knowledge) revealed that the codons located at a long distance from the ribosomal A site may also have an association on the translation elongation rate. This finding of long-range impact on translation elongation velocity may shed new light on the regulatory mechanisms of protein synthesis. Overall, these results indicated that RiboMIMO can provide a useful tool for studying the regulation of translation elongation in the range of full-length CDS. Public Library of Science 2021-03-26 /pmc/articles/PMC8026034/ /pubmed/33770074 http://dx.doi.org/10.1371/journal.pcbi.1008842 Text en © 2021 Tian et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tian, Tingzhong
Li, Shuya
Lang, Peng
Zhao, Dan
Zeng, Jianyang
Full-length ribosome density prediction by a multi-input and multi-output model
title Full-length ribosome density prediction by a multi-input and multi-output model
title_full Full-length ribosome density prediction by a multi-input and multi-output model
title_fullStr Full-length ribosome density prediction by a multi-input and multi-output model
title_full_unstemmed Full-length ribosome density prediction by a multi-input and multi-output model
title_short Full-length ribosome density prediction by a multi-input and multi-output model
title_sort full-length ribosome density prediction by a multi-input and multi-output model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8026034/
https://www.ncbi.nlm.nih.gov/pubmed/33770074
http://dx.doi.org/10.1371/journal.pcbi.1008842
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