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Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

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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.
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Ribosomal RNA Synthesis02:53

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Ribosome synthesis is a highly complex and coordinated process involving more than 200 assembly factors. The synthesis and processing of ribosomal components occurs not only in the nucleolus but also in the nucleoplasm and the cytoplasm of eukaryotic cells.
Ribosome biogenesis begins with the synthesis of 5S and 45S pre-rRNAs by distinct RNA polymerases. The primary transcripts are extensively processed and modified before they are bound and folded by ribosomal proteins and assembly factors,...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Ribosomes01:27

Ribosomes

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Ribosomes translate genetic information encoded by messenger RNA (mRNA) into proteins. Both prokaryotic and eukaryotic cells have ribosomes. Cells that synthesize large quantities of protein—such as secretory cells in the human pancreas—can contain millions of ribosomes.
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Ribosomes01:27

Ribosomes

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Ribosomes translate genetic information encoded by messenger RNA (mRNA) into proteins. Both prokaryotic and eukaryotic cells have ribosomes. Cells that synthesize large quantities of protein—such as secretory cells in the human pancreas—can contain millions of ribosomes.
Ribosome Structure and Assembly
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Related Experiment Video

Updated: Mar 7, 2026

Genome-wide Quantification of Translation in Budding Yeast by Ribosome Profiling
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Genome-wide Quantification of Translation in Budding Yeast by Ribosome Profiling

Published on: December 21, 2017

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Ribo-seq enlightens codon usage bias.

Damien Paulet1,2, Alexandre David3, Eric Rivals1,2

  • 1Department of Computer Science, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS et Université de Montpellier, 34095 Montpellier Cedex 5, France.

DNA Research : an International Journal for Rapid Publication of Reports on Genes and Genomes
|February 8, 2017
PubMed
Summary
This summary is machine-generated.

We developed a new method using ribosome protected mRNA fragments (Ribo-seq) to precisely measure codon usage bias across species. This approach reveals a decreasing bias in multicellular organisms and identifies preferred codons.

Keywords:
codon usageevolutionhigh throughput sequencingsynonymous codontranslation

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

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Codon usage bias, the non-uniform usage of synonymous codons, is genome-specific and linked to gene expression levels.
  • Assessing codon usage bias is challenging in complex species, particularly mammals.
  • Previous methods lacked the precision required for comprehensive analysis in diverse organisms.

Purpose of the Study:

  • To introduce a novel method for computing codon usage bias using genome-wide translational data.
  • To enable high-precision assessment of codon usage bias across a wide range of species, including mammals.
  • To investigate the relationship between gene expression, tRNA availability, and codon usage bias.

Main Methods:

  • Utilized sequencing of ribosome protected mRNA fragments (Ribo-seq) to obtain genome-wide translational data.
  • Developed a new computational approach to rank genes based on Ribo-seq data.
  • Applied the method to analyze codon usage bias in various species, including multicellular organisms.

Main Results:

  • Ribo-seq enabled precise gene ranking and codon usage bias computation across diverse species.
  • Gene ranking confirmed the significant influence of the tRNA pool on codon usage bias.
  • A decreasing trend in codon usage bias was observed in multicellular species compared to unicellular ones.
  • The method successfully identified preferred codons without prior knowledge of gene function.

Conclusions:

  • The Ribo-seq based method offers a powerful and precise tool for analyzing codon usage bias.
  • Codon usage bias is modulated by translational efficiency and tRNA availability, with variations across species complexity.
  • This technique advances our understanding of translational regulation and genome evolution in eukaryotes.