Open AccessdeltaTE: Detection of Translationally Regulated Genes by Integrative Analysis of Ribo‐seq and RNA‐seq Data
Author(s) -
Chothani Sonia,
Adami Eleonora,
Ouyang John F.,
Viswanathan Sivakumar,
Hubner Norbert,
Cook Stuart A.,
Schafer Sebastian,
Rackham Owen J. L.
Publication year - 2019
Publication title -
current protocols in molecular biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.533
H-Index - 42
eISSN - 1934-3647
pISSN - 1934-3639
DOI - 10.1002/cpmb.108
Subject(s) - computational biology , rna seq , gene , ribosome profiling , biology , genome , gene expression , genetics , transcriptome , computer science , ribosome , data mining , rna
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