Chothani Sonia, Adami Eleonora, Ouyang John F, Viswanathan Sivakumar, Hubner Norbert, Cook Stuart A, Schafer Sebastian, Rackham Owen J L
Program in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore.
Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
Curr Protoc Mol Biol. 2019 Dec;129(1):e108. doi: 10.1002/cpmb.108.
Ribosome profiling quantifies the genome-wide ribosome occupancy of transcripts. With the integration of matched RNA sequencing data, the translation efficiency (TE) of genes can be calculated to reveal translational regulation. This layer of gene-expression regulation is otherwise difficult to assess on a global scale and generally not well understood in the context of human disease. Current statistical methods to calculate differences in TE have low accuracy, cannot accommodate complex experimental designs or confounding factors, and do not categorize genes into buffered, intensified, or exclusively translationally regulated genes. This article outlines a method [referred to as deltaTE (ΔTE), standing for change in TE] to identify translationally regulated genes, which addresses the shortcomings of previous methods. In an extensive benchmarking analysis, ΔTE outperforms all methods tested. Furthermore, applying ΔTE on data from human primary cells allows detection of substantially more translationally regulated genes, providing a clearer understanding of translational regulation in pathogenic processes. In this article, we describe protocols for data preparation, normalization, analysis, and visualization, starting from raw sequencing files. © 2019 The Authors. Basic Protocol: One-step detection and classification of differential translation efficiency genes using DTEG.R Alternate Protocol: Step-wise detection and classification of differential translation efficiency genes using R Support Protocol: Workflow from raw data to read counts.
核糖体图谱分析可对转录本的全基因组核糖体占有率进行定量分析。通过整合匹配的RNA测序数据，可以计算基因的翻译效率（TE），以揭示翻译调控情况。基因表达调控的这一层面在全球范围内很难评估，而且在人类疾病背景下通常也未被充分理解。目前计算TE差异的统计方法准确性较低，无法适应复杂的实验设计或混杂因素，也不能将基因分类为缓冲型、增强型或仅受翻译调控的基因。本文概述了一种用于识别受翻译调控基因的方法[称为deltaTE（ΔTE），代表TE的变化]，该方法解决了先前方法的缺点。在广泛的基准分析中，ΔTE优于所有测试方法。此外，将ΔTE应用于来自人类原代细胞的数据，可以检测到更多受翻译调控的基因，从而更清楚地了解致病过程中的翻译调控。在本文中，我们描述了从原始测序文件开始的数据准备、标准化、分析和可视化方案。©2019作者。基本方案：使用DTEG.R一步检测和分类差异翻译效率基因。替代方案：使用R逐步检测和分类差异翻译效率基因。支持方案：从原始数据到读取计数的工作流程。
