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Visualizing Codon Usage Within and Across Genomes: Concepts and Tools

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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Cost and time of genome sequencing have plummeted over the last decade. This leads to explosive growth of genetic databases and development of novel sequencing-based approaches to study various biological phenomena. The database growth was particularly beneficial for investigation of protein-coding sequences at the codon level, requiring the access to large sets of related genomes. Such studies are expected to illuminate biological forces that shape primary structure of coding sequences and predict their evolutionary trajectories more precisely. In addition to fundamental interest, codon usage studies are of ample practical value, for example, in drug discovery and genomic medicine areas. Nevertheless, the depth of our understanding of codon-related issues is currently shallower as compared to what we know about nucleotide and amino acid sequences. Besides the lack of adequate datasets in the early days of molecular biology, codon usage studies, in our opinion, suffer from underdevelopment of easy-to-use tools to analyze and visualize how codon sequence changes along the gene and across the homologous genes in course of evolution. In this review, we aim to describe main areas of codon usage studies with an emphasis on the tools that allow visual interpretation of the data. We discuss underlying principles of different approaches, what kind of statistics lends confidence in their results and what has to be done to further boost the field of codon usage research.

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References

  1. Cannarozzi GM, Schneider A (eds) (2012) Codon evolution. Mechanisms and models. Oxford University Press, New York, 297 p. ISBN 978–0–19–960116–5

    Google Scholar 

  2. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z, Dewell SB, Du L, Fierro JM, Gomes XV, Godwin BC, He W, Helgesen S, Ho CH, Irzyk GP, Jando SC, Alenquer ML, Jarvie TP, Jirage KB, Kim JB, Knight JR, Lanza JR, Leamon JH, Lefkowitz SM, Lei M, Li J, Lohman KL, Lu H, Makhijani VB, McDade KE, McKenna MP, Myers EW, Nickerson E, Nobile JR, Plant R, Puc BP, Ronan MT, Roth GT, Sarkis GJ, Simons JF, Simpson JW, Srinivasan M, Tartaro KR, Tomasz A, Vogt KA, Volkmer GA, Wang SH, Wang Y, Weiner MP, Yu P, Begley RF, Rothberg JM (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437(7057):376–380

    Article  Google Scholar 

  3. Rothberg JM, Leamon JH (2008) The development and impact of 454 sequencing. Nat Biotechnol 26(10):1117–1124. https://doi.org/10.1038/nbt1485

    Article  Google Scholar 

  4. Slatko BE, Gardner AF, Ausubel FM (2018) Overview of next-generation sequencing technologies. Curr Protoc Mol Biol 122(1):e59. https://doi.org/10.1002/cpmb.59

    Article  Google Scholar 

  5. O’Donoghue SI, Baldi BF, Clark SJ, Darling AE, Hogan JM, Kaur S, Maier-Hein L, McCarthy DJ, Moore WJ, Stenau E, Swedlow JR, Vuong J, Procter JB (2018) Visualization of biomedical data. Annu Rev Biomed Data Sci 1:275–304. https://doi.org/10.1146/annurev-biodatasci-080917-013424

    Article  Google Scholar 

  6. Liu X, Zhang J, Ni F, Dong X, Han B, Han D, Ji Z, Zhao Y (2010) Genome wide exploration of the origin and evolution of amino acids. BMC Evol Biol 15(10):77. https://doi.org/10.1186/1471-2148-10-77

    Article  Google Scholar 

  7. Jordan IK, Kondrashov FA, Adzhubei IA, Wolf YI, Koonin EV, Kondrashov AS, Sunyaev S (2005) A universal trend of amino acid gain and loss in protein evolution. Nature 433(7026):633–638

    Article  Google Scholar 

  8. Fimmel E, Strüngmann L (2018) Mathematical fundamentals for the noise immunity of the genetic code. Biosystems 164:186–198. https://doi.org/10.1016/j.biosystems.2017.09.007

    Article  Google Scholar 

  9. Keeling PJ (2016) Genomics: evolution of the genetic code. Curr Biol 26(18):R851–R853. https://doi.org/10.1016/j.cub.2016.08.005

    Article  Google Scholar 

  10. Koonin EV, Novozhilov AS (2017) Origin and evolution of the universal genetic code. Annu Rev Genet 27(51):45–62. https://doi.org/10.1146/annurev-genet-120116-024713

    Article  Google Scholar 

  11. Heaphy SM, Mariotti M, Gladyshev VN, Atkins JF, Baranov PV (2016) Novel ciliate genetic code variants including the reassignment of all three stop codons to sense codons in Condylostoma magnum. Mol Biol Evol 33(11):2885–2889

    Article  Google Scholar 

  12. Mühlhausen S, Schmitt HD, Pan KT, Plessmann U, Urlaub H, Hurst LD, Kollmar M (2018) Endogenous stochastic decoding of the CUG codon by competing Ser- and Leu-tRNAs in Ascoidea asiatica. Curr Biol 28(13):2046–2057.e5. https://doi.org/10.1016/j.cub.2018.04.085

    Article  Google Scholar 

  13. Miranda I, Rocha R, Santos MC, Mateus DD, Moura GR, Carreto L, Santos MA (2007) A genetic code alteration is a phenotype diversity generator in the human pathogen Candida albicans. PLoS ONE 2(10):e996

    Article  Google Scholar 

  14. Väre VY, Eruysal ER, Narendran A, Sarachan KL, Agris PF (201) Chemical and conformational diversity of modified nucleosides affects tRNA structure and function. Biomolecules 7(1):pii: E29. https://doi.org/10.3390/biom7010029

    Article  Google Scholar 

  15. Agris PF, Narendran A, Sarachan K, Väre VYP, Eruysal E (2017) The importance of being modified: the role of RNA modifications in translational fidelity. Enzymes 41:1–50. https://doi.org/10.1016/bs.enz.2017.03.005

    Article  Google Scholar 

  16. Schweizer U, Bohleber S, Fradejas-Villar N (2017) The modified base isopentenyladenosine and its derivatives in tRNA. RNA Biol 14(9):1197–1208. https://doi.org/10.1080/15476286.2017.1294309

    Article  Google Scholar 

  17. Hori H (2017) Transfer RNA methyltransferases with a SpoU-TrmD (SPOUT) fold and their modified nucleosides in tRNA. Biomolecules 7(1):pii: E23. https://doi.org/10.3390/biom7010023

    Article  MathSciNet  Google Scholar 

  18. Hou YM, Masuda I, Gamper H (2019) Codon-Specific Translation by m(1)G37 Methylation of tRNA. Front Genet 10(9):713. https://doi.org/10.3389/fgene.2018.00713

    Article  Google Scholar 

  19. Pan T (2018) Modifications and functional genomics of human transfer RNA. Cell Res 28(4):395–404. https://doi.org/10.1038/s41422-018-0013-y

    Article  Google Scholar 

  20. Schimmel P (2018) The emerging complexity of the tRNA world: mammalian tRNAs beyond protein synthesis. Nat Rev Mol Cell Biol 19(1):45–58. https://doi.org/10.1038/nrm.2017.77

    Article  Google Scholar 

  21. Silva RM, Paredes JA, Moura GR, Manadas B, Lima-Costa T, Rocha R, Miranda I, Gomes AC, Koerkamp MJ, Perrot M, Holstege FC, Boucherie H, Santos MA (2007) Critical roles for a genetic code alteration in the evolution of the genus Candida. EMBO J 26(21):4555–4565

    Article  Google Scholar 

  22. Zhang Z, Yu J (2011) On the organizational dynamics of the genetic code. Genomics Proteomics Bioinformatics. 9(1–2):21–29. https://doi.org/10.1016/S1672-0229(11)60004-1

    Article  Google Scholar 

  23. Rosandić M, Paar V (2014) Codon sextets with leading role of serine create “ideal” symmetry classification scheme of the genetic code. Gene 543(1):45–52. https://doi.org/10.1016/j.gene.2014.04.009

    Article  Google Scholar 

  24. José MV, Zamudio GS, Morgado ER (2017) A unified model of the standard genetic code. R Soc Open Sci 4(3):160908. https://doi.org/10.1098/rsos.160908

    Article  MathSciNet  Google Scholar 

  25. Acevedo-Rocha CG, Budisa N (2016) Xenomicrobiology: a roadmap for genetic code engineering. Microb Biotechnol 9(5):666–676. https://doi.org/10.1111/1751-7915.12398

    Article  Google Scholar 

  26. van der Gulik PT, Hoff WD (2016) Anticodon modifications in the tRNA set of LUCA and the fundamental regularity in the standard genetic code. PLoS ONE 11(7):e0158342. https://doi.org/10.1371/journal.pone.0158342

    Article  Google Scholar 

  27. Grosjean H, Westhof E (2016) An integrated, structure- and energy-based view of the genetic code. Nucleic Acids Res 44(17):8020–8040. https://doi.org/10.1093/nar/gkw608

    Article  Google Scholar 

  28. Subramaniam AR, Pan T, Cluzel P (2013) Environmental perturbations lift the degeneracy of the genetic code to regulate protein levels in bacteria. Proc Natl Acad Sci U S A 110(6):2419–2424. https://doi.org/10.1073/pnas.1211077110

    Article  Google Scholar 

  29. Moukadiri I, Garzón MJ, Björk GR, Armengod ME (2014) The output of the tRNA modification pathways controlled by the Escherichia coli MnmEG and MnmC enzymes depends on the growth conditions and the tRNA species. Nucleic Acids Res 42(4):2602–2623. https://doi.org/10.1093/nar/gkt1228

    Article  Google Scholar 

  30. Asano K, Suzuki T, Saito A, Wei FY, Ikeuchi Y, Numata T, Tanaka R, Yamane Y, Yamamoto T, Goto T, Kishita Y, Murayama K, Ohtake A, Okazaki Y, Tomizawa K, Sakaguchi Y, Suzuki T (2018) Metabolic and chemical regulation of tRNA modification associated with taurine deficiency and human disease. Nucleic Acids Res 46(4):1565–1583. https://doi.org/10.1093/nar/gky068

    Article  Google Scholar 

  31. Kirchner S, Ignatova Z (2015) Emerging roles of tRNA in adaptive translation, signalling dynamics and disease. Nat Rev Genet 16(2):98–112. https://doi.org/10.1038/nrg3861

    Article  Google Scholar 

  32. Rogers SO (2019) Evolution of the genetic code based on conservative changes of codons, amino acids, and aminoacyl tRNA synthetases. J Theor Biol 7(466):1–10. https://doi.org/10.1016/j.jtbi.2019.01.022

    Article  MATH  Google Scholar 

  33. Itzkovitz S, Alon U (2007) The genetic code is nearly optimal for allowing additional information within protein-coding sequences. Genome Res 17(4):405–412

    Article  Google Scholar 

  34. Itzkovitz S, Hodis E, Segal E (2010) Overlapping codes within protein-coding sequences. Genome Res 20(11):1582–1589. https://doi.org/10.1101/gr.105072.110

    Article  Google Scholar 

  35. Bollenbach T, Vetsigian K, Kishony R (2007) Evolution and multilevel optimization of the genetic code. Genome Res 17(4):401–404

    Article  Google Scholar 

  36. Wnętrzak M, Błażej P, Mackiewicz D, Mackiewicz P (2018) The optimality of the standard genetic code assessed by an eight-objective evolutionary algorithm. BMC Evol Biol 18(1):192. https://doi.org/10.1186/s12862-018-1304-0

    Article  MATH  Google Scholar 

  37. Błażej P, Wnętrzak M, Mackiewicz D, Gagat P, Mackiewicz P (2019) Many alternative and theoretical genetic codes are more robust to amino acid replacements than the standard genetic code. J Theor Biol 7(464):21–32. https://doi.org/10.1016/j.jtbi.2018.12.030

    Article  MATH  Google Scholar 

  38. Kuruoglu EE, Arndt PF (2017) The information capacity of the genetic code: is the natural code optimal? J Theor Biol 21(419):227–237. https://doi.org/10.1016/j.jtbi.2017.01.046

    Article  Google Scholar 

  39. Agarwal D, Gregory ST, O’Connor M (2011) Error-prone and error-restrictive mutations affecting ribosomal protein S12. J Mol Biol 410(1):1–9. https://doi.org/10.1016/j.jmb.2011.04.068

    Article  Google Scholar 

  40. Robinson LJ, Cameron AD, Stavrinides J (2015) Spontaneous and on point: do spontaneous mutations used for laboratory experiments cause pleiotropic effects that might confound bacterial infection and evolution assays? FEMS Microbiol Lett 362(21):pii: fnv177. https://doi.org/10.1093/femsle/fnv177

    Article  Google Scholar 

  41. An W, Chin JW (2011) Orthogonal gene expression in Escherichia coli. Methods Enzymol 497:115–134. https://doi.org/10.1016/B978-0-12-385075-1.00005-6

    Article  Google Scholar 

  42. Liu CC, Jewett MC, Chin JW, Voigt CA (2018) Toward an orthogonal central dogma. Nat Chem Biol 14(2):103–106. https://doi.org/10.1038/nchembio.2554

    Article  Google Scholar 

  43. Ishikawa J, Hotta K (1999) FramePlot: a new implementation of the frame analysis for predicting protein-coding regions in bacterial DNA with a high G+C content. FEMS Microbiol Lett 174(2):251–253

    Article  Google Scholar 

  44. Fickett JW, Tung CS (1992) Assessment of protein coding measures. Nucleic Acids Res 20(24):6441–6450

    Article  Google Scholar 

  45. Azad RK, Borodovsky M (2004) Probabilistic methods of identifying genes in prokaryotic genomes: connections to the HMM theory. Brief Bioinform 5(2):118–130

    Article  Google Scholar 

  46. Pride DT, Meinersmann RJ, Wassenaar TM, Blaser MJ (2003) Evolutionary implications of microbial genome tetranucleotide frequency biases. Genome Res 13(2):145–158

    Article  Google Scholar 

  47. Teeling H, Waldmann J, Lombardot T, Bauer M, Glöckner FO (2004) TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences. BMC Bioinform 26(5):163

    Article  Google Scholar 

  48. Richter M, Rosselló-Móra R, Oliver Glöckner F, Peplies J (2016) JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison. Bioinformatics 32(6):929–931. https://doi.org/10.1093/bioinformatics/btv681

    Article  Google Scholar 

  49. Wang Y, Zeng Z, Liu TL, Sun L, Yao Q, Chen KP (2019) TA, GT and AC are significantly under-represented in open reading frames of prokaryotic and eukaryotic protein-coding genes. Mol Genet Genomics. https://doi.org/10.1007/s00438-019-01535-1

    Article  Google Scholar 

  50. Akogwu I, Wang N, Zhang C, Gong P (2016) A comparative study of k-spectrum-based error correction methods for next-generation sequencing data analysis. Hum Genomics 10(Suppl 2):20. https://doi.org/10.1186/s40246-016-0068-0

    Article  Google Scholar 

  51. Mapleson D, Garcia Accinelli G, Kettleborough G, Wright J, Clavijo BJ (2017) KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies. Bioinformatics 33(4):574–576. https://doi.org/10.1093/bioinformatics/btw663

    Article  Google Scholar 

  52. Sheppard SK, Guttman DS, Fitzgerald JR (2018) Population genomics of bacterial host adaptation. Nat Rev Genet 19(9):549–565. https://doi.org/10.1038/s41576-018-0032-z

    Article  Google Scholar 

  53. Camiolo S, Porceddu A (2018) corseq: fast and efficient identification of favoured codons from next generation sequencing reads. PeerJ. 4(6):e5099. https://doi.org/10.7717/peerj.5099

    Article  Google Scholar 

  54. Lees JA, Vehkala M, Välimäki N, Harris SR, Chewapreecha C, Croucher NJ, Marttinen P, Davies MR, Steer AC, Tong SY, Honkela A, Parkhill J, Bentley SD, Corander J (2016) Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes. Nat Commun 16(7):12797. https://doi.org/10.1038/ncomms12797

    Article  Google Scholar 

  55. Mohamadi H, Khan H, Birol I (2017) ntCard: a streaming algorithm for cardinality estimation in genomics data. Bioinformatics 33(9):1324–1330. https://doi.org/10.1093/bioinformatics/btw832

    Article  Google Scholar 

  56. Manekar SC, Sathe SR (2018) A benchmark study of k-mer counting methods for high-throughput sequencing. Gigascience 7(12). https://doi.org/10.1093/gigascience/giy125

  57. Fuglsang A (2004) Nucleotides downstream of start codons show marked non-randomness in Escherichia coli but not in Bacillus subtilis. Antonie Van Leeuwenhoek 86(2):149–158

    Article  Google Scholar 

  58. Rokytskyy I, Kulaha S, Mutenko H, Rabyk M, Ostash B (2017) Peculiarities of codon context and substitution within streptomycete genomes. Visn Lviv Univ Ser Biol 75:66–74. https://doi.org/10.30970/vlubs.2017.75.07

  59. Knight RD, Freeland SJ, Landweber LF (2001) A simple model based on mutation and selection explains trends in codon and amino-acid usage and GC composition within and across genomes. Genome Biol 2(4):RESEARCH0010

    Article  Google Scholar 

  60. Ikemura T (1985) Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol 2(1):13–34

    Google Scholar 

  61. Higgs PG, Ran W (2008) Coevolution of codon usage and tRNA genes leads to alternative stable states of biased codon usage. Mol Biol Evol 25(11):2279–2291. https://doi.org/10.1093/molbev/msn173

    Article  Google Scholar 

  62. Kanaya S, Yamada Y, Kinouchi M, Kudo Y, Ikemura T (2001) Codon usage and tRNA genes in eukaryotes: correlation of codon usage diversity with translation efficiency and with CG-dinucleotide usage as assessed by multivariate analysis. J Mol Evol 53(4–5):290–298

    Article  Google Scholar 

  63. Yu C-H, Dang Y, Zhou Z et al (2015) Codon usage influences the local rate of translation elongation to regulate co-translational protein folding. Mol Cell 59:744–754

    Article  Google Scholar 

  64. Quax TE, Claassens NJ, Söll D, van der Oost J (2015) Codon bias as a means to fine-tune gene expression. Mol Cell 59(2):149–161. https://doi.org/10.1016/j.molcel.2015.05.035

    Article  Google Scholar 

  65. Ikemura T (1981) Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes: a proposal for a synonymous codon choice that is optimal for the E. coli translational system. J Mol Biol 151(3):389–409

    Article  Google Scholar 

  66. dos Reis M, Savva R, Wernisch L (2004) Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res 32(17):5036–5044

    Article  Google Scholar 

  67. Hershberg R, Petrov DA (2008) Selection on codon bias. Annu Rev Genet 42:287–299. https://doi.org/10.1146/annurev.genet.42.110807.091442

    Article  Google Scholar 

  68. Gribskov M, Devereux J, Burgess RR (1984) The codon preference plot: graphic analysis of protein coding sequences and prediction of gene expression. Nucleic Acids Res 12(1 Pt 2):539–549

    Article  Google Scholar 

  69. Garcia-Vallve S, Guzman E, Montero MA, Romeu A (2003) HGT-DB: a database of putative horizontally transferred genes in prokaryotic complete genomes. Nucleic Acids Res 31(1):187–189

    Article  Google Scholar 

  70. Puigbò P, Romeu A, Garcia-Vallvé S (2008) HEG-DB: a database of predicted highly expressed genes in prokaryotic complete genomes under translational selection. Nucleic Acids Res 36(Database issue):D524–D527

    Article  Google Scholar 

  71. Paulet D, David A, Rivals E (2017) Ribo-seq enlightens codon usage bias. DNA Res 24(3):303–2100. https://doi.org/10.1093/dnares/dsw062

    Article  Google Scholar 

  72. Sharp PM, Li WH (1987) The codon Adaptation Index—a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15(3):1281–1295

    Article  Google Scholar 

  73. Dittmar KA, Sørensen MA, Elf J, Ehrenberg M, Pan T (2005) Selective charging of tRNA isoacceptors induced by amino-acid starvation. EMBO Rep 6(2):151–157

    Article  Google Scholar 

  74. Welch M, Govindarajan S, Ness JE, Villalobos A, Gurney A, Minshull J, Gustafsson C (2009) Design parameters to control synthetic gene expression in Escherichia coli. PLoS ONE 4(9):e7002. https://doi.org/10.1371/journal.pone.0007002

    Article  Google Scholar 

  75. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, Bewtra M, Knights D, Walters WA, Knight R, Sinha R, Gilroy E, Gupta K, Baldassano R, Nessel L, Li H, Bushman FD, Lewis JD (2011) Linking long-term dietary patterns with gut microbial enterotypes. Science 334(6052):105–108. https://doi.org/10.1126/science.1208344

    Article  Google Scholar 

  76. Koropatkin NM, Cameron EA, Martens EC (2012) How glycan metabolism shapes the human gut microbiota. Nat Rev Microbiol 10(5):323–335. https://doi.org/10.1038/nrmicro2746

    Article  Google Scholar 

  77. Hodgson DA (2000) Primary metabolism and its control in streptomycetes: a most unusual group of bacteria. Adv Microb Physiol 42:47–238

    Article  Google Scholar 

  78. Ho A, Di Lonardo DP, Bodelier PL (2017) Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol Ecol 93(3). https://doi.org/10.1093/femsec/fix006

  79. Nakao A, Yoshihama M, Kenmochi N (2004) RPG: the Ribosomal Protein Gene database. Nucleic Acids Res 32(Database issue):D168–D170

    Article  Google Scholar 

  80. Carbone A, Zinovyev A, Képès F (2003) Codon adaptation index as a measure of dominating codon bias. Bioinformatics 19(16):2005–2015

    Article  Google Scholar 

  81. Raiford DW, Doom TE, Krane DE, Raymer ME (2011) A genetic optimization approach for isolating translational efficiency bias. IEEE/ACM Trans Comput Biol Bioinf 8(2):342–352

    Article  Google Scholar 

  82. Xia X (2015) A major controversy in codon-anticodon adaptation resolved by a new codon usage index. Genetics 199(2):573–579. https://doi.org/10.1534/genetics.114.172106

    Article  Google Scholar 

  83. Kudla G, Murray AW, Tollervey D, Plotkin JB (2009) Coding-sequence determinants of gene expression in Escherichia coli. Science 324(5924):255–258. https://doi.org/10.1126/science.1170160

    Article  Google Scholar 

  84. Garcia V, Zoller S, Anisimova M (2018) Accounting for programmed ribosomal frameshifting in the computation of codon usage bias indices. G3 (Bethesda) 8(10):3173–3183. https://doi.org/10.1534/g3.118.200185

    Article  Google Scholar 

  85. Wei Y, Silke JR, Xia X (2019) An improved estimation of tRNA expression to better elucidate the coevolution between tRNA abundance and codon usage in bacteria. Sci Rep 9(1):3184. https://doi.org/10.1038/s41598-019-39369-x

    Article  Google Scholar 

  86. Pechmann S, Frydman J (2013) Evolutionary conservation of codon optimality reveals hidden signatures of cotranslational folding. Nat Struct Mol Biol 20(2):237–243. https://doi.org/10.1038/nsmb.2466

    Article  Google Scholar 

  87. Sabi R, Tuller T (2014) Modelling the efficiency of codon-tRNA interactions based on codon usage bias. DNA Res 21(5):511–526. https://doi.org/10.1093/dnares/dsu017

    Article  Google Scholar 

  88. Roymondal U, Das S, Sahoo S (2009) Predicting gene expression level from relative codon usage bias: an application to Escherichia coli genome. DNA Res 16(1):13–30. https://doi.org/10.1093/dnares/dsn029

    Article  Google Scholar 

  89. Wright F (1990) The ‘effective number of codons’ used in a gene. Gene 87(1):23–29

    Article  MathSciNet  Google Scholar 

  90. Fuglsang A (2004) The ‘effective number of codons’ revisited. Biochem Biophys Res Commun 317(3):957–964

    Article  Google Scholar 

  91. Novembre JA (2002) Accounting for background nucleotide composition when measuring codon usage bias. Mol Biol Evol 19(8):1390–1394

    Article  Google Scholar 

  92. Liu X (2013) A more accurate relationship between ‘effective number of codons’ and GC3s under assumptions of no selection. Comput Biol Chem 42:35–39. https://doi.org/10.1016/j.compbiolchem.2012.11.003

    Article  Google Scholar 

  93. Sun X, Yang Q, Xia X (2013) An improved implementation of effective number of codons (Nc). Mol Biol Evol 30(1):191–196. https://doi.org/10.1093/molbev/mss201

    Article  Google Scholar 

  94. Liu SS, Hockenberry AJ, Jewett MC, Amaral LAN (2018) A novel framework for evaluating the performance of codon usage bias metrics. J R Soc Interface 15(138):pii: 20170667. https://doi.org/10.1098/rsif.2017.0667

    Article  Google Scholar 

  95. Zhang Z, Li J, Cui P, Ding F, Li A, Townsend JP, Yu J (2012) Codon Deviation Coefficient: a novel measure for estimating codon usage bias and its statistical significance. BMC Bioinform 22(13):43. https://doi.org/10.1186/1471-2105-13-43

    Article  Google Scholar 

  96. Gilchrist MA, Shah P, Zaretzki R (2009) Measuring and detecting molecular adaptation in codon usage against nonsense errors during protein translation. Genetics 183(4):1493–1505. https://doi.org/10.1534/genetics.109.108209

    Article  Google Scholar 

  97. Chou T (2003) Ribosome recycling, diffusion, and mRNA loop formation in translational regulation. Biophys J 85(2):755–773

    Article  Google Scholar 

  98. Mitarai N, Sneppen K, Pedersen S (2008) Ribosome collisions and translation efficiency: optimization by codon usage and mRNA destabilization. J Mol Biol 382(1):236–245. https://doi.org/10.1016/j.jmb.2008.06.068

    Article  Google Scholar 

  99. Gilchrist MA, Chen WC, Shah P, Landerer CL, Zaretzki R (2015) Estimating gene expression and codon-specific translational efficiencies, mutation biases, and selection coefficients from genomic data alone. Genome Biol Evol 7(6):1559–1579. https://doi.org/10.1093/gbe/evv087

    Article  Google Scholar 

  100. Proshkin S, Rahmouni AR, Mironov A, Nudler E (2010) Cooperation between translating ribosomes and RNA polymerase in transcription elongation. Science 328(5977):504–508. https://doi.org/10.1126/science.1184939

    Article  Google Scholar 

  101. Boël G, Letso R, Neely H, Price WN, Wong KH, Su M, Luff J, Valecha M, Everett JK, Acton TB, Xiao R, Montelione GT, Aalberts DP, Hunt JF (2016) Codon influence on protein expression in E. coli correlates with mRNA levels. Nature 529(7586):358–363. https://doi.org/10.1038/nature16509

    Article  Google Scholar 

  102. Bellaousov S, Reuter JS, Seetin MG, Mathews DH (2013) RNAstructure: web servers for RNA secondary structure prediction and analysis. Nucleic Acids Res 41(Web Server issue):W471–W474. https://doi.org/10.1093/nar/gkt290

    Article  Google Scholar 

  103. Bentele K, Saffert P, Rauscher R, Ignatova Z, Blüthgen N (2013) Efficient translation initiation dictates codon usage at gene start. Mol Syst Biol 18(9):675. https://doi.org/10.1038/msb.2013.32

    Article  Google Scholar 

  104. Kelsic ED, Chung H, Cohen N, Park J, Wang HH, Kishony R (2016) RNA structural determinants of optimal codons revealed by MAGE-Seq. Cell Syst. 3(6):563–571.e6. https://doi.org/10.1016/j.cels.2016.11.004

    Article  Google Scholar 

  105. Frumkin I, Schirman D, Rotman A, Li F, Zahavi L, Mordret E, Asraf O, Wu S, Levy SF, Pilpel Y (2017) Gene architectures that minimize cost of gene expression. Mol Cell 65(1):142–153. https://doi.org/10.1016/j.molcel.2016.11.007

    Article  Google Scholar 

  106. Hanson G, Alhusaini N, Morris N, Sweet T, Coller J (2018) Translation elongation and mRNA stability are coupled through the ribosomal A-site. RNA 24(10):1377–1389. https://doi.org/10.1261/rna.066787.118

    Article  Google Scholar 

  107. Arango D, Sturgill D, Alhusaini N, Dillman AA, Sweet TJ, Hanson G, Hosogane M, Sinclair WR, Nanan KK, Mandler MD, Fox SD, Zengeya TT, Andresson T, Meier JL, Coller J, Oberdoerffer S (2018) Acetylation of cytidine in mRNA promotes translation efficiency. Cell 175(7):1872–1886.e24. https://doi.org/10.1016/j.cell.2018.10.030

    Article  Google Scholar 

  108. Schikora-Tamarit MÀ, Carey LB (2018) Poor codon optimality as a signal to degrade transcripts with frameshifts. Transcription 9(5):327–333. https://doi.org/10.1080/21541264.2018.1511676

    Article  Google Scholar 

  109. Lykke-Andersen S, Jensen TH (2015) Nonsense-mediated mRNA decay: an intricate machinery that shapes transcriptomes. Nat Rev Mol Cell Biol 16(11):665–677. https://doi.org/10.1038/nrm4063

    Article  Google Scholar 

  110. Radhakrishnan A, Chen YH, Martin S, Alhusaini N, Green R, Coller J (2016) The DEAD-box protein Dhh1p couples mRNA decay and translation by monitoring codon optimality. Cell 167(1):122–132.e9. https://doi.org/10.1016/j.cell.2016.08.053

    Article  Google Scholar 

  111. Presnyak V, Alhusaini N, Chen YH, Martin S, Morris N, Kline N, Olson S, Weinberg D, Baker KE, Graveley BR, Coller J (2015) Codon optimality is a major determinant of mRNA stability. Cell 160(6):1111–1124. https://doi.org/10.1016/j.cell.2015.02.029

    Article  Google Scholar 

  112. Carneiro RL, Requião RD, Rossetto S, Domitrovic T, Palhano FL (2019) Codon stabilization coefficient as a metric to gain insights into mRNA stability and codon bias and their relationships with translation. Nucleic Acids Res 47(5):2216–2228. https://doi.org/10.1093/nar/gkz033

    Article  Google Scholar 

  113. Kumar S, Stecher G, Li M, Knyaz C, Tamura K (2018) MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 35(6):1547–1549. https://doi.org/10.1093/molbev/msy096

    Article  Google Scholar 

  114. Xia X (2018) DAMBE7: new and improved tools for data analysis in molecular biology and evolution. Mol Biol Evol 35(6):1550–1552. https://doi.org/10.1093/molbev/msy073

    Article  Google Scholar 

  115. Supek F, Vlahovicek K (2004) INCA: synonymous codon usage analysis and clustering by means of self-organizing map. Bioinformatics 20(14):2329–2330

    Article  Google Scholar 

  116. Peden JF (2005) CodonW, p. 1. https://sourceforge.net/projects/codonw/. Last accessed Apr 2019

  117. Vetrivel U, Arunkumar V, Dorairaj S (2007) ACUA: a software tool for automated codon usage analysis. Bioinformation 2(2):62–63

    Article  Google Scholar 

  118. Angellotti MC, Bhuiyan SB, Chen G, Wan XF (2007) CodonO: codon usage bias analysis within and across genomes. Nucleic Acids Res 35(Web Server issue):W132–W136

    Article  Google Scholar 

  119. Miller JB, Brase LR, Ridge PG (2019) ExtRamp: a novel algorithm for extracting the ramp sequence based on the tRNA adaptation index or relative codon adaptiveness. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1193

    Article  Google Scholar 

  120. Tuller T, Carmi A, Vestsigian K, Navon S, Dorfan Y, Zaborske J, Pan T, Dahan O, Furman I, Pilpel Y (2010) An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell 141(2):344–354. https://doi.org/10.1016/j.cell.2010.03.031

    Article  Google Scholar 

  121. Wu G, Culley DE, Zhang W (2005) Predicted highly expressed genes in the genomes of Streptomyces coelicolor and Streptomyces avermitilis and the implications for their metabolism. Microbiology 151(Pt 7):2175–2187

    Article  Google Scholar 

  122. Grote A, Hiller K, Scheer M, Münch R, Nörtemann B, Hempel DC, Jahn D (2005) JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res 33(Web Server issue):W526–W531

    Article  Google Scholar 

  123. Puigbò P, Bravo IG, Garcia-Vallve S (2008) CAIcal: a combined set of tools to assess codon usage adaptation. Biol Direct 16(3):38. https://doi.org/10.1186/1745-6150-3-38

    Article  Google Scholar 

  124. McInerney JO (1998) GCUA: general codon usage analysis. Bioinformatics 14(4):372–373

    Article  Google Scholar 

  125. Sabi R, Volvovitch Daniel R, Tuller T (2017) stAIcalc: tRNA adaptation index calculator based on species-specific weights. Bioinformatics 33(4):589–591. https://doi.org/10.1093/bioinformatics/btw647

    Article  Google Scholar 

  126. Athey J, Alexaki A, Osipova E, Rostovtsev A, Santana-Quintero LV, Katneni U, Simonyan V, Kimchi-Sarfaty C (2017) A new and updated resource for codon usage tables. BMC Bioinform 18(1):391. https://doi.org/10.1186/s12859-017-1793-7

    Article  Google Scholar 

  127. Yoon J, Chung YJ, Lee M (2018) STADIUM: species-specific tRNA adaptive index compendium. Genomics Inform 16(4):e28. https://doi.org/10.5808/GI.2018.16.4.e28

    Article  Google Scholar 

  128. Plotkin JB, Kudla G (2011) Synonymous but not the same: the causes and consequences of codon bias. Nat Rev Genet 12(1):32–42. https://doi.org/10.1038/nrg2899

    Article  Google Scholar 

  129. Ghaemmaghami S, Huh WK, Bower K, Howson RW, Belle A, Dephoure N, O’Shea EK, Weissman JS (2003) Global analysis of protein expression in yeast. Nature 425(6959):737–741

    Article  Google Scholar 

  130. Ishihama Y, Schmidt T, Rappsilber J, Mann M, Hartl FU, Kerner MJ, Frishman D (2008) Protein abundance profiling of the Escherichia coli cytosol. BMC Genom 27(9):102. https://doi.org/10.1186/1471-2164-9-102

    Article  Google Scholar 

  131. Liu Y, Beyer A, Aebersold R (2016) On the dependency of cellular protein levels on mRNA abundance. Cell 165(3):535–550. https://doi.org/10.1016/j.cell.2016.03.014

    Article  Google Scholar 

  132. Hanson G, Coller J (2018) Codon optimality, bias and usage in translation and mRNA decay. Nat Rev Mol Cell Biol 19(1):20–30. https://doi.org/10.1038/nrm.2017.91

    Article  Google Scholar 

  133. Frumkin I, Lajoie MJ, Gregg CJ, Hornung G, Church GM, Pilpel Y (2018) Codon usage of highly expressed genes affects proteome-wide translation efficiency. Proc Natl Acad Sci U S A. 115(21):E4940–E4949. https://doi.org/10.1073/pnas.1719375115

    Article  Google Scholar 

  134. Puigbò P, Guzmán E, Romeu A, Garcia-Vallvé S (2007) OPTIMIZER: a web server for optimizing the codon usage of DNA sequences. Nucleic Acids Res 35(Web Server issue):W126–W131

    Article  Google Scholar 

  135. Hatfield GW, Roth DA (2007) Optimizing scaleup yield for protein production: computationally optimized DNA assembly (CODA) and translation engineering. Biotechnol Annu Rev 13:27–42

    Article  Google Scholar 

  136. Cheng BYH, Nogales A, de la Torre JC, Martínez-Sobrido L (2017) Development of live-attenuated arenavirus vaccines based on codon deoptimization of the viral glycoprotein. Virology 15(501):35–46. https://doi.org/10.1016/j.virol.2016.11.001

    Article  Google Scholar 

  137. Jia W, Higgs PG (2008) Codon usage in mitochondrial genomes: distinguishing context-dependent mutation from translational selection. Mol Biol Evol 25(2):339–351

    Article  Google Scholar 

  138. Aalberts DP, Boël G, Hunt JF (2017) Codon clarity or conundrum? Cell Syst. 4(1):16–19. https://doi.org/10.1016/j.cels.2017.01.004

    Article  Google Scholar 

  139. Webster GR, Teh AY, Ma JK (2017) Synthetic gene design-The rationale for codon optimization and implications for molecular pharming in plants. Biotechnol Bioeng 114(3):492–502. https://doi.org/10.1002/bit.26183

    Article  Google Scholar 

  140. Mauro VP, Chappell SA (2018) Considerations in the use of codon optimization for recombinant protein expression. Methods Mol Biol 1850:275–288. https://doi.org/10.1007/978-1-4939-8730-6_18

    Article  Google Scholar 

  141. Mauro VP (2018) Codon optimization in the production of recombinant biotherapeutics: potential risks and considerations. BioDrugs 32(1):69–81. https://doi.org/10.1007/s40259-018-0261-x

    Article  Google Scholar 

  142. Mandad S, Rahman RU, Centeno TP, Vidal RO, Wildhagen H, Rammner B, Keihani S, Opazo F, Urban I, Ischebeck T, Kirli K, Benito E, Fischer A, Yousefi RY, Dennerlein S, Rehling P, Feussner I, Urlaub H, Bonn S, Rizzoli SO, Fornasiero EF (2018) The codon sequences predict protein lifetimes and other parameters of the protein life cycle in the mouse brain. Sci Rep 8(1):16913. https://doi.org/10.1038/s41598-018-35277-8

    Article  Google Scholar 

  143. Liu Y, Mi Y, Mueller T, Kreibich S, Williams EG, Van Drogen A, Borel C, Frank M, Germain PL, Bludau I, Mehnert M, Seifert M, Emmenlauer M, Sorg I, Bezrukov F, Bena FS, Zhou H, Dehio C, Testa G, Saez-Rodriguez J, Antonarakis SE, Hardt WD, Aebersold R (2019) Multi-omic measurements of heterogeneity in HeLa cells across laboratories. Nat Biotechnol 37(3):314–322. https://doi.org/10.1038/s41587-019-0037-y

    Article  Google Scholar 

  144. Xu Y, Ma P, Shah P, Rokas A, Liu Y, Johnson CH (2013) Non-optimal codon usage is a mechanism to achieve circadian clock conditionality. Nature 495(7439):116–120. https://doi.org/10.1038/nature11942

    Article  Google Scholar 

  145. Yourno J, Tanemura S (1970) Restoration of in-phase translation by an unlinked suppressor of a frameshift mutation in Salmonella typhimurium. Nature 225(5231):422–426

    Article  Google Scholar 

  146. Bossi L, Roth JR (1980) The influence of codon context on genetic code translation. Nature 286(5769):123–127

    Article  Google Scholar 

  147. Giliberti J, O’Donnell S, Etten WJ, Janssen GR (2012) A 5′-terminal phosphate is required for stable ternary complex formation and translation of leaderless mRNA in Escherichia coli. RNA 18(3):508–518. https://doi.org/10.1261/rna.027698.111

    Article  Google Scholar 

  148. Akulich KA, Andreev DE, Terenin IM, Smirnova VV, Anisimova AS, Makeeva DS, Arkhipova VI, Stolboushkina EA, Garber MB, Prokofjeva MM, Spirin PV, Prassolov VS, Shatsky IN, Dmitriev SE (2016) Four translation initiation pathways employed by the leaderless mRNA in eukaryotes. Sci Rep 28(6):37905. https://doi.org/10.1038/srep37905

    Article  Google Scholar 

  149. Brar GA (2016) Beyond the triplet code: context cues transform translation. Cell 167(7):1681–1692. https://doi.org/10.1016/j.cell.2016.09.022

    Article  Google Scholar 

  150. Li GW, Oh E, Weissman JS (2012) The anti-Shine-Dalgarno sequence drives translational pausing and codon choice in bacteria. Nature 484(7395):538–541. https://doi.org/10.1038/nature10965

    Article  Google Scholar 

  151. Yurovsky A, Amin MR, Gardin J, Chen Y, Skiena S, Futcher B (2018) Prokaryotic coding regions have little if any specific depletion of Shine-Dalgarno motifs. PLoS ONE 13(8):e0202768. https://doi.org/10.1371/journal.pone.0202768

    Article  Google Scholar 

  152. Mohammad F, Woolstenhulme CJ, Green R, Buskirk AR (2016) Clarifying the translational pausing landscape in bacteria by ribosome profiling. Cell Rep 14(4):686–694. https://doi.org/10.1016/j.celrep.2015.12.073

    Article  Google Scholar 

  153. Mohammad F, Green R, Buskirk AR (2019) A systematically-revised ribosome profiling method for bacteria reveals pauses at single-codon resolution. Elife 8:pii: e42591. https://doi.org/10.7554/elife.42591

  154. Ogle JM, Ramakrishnan V (2005) Structural insights into translational fidelity. Annu Rev Biochem 74:129–177

    Article  Google Scholar 

  155. Gamper HB, Masuda I, Frenkel-Morgenstern M, Hou YM (2015) The UGG isoacceptor of tRNAPro is naturally prone to frameshifts. Int J Mol Sci 16(7):14866–14883. https://doi.org/10.3390/ijms160714866

    Article  Google Scholar 

  156. Gamper HB, Masuda I, Frenkel-Morgenstern M, Hou YM (2015) Maintenance of protein synthesis reading frame by EF-P and m(1)G37-tRNA. Nat Commun 26(6):7226. https://doi.org/10.1038/ncomms8226

    Article  Google Scholar 

  157. Gutman GA, Hatfield GW (1989) Nonrandom utilization of codon pairs in Escherichia coli. Proc Natl Acad Sci U S A. 86(10):3699–3703

    Article  Google Scholar 

  158. Fedorov A, Saxonov S, Gilbert W (2002) Regularities of context-dependent codon bias in eukaryotic genes. Nucleic Acids Res 30(5):1192–1197

    Article  Google Scholar 

  159. Ciandrini L, Stansfield I, Romano MC (2013) Ribosome traffic on mRNAs maps to gene ontology: genome-wide quantification of translation initiation rates and polysome size regulation. PLoS Comput Biol 9(1):e1002866. https://doi.org/10.1371/journal.pcbi.1002866

    Article  Google Scholar 

  160. Letzring DP, Wolf AS, Brule CE, Grayhack EJ (2013) Translation of CGA codon repeats in yeast involves quality control components and ribosomal protein L1. RNA 19(9):1208–1217. https://doi.org/10.1261/rna.039446.113

    Article  Google Scholar 

  161. Chevance FF, Le Guyon S, Hughes KT (2014) The effects of codon context on in vivo translation speed. PLoS Genet 10(6):e1004392. https://doi.org/10.1371/journal.pgen.1004392

    Article  Google Scholar 

  162. Coleman JR, Papamichail D, Skiena S, Futcher B, Wimmer E, Mueller S (2008) Virus attenuation by genome-scale changes in codon pair bias. Science 320(5884):1784–1787. https://doi.org/10.1126/science.1155761

    Article  Google Scholar 

  163. Tulloch F, Atkinson NJ, Evans DJ, Ryan MD, Simmonds P (2014) RNA virus attenuation by codon pair deoptimisation is an artefact of increases in CpG/UpA dinucleotide frequencies. Elife 9(3):e04531. https://doi.org/10.7554/eLife 04531

    Article  Google Scholar 

  164. Peil L, Starosta AL, Lassak J, Atkinson GC, Virumäe K, Spitzer M, Tenson T, Jung K, Remme J, Wilson DN (2013) Distinct XPPX sequence motifs induce ribosome stalling, which is rescued by the translation elongation factor EF-P. Proc Natl Acad Sci U S A. 110(38):15265–15270. https://doi.org/10.1073/pnas.1310642110

    Article  Google Scholar 

  165. Starosta AL, Lassak J, Peil L, Atkinson GC, Virumäe K, Tenson T, Remme J, Jung K, Wilson DN (2014) Translational stalling at polyproline stretches is modulated by the sequence context upstream of the stall site. Nucleic Acids Res 42(16):10711–10719. https://doi.org/10.1093/nar/gku768

    Article  Google Scholar 

  166. Gamble CE, Brule CE, Dean KM, Fields S, Grayhack EJ (2016) Adjacent codons act in concert to modulate translation efficiency in yeast. Cell 166(3):679–690. https://doi.org/10.1016/j.cell.2016.05.070

    Article  Google Scholar 

  167. McCarthy C, Carrea A, Diambra L (2017) Bicodon bias can determine the role of synonymous SNPs in human diseases. BMC Genom 18(1):227. https://doi.org/10.1186/s12864-017-3609-6

    Article  Google Scholar 

  168. Chevance FFV, Hughes KT (2017) Case for the genetic code as a triplet of triplets. Proc Natl Acad Sci U S A. 114(18):4745–4750. https://doi.org/10.1073/pnas.1614896114

    Article  Google Scholar 

  169. Ghoneim DH, Zhang X, Brule CE, Mathews DH, Grayhack EJ (2018) Conservation of location of several specific inhibitory codon pairs in the Saccharomyces sensu stricto yeasts reveals translational selection. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1262

    Article  Google Scholar 

  170. Komar AA, Lesnik T, Reiss C (1999) Synonymous codon substitutions affect ribosome traffic and protein folding during in vitro translation. FEBS Lett 462(3):387–391

    Article  Google Scholar 

  171. Zhang G, Hubalewska M, Ignatova Z (2009) Transient ribosomal attenuation coordinates protein synthesis and co-translational folding. Nat Struct Mol Biol 16(3):274–280. https://doi.org/10.1038/nsmb.1554

    Article  Google Scholar 

  172. Buhr F, Jha S, Thommen M, Mittelstaet J, Kutz F, Schwalbe H, Rodnina MV, Komar AA (2016) Synonymous codons direct cotranslational folding toward different protein conformations. Mol Cell 61(3):341–351. https://doi.org/10.1016/j.molcel.2016.01.008

    Article  Google Scholar 

  173. Pechmann S, Chartron JW, Frydman J (2014) Local slowdown of translation by nonoptimal codons promotes nascent-chain recognition by SRP in vivo. Nat Struct Mol Biol 21(12):1100–1105. https://doi.org/10.1038/nsmb.2919

    Article  Google Scholar 

  174. Lee Y, Zhou T, Tartaglia GG, Vendruscolo M, Wilke CO (2010) Translationally optimal codons associate with aggregation-prone sites in proteins. Proteomics 10(23):4163–4171. https://doi.org/10.1002/pmic.201000229

    Article  Google Scholar 

  175. Cannarozzi G, Schraudolph NN, Faty M, von Rohr P, Friberg MT, Roth AC, Gonnet P, Gonnet G, Barral Y (2010) A role for codon order in translation dynamics. Cell 141(2):355–367. https://doi.org/10.1016/j.cell.2010.02.036

    Article  Google Scholar 

  176. Carrier MJ, Buckingham RH (1984) An effect of codon context on the mistranslation of UGU codons in vitro. J Mol Biol 175(1):29–38

    Article  Google Scholar 

  177. Buckingham RH (1994) Codon context and protein synthesis: enhancements of the genetic code. Biochimie 76(5):351–354

    Article  Google Scholar 

  178. Baranov PV, Atkins JF, Yordanova MM (2015) Augmented genetic decoding: global, local and temporal alterations of decoding processes and codon meaning. Nat Rev Genet 16(9):517–529. https://doi.org/10.1038/nrg3963

    Article  Google Scholar 

  179. Skuzeski JM, Nichols LM, Gesteland RF, Atkins JF (1991) The signal for a leaky UAG stop codon in several plant viruses includes the two downstream codons. J Mol Biol 218(2):365–373

    Article  Google Scholar 

  180. Chan CS, Jungreis I, Kellis M (2013) Heterologous stop codon readthrough of metazoan readthrough candidates in yeast. PLoS ONE 8(3):e59450. https://doi.org/10.1371/journal.pone.0059450

    Article  Google Scholar 

  181. Loughran G, Jungreis I, Tzani I, Power M, Dmitriev RI, Ivanov IP, Kellis M, Atkins JF (2018) Stop codon readthrough generates a C-terminally extended variant of the human vitamin D receptor with reduced calcitriol response. J Biol Chem 293(12):4434–4444. https://doi.org/10.1074/jbc.M117.818526

    Article  Google Scholar 

  182. Jungreis I, Lin MF, Spokony R, Chan CS, Negre N, Victorsen A, White KP, Kellis M (2011) Evidence of abundant stop codon readthrough in Drosophila and other metazoa. Genome Res 21(12):2096–2113. https://doi.org/10.1101/gr.119974.110

    Article  Google Scholar 

  183. Jungreis I, Chan CS, Waterhouse RM, Fields G, Lin MF, Kellis M (2016) Evolutionary dynamics of abundant stop codon readthrough. Mol Biol Evol 33(12):3108–3132

    Article  Google Scholar 

  184. Rajput B, Pruitt KD, Murphy TD (2019) RefSeq curation and annotation of stop codon recoding in vertebrates. Nucleic Acids Res 47(2):594–606. https://doi.org/10.1093/nar/gky1234

    Article  Google Scholar 

  185. Swart EC, Serra V, Petroni G, Nowacki M (2016) Genetic codes with no dedicated stop codon: context-dependent translation termination. Cell 166(3):691–702. https://doi.org/10.1016/j.cell.2016.06.020

    Article  Google Scholar 

  186. Belew AT, Dinman JD (2015) Cell cycle control (and more) by programmed −1 ribosomal frameshifting: implications for disease and therapeutics. Cell Cycle 14(2):172–178. https://doi.org/10.4161/15384101.2014.989123

    Article  Google Scholar 

  187. Belew AT, Hepler NL, Jacobs JL, Dinman JD (2008) PRFdb: a database of computationally predicted eukaryotic programmed −1 ribosomal frameshift signals. BMC Genom 17(9):339. https://doi.org/10.1186/1471-2164-9-339

    Article  Google Scholar 

  188. Pinheiro M, Afreixo V, Moura G, Freitas A, Santos MA, Oliveira JL (2006) Statistical, computational and visualization methodologies to unveil gene primary structure features. Methods Inf Med 45(2):163–168

    Article  Google Scholar 

  189. Moura G, Pinheiro M, Silva R, Miranda I, Afreixo V, Dias G, Freitas A, Oliveira JL, Santos MA (2005) Comparative context analysis of codon pairs on an ORFeome scale. Genome Biol 6(3):R28

    Article  Google Scholar 

  190. Moura G, Pinheiro M, Arrais J, Gomes AC, Carreto L, Freitas A, Oliveira JL, Santos MA (2007) Large scale comparative codon-pair context analysis unveils general rules that fine-tune evolution of mRNA primary structure. PLoS ONE 2(9):e847

    Article  Google Scholar 

  191. Tats A, Tenson T, Remm M (2008) Preferred and avoided codon pairs in three domains of life. BMC Genom 8(9):463. https://doi.org/10.1186/1471-2164-9-463

    Article  Google Scholar 

  192. Doyle F, Leonardi A, Endres L, Tenenbaum SA, Dedon PC, Begley TJ (2016) Gene- and genome-based analysis of significant codon patterns in yeast, rat and mice genomes with the CUT Codon UTilization tool. Methods 1(107):98–109. https://doi.org/10.1016/j.ymeth.2016.05.010

    Article  Google Scholar 

  193. Alexaki A, Kames J, Holcomb DD, Athey J, Santana-Quintero LV, Lam PVN, Hamasaki-Katagiri N, Osipova E, Simonyan V, Bar H, Komar AA, Kimchi-Sarfaty C (2019) Codon and codon-pair usage tables (CoCoPUTs): facilitating genetic variation analyses and recombinant gene design. J Mol Biol pii: S0022-2836(19)30228-1. https://doi.org/10.1016/j.jmb.2019.04.021

    Article  Google Scholar 

  194. Kucukyildirim S, Long H, Sung W, Miller SF, Doak TG, Lynch M (2016) The rate and spectrum of spontaneous mutations in Mycobacterium smegmatis, a bacterium naturally devoid of the postreplicative mismatch repair pathway. G3 (Bethesda) 6(7):2157–2163. https://doi.org/10.1534/g3.116.030130

    Article  Google Scholar 

  195. Aslam S, Lan XR, Zhang BW, Chen ZL, Wang L, Niu DK (2019) Aerobic prokaryotes do not have higher GC contents than anaerobic prokaryotes, but obligate aerobic prokaryotes have. BMC Evol Biol 19(1):35. https://doi.org/10.1186/s12862-019-1365-8

    Article  Google Scholar 

  196. Hershberg R, Petrov DA (2010) Evidence that mutation is universally biased towards AT in bacteria. PLoS Genet 6(9):e1001115. https://doi.org/10.1371/journal.pgen.1001115

    Article  Google Scholar 

  197. Lassalle F, Périan S, Bataillon T, Nesme X, Duret L, Daubin V (2015) GC-Content evolution in bacterial genomes: the biased gene conversion hypothesis expands. PLoS Genet 11(2):e1004941. https://doi.org/10.1371/journal.pgen.1004941

    Article  Google Scholar 

  198. Hildebrand F, Meyer A, Eyre-Walker A (2010) Evidence of selection upon genomic GC-content in bacteria. PLoS Genet 6(9):e1001107. https://doi.org/10.1371/journal.pgen.1001107

    Article  Google Scholar 

  199. Bobay LM, Ochman H (2017) Impact of recombination on the base composition of bacteria and archaea. Mol Biol Evol 34(10):2627–2636. https://doi.org/10.1093/molbev/msx189

    Article  Google Scholar 

  200. Trotta E (2016) Selective forces and mutational biases drive stop codon usage in the human genome: a comparison with sense codon usage. BMC Genom 17(17):366. https://doi.org/10.1186/s12864-016-2692-4

    Article  Google Scholar 

  201. Wilke CO, Drummond DA (2006) Population genetics of translational robustness. Genetics 173(1):473–481

    Article  Google Scholar 

  202. Zhou T, Weems M, Wilke CO (2009) Translationally optimal codons associate with structurally sensitive sites in proteins. Mol Biol Evol 26(7):1571–1580. https://doi.org/10.1093/molbev/msp070

    Article  Google Scholar 

  203. Yu CH, Dang Y, Zhou Z, Wu C, Zhao F, Sachs MS, Liu Y (2015) Codon usage influences the local rate of translation elongation to regulate co-translational protein folding. Mol Cell 59(5):744–754. https://doi.org/10.1016/j.molcel.2015.07.018

    Article  Google Scholar 

  204. Yan X, Hoek TA, Vale RD, Tanenbaum ME (2016) Dynamics of translation of single mRNA molecules in vivo. Cell 165(4):976–989. https://doi.org/10.1016/j.cell.2016.04.034

    Article  Google Scholar 

  205. Zhao F, Yu CH, Liu Y (2017) Codon usage regulates protein structure and function by affecting translation elongation speed in Drosophila cells. Nucleic Acids Res 45(14):8484–8492. https://doi.org/10.1093/nar/gkx501

    Article  Google Scholar 

  206. Li GW, Burkhardt D, Gross C, Weissman JS (2014) Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157(3):624–635. https://doi.org/10.1016/j.cell.2014.02.033

    Article  Google Scholar 

  207. Shah P, Gilchrist MA (2010) Effect of correlated tRNA abundances on translation errors and evolution of codon usage bias. PLoS Genet 6(9):e1001128. https://doi.org/10.1371/journal.pgen.1001128

    Article  Google Scholar 

  208. Chamary JV, Parmley JL, Hurst LD (2006) Hearing silence: non-neutral evolution at synonymous sites in mammals. Nat Rev Genet 7(2):98–108

    Article  Google Scholar 

  209. Sauna ZE, Kimchi-Sarfaty C (2011) Understanding the contribution of synonymous mutations to human disease. Nat Rev Genet 12(10):683–691. https://doi.org/10.1038/nrg3051

    Article  Google Scholar 

  210. Kirchner S, Cai Z, Rauscher R, Kastelic N, Anding M, Czech A, Kleizen B, Ostedgaard LS, Braakman I, Sheppard DN, Ignatova Z (2017) Alteration of protein function by a silent polymorphism linked to tRNA abundance. PLoS Biol 15(5):e2000779. https://doi.org/10.1371/journal.pbio.2000779

    Article  Google Scholar 

  211. Zhou Z, Dang Y, Zhou M, Li L, Yu CH, Fu J, Chen S, Liu Y (2016) Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc Natl Acad Sci U S A 113(41):E6117–E6125

    Article  Google Scholar 

  212. Mittal P, Brindle J, Stephen J, Plotkin JB, Kudla G (2018) Codon usage influences fitness through RNA toxicity. Proc Natl Acad Sci U S A 115(34):8639–8644. https://doi.org/10.1073/pnas.1810022115

    Article  Google Scholar 

  213. Weinberg DE, Shah P, Eichhorn SW, Hussmann JA, Plotkin JB, Bartel DP (2016) Improved ribosome-footprint and mRNA measurements provide insights into dynamics and regulation of yeast translation. Cell Rep 14(7):1787–1799. https://doi.org/10.1016/j.celrep.2016.01.043

    Article  Google Scholar 

  214. Chu D, Kazana E, Bellanger N, Singh T, Tuite MF, von der Haar T (2014) Translation elongation can control translation initiation on eukaryotic mRNAs. EMBO J 33(1):21–34. https://doi.org/10.1002/embj.201385651

    Article  Google Scholar 

  215. Chan LY, Mugler CF, Heinrich S, Vallotton P, Weis K (2018) Non-invasive measurement of mRNA decay reveals translation initiation as the major determinant of mRNA stability. Elife 7:pii: e32536. https://doi.org/10.7554/elife.32536

  216. Eraslan B, Wang D, Gusic M, Prokisch H, Hallström BM, Uhlén M, Asplund A, Pontén F, Wieland T, Hopf T, Hahne H, Kuster B, Gagneur J (2019) Quantification and discovery of sequence determinants of protein-per-mRNA amount in 29 human tissues. Mol Syst Biol 15(2):e8513. https://doi.org/10.15252/msb.20188513

    Article  Google Scholar 

  217. Zhou M, Guo J, Cha J, Chae M, Chen S, Barral JM, Sachs MS, Liu Y (2013) Non-optimal codon usage affects expression, structure and function of clock protein FRQ. Nature 495(7439):111–115. https://doi.org/10.1038/nature11833

    Article  Google Scholar 

  218. Chan C, Pham P, Dedon PC, Begley TJ (2018) Lifestyle modifications: coordinating the tRNA epitranscriptome with codon bias to adapt translation during stress responses. Genome Biol 19(1):228. https://doi.org/10.1186/s13059-018-1611-1

    Article  Google Scholar 

  219. Novoa EM, Pavon-Eternod M, Pan T, de Pouplana LR (2012) A role for tRNA modifications in genome structure and codon usage. Cell 149(1):202–213. https://doi.org/10.1016/j.cell.2012.01.050

    Article  Google Scholar 

  220. Fuglsang A (2005) Intragenic position of UUA codons in streptomycetes. Microbiology 151(Pt 10):3150–3152

    Article  Google Scholar 

  221. Zaburannyy N, Ostash B, Fedorenko V (2009) TTA Lynx: a web-based service for analysis of actinomycete genes containing rare TTA codon. Bioinformatics 25(18):2432–2433. https://doi.org/10.1093/bioinformatics/btp402

    Article  Google Scholar 

  222. Jee J, Rasouly A, Shamovsky I, Akivis Y, Steinman SR, Mishra B, Nudler E (2016) Rates and mechanisms of bacterial mutagenesis from maximum-depth sequencing. Nature 534(7609):693–696

    Article  Google Scholar 

  223. Kosiol C, Goldman N (2011) Markovian and non-Markovian protein sequence evolution: aggregated Markov process models. J Mol Biol 411(4):910–923. https://doi.org/10.1016/j.jmb.2011.06.005

    Article  Google Scholar 

  224. Anisimova M, Kosiol C (2009) Investigating protein-coding sequence evolution with probabilistic codon substitution models. Mol Biol Evol 26(2):255–271. https://doi.org/10.1093/molbev/msn232

    Article  Google Scholar 

  225. Schneider A, Cannarozzi GM, Gonnet GH (2005) Empirical codon substitution matrix. BMC Bioinform 1(6):134

    Article  Google Scholar 

  226. Beaumont MA, Rannala B (2004) The Bayesian revolution in genetics. Nat Rev Genet 5(4):251–261

    Article  Google Scholar 

  227. Eddy SR (2004) What is Bayesian statistics? Nat Biotechnol 22(9):1177–1178

    Article  Google Scholar 

  228. Do CB, Batzoglou S (2008) What is the expectation maximization algorithm? Nat Biotechnol 26(8):897–899. https://doi.org/10.1038/nbt1406

    Article  Google Scholar 

  229. Anisimova M, Bielawski JP, Yang Z (2001) Accuracy and power of the likelihood ratio test in detecting adaptive molecular evolution. Mol Biol Evol 18(8):1585–1592

    Article  Google Scholar 

  230. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723. https://doi.org/10.1109/TAC.1974.1100705

    Article  MathSciNet  MATH  Google Scholar 

  231. Davydov II, Salamin N, Robinson-Rechavi M (2019) Large-scale comparative analysis of codon models accounting for protein and nucleotide selection. Mol Biol Evol pii: msz048. https://doi.org/10.1093/molbev/msz048

    Article  Google Scholar 

  232. Arenas M (2015) Trends in substitution models of molecular evolution. Front Genet 26(6):319. https://doi.org/10.3389/fgene.2015.00319

    Article  Google Scholar 

  233. Yang Z (2006) Computational molecular evolution. Oxford University Press, Oxford, 324 p. ISBN 978–0–19–856699–1

    Google Scholar 

  234. Venkat A, Hahn MW, Thornton JW (2018) Multinucleotide mutations cause false inferences of lineage-specific positive selection. Nat Ecol Evol 2(8):1280–1288. https://doi.org/10.1038/s41559-018-0584-5

    Article  Google Scholar 

  235. Liu X, Liu H, Guo W, Yu K (2012) Codon substitution models based on residue similarity and their applications. Gene 509(1):136–141. https://doi.org/10.1016/j.gene.2012.07.075

    Article  Google Scholar 

  236. Delport W, Scheffler K, Botha G, Gravenor MB, Muse SV, Kosakovsky Pond SL (2010) CodonTest: modeling amino acid substitution preferences in coding sequences. PLoS Comput Biol 6(8):pii: e1000885. https://doi.org/10.1371/journal.pcbi.1000885

    Article  MathSciNet  Google Scholar 

  237. Huttley GA (2004) Modeling the impact of DNA methylation on the evolution of BRCA1 in mammals. Mol Biol Evol 21(9):1760–1768

    Article  Google Scholar 

  238. Mayrose I, Doron-Faigenboim A, Bacharach E, Pupko T (2007) Towards realistic codon models: among site variability and dependency of synonymous and non-synonymous rates. Bioinformatics 23(13):i319–i327

    Article  Google Scholar 

  239. Higgs PG, Hao W, Golding GB (2007) Identification of conflicting selective effects on highly expressed genes. Evol Bioinform Online 14(3):1–13

    Google Scholar 

  240. Kubatko L, Shah P, Herbei R, Gilchrist MA (2016) A codon model of nucleotide substitution with selection on synonymous codon usage. Mol Phylogenet Evol 94(Pt A):290–297. https://doi.org/10.1016/j.ympev.2015.08.026

    Article  Google Scholar 

  241. Beaulieu JM, O’Meara BC, Zaretzki R, Landerer C, Chai J, Gilchrist MA (2019) Population genetics based phylogenetics under stabilizing selection for an optimal amino acid sequence: a nested modeling approach. Mol Biol Evol 36(4):834–851. https://doi.org/10.1093/molbev/msy222

    Article  Google Scholar 

  242. Higgs PG (2008) Linking population genetics to phylogenetics. Banach Center Publ 80(1):145–166

    Article  MathSciNet  MATH  Google Scholar 

  243. Pouyet F, Bailly-Bechet M, Mouchiroud D, Guéguen L (2016) SENCA: a multilayered codon model to study the origins and dynamics of codon usage. Genome Biol Evol 8(8):2427–2441. https://doi.org/10.1093/gbe/evw165

    Article  Google Scholar 

  244. Rodrigue N, Lartillot N (2017) Detecting adaptation in protein-coding genes using a bayesian site-heterogeneous mutation-selection codon substitution model. Mol Biol Evol 34(1):204–214. https://doi.org/10.1093/molbev/msw220

    Article  Google Scholar 

  245. Teufel AI, Ritchie AM, Wilke CO, Liberles DA (2018) Using the mutation-selection framework to characterize selection on protein sequences. Genes (Basel) 9(8):pii: E409. https://doi.org/10.3390/genes9080409

    Article  Google Scholar 

  246. Dunn KA, Kenney T, Gu H, Bielawski JP (2019) Improved inference of site-specific positive selection under a generalized parametric codon model when there are multinucleotide mutations and multiple nonsynonymous rates. BMC Evol Biol 19(1):22. https://doi.org/10.1186/s12862-018-1326-7

    Article  Google Scholar 

  247. Dayhoff MO, Schwartz RM, Orcutt BC (1978) A model of evolutionary change in proteins. In: Atlas of protein sequence and structure, vol 5, pp 345–352

    Google Scholar 

  248. Gonnet GH, Cohen MA, Benner SA (1992) Exhaustive matching of the entire protein sequence database. Science 256(5062):1443–1445

    Article  Google Scholar 

  249. De Maio N, Holmes I, Schlötterer C, Kosiol C (2013) Estimating empirical codon hidden Markov models. Mol Biol Evol 30(3):725–736. https://doi.org/10.1093/molbev/mss266

    Article  Google Scholar 

  250. Zoller S, Schneider A (2010) Empirical analysis of the most relevant parameters of codon substitution models. J Mol Evol 70(6):605–612. https://doi.org/10.1007/s00239-010-9356-9

    Article  Google Scholar 

  251. Kosiol C, Holmes I, Goldman N (2007) An empirical codon model for protein sequence evolution. Mol Biol Evol 24(7):1464–1479

    Article  Google Scholar 

  252. Doron-Faigenboim A, Pupko T (2007) A combined empirical and mechanistic codon model. Mol Biol Evol 24(2):388–397

    Article  Google Scholar 

  253. Zoller S, Schneider A (2012) A new semiempirical codon substitution model based on principal component analysis of mammalian sequences. Mol Biol Evol 29(1):271–277. https://doi.org/10.1093/molbev/msr198

    Article  Google Scholar 

  254. Hoban S, Bertorelle G, Gaggiotti OE (2012) Computer simulations: tools for population and evolutionary genetics. Nat Rev Genet 13(2):110–122. https://doi.org/10.1038/nrg3130

    Article  Google Scholar 

  255. Arenas M (2013) Computer programs and methodologies for the simulation of DNA sequence data with recombination. Front Genet 1(4):9. https://doi.org/10.3389/fgene.2013.00009

    Article  Google Scholar 

  256. Anisimova M, Nielsen R, Yang Z (2003) Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites. Genetics 164(3):1229–1236

    Google Scholar 

  257. Dalquen DA, Anisimova M, Gonnet GH, Dessimoz C (2012) ALF—a simulation framework for genome evolution. Mol Biol Evol 29(4):1115–1123. https://doi.org/10.1093/molbev/msr268

    Article  Google Scholar 

  258. Arenas M, Posada D (2014) Simulation of genome-wide evolution under heterogeneous substitution models and complex multispecies coalescent histories. Mol Biol Evol 31(5):1295–1301. https://doi.org/10.1093/molbev/msu078

    Article  Google Scholar 

  259. Mallo D, De Oliveira Martins L, Posada D (2016) SimPhy: phylogenomic simulation of gene, locus, and species trees. Syst Biol 65(2):334–344. https://doi.org/10.1093/sysbio/syv082

    Article  Google Scholar 

  260. Haller BC, Messer PW (2019) SLiM 3: forward genetic simulations beyond the Wright-Fisher model. Mol Biol Evol 36(3):632–637. https://doi.org/10.1093/molbev/msy228

    Article  Google Scholar 

  261. Klosterman PS, Uzilov AV, Bendaña YR, Bradley RK, Chao S, Kosiol C, Goldman N, Holmes I (2006) XRate: a fast prototyping, training and annotation tool for phylo-grammars. BMC Bioinform 3(7):428

    Article  Google Scholar 

  262. Barquist L, Holmes I (2008) xREI: a phylo-grammar visualization webserver. Nucleic Acids Res 36(Web Server issue):W65–W69. https://doi.org/10.1093/nar/gkn283

    Article  Google Scholar 

  263. Wernersson R, Pedersen AG (2003) RevTrans: multiple alignment of coding DNA from aligned amino acid sequences. Nucleic Acids Res 31(13):3537–3539

    Article  Google Scholar 

  264. Ranwez V, Douzery EJP, Cambon C, Chantret N, Delsuc F (2018) MACSE v2: toolkit for the alignment of coding sequences accounting for frameshifts and stop codons. Mol Biol Evol 35(10):2582–2584. https://doi.org/10.1093/molbev/msy159

    Article  Google Scholar 

  265. Noens EE, Mersinias V, Traag BA, Smith CP, Koerten HK, van Wezel GP (2005) SsgA-like proteins determine the fate of peptidoglycan during sporulation of Streptomyces coelicolor. Mol Microbiol 58(4):929–944

    Article  Google Scholar 

  266. Rabyk M, Yushchuk O, Rokytskyy I, Anisimova M, Ostash B (2018) Genomic insights into evolution of AdpA family master regulators of morphological differentiation and secondary metabolism in Streptomyces. J Mol Evol 86(3–4):204–215. https://doi.org/10.1007/s00239-018-9834-z

    Article  Google Scholar 

  267. Wang M, Kapralov MV, Anisimova M (2011) Coevolution of amino acid residues in the key photosynthetic enzyme Rubisco. BMC Evol Biol 23(11):266. https://doi.org/10.1186/1471-2148-11-266

    Article  Google Scholar 

  268. Kapralov MV, Filatov DA (2007) Widespread positive selection in the photosynthetic Rubisco enzyme. BMC Evol Biol 11(7):73

    Article  Google Scholar 

  269. Elena SF, Lenski RE (2003) Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat Rev Genet 4(6):457–469

    Article  Google Scholar 

  270. Charlesworth B (2013) Stabilizing selection, purifying selection, and mutational bias in finite populations. Genetics 194(4):955–971. https://doi.org/10.1534/genetics.113.151555

    Article  Google Scholar 

  271. Kimura M (1991) Recent development of the neutral theory viewed from the Wrightian tradition of theoretical population genetics. Proc Natl Acad Sci U S A 88(14):5969–5973

    Article  Google Scholar 

  272. Jensen JD, Payseur BA, Stephan W, Aquadro CF, Lynch M, Charlesworth D, Charlesworth B (2019) The importance of the Neutral Theory in 1968 and 50 years on: a response to Kern and Hahn 2018. Evolution 73(1):111–114. https://doi.org/10.1111/evo.13650

    Article  Google Scholar 

  273. Kimura M (1981) Possibility of extensive neutral evolution under stabilizing selection with special reference to nonrandom usage of synonymous codons. Proc Natl Acad Sci U S A 78(9):5773–5777

    Article  MATH  Google Scholar 

  274. Fuller ZL, Haynes GD, Zhu D, Batterton M, Chao H, Dugan S, Javaid M, Jayaseelan JC, Lee S, Li M, Ongeri F, Qi S, Han Y, Doddapaneni H, Richards S, Schaeffer SW (2014) Evidence for stabilizing selection on codon usage in chromosomal rearrangements of Drosophila pseudoobscura. G3 (Bethesda) 4(12):2433–2449. https://doi.org/10.1534/g3.114.014860

    Article  Google Scholar 

  275. Jackson BC, Campos JL, Haddrill PR, Charlesworth B, Zeng K (2017) Variation in the intensity of selection on codon bias over time causes contrasting patterns of base composition evolution in Drosophila. Genome Biol Evol 9(1):102–123. https://doi.org/10.1093/gbe/evw291

    Article  Google Scholar 

  276. Plotkin JB, Dushoff J, Fraser HB (2004) Detecting selection using a single genome sequence of M. tuberculosis and P. falciparum. Nature 428(6986):942–945

    Article  Google Scholar 

  277. Plotkin JB, Dushoff J, Desai MM, Fraser HB (2006) Codon usage and selection on proteins. J Mol Evol 63(5):635–653

    Article  Google Scholar 

  278. Zhang J (2005) On the evolution of codon volatility. Genetics 169(1):495–501

    Article  Google Scholar 

  279. Dagan T, Graur D (2005) The comparative method rules! Codon volatility cannot detect positive Darwinian selection using a single genome sequence. Mol Biol Evol 22(3):496–500

    Article  Google Scholar 

  280. O’Connell MJ, Doyle AM, Juenger TE, Donoghue MT, Keshavaiah C, Tuteja R, Spillane C (2012) In Arabidopsis thaliana codon volatility scores reflect GC3 composition rather than selective pressure. BMC Res Notes 17(5):359. https://doi.org/10.1186/1756-0500-5-359

    Article  Google Scholar 

  281. Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123(3):585–595

    Google Scholar 

  282. McDonald JH, Kreitman M (1991) Adaptive protein evolution at the Adh locus in Drosophila. Nature 351(6328):652–654

    Article  Google Scholar 

  283. Zhai W, Slatkin M, Nielsen R (2007) Exploring variation in the d(N)/d(S) ratio among sites and lineages using mutational mappings: applications to the influenza virus. J Mol Evol 65(3):340–348

    Article  Google Scholar 

  284. Gelman A, Meng X-L, Stern H (1996) Posterior predictive assessment of model fitness via realized discrepancies. Stat Sin 6:733–807

    MathSciNet  MATH  Google Scholar 

  285. Kosakovsky Pond SL, Frost SD (2005) Not so different after all: a comparison of methods for detecting amino acid sites under selection. Mol Biol Evol 22(5):1208–1222

    Article  Google Scholar 

  286. Lemey P, Minin VN, Bielejec F, Kosakovsky Pond SL, Suchard MA (2012) A counting renaissance: combining stochastic mapping and empirical Bayes to quickly detect amino acid sites under positive selection. Bioinformatics 28(24):3248–3256. https://doi.org/10.1093/bioinformatics/bts580

    Article  Google Scholar 

  287. Yang Z, Nielsen R (2000) Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol Biol Evol 17(1):32–43

    Article  Google Scholar 

  288. Gil M, Zanetti MS, Zoller S, Anisimova M (2013) CodonPhyML: fast maximum likelihood phylogeny estimation under codon substitution models. Mol Biol Evol 30(6):1270–1280. https://doi.org/10.1093/molbev/mst034

    Article  Google Scholar 

  289. Hedge J, Wilson DJ (2016) Practical approaches for detecting selection in microbial genomes. PLoS Comput Biol 12(2):e1004739. https://doi.org/10.1371/journal.pcbi.1004739

    Article  Google Scholar 

  290. Yang Z (2007) PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol 24(8):1586–1591

    Article  Google Scholar 

  291. Gao F, Chen C, Arab DA, Du Z, He Y, Ho SYW (2019) EasyCodeML: a visual tool for analysis of selection using CodeML. Ecol Evol 9(7):3891–3898. https://doi.org/10.1002/ece3.5015

    Article  Google Scholar 

  292. Zhao K, Henderson E, Bullard K, Oberste MS, Burns CC, Jorba J (2018) PoSE: visualization of patterns of sequence evolution using PAML and MATLAB. BMC Bioinform 19(Suppl 11):364. https://doi.org/10.1186/s12859-018-2335-7

    Article  Google Scholar 

  293. Pond SL, Frost SD, Muse SV (2005) HyPhy: hypothesis testing using phylogenies. Bioinformatics 21(5):676–679

    Article  Google Scholar 

  294. Weaver S, Shank SD, Spielman SJ, Li M, Muse SV, Kosakovsky Pond SL (2018) Datamonkey 2.0: a modern web application for characterizing selective and other evolutionary processes. Mol Biol Evol 35:773–777. https://doi.org/10.1093/molbev/msx335

    Article  Google Scholar 

  295. Bouckaert R, Vaughan TG, Barido-Sottani J, Duchêne S, Fourment M, Gavryushkina A, Heled J, Jones G, Kühnert D, De Maio N, Matschiner M, Mendes FK, Müller NF, Ogilvie HA, du Plessis L, Popinga A, Rambaut A, Rasmussen D, Siveroni I, Suchard MA, Wu CH, Xie D, Zhang C, Stadler T, Drummond AJ (2019) BEAST 2.5: an advanced software platform for Bayesian evolutionary analysis. PLoS Comput Biol 15(4):e1006650. https://doi.org/10.1371/journal.pcbi.1006650

    Article  Google Scholar 

  296. Sealfon RS, Lin MF, Jungreis I, Wolf MY, Kellis M, Sabeti PC (2015) FRESCo: finding regions of excess synonymous constraint in diverse viruses. Genome Biol 17(16):38. https://doi.org/10.1186/s13059-015-0603-7

    Article  Google Scholar 

  297. Stern A, Doron-Faigenboim A, Erez E, Martz E, Bacharach E, Pupko T (2007) Selecton 2007: advanced models for detecting positive and purifying selection using a Bayesian inference approach. Nucleic Acids Res 35(Web Server issue):W506-W511

    Article  Google Scholar 

  298. Supek F, Šmuc T (2010) On relevance of codon usage to expression of synthetic and natural genes in Escherichia coli. Genetics 185(3):1129–1134. https://doi.org/10.1534/genetics.110.115477

    Article  Google Scholar 

  299. Pokusaeva VO, Usmanova DR, Putintseva EV, Espinar L, Sarkisyan KS, Mishin AS, Bogatyreva NS, Ivankov DN, Akopyan AV, Avvakumov SY, Povolotskaya IS, Filion GJ, Carey LB, Kondrashov FA (2019) An experimental assay of the interactions of amino acids from orthologous sequences shaping a complex fitness landscape. PLoS Genet 15(4):e1008079. https://doi.org/10.1371/journal.pgen.1008079

    Article  Google Scholar 

  300. Darriba D, Flouri T, Stamatakis A (2018) The state of software for evolutionary biology. Mol Biol Evol 35(5):1037–1046. https://doi.org/10.1093/molbev/msy014

    Article  Google Scholar 

  301. Abadi S, Azouri D, Pupko T, Mayrose I (2019) Model selection may not be a mandatory step for phylogeny reconstruction. Nat Commun 10(1):934. https://doi.org/10.1038/s41467-019-08822-w

    Article  Google Scholar 

  302. Spielman SJ, Kosakovsky Pond SL (2018) Relative evolutionary rates in proteins are largely insensitive to the substitution model. Mol Biol Evol. https://doi.org/10.1093/molbev/msy127

    Article  Google Scholar 

  303. Chionh YH, McBee M, Babu IR, Hia F, Lin W, Zhao W, Cao J, Dziergowska A, Malkiewicz A, Begley TJ, Alonso S, Dedon PC (2016) tRNA-mediated codon-biased translation in mycobacterial hypoxic persistence. Nat Commun 11(7):13302. https://doi.org/10.1038/ncomms13302

    Article  Google Scholar 

  304. Gingold H, Tehler D, Christoffersen NR, Nielsen MM, Asmar F, Kooistra SM, Christophersen NS, Christensen LL, Borre M, Sørensen KD, Andersen LD, Andersen CL, Hulleman E, Wurdinger T, Ralfkiær E, Helin K, Grønbæk K, Ørntoft T, Waszak SM, Dahan O, Pedersen JS, Lund AH, Pilpel Y (2014) A dual program for translation regulation in cellular proliferation and differentiation. Cell 158(6):1281–1292. https://doi.org/10.1016/j.cell.2014.08.011

    Article  Google Scholar 

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Acknowledgements

B.O. thanks numerous students and coworkers who investigated various aspects of codon usage. Work in the laboratory of B.O. was supported by the grants from Ministry of Education and Science of Ukraine and State Fund for Fundamental Research. M.A. thanks the Swiss National Science Foundation for research funding (grant 31003A_182330/1).

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  1. Department of Genetics and Biotechnology, Ivan Franko National University of Lviv, Lviv, 79005, Ukraine

    Bohdan Ostash

  2. School of Life Sciences and Facility Management, Institute of Applied Simulations, Zurich University of Applied Sciences ZHAW, 8820, Wädenswil, Switzerland

    Maria Anisimova

  3. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Maria Anisimova

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    K. G. Srinivasa

  2. Department of Information Science and Engineering, Ramaiah Institute of Technology, Bengaluru, Karnataka, India

    G. M. Siddesh

  3. Department of Information Science and Engineering, Ramaiah Institute of Technology, Bengaluru, Karnataka, India

    S. R. Manisekhar

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Ostash, B., Anisimova, M. (2020). Visualizing Codon Usage Within and Across Genomes: Concepts and Tools. In: Srinivasa, K., Siddesh, G., Manisekhar, S. (eds) Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-2445-5_13

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