Author: Daniela Cassol (danielac@ucr.edu) and Thomas Girke (thomas.girke@ucr.edu)

Last update: 21 November, 2019

Alternative formats of this tutorial: [ HTML ], [ PDF ], [ .Rmd ], [ .R ]

Ribo-Seq and polyRibo-Seq are a specific form of RNA-Seq gene expression experiments utilizing mRNA subpopulations directly bound to ribosomes. Compared to standard RNA-Seq, their readout of gene expression provides a better approximation of downstream protein abundance profiles due to their close association with translational processes. The most important difference among the two is that polyRibo-Seq utilizes polyribosomal RNA for sequencing, whereas Ribo-Seq is a footprinting approach restricted to sequencing RNA fragments protected by ribosomes (Ingolia et al., 2009; Aspden et al., 2014; Juntawong et al., 2015).

The workflow presented in this vignette contains most of the data analysis steps described by (Juntawong et al., 2014) including functionalities useful for processing both polyRibo-Seq and Ribo-Seq experiments. To improve re-usability and adapt to recent changes of software versions (e.g. R, Bioconductor and short read aligners), the code has been optimized accordingly. Thus, the results obtained with the updated workflow are expected to be similar but not necessarily identical with the published results described in the original paper.

Relevant analysis steps of this workflow include read preprocessing, read alignments against a reference genome, counting of reads overlapping with a wide range of genomic features (e.g. CDSs, UTRs, uORFs, rRNAs, etc.), differential gene expression and differential ribosome binding analyses, as well as a variety of genome-wide summary plots for visualizing RNA expression trends. Functions are provided for evaluating the quality of Ribo-seq data, for identifying novel expressed regions in the genomes, and for gaining insights into gene regulation at the post-transcriptional and translational levels. For example, the functions genFeatures and featuretypeCounts can be used to quantify the expression output for all feature types included in a genome annotation (e.g. genes, introns, exons, miRNAs, intergenic regions, etc.). To determine the approximate read length of ribosome footprints in Ribo-Seq experiments, these feature type counts can be obtained and plotted for specific read lengths separately. Typically, the most abundant read length obtained for translated features corresponds to the approximate footprint length occupied by the ribosomes of a given organism group. Based on the results from several Ribo-Seq studies, these ribosome footprints are typically ~30 nucleotides long (Ingolia et al., 2011; Ingolia et al., 2009; Juntawong et al., 2014). However, their length can vary by several nucleotides depending upon the optimization of the RNA digestion step and various factors associated with translational regulation. For quality control purposes of Ribo-Seq experiments it is also useful to monitor the abundance of reads mapping to rRNA genes due to the high rRNA content of ribosomes. This information can be generated with the featuretypeCounts function described above.

Coverage trends along transcripts summarized for any number of transcripts can be obtained and plotted with the functions featureCoverage and plotfeatureCoverage, respectively. Their results allow monitoring of the phasing of ribosome movements along triplets of coding sequences. Commonly, high quality data will display here for the first nucleotide of each codon the highest depth of coverage computed for the 5’ ends of the aligned reads.

Ribo-seq data can also be used to evaluate various aspects of translational control due to ribosome occupancy in upstream open reading frames (uORFs). The latter are frequently present in (or near) 5’ UTRs of transcripts. For this, the function predORFs can be used to identify ORFs in the nucleotide sequences of transcripts or their subcomponents such as UTR regions. After scaling the resulting ORF coordinates back to the corresponding genome locations using scaleRanges, one can use these novel features (e.g. uORFs) for expression analysis routines similar to those employed for pre-existing annotations, such as the exonic regions of genes. For instance, in Ribo-Seq experiments one can use this approach to systematically identify all transcripts occupied by ribosomes in their uORF regions. The binding of ribosomes to uORF regions may indicate a regulatory role in the translation of the downstream main ORFs and/or translation of the uORFs into functionally relevant peptides.

## Experimental design

Typically, users want to specify here all information relevant for the analysis of their NGS study. This includes detailed descriptions of FASTQ files, experimental design, reference genome, gene annotations, etc.

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