By mapping the positions of millions of translating ribosomes in the cell, ribosome profiling (Ribo-seq) has established its role as a powerful tool to study gene expression. Several laboratories have introduced modifications to the experimental protocol and expanded the repertoire of biochemical methods to study translation transcriptome-wide. However, the diversity of protocols highlights a need for standardization. At the same time, different computational analysis strategies have used Ribo-seq data to identify the set of translated sequences with high confidence. In this review we present an overview of such methodologies, outlining their assumptions, data requirements, and availability. At the interface between RNA and proteins, Ribo-seq can complement data from multiple omics approaches, zooming in on the central role of translation in the molecular cell.

通过绘制细胞中数百万个翻译核糖体的位置图，核糖体谱（Ribo-seq）已确立了其作为研究基因表达的强大工具的作用。几个实验室已经对实验方案进行了修改，并扩大了生化方法库，以研究翻译转录组范围。但是，协议的多样性凸显了对标准化的需求。同时，不同的计算分析策略已使用Ribo-seq数据以高置信度来识别一组翻译序列。在这篇综述中，我们概述了这些方法，概述了它们的假设，数据要求和可用性。在RNA和蛋白质之间的界面上，Ribo-seq可以补充来自多种组学方法的数据，