[HTML][HTML] RiboProfiling: a Bioconductor package for standard Ribo-seq pipeline processing

A Popa, K Lebrigand, A Paquet, N Nottet… - …, 2016 - ncbi.nlm.nih.gov
A Popa, K Lebrigand, A Paquet, N Nottet, K Robbe-Sermesant, R Waldmann, P Barbry
F1000Research, 2016ncbi.nlm.nih.gov
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 …
Abstract
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.
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