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.
Researchers at Duke-NUS Medical School have developed 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, the developers describe protocols for data preparation, normalization, analysis, and visualization, starting from raw sequencing files.
Transcriptional and translational regulation
(A). Genome‐wide quantification of mRNA counts and ribosome‐protected mRNA fragments (RPFs) using RNA sequencing (RNA‐seq) and ribosome profiling (Ribo‐seq), respectively. Lines are not drawn to scale. In a hypothetical study with two conditions, control and treatment, (B) a gene with change in mRNA counts and RPFs at the same rate is a differentially transcribed gene (DTG) and, (C) a gene with change in RPFs independent of change in mRNA counts, which leads to a change in translation efficiency, is defined as a differential translation efficiency gene (DTEG). TE = translation efficiency = RPF/mRNA. (D‐E) Classification of genes based on fold changes of RPF, mRNA, and TE.