Ribo-seq enlightens codon usage bias.
Codon usage is biased between lowly and highly expressed genes in a genome-specific manner. This universal bias has been well assessed in some unicellular species, but remains problematic to assess in more complex species. We propose a new method to compute codon usage bias based on genome wide translational data. A new technique based on sequencing of ribosome protected mRNA fragments (Ribo-seq) allowed us to rank genes and compute codon usage bias with high precision for a great variety of species, including mammals. Genes ranking using Ribo-Seq data confirms the influence of the tRNA pool on codon usage bias and shows a decreasing bias in multicellular species. Ribo-Seq analysis also makes possible to detect preferred codons without information on genes function.
Department of Computer Science, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS et Université de Montpellier, 34095 Montpellier Cedex 5, France.|Institut de Biologie Computationnelle (IBC); Université de Montpellier, France.
Damien
D
Paulet
Cancer Biology Department, Institut de Génomique Fonctionnelle, CNRS, INSERM, Université de Montpellier, F-34094 Montpellier, France.
Alexandre
A
David
Department of Computer Science, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS et Université de Montpellier, 34095 Montpellier Cedex 5, France.|Institut de Biologie Computationnelle (IBC); Université de Montpellier, France.
Eric
E
Rivals
D000818:Animals; D003062:Codon / Q000032:analysis / Q000235:genetics*; D056890:Eukaryota / Q000235:genetics; D019143:Evolution, Molecular; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D014176:Protein Biosynthesis; D012333:RNA, Messenger; D012270:Ribosomes; D017423:Sequence Analysis, RNA; D059467:Transcriptome
D003062:Codon; D012333:RNA, Messenger
codon usage; evolution; high throughput sequencing; synonymous codon; translation