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RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing
The ribosome profiling technique (Ribo-seq) allows the selective sequencing of translated RNA regions. Recently, the analysis of genomic sequences associated to Ribo-seq reads has been widely employed to assess their coding potential. These analyses led to the identification of differentially transl...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000Research
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4918025/ https://www.ncbi.nlm.nih.gov/pubmed/27347386 http://dx.doi.org/10.12688/f1000research.8964.1 |
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author | Popa, Alexandra Lebrigand, Kevin Paquet, Agnes Nottet, Nicolas Robbe-Sermesant, Karine Waldmann, Rainer Barbry, Pascal |
author_facet | Popa, Alexandra Lebrigand, Kevin Paquet, Agnes Nottet, Nicolas Robbe-Sermesant, Karine Waldmann, Rainer Barbry, Pascal |
author_sort | Popa, Alexandra |
collection | PubMed |
description | The ribosome profiling technique (Ribo-seq) allows the selective sequencing of translated RNA regions. Recently, the analysis of genomic sequences associated to Ribo-seq reads has been widely employed to assess their coding potential. These analyses led to the identification of differentially translated transcripts under different experimental conditions, and/or ribosome pausing on codon motifs. In the context of the ever-growing need for tools analyzing Ribo-seq reads, we have developed ‘RiboProfiling’, a new Bioconductor open-source package. ‘RiboProfiling’ provides a full pipeline to cover all key steps for the analysis of ribosome footprints. This pipeline has been implemented in a single R workflow. The package takes an alignment (BAM) file as input and performs ribosome footprint quantification at a transcript level. It also identifies footprint accumulation on particular amino acids or multi amino-acids motifs. Report summary graphs and data quantification are generated automatically. The package facilitates quality assessment and quantification of Ribo-seq experiments. Its implementation in Bioconductor enables the modeling and statistical analysis of its output through the vast choice of packages available in R. This article illustrates how to identify codon-motifs accumulating ribosome footprints, based on data from Escherichia coli. |
format | Online Article Text |
id | pubmed-4918025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | F1000Research |
record_format | MEDLINE/PubMed |
spelling | pubmed-49180252016-06-23 RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing Popa, Alexandra Lebrigand, Kevin Paquet, Agnes Nottet, Nicolas Robbe-Sermesant, Karine Waldmann, Rainer Barbry, Pascal F1000Res Software Tool Article The ribosome profiling technique (Ribo-seq) allows the selective sequencing of translated RNA regions. Recently, the analysis of genomic sequences associated to Ribo-seq reads has been widely employed to assess their coding potential. These analyses led to the identification of differentially translated transcripts under different experimental conditions, and/or ribosome pausing on codon motifs. In the context of the ever-growing need for tools analyzing Ribo-seq reads, we have developed ‘RiboProfiling’, a new Bioconductor open-source package. ‘RiboProfiling’ provides a full pipeline to cover all key steps for the analysis of ribosome footprints. This pipeline has been implemented in a single R workflow. The package takes an alignment (BAM) file as input and performs ribosome footprint quantification at a transcript level. It also identifies footprint accumulation on particular amino acids or multi amino-acids motifs. Report summary graphs and data quantification are generated automatically. The package facilitates quality assessment and quantification of Ribo-seq experiments. Its implementation in Bioconductor enables the modeling and statistical analysis of its output through the vast choice of packages available in R. This article illustrates how to identify codon-motifs accumulating ribosome footprints, based on data from Escherichia coli. F1000Research 2016-06-09 /pmc/articles/PMC4918025/ /pubmed/27347386 http://dx.doi.org/10.12688/f1000research.8964.1 Text en Copyright: © 2016 Popa A et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Tool Article Popa, Alexandra Lebrigand, Kevin Paquet, Agnes Nottet, Nicolas Robbe-Sermesant, Karine Waldmann, Rainer Barbry, Pascal RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing |
title | RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing |
title_full | RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing |
title_fullStr | RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing |
title_full_unstemmed | RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing |
title_short | RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing |
title_sort | riboprofiling: a bioconductor package for standard ribo-seq pipeline processing |
topic | Software Tool Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4918025/ https://www.ncbi.nlm.nih.gov/pubmed/27347386 http://dx.doi.org/10.12688/f1000research.8964.1 |
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