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Abstract
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
Highlights
Next-generation sequencing methods have become commonplace tools in the life sciences, allowing researchers to understand the molecular mechanisms underpinning cellular processes, shaping phenotypic differences, and modifying disease susceptibility
RNA sequencing (RNA-seq) is a methodology that quantifies fragments of RNA molecules to assess the level of gene transcription
While transcription serves to generate a broad collection of transcripts, the final expression of a gene is refined, and its fate determined, in the downstream stages of gene expression regulation, such as translational regulation, protein stability, protein degradation, and others
Summary
Next-generation sequencing methods have become commonplace tools in the life sciences, allowing researchers to understand the molecular mechanisms underpinning cellular processes, shaping phenotypic differences, and modifying disease susceptibility. ONE-STEP DETECTION AND CLASSIFICATION OF DIFFERENTIAL TRANSLATION EFFICIENCY GENES (DTEG) USING DTEG.R. The RNA-seq and Ribo-seq data should be processed first as described in the Support Protocol, in order to determine translationally regulated genes. Argument 2 (arg2): RNA-seq count matrix file path Argument 3 (arg3): Sample information file path Argument 4 (arg4): Batch effect covariate: yes=1, or no=0 Argument 5 (arg5): Save Rdata file as a record for future use (optional, Default = 1) Argument 6 (arg6): Verbose mode (optional, Default = 0) R: https://cran.r-project.org/bin/windows/base/ Rstudio: https://www.rstudio.com/products/rstudio/download/ DESeq: https://bioconductor.org/packages/release/bioc/html/DESeq2.html DESeq can be installed in R by typing the following command: sample_info.txt: Sample-wise information on sequencing methodology used, condition and batch. Using this protocol, the sample information file can have more columns for other covariates that can be included in the model design, as described in step 3
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