Ribosome decision graphs for the representation of eukaryotic RNA translation complexity.

The application of ribosome profiling has revealed an unexpected abundance of translation in addition to that responsible for the synthesis of previously annotated protein-coding regions. Multiple short sequences have been found to be translated within single RNA molecules, within both annotated protein-coding and noncoding regions. The biological significance of this translation is a matter of intensive investigation. However, current schematic or annotation-based representations of mRNA translation generally do not account for the apparent multitude of translated regions within the same molecules. They also do not take into account the stochasticity of the process that allows alternative translations of the same RNA molecules by different ribosomes. There is a need for formal representations of mRNA complexity that would enable the analysis of quantitative information on translation and more accurate models for predicting the phenotypic effects of genetic variants affecting translation. To address this, we developed a conceptually novel abstraction that we term ribosome decision graphs (RDGs). RDGs represent translation as multiple ribosome paths through untranslated and translated mRNA segments. We termed the latter "translons." Nondeterministic events, such as initiation, reinitiation, selenocysteine insertion, or ribosomal frameshifting, are then represented as branching points. This representation allows for an adequate representation of eukaryotic translation complexity and focuses on locations critical for translation regulation. We show how RDGs can be used for depicting translated regions and for analyzing genetic variation and quantitative genome-wide data on translation for characterization of regulatory modulators of translation.

Ribosome Decision Graphs for the Representation of Eukaryotic RNA Translation Complexity.

The application of ribosome profiling has revealed an unexpected abundance of translation in addition to that responsible for the synthesis of previously annotated protein-coding regions. Multiple short sequences have been found to be translated within single RNA molecules, both within annotated protein-coding and non-coding regions. The biological significance of this translation is a matter of intensive investigation. However, current schematic or annotation-based representations of mRNA translation generally do not account for the apparent multitude of translated regions within the same molecules. They also do not take into account the stochasticity of the process that allows alternative translations of the same RNA molecules by different ribosomes. There is a need for formal representations of mRNA complexity that would enable the analysis of quantitative information on translation and more accurate models for predicting the phenotypic effects of genetic variants affecting translation. To address this, we developed a conceptually novel abstraction that we term Ribosome Decision Graphs (RDGs). RDGs represent translation as multiple ribosome paths through untranslated and translated mRNA segments. We termed the later 'translons'. Non-deterministic events, such as initiation, re-initiation, selenocysteine insertion or ribosomal frameshifting are then represented as branching points. This representation allows for an adequate representation of eukaryotic translation complexity and focuses on locations critical for translation regulation. We show how RDGs can be used for depicting translated regions, analysis of genetic variation and quantitative genome-wide data on translation for characterisation of regulatory modulators of translation.

RiboGalaxy: A Galaxy-based Web Platform for Ribosome Profiling Data Processing - 2023 Update.

Ribosome profiling (Ribo-Seq) captures a "snapshot" of ribosomes' locations at the entire transcriptome of a cell at sub-codon resolution providing insights into gene expression and enabling the discovery of novel translated regions. RiboGalaxy (https://ribogalaxy.genomicsdatascience.ie/), a Galaxy-based platform for processing Ribo-Seq data is a RiboSeq.Org (https://riboseq.org/) resource. RiboSeq.Org is an online gateway to a set of integrated tools for the processing and analysis of Ribo-Seq data. In this RiboGalaxy update we introduce changes to both the tools available on RiboGalaxy and to how the resource is managed on the backend. For example, in order to improve interoperability between Riboseq.Org resources, we added tools that link RiboGalaxy outputs with Trips-Viz and GWIPS-viz browsers for downstream analysis and visualisation. RiboGalaxy's backend now utilises Ansible configuration management which enhances its stability and jobs are executed within Singularity containers and are managed by Slurm, strengthening reproducibility and performance respectively.