Metagenomic analyses

Nucleotide periodicity of mapped reads

All mapped reads are aligned by their 5' ends. Ribosome-footprint reads exhibit a three-nucleotide periodicity along the ORF. Footprinting reads also show accumulation at the start and stop codons.

Read count histogram

Distribution of lengths of reads mapped to coding sequences

Position-specific distribution of reads

Metagene analyses of RPF and RNA-seq reads. Mapped reads at each position (codon for RPF and nucleotide for mRNA) are normalized by the mean within each ORF, and then averaged with equal weight for each position across all ORFs. The size of the RPF 'ramp' varies between studies and can be largely attributed to use of CHX pre-treatment. Each RPF dataset is compared with an average of representative studies with and without CHX pre-treatment. Each mRNA dataset is compared with an average of representative studies. Shaded areas represent standard errors around the mean lines. Select either 3' or 5' ends below :

Excess codon-specific RPF reads in 5' ends of ORFs

For each of the codons, densities of RPFs with ribosomal A sites mapping to that codon were calculated using either only the ramp region of each ORF (codons 1-200) or the remainder of each ORF. Datasets with CHX pre-treatment typically have higher excess codon-specific reads compared to non-CHX datasets. Shaded areas represent standard deviation.

Nucleotide frequencies along mapped reads

All mapped reads of a particular length are aligned and nucleotide frequencies are estimated at each position.

Select Frame:

Sequence-based features and normalized reads

Correlations between coding sequence features and gene-specific RPKM values. Correlations are estimated based on genes with at least 64 mapped reads (blue dots).

tRNA abundances and codon-specific reads

Correlation between codon-specific normalized reads mapped to either A, P, or E-site of the read and various estimates of tRNA abundances.

Gene based analyses

Gene by gene analyses are available here or through the Gene of interest tab.

Rationale and details

Ribosome profiling provides a detailed global snapshot of protein synthesis in a cell. At its core, this technique makes use of the observation that a translating ribosome protects around 30 nucleotides of the mRNA from nuclease activity. High-throughput sequencing of these ribosome protected fragments (called ribosome footprints) offers a precise record of the number and location of the ribosomes at the time at which translation is stopped. Mapping the position of the ribosome protected fragments indicates the translated regions within the transcriptome. Moreover, ribosomes spend different periods of time at different positions, leading to variation in the footprint density along the mRNA transcript. This provides an estimate of how much protein is being produced from each mRNA. Importantly, ribosome profiling is as precise and detailed as RNA sequencing. Even in a short time, since its introduction in 2009, ribosome profiling has been playing a key role in driving biological discovery.

We have developed a bioinformatics tool-kit, riboviz, for analyzing and visualizing ribosome profiling datasets. riboviz consists of a comprehensive and flexible backend analysis pipeline and a web application for visualization. The current iteration of riboviz is designed for yeast datasets.

Existing yeast datasets consist of a mix of studies, some of which use elongation inhibitors such as cycloheximide (CHX) and others that flash freeze (FF) the samples to prevent initiation and elongation during sample preparation. In general, each experimental step can potentially introduce biases in processed datasets. riboviz can help identify these biases by allowing users to compare and contrast datasets obtained under different experimental conditions.

Codes for processing the raw reads are available on GitHub. In addition to the metagenomic analyses, a R/Shiny integration allows the user to select a gene of interest and compare ribosomal densities of its ORF simultaneously across nine data sets.

© 2017 Oana Carja and Premal Shah