RSCU_RS: Measuring the bias in codon usage from ribosomal activity
Overview
Overview: In the protein coding sequences of a species, the 61 possible codons of the genetic code are not equally distributed. This observation is referred to as the Codon Usage Bias (CUB) of a species. Several measures have been proposed to quantify the CUB using the frequencies of codons in all RNA coding sequences (or at least a representative subset of these). This yields a static analysis since sequences change slowly over time. But in living cells the translation varies over time, since the ribosome, the molecular machine that performs translation, has a role in the selection of which RNA sequences are indeed translated. The precise location of ribosomes translating RNA can be monitor by Ribo-seq (aka ribosome profiling), a high throughput sequencing assay that captures the portions of RNA located inside the ribosomes. Thus, the sequencing reads produced by Ribo-seq in a given condition give us access to which codons ribosomes are translating.
We proposed to measure the codon usage bias in a transcriptome wide manner using Ribo-seq sequencing data. This delivers a dynamic and precise estimation of Codon Usage Bias, since it integrates the location of ribosomes during translation. The codon usage bias can be computed classicaly for a given species, for a subset of genes, but also for any given condition for which Ribo-seq data is available. The CUB can thus be compared across species, across subsets of genes, or across conditions.
For this, we develop a stand-alone software, called RSCU_RS, and report experiments in estimating and comparing CUB across species in a journal article.
Reference
Journal article
Ribo-seq enlightens Codon Usage Bias
D. Paulet, A. David, E. Rivals
DNA Research, dsw062. https://doi.org/10.1093/dnares/dsw062, 2017.
Documentation and ressources
A documentation about the software RSCU_RS is available at
https://www.lirmm.fr/~rivals/rscu/
Funding
Fondation pour la Recherche Médicale, grant DBI20131228574.
ANR grant (ANR-11-BINF-0002), Institut de Biologie Computationnelle (IBC).
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