De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data

Summary

Translating ribosomes decode three nucleotides per codon into peptides. Their movement along mRNA, captured by ribosome profiling, produces the footprints exhibiting characteristic triplet periodicity. This protocol describes how to use RiboCode to decipher this prominent feature from ribosome profiling data to identify actively translated open reading frames at the whole-transcriptome level.

Abstract

Identification of open reading frames (ORFs), especially those encoding small peptides and being actively translated under specific physiological contexts, is critical for comprehensive annotations of context-dependent translatomes. Ribosome profiling, a technique for detecting the binding locations and densities of translating ribosomes on RNA, offers an avenue to rapidly discover where translation is occurring at the genome-wide scale. However, it is not a trivial task in bioinformatics to efficiently and comprehensively identify the translating ORFs for ribosome profiling. Described here is an easy-to-use package, named RiboCode, designed to search for actively translating ORFs of any size from distorted and ambiguous signals in ribosome profiling data. Taking our previously published dataset as an example, this article provides step-by-step instructions for the entire RiboCode pipeline, from preprocessing of the raw data to interpretation of the final output result files. Furthermore, for evaluating the translation rates of the annotated ORFs, procedures for visualization and quantification of ribosome densities on each ORF are also described in detail. In summary, the present article is a useful and timely instruction for the research fields related to translation, small ORFs, and peptides.

Introduction

Recently, a growing body of studies has revealed widespread production of peptides translated from ORFs of coding genes and the previously annotated genes as noncoding, such as long noncoding RNAs (lncRNAs)^{1,2,3,4,5,6,7,8}. These translated ORFs are regulated or induced by cells to respond to environmental changes, stress, and cell differentiation^1,8

Protocol

1. Environment setup and RiboCode installation

1. Open a Linux terminal window and create a conda environment:
   conda create -n RiboCode python=3.8
2. Switch to the created environment and install RiboCode and dependencies:
   conda activate RiboCode
   conda install -c bioconda ribocode ribominer sra-tools fastx_toolkit cutadapt bowtie star samtools

2. Data preparation

1. Get genome reference files.
   1. For the reference sequence, go to the Ensemble website at https://www.ensembl.org/index.html, click the t....

Results

The example ribosome profiling datasets were deposited in the GEO database under the accession number GSE131074. All the files and codes used in this protocol are available from Supplemental files 1-4. By applying RiboCode to a set of published ribosome profiling datasets²³, we identified the novel ORFs actively translated in MCF-10A cells treated with control and EIF3E siRNAs. To select the RPF reads that are most likely bound by the tra.......

Discussion

Ribosome profiling offers an unprecedented opportunity to study the ribosomes' action in cells at a genome scale. Precisely deciphering the information carried by the ribosome profiling data could provide insight into which regions of genes or transcripts are actively translating. This step-by-step protocol provides guidance on how to use RiboCode to analyze ribosome profiling data in detail, including package installation, data preparation, command execution, result explanation, and data visualization. The analysis resu.......

Materials

Name	Company	Catalog Number	Comments
A computer/server running Linux	Any	-	-
Anaconda or Miniconda	Anaconda	-	Anaconda: https://www.anaconda.com; Miniconda:https://docs.conda.io/en/latest/miniconda.html
R	R Foundation	-	https://www.r-project.org/
Rstudio	Rstudio	-	https://www.rstudio.com/