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Riboswitches are non-coding mRNA domains that regulate the transcription and translation of downstream genes without the help of proteins. Riboswitches bind directly to a metabolite and can form unique stem-loop or hairpin structures in response to the amount of the metabolite present. They have two distinct regions – a metabolite-binding aptamer and an expression platform.
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RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing.

Alexandra Popa1, Kevin Lebrigand1, Agnes Paquet1

  • 1Institut de Pharmacologie Mol´eculaire et Cellulaire, University Nice Sophia Antipolis and CNRS, Sophia- Antipolis, 06560, France.

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|June 28, 2016
PubMed
Summary
This summary is machine-generated.

We introduce RiboProfiling, an open-source R package for analyzing ribosome profiling (Ribo-seq) data. It quantifies ribosome footprints and identifies codon motifs associated with ribosome pausing, aiding in the assessment of translation efficiency.

Keywords:
genomicsribosome footprintsribosome profiling

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Ribosome profiling (Ribo-seq) sequences translated RNA regions to analyze gene expression at the translational level.
  • Analysis of Ribo-seq reads is crucial for understanding coding potential and identifying ribosome pausing on specific codon motifs.
  • There is an increasing demand for robust computational tools to analyze Ribo-seq data.

Purpose of the Study:

  • To develop and present 'RiboProfiling', a novel Bioconductor open-source package for comprehensive Ribo-seq data analysis.
  • To provide a streamlined R workflow for key Ribo-seq analysis steps, from footprint quantification to motif identification.
  • To facilitate quality assessment and statistical analysis of Ribo-seq experiments.

Main Methods:

  • Developed 'RiboProfiling', an R package integrated into the Bioconductor platform.
  • Implemented a full R workflow pipeline for Ribo-seq data analysis.
  • The package accepts alignment (BAM) files, quantifies ribosome footprints at the transcript level, and identifies accumulation on amino acid or multi-amino acid motifs.

Main Results:

  • 'RiboProfiling' automates the generation of report summary graphs and data quantification.
  • The package enables the identification of specific codon motifs where ribosome footprints accumulate.
  • Demonstrated the utility of 'RiboProfiling' using data from Escherichia coli.

Conclusions:

  • 'RiboProfiling' offers a comprehensive and user-friendly solution for Ribo-seq data analysis.
  • The package enhances the ability to study translational regulation and ribosome dynamics.
  • Its integration with Bioconductor allows for advanced statistical modeling and analysis of Ribo-seq outputs.