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A review of Ribosome profiling and tools used in Ribo-seq data analysis

Authors :
Mingso Sherma Limbu
Tianze Xiong
Sufang Wang
Source :
Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 1912-1918 (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

Abstract

Translational regulation plays the most critical role in gene expression. Ribosome profiling sequencing (Ribo-Seq) is one of the methods to study translation and its regulation. It is a high throughput technology based on deep sequencing, which targets ribosome protected mRNA fragments to produce a ‘global snapshot’ of translatome. There has been an annual increase in the number of publications incorporating Ribo-seq technology. Because of its importance, we used PubMed database to conduct a comprehensive bibliometric analysis on Ribo-seq. We identified 2744 published articles that utilized the term ‘Ribo-seq’ between 2009 and Jan 2024, and 684 articles that contained both Ribo-seq and RNA-seq terms. Based on keywords correlation analysis, we discovered that the primary focus of Ribo-seq articles lies in the areas of translation, transcriptome, and ribosome in the past few years and other topics such as single-cell ribo-seq and crispr within two years, reflecting current areas of interests in Ribo-seq research. The Ribo-seq data analysis applications were also explored and summarized, providing a guide for researchers to choose corresponding tools for different types of analysis. Overall, we highlighted the advances made by Ribo-seq technologies, and the possibilities of utilizing machine learning models to unravel information from multi-omics data. The integration of Ribo-seq with other omics data, such as RNA-seq, is essential to understand the gene expression in complex biological systems.

Details

Language :
English
ISSN :
20010370
Volume :
23
Issue :
1912-1918
Database :
Directory of Open Access Journals
Journal :
Computational and Structural Biotechnology Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.48f8e969ab64f28ae7e647473b3c4fb
Document Type :
article
Full Text :
https://doi.org/10.1016/j.csbj.2024.04.051