A new and updated resource for codon usage tables

Abstract

BackgroundDue to the degeneracy of the genetic code, most amino acids can be encoded by multiple synonymous codons. Synonymous codons naturally occur with different frequencies in different organisms. The choice of codons may affect protein expression, structure, and function. Recombinant gene technologies commonly take advantage of the former effect by implementing a technique termed codon optimization, in which codons are replaced with synonymous ones in order to increase protein expression. This technique relies on the accurate knowledge of codon usage frequencies. Accurately quantifying codon usage bias for different organisms is useful not only for codon optimization, but also for evolutionary and translation studies: phylogenetic relations of organisms, and host-pathogen co-evolution relationships, may be explored through their codon usage similarities. Furthermore, codon usage has been shown to affect protein structure and function through interfering with translation kinetics, and cotranslational protein folding.ResultsDespite the obvious need for accurate codon usage tables, currently available resources are either limited in scope, encompassing only organisms from specific domains of life, or greatly outdated. Taking advantage of the exponential growth of GenBank and the creation of NCBI’s RefSeq database, we have developed a new database, the High-performance Integrated Virtual Environment-Codon Usage Tables (HIVE-CUTs), to present and analyse codon usage tables for every organism with publicly available sequencing data. Compared to existing databases, this new database is more comprehensive, addresses concerns that limited the accuracy of earlier databases, and provides several new functionalities, such as the ability to view and compare codon usage between individual organisms and across taxonomical clades, through graphical representation or through commonly used indices. In addition, it is being routinely updated to keep up with the continuous flow of new data in GenBank and RefSeq.ConclusionGiven the impact of codon usage bias on recombinant gene technologies, this database will facilitate effective development and review of recombinant drug products and will be instrumental in a wide area of biological research. The database is available at hive.biochemistry.gwu.edu/review/codon.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1793-7) contains supplementary material, which is available to authorized users.

OUP accepted manuscript

“…Using the RefSeq database from NCBI, as tabulated in the latest release of the codon usage table database (CUTD) (Athey et al 2017), we assessed the relative proportions of the different leucine and serine codons across species. We found that global usage of CUA, UUA, and UCA (per associated amino acid) scales directly and inversely with GC content at the third position of codons (GC3) and only species with extremely high GC3 had bias against CUA, UUA, and UCA as extreme as that in the rORF-containing narnaviruses (Fig.…”

Transient genetic transformation of Mougeotia scalaris (Zygnematophyceae) mediated by the endogenous α‐tubulin1 promoter

“…Both factors can influence the efficiency of heterologous gene expression. We compared the GC content (of open reading frames) and codon usage of M. scalaris, Closterium peracerosum-strigosum-litorale complex, C. reinhardtii, and Arabidopsis thaliana using the Codon Usage Table Database (Athey et al 2017). This web-based tool analyzes user-defined genbank accessions.…”

Absent from DNA and protein: genomic characterization of nullomers and nullpeptides across functional categories and evolution

“…When analyzing the amino acid composition of the annotated nullpeptides, we identified the amino acids Tryptophan (W), Methionine (M) and Cysteine (C) to appear much more in nullpeptides than in non-nullomeric amino acid k-mers of the same length ( Supplementary Figure 4a ). This might be due to the importance of the M codon for translation initiation and that a single base pair change can cause W and C codons to become a TAA stop codon, the second most frequent stop codon in humans 14,15 . Similarly to nullomers, the third metric we used was the number of species in which we identified each human nullpeptide in their proteome ( Figure 5g, Supplementary Table 2 ).…”