deltaTE: Detection of Translationally Regulated Genes by Integrative Analysis of Ribo-seq and RNA-seq Data.
deltaTE: Detection of Translationally Regulated Genes by Integrative Analysis of Ribo-seq and RNA-seq Data.
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DOI：
10.1002/cpmb.108
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发表时间：
2019-12
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核糖体图谱可以量化转录本的全基因组核糖体占有率。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。否则，这一层的基因表达调控很难在全球范围内进行评估，而且通常在人类疾病的背景下也不能很好地理解。目前计算TE差异的统计方法精确度较低，不能适应复杂的实验设计或混杂因素，并且不能将基因归类为缓冲、强化或唯一翻译调控基因。这篇文章概述了一种识别翻译调控基因的方法[称为增量TE(ΔTE)，代表TE的变化]，它解决了以前方法的缺点。在广泛的基准分析中，ΔTE的性能优于所有测试的方法。此外，对来自人类原代细胞的数据应用ΔTE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，我们从原始测序文件开始，描述数据准备、标准化、分析和可视化的协议。©2019作者。基本协议：使用DTEG.R替代协议一步检测和分类差异翻译效率基因：使用R支持协议逐步检测和分类差异翻译效率基因：从原始数据到读取计数的工作流程
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