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Abstract

Bacterial genomes often reflect a bias in the usage of codons. These biases are often most notable within highly expressed genes. While deviations in codon usage can be attributed to selection or mutational biases, they can also be functional, for example controlling gene expression or guiding protein structure. Several different metrics have been developed to identify biases in codon usage. Previously we released a database, CBDB: The Codon Bias Database, in which users could retrieve precalculated codon bias data for bacterial RefSeq genomes. With the increase of bacterial genome sequence data since its release a new tool was needed. Here we present the Dynamic Codon Biaser (DCB) tool, a web application that dynamically calculates the codon usage bias statistics of prokaryotic genomes. DCB bases these calculations on 40 different highly expressed genes (HEGs) that are highly conserved across different prokaryotic species. A user can either specify an NCBI accession number or upload their own sequence. DCB returns both the bias statistics and the genome’s HEG sequences. These calculations have several downstream applications, such as evolutionary studies and phage–host predictions. The source code is freely available, and the website is hosted at www.cbdb.info.

Funding
This study was supported by the:
  • National Science Foundation (Award 1661357)
    • Principle Award Recipient: CatherinePutonti
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2021-10-26
2024-12-12
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References

  1. Sharp PM, Stenico M, Peden JF, Lloyd AT. Codon usage: mutational bias, translational selection, or both?. Biochem Soc Trans 1993; 21:835–841 [View Article] [PubMed]
    [Google Scholar]
  2. Quax TEF, Claassens NJ, Söll D, van der Oost J. Codon bias as a means to fine-tune gene expBias as a Means to Fine-Tune Gene Expression. Mol Cell 2015; 59:149–161 [View Article] [PubMed]
    [Google Scholar]
  3. Sharp PM, Li WH. The codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 1987; 15:1281–1295 [View Article] [PubMed]
    [Google Scholar]
  4. Sharp PM, Bailes E, Grocock RJ, Peden JF, Sockett RE. Variation in the strength of selected codon usage bias among bacteria. Nucleic Acids Res 2005; 33:1141–1153 [View Article] [PubMed]
    [Google Scholar]
  5. López JL, Lozano MJ, Fabre ML, Lagares A. Codon usage optimization in the prokaryotic tree of life: how synonymous codons are differentially selected in sequence domains with different expression levels and degrees of conservation. mBio 2020; 11:e00766-20 [View Article] [PubMed]
    [Google Scholar]
  6. Gouy M, Gautier C. Codon usage in bacteria: correlation with gene expressivity. Nucleic Acids Res 1982; 10:7055–7074 [View Article] [PubMed]
    [Google Scholar]
  7. Cambray G, Guimaraes JC, Arkin AP. Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli. Nat Biotechnol 2018; 36:1005–1015 [View Article] [PubMed]
    [Google Scholar]
  8. Ceroni F, Algar R, Stan G-. B, Ellis T. Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat Methods 2015; 12:415–418 [View Article] [PubMed]
    [Google Scholar]
  9. Frumkin I, Schirman D, Rotman A, Li F, Zahavi L. Gene architectures that minimize cost of gene expreArchitectures that Minimize Cost of Gene Expression. Mol Cell 2017; 65:142–153 [View Article] [PubMed]
    [Google Scholar]
  10. Guimaraes JC, Rocha M, Arkin AP. Transcript level and sequence determinants of protein abundance and noise in Escherichia coli. Nucleic Acids Res 2014; 42:4791–4799 [View Article] [PubMed]
    [Google Scholar]
  11. Buhr F, Jha S, Thommen M, Mittelstaet J, Kutz F. Synonymous codons direct cotranslational folding toward different protein conformations. Mol Cell 2016; 61:341–351 [View Article] [PubMed]
    [Google Scholar]
  12. Presnyak V, Alhusaini N, Chen Y-. H, Martin S, Morris N. Codon optimality is a major determinant of mRNA stability. Cell 2015; 160:1111–1124 [View Article] [PubMed]
    [Google Scholar]
  13. Kanaya S, Yamada Y, Kudo Y, Ikemura T. Studies of codon usage and tRNA genes of 18 unicellular organisms and quantification of Bacillus subtilis tRNAs: gene expression level and species-specific diversity of codon usage based on multivariate analysis. Gene 1999; 238:143–155 [View Article] [PubMed]
    [Google Scholar]
  14. Hanson G, Coller J. Codon optimality, bias and usage in translation and mRNA decay. Nat Rev Mol Cell Biol 2018; 19:20–30 [View Article] [PubMed]
    [Google Scholar]
  15. Guo F-B, Ye Y-N, Zhao H-L, Lin D, Wei W. Universal pattern and diverse strengths of successive synonymous codon bias in three domains of life, particularly among prokaryotic genomes. DNA Res 2012; 19:477–485 [View Article] [PubMed]
    [Google Scholar]
  16. Gamble CE, Brule CE, Dean KM, Fields S, Grayhack EJ. Adjacent codons act in concert to modulate translation efficiency in yeaCodons Act in Concert to Modulate Translation Efficiency in Yeast. Cell 2016; 166:679–690 [View Article] [PubMed]
    [Google Scholar]
  17. Diambra LA. Differential bicodon usage in lowly and highly abundant proteins. PeerJ 2017; 5:e3081 [View Article] [PubMed]
    [Google Scholar]
  18. Carbone A. Codon bias is a major factor explaining phage evolution in translationally biased hosts. J Mol Evol 2008; 66:210–223 [View Article] [PubMed]
    [Google Scholar]
  19. Cardinale DJ, Duffy S. Single-stranded genomic architecture constrains optimal codon usage. Bacteriophage 2011; 1:219–224 [View Article] [PubMed]
    [Google Scholar]
  20. Lucks JB, Nelson DR, Kudla GR, Plotkin JB. Genome landscapes and bacteriophage codon usage. PLoS Comput Biol 2008; 4:e1000001 [View Article] [PubMed]
    [Google Scholar]
  21. Pride DT, Wassenaar TM, Ghose C, Blaser MJ. Evidence of host-virus co-evolution in tetranucleotide usage patterns of bacteriophages and eukaryotic viruses. BMC Genomics 2006; 7:8 [View Article] [PubMed]
    [Google Scholar]
  22. Kunisawa T, Kanaya S, Kutter E. Comparison of synonymous codon distribution patterns of bacteriophage and host genomes. DNA Res 1998; 5:319–326 [View Article] [PubMed]
    [Google Scholar]
  23. Roux S, Hallam SJ, Woyke T, Sullivan MB. Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. eLife 2015; 4: [View Article] [PubMed]
    [Google Scholar]
  24. Edwards RA, McNair K, Faust K, Raes J, Dutilh BE. Computational approaches to predict bacteriophage-host relationships. FEMS Microbiol Rev 2016; 40:258–272 [View Article] [PubMed]
    [Google Scholar]
  25. Hilterbrand A, Saelens J, Putonti C. CBDB: the codon bias database. BMC Bioinformatics 2012; 13:62 [View Article] [PubMed]
    [Google Scholar]
  26. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 2016; 44:D733–745 [View Article] [PubMed]
    [Google Scholar]
  27. Sharp PM, Tuohy TM, Mosurski KR. Codon usage in yeast: cluster analysis clearly differentiates highly and lowly expressed genes. Nucleic Acids Res 1986; 14:5125–5143 [View Article] [PubMed]
    [Google Scholar]
  28. Carbone A, Zinovyev A, Képès F. Codon adaptation index as a measure of dominating codon bias. Bioinformatics 2003; 19:2005–2015 [View Article] [PubMed]
    [Google Scholar]
  29. Lee S, Weon S, Lee S, Kang C. Relative codon adaptation index, a sensitive measure of codon usage bias. Evol Bioinform Online 2010; 6:47–55 [View Article] [PubMed]
    [Google Scholar]
  30. dos Reis M, Wernisch L, Savva R. Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome. Nucleic Acids Res 2003; 31:6976–6985 [View Article] [PubMed]
    [Google Scholar]
  31. Alexaki A, Kames J, Holcomb DD, Athey J, Santana-Quintero LV. Codon and Codon-Pair Usage Tables (CoCoPUTs): facilitating genetic variation analyses and recombinant gene desigFacilitating Genetic Variation Analyses and Recombinant Gene Design. J Mol Biol 2019; 431:2434–2441 [View Article] [PubMed]
    [Google Scholar]
  32. Puigbò P, Bravo IG, Garcia-Vallve S. CAIcal: a combined set of tools to assess codon usage adaptation. Biol Direct 2008; 3:38 [View Article] [PubMed]
    [Google Scholar]
  33. Puigbò P, Romeu A, Garcia-Vallvé S. HEG-DB: a database of predicted highly expressed genes in prokaryotic complete genomes under translational selection. Nucleic Acids Res 2008; 36:D524–527 [View Article] [PubMed]
    [Google Scholar]
  34. Bourret J, Alizon S, Bravo IG. COUSIN (COdon Usage Similarity INdex): A Normalized Measure of Codon Usage Preferences. Genome Biol Evol 2019; 11:3523–3528 [View Article] [PubMed]
    [Google Scholar]
  35. Athey J, Alexaki A, Osipova E, Rostovtsev A, Santana-Quintero LV. A new and updated resource for codon usage tables. BMC Bioinformatics 2017; 18:391 [View Article] [PubMed]
    [Google Scholar]
  36. Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010; 11:119 [View Article] [PubMed]
    [Google Scholar]
  37. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods 2015; 12:59–60 [View Article] [PubMed]
    [Google Scholar]
  38. Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 2009; 25:1422–1423 [View Article] [PubMed]
    [Google Scholar]
  39. UniProt Consortium UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 2019; 47:D506–D515 [View Article] [PubMed]
    [Google Scholar]
  40. Rocha J, Botelho J, Ksiezarek M, Perovic SU, Machado M. Lactobacillus mulieris sp. nov., a new species of Lactobacillus delbrueckii group. Int J Syst Evol Microbiol 2020; 70:1522–1527 [View Article] [PubMed]
    [Google Scholar]
  41. Putonti C, Shapiro JW, Ene A, Tsibere O, Wolfe AJ. Comparative genomic study of Lactobacillus jensenii and the newly defined Lactobacillus mulieris species identifies species-specific functionality. mSphere 2020; 5:e00560-20 [View Article] [PubMed]
    [Google Scholar]
  42. Salvetti E, Harris HMB, Felis GE, O’Toole PW. Comparative genomics of the genus Lactobacillus reveals robust phylogroups that provide the basis for reclassification. Appl Environ Microbiol 2018; 84:e00993-18 [View Article] [PubMed]
    [Google Scholar]
  43. Roller M, Lucić V, Nagy I, Perica T, Vlahovicek K. Environmental shaping of codon usage and functional adaptation across microbial communities. Nucleic Acids Res 2013; 41:8842–8852 [View Article] [PubMed]
    [Google Scholar]
  44. Malki K, Kula A, Bruder K, Sible E, Hatzopoulos T. Bacteriophages isolated from Lake Michigan demonstrate broad host-range across several bacterial phyla. Virol J 2015; 12:164 [View Article] [PubMed]
    [Google Scholar]
  45. Jack BR, Boutz DR, Paff ML, Smith BL, Bull JJ. Reduced protein expression in a virus attenuated by codon deoptimization. G3 (Bethesda) 2017; 7:2957–2968 [View Article] [PubMed]
    [Google Scholar]
  46. Kula A, Saelens J, Cox J, Schubert AM, Travisano M. The evolution of molecular compatibility between bacteriophage ΦX174 and its host. Sci Rep 2018; 8:8350 [View Article] [PubMed]
    [Google Scholar]
  47. Ranjan A, Vidyarthi AS, Poddar R. Evaluation of codon bias perspectives in phage therapy of Mycobacterium tuberculosis by multivariate analysis. In Silico Biol 2007; 7:423–431 [PubMed]
    [Google Scholar]
  48. Chen Y, Batra H, Dong J, Chen C, Rao VB. Genetic engineering of bacteriophages against infectious diseases. Front Microbiol 2019; 10:954 [View Article] [PubMed]
    [Google Scholar]
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