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Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

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Related Experiment Video

Updated: Jan 3, 2026

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

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

Published on: February 18, 2022

Computational methods for ribosome profiling data analysis.

Stephen J Kiniry¹, Audrey M Michel¹, Pavel V Baranov^1,2

¹School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland.

Wiley Interdisciplinary Reviews. RNA

|November 25, 2019

Summary

This summary is machine-generated.

Ribosome profiling (Ribo-seq) is a powerful technique for studying gene expression and translation. This review details computational tools and methods for analyzing Ribo-seq data, addressing current challenges and offering solutions.

Keywords:

Ribo-Seq mRNA translation protein synthesis ribosome profiling translatome

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Last Updated: Jan 3, 2026

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

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

Published on: February 18, 2022

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Published on: August 7, 2021

Area of Science:

Genomics and Computational Biology
Molecular Biology
Translational Regulation

Background:

Ribosome profiling (Ribo-seq) has become a popular technique for assessing gene expression and translational regulation since its introduction in 2009.
The increasing volume of Ribo-seq data necessitates robust computational tools for comprehensive analysis.

Purpose of the Study:

To review computational methods and software for analyzing ribosome profiling data.
To identify challenges in Ribo-seq data analysis and discuss current solutions.
To provide a resource list of publicly available software for Ribo-seq analysis.

Main Methods:

Review of computational approaches for Ribo-seq data processing, from raw data to advanced analyses.
Identification of tools for tasks including translated open reading frame (tORF) detection, differential gene expression, and codon decoding rate evaluation.
Discussion of challenges and existing solutions at each stage of Ribo-seq data analysis.

Main Results:

A comprehensive overview of computational strategies for ribosome profiling data analysis.
Identification of key challenges in Ribo-seq data processing and interpretation.
A curated list of publicly available software tools for various Ribo-seq analysis steps.

Conclusions:

Effective computational analysis is crucial for extracting biological insights from ribosome profiling data.
Addressing current challenges in Ribo-seq bioinformatics can inspire novel solutions and improve analytical tools.
This review serves as a starting point for researchers new to ribosome profiling analysis and for computational biologists seeking advanced methods.