Ribosome Decision Graphs for the Representation of Eukaryotic RNA Translation Complexity

Abstract

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.

mRNA context and translation factors determine decoding in alternative nuclear genetic codes

“…It represents eukaryotic translation as multiple ribosome paths, reflects translation complexity, and focuses on locations critical for translation regulation. [37] At the moment, we have only fragmentary information about context-depending decoding, and it is not yet possible to predict events at the particular codon accurately. However, the analysis of alternative genetic codes, carried out, in particular, in this review, allows us to approach describing them.…”

The Roles of eIF4G2 in Leaky Scanning and Reinitiation on the Human Dual-Coding POLG mRNA

“…The light grey path represents the post-terminating small ribosome subunit that resumes scanning and can initiate on the downstream POLGARF or POLG start codons (the reinitiation path). The concept of visualization was adopted from [20].…”

“…The light grey path represents the post-terminating small ribosome subunit that resumes scanning and can initiate on the downstream POLGARF or POLG start codons (the reinitiation path). The concept of visualization was adopted from [20] POLG is one of the few nuclear encoded genes associated with mtDNA disorders [21]. It bears a polyglutamine tract, and this tract's expansion has been linked to Parkinson's disease and other neuropathological conditions [22,23].…”

Current limitations in predicting mRNA translation with deep learning models

“…The ratio of normalized RPFs and RNA-seq reads over the coding region is used as an estimate of "translation efficiency" (TE), which is considered a proxy for the synthesis rate of the encoded protein [2]. Ribosome footprinting has been applied to a variety of cells and organisms [11], yielding new mechanistic and regulatory insights (e.g. [12,13]).…”