关键词: Gene expression Multi-omics analysis RNA-seq Ribo-seq Ribosome profiling

来  源:   DOI:10.1016/j.csbj.2024.04.051   PDF(Pubmed)

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.
摘要:
翻译调控在基因表达中起着最关键的作用。核糖体谱分析测序(Ribo-Seq)是研究翻译及其调控的方法之一。它是一种基于深度测序的高通量技术,它靶向核糖体保护的mRNA片段,以产生翻译体的“全局快照”。采用Ribo-seq技术的出版物数量每年都在增加。因为它的重要性,我们使用PubMed数据库对Ribo-seq进行了全面的文献计量分析。我们确定了2009年至2024年1月之间使用术语“Ribo-seq”的2744篇已发表的文章,以及包含Ribo-seq和RNA-seq术语的684篇文章。基于关键词相关性分析,我们发现Ribo-seq文章的主要焦点在于翻译领域,转录组,和核糖体在过去几年和其他主题,如单细胞ribo-seq和crispr在两年内,反映了Ribo-seq研究的当前兴趣领域。还对Ribo-seq数据分析应用进行了探索和总结,为研究人员选择相应的工具进行不同类型的分析提供了指导。总的来说,我们强调了Ribo-seq技术所取得的进步,以及利用机器学习模型从多组数据中解开信息的可能性。Ribo-seq与其他组学数据的整合,例如RNA-seq,对于理解复杂生物系统中的基因表达至关重要。
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