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A Beginners Guide to Estimating the Non-synonymous to Synonymous Rate Ratio of all Protein-Coding Genes in a Genome

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Parasite Genomics Protocols

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1201))

Abstract

The ratio of non-synonymous to synonymous substitutions (dN/dS) is a useful measure of the strength and mode of natural selection acting on protein-coding genes. It is widely used to study patterns of selection on protein genes on a genomic scale—from the small genomes of viruses, bacteria, and parasitic eukaryotes to the largest eukaryotic genomes. In this chapter we describe all the steps necessary to calculate the dN/dS of all the genes using at least two genomes. We include a brief discussion on assigning orthologs, and of codon-aware alignment of orthologs. We then describe how to use the CODEML program of the PAML package for phylogenetic analysis to calculate the dN/dS and how to perform some statistical tests for positive selection. We then outline some methods for interpreting output and describe how one may use this data to make discoveries about the biology of your species. Finally, as a worked example we show all the steps we used to calculate dN/dS for 3,261 orthologs from six Plasmodium species, including tests for adaptive evolution (see worked_example.pdf).

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References

  1. Kosiol C, Vinar T, da Fonseca RR, Hubisz MJ, Bustamante CD, Nielsen R, Siepel A (2008) Patterns of positive selection in six Mammalian genomes. PLoS Genet 4:e1000144

    Article  PubMed Central  PubMed  Google Scholar 

  2. Yang W, Bielawski JP, Yang Z (2003) Widespread adaptive evolution in the human immunodeficiency virus type 1 genome. J Mol Evol 57:212–221

    Article  CAS  PubMed  Google Scholar 

  3. Lefébure T, Stanhope MJ (2009) Pervasive, genome-wide positive selection leading to functional divergence in the bacterial genus Campylobacter. Genome Res 19:1224–1232

    Article  PubMed Central  PubMed  Google Scholar 

  4. Yang Z (1998) Likelihood ratio tests for detecting positive selection and application to primate lysozyme evolution. Mol Biol Evol 15:568–573

    Article  CAS  PubMed  Google Scholar 

  5. Clark AG, Eisen MB, Smith DR, Bergman CM, Oliver B, Markow TA, Kaufman TC, Kellis M, Gelbart W, Iyer VN, Pollard DA, Sackton TB, Larracuente AM, Singh ND, Abad JP, Abt DN, Adryan B, Aguade M, Akashi H, Anderson WW, Aquadro CF, Ardell DH, Arguello R, Artieri CG, Barbash DA, Barker D, Barsanti P, Batterham P, Batzoglou S, Begun D, Bhutkar A, Blanco E, Bosak SA, Bradley RK, Brand AD, Brent MR, Brooks AN, Brown RH, Butlin RK, Caggese C, Calvi BR, de Carvalho AB, Caspi A, Castrezana S, Celniker SE, Chang JL, Chapple C, Chatterji S, Chinwalla A, Civetta A, Clifton SW, Comeron JM, Costello JC, Coyne JA, Daub J, David RG, Delcher AL, Delehaunty K, Do CB, Ebling H, Edwards K, Eickbush T, Evans JD, Filipski A, Szlig SF, Freyhult E, Fulton L, Fulton R, Garcia ACL, Gardiner A, Garfield DA, Garvin BE, Gibson G, Gilbert D, Gnerre S, Godfrey J, Good R, Gotea V, Gravely B, Greenberg AJ, Griffiths-Jones S, Gross S, Guigó R, Gustafson EA, Haerty W, Hahn MW, Halligan DL, Halpern AL, Halter GM, Han MV, Heger A, Hillier L, Hinrichs AS, Holmes I, Hoskins RA, Hubisz MJ, Hultmark D, Huntley MA, Jaffe DB, Jagadeeshan S, Jeck WR, Johnson J, Jones CD, Jordan WC, Karpen GH, Kataoka E, Keightley PD, Kheradpour P, Kirkness EF, Koerich LB, Kristiansen K, Kudrna D, Kulathinal RJ, Kumar S, Kwok R, Lander E, Langley CH, Lapoint R, Lazzaro BP, Lee S-J, Levesque L, Li R, Lin C-F, Lin MF, Lindblad-Toh K, Llopart A, Long M, Low L, Lozovsky E, Lu J, Luo M, Machado CA, Makalowski W, Marzo M, Matsuda M, Matzkin L, McAllister B, McBride CS, McKernan B, McKernan K, Mendez-Lago M, Minx P, Mollenhauer MU, Montooth K, Mount SM, Mu X, Myers E, Negre B, Newfeld S, Nielsen R, Noor MAF, O’Grady P, Pachter L, Papaceit M, Parisi MJ, Parisi M, Parts L, Pedersen JS, Pesole G, Phillippy AM, Ponting CP, Pop M, Porcelli D, Powell JR, Prohaska S, Pruitt K, Puig M, Quesneville H, Ram KR, Rand D, Rasmussen MD, Reed LK, Reenan R, Reily A, Remington KA, Rieger TT, Ritchie MG, Robin C, Rogers Y-H, Rohde C, Rozas J, Rubenfield MJ, Ruiz A, Russo S, Salzberg SL, Sanchez-Gracia A, Saranga DJ, Sato H, Schaeffer SW, Schatz MC, Schlenke T, Schwartz R, Segarra C, Singh RS, Sirot L, Sirota M, Sisneros NB, Smith CD, Smith TF, Spieth J, Stage DE, Stark A, Stephan W, Strausberg RL, Strempel S, Sturgill D, Sutton G, Sutton GG, Tao W, Teichmann S, Tobari YN, Tomimura Y, Tsolas JM, Valente VLS, Venter E, Venter JC, Vicario S, Vieira FG, Vilella AJ, Villasante A, Walenz B, Wang J, Wasserman M, Watts T, Wilson D, Wilson RK, Wing RA, Wolfner MF, Wong A, Wong GK-S, Wu C-I, Wu G, Yamamoto D, Yang H-P, Yang S-P, Yorke JA, Yoshida K, Zdobnov E, Zhang P, Zhang Y, Zimin AV, Baldwin J, Abdouelleil A, Abdulkadir J, Abebe A, Abera B, Abreu J, Acer SC, Aftuck L, Alexander A, An P, Anderson E, Anderson S, Arachi H, Azer M, Bachantsang P, Barry A, Bayul T, Berlin A, Bessette D, Bloom T, Blye J, Boguslavskiy L, Bonnet C, Boukhgalter B, Bourzgui I, Brown A, Cahill P, Channer S, Cheshatsang Y, Chuda L, Citroen M, Collymore A, Cooke P, Costello M, D’Aco K, Daza R, De Haan G, DeGray S, DeMaso C, Dhargay N, Dooley K, Dooley E, Doricent M, Dorje P, Dorjee K, Dupes A, Elong R, Falk J, Farina A, Faro S, Ferguson D, Fisher S, Foley CD, Franke A, Friedrich D, Gadbois L, Gearin G, Gearin CR, Giannoukos G, Goode T, Graham J, Grandbois E, Grewal S, Gyaltsen K, Hafez N, Hagos B, Hall J, Henson C, Hollinger A, Honan T, Huard MD, Hughes L, Hurhula B, Husby ME, Kamat A, Kanga B, Kashin S, Khazanovich D, Kisner P, Lance K, Lara M, Lee W, Lennon N, Letendre F, Levine R, Lipovsky A, Liu X, Liu J, Liu S, Lokyitsang T, Lokyitsang Y, Lubonja R, Lui A, MacDonald P, Magnisalis V, Maru K, Matthews C, McCusker W, McDonough S, Mehta T, Meldrim J, Meneus L, Mihai O, Mihalev A, Mihova T, Mittelman R, Mlenga V, Montmayeur A, Mulrain L, Navidi A, Naylor J, Negash T, Nguyen T, Nguyen N, Nicol R, Norbu C, Norbu N, Novod N, O’Neill B, Osman S, Markiewicz E, Oyono OL, Patti C, Phunkhang P, Pierre F, Priest M, Raghuraman S, Rege F, Reyes R, Rise C, Rogov P, Ross K, Ryan E, Settipalli S, Shea T, Sherpa N, Shi L, Shih D, Sparrow T, Spaulding J, Stalker J, Stange-Thomann N, Stavropoulos S, Stone C, Strader C, Tesfaye S, Thomson T, Thoulutsang Y, Thoulutsang D, Topham K, Topping I, Tsamla T, Vassiliev H, Vo A, Wangchuk T, Wangdi T, Weiand M, Wilkinson J, Wilson A, Yadav S, Young G, Yu Q, Zembek L, Zhong D, Zimmer A, Zwirko Z, Alvarez P, Brockman W, Butler J, Chin C, Grabherr M, Kleber M, Mauceli E, MacCallum I (2007) Evolution of genes and genomes on the Drosophila phylogeny. Nature 450:203–218

    Google Scholar 

  6. Kimura M (1983) The neutral theory of molecular evolution. Cambridge University Press, Cambridge, 1968. ISBN 0-521-23109-4

    Google Scholar 

  7. Bustamante CD (2005) Population genetics of molecular evolution. In: Nielsen R (ed) Statistical methods in molecular evolution. Springer, New York

    Google Scholar 

  8. Sharp PM, Bailes E, Grocock RJ, Peden JF, Sockett RE (2005) Variation in the strength of selected codon usage bias among bacteria. Nucleic Acids Res 33:1141–1153

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. dos Reis M, Wernisch L (2009) Estimating translational selection in eukaryotic genomes. Mol Biol Evol 26:451–461

    Article  PubMed Central  PubMed  Google Scholar 

  10. Yang Z, Nielsen R (2008) Mutation-selection models of codon substitution and their use to estimate selective strengths on codon usage. Mol Biol Evol 25:568–579

    Article  CAS  PubMed  Google Scholar 

  11. Jeffares DC, Pain A, Berry A, Cox AV, Stalker J, Ingle CE, Thomas A, Quail MA, Siebenthall K, Uhlemann A-C, Kyes S, Krishna S, Newbold C, Dermitzakis ET, Berriman M (2007) Genome variation and evolution of the malaria parasite Plasmodium falciparum. Nat Genet 39:120–125

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Ziheng Y, Bielawski JP (2000) Statistical methods for detecting molecular adaptation. Trends Ecol Evol 15:496–503

    Article  Google Scholar 

  13. Swanson, W. J., Z. Yang, M. F. Wolfner, and C. F. Aquadro. 2001. Positive Darwinian selection drives the evolution of several female reproductive proteins in mammals. Proc. Natl.Acad. Sci. USA 98:2509-2514

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  15. Anisimova M, Bielawski JP, Yang Z (2002) Accuracy and power of bayes prediction of amino acid sites under positive selection. Mol Biol Evol 19:950–958

    Article  CAS  PubMed  Google Scholar 

  16. Yang Z, Nielsen R (2002) Codon-substitution models for detecting molecular adaptation at individual sites along specific lineages. Mol Biol Evol 19:908–917

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  18. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Söding J, Thompson JD, Higgins DG (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7:539

    Article  PubMed Central  PubMed  Google Scholar 

  19. Löytynoja A, Goldman N (2008) Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis. Science 320:1632–1635

    Article  PubMed  Google Scholar 

  20. Stamatakis A, Ludwig T, Meier H (2005) RAxML-III: a fast program for maximum likelihood-based inference of large phylogenetic trees. Bioinformatics 21:456–463

    Article  CAS  PubMed  Google Scholar 

  21. Fitch WM (1970) Distinguishing homologous from analogous proteins. Syst Zool 19:99

    Article  CAS  PubMed  Google Scholar 

  22. Koonin EV (2005) Orthologs, paralogs, and evolutionary genomics. Annu Rev Genet 39:309–338

    Article  CAS  PubMed  Google Scholar 

  23. Kuzniar A, van Ham RCHJ, Pongor S, Leunissen JAM (2008) The quest for orthologs: finding the corresponding gene across genomes. Trends Genet 24:539–551

    Article  CAS  PubMed  Google Scholar 

  24. Altenhoff AM, Dessimoz C (2012) Inferring orthology and paralogy. Methods Mol Biol 855:259–279

    Article  CAS  PubMed  Google Scholar 

  25. Trachana K, Larsson TA, Powell S, Chen W-H, Doerks T, Muller J, Bork P (2011) Orthology prediction methods: a quality assessment using curated protein families. Bioessays 33:769–780

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  26. Kristensen DM, Wolf YI, Mushegian AR, Koonin EV (2011) Computational methods for gene orthology inference. Brief Bioinform 12:379–391

    Article  PubMed Central  PubMed  Google Scholar 

  27. Salichos L, Rokas A (2011) Evaluating ortholog prediction algorithms in a yeast model clade. PLoS One 6:e18755

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Moreno-Hagelsieb G, Latimer K (2008) Choosing BLAST options for better detection of orthologs as reciprocal best hits. Bioinformatics 24:319–324

    Article  CAS  PubMed  Google Scholar 

  29. Bork P, Dandekar T, Diaz-Lazcoz Y, Eisenhaber F, Huynen M, Yuan Y (1998) Predicting function: from genes to genomes and back. J Mol Biol 283:707–725

    Article  CAS  PubMed  Google Scholar 

  30. Tatusov RL, Koonin EV, Lipman DJ (1997) A genomic perspective on protein families. Science 278:631–637

    Article  CAS  PubMed  Google Scholar 

  31. Camacho C, Coulouris G, Avagyan V (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421

    Article  PubMed Central  PubMed  Google Scholar 

  32. Yang Z, dos Reis M (2011) Statistical properties of the branch-site test of positive selection. Mol Biol Evol 28:1217–1228

    Article  CAS  PubMed  Google Scholar 

  33. Jordan G, Goldman N (2012) The effects of alignment error and alignment filtering on the sitewise detection of positive selection. Mol Biol Evol 29:1125–1139

    Article  CAS  PubMed  Google Scholar 

  34. Markova-Raina P, Petrov D (2011) High sensitivity to aligner and high rate of false positives in the estimates of positive selection in the 12 Drosophila genomes. Genome Res 21:863–874

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Fletcher W, Yang Z (2010) The effect of insertions, deletions, and alignment errors on the branch-site test of positive selection. Mol Biol Evol 27:2257–2267

    Article  CAS  PubMed  Google Scholar 

  36. Castresana J (2000) Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol 17:540–552

    Article  CAS  PubMed  Google Scholar 

  37. Notredame C, and Abergel C (2003) Using Multiple Alignment Methods to Assess the Quality of Genomic Data Analysis, in Bioinformatics and Genomes: Current Perspectives, M. Andrade, Editor. 2003, Horizon Scientific Press. p. 30–50

    Google Scholar 

  38. Penn O, Privman E, Landan G, Graur D, Pupko T (2010) An alignment confidence score capturing robustness to guide tree uncertainty. Mol Biol Evol 27:1759–1767

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  39. Privman E, Penn O, Pupko T (2012) Improving the performance of positive selection inference by filtering unreliable alignment regions. Mol Biol Evol 29:1–5

    Article  CAS  PubMed  Google Scholar 

  40. Suyama M, Torrents D, Bork P (2006) PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res 34:W609–W612

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Ranwez V, Harispe S, Delsuc F, Douzery EJP (2011) MACSE: multiple alignment of coding SEquences accounting for frameshifts and stop codons. PLoS One 6:e22594

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  42. Yang Z (2006) Computational molecular evolution. Oxford University Press, UK

    Book  Google Scholar 

  43. Yang Z, Nielsen R, Goldman N (2009) In defense of statistical methods for detecting positive selection. Proc Natl Acad Sci U S A 106:E95–E95

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  44. Sergei L, Pond S, Frost S (2005) HyPhy: hypothesis testing using phylogenies. Bioinformatics 21:676–679, Advance Access published on March 1, 2005

    Article  Google Scholar 

  45. Massingham T, Goldman N (2005) Detecting amino acid sites under positive selection and purifying selection. Genetics 169:1753–1762

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  46. Messier W, Stewart CB (1997) Episodic adaptive evolution of primate lysozymes. Nature 385:151–154

    Article  CAS  PubMed  Google Scholar 

  47. Swanson WJ, Nielsen R, Yang Q (2003) Pervasive adaptive evolution in mammalian fertilization proteins. Mol Biol Evol 20:18–20

    Article  CAS  PubMed  Google Scholar 

  48. Wong WSW, Yang Z, Goldman N, Nielsen R (2004) Accuracy and power of statistical methods for detecting adaptive evolution in protein coding sequences and for identifying positively selected sites. Genetics 168:1041–1051

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate—a practical and powerful approach to multiple testing. J Roy Stat Soc B Met 57:289–300

    Google Scholar 

  50. Yang Z, Wong W (2005) Bayes empirical Bayes inference of amino acid sites under positive selection. Mol Biol Evol 22:1107–1118

    Article  CAS  PubMed  Google Scholar 

  51. Zhang J, Nielsen R, Yang Z (2005) Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Mol Biol Evol 22:2472–2479

    Article  CAS  PubMed  Google Scholar 

  52. Krylov DM, Wolf YI, Rogozin IB, Koonin EV (2003) Gene loss, protein sequence divergence, gene dispensability, expression level, and interactivity are correlated in eukaryotic evolution. Genome Res 13:2229–2235

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  53. Fraser HB, Hirsh AE (2004) Evolutionary rate depends on number of protein-protein interactions independently of gene expression level. BMC Evol Biol 4:13

    Article  PubMed Central  PubMed  Google Scholar 

  54. Park S, Choi S (2010) Expression breadth and expression abundance behave differently in correlations with evolutionary rates. BMC Evol Biol 10:241

    Article  PubMed Central  PubMed  Google Scholar 

  55. Lindblad-Toh K, Garber M, Zuk O, Lin MF, Parker BJ, Washietl S, Kheradpour P, Ernst J, Jordan G, Mauceli E, Ward LD, Lowe CB, Holloway AK, Clamp M, Gnerre S, Alföldi J, Beal K, Chang J., Clawson H, Cuff J, Di Palma F, Fitzgerald S, Flicek P, Guttman M, Hubisz MJ, Jaffe DB, Jungreis I, Kent WJ, Kostka D, Lara M, Martins AL, Massingham T, Moltke I, Raney BJ, Rasmussen MD, Robinson J, Stark A, Vilella AJ, Wen J, Xie X, Zody MC, Broad Institute Sequencing Platform and Whole Genome Assembly Team, Baldwin J, Bloom T, Chin CW, Heiman D, Nicol R, Nusbaum C, Young S, Wilkinson J, Worley KC, Kovar CL, Muzny DM, Gibbs RA, Baylor College of Medicine Human Genome Sequencing Center Sequencing Team, Cree A, Dihn HH, Fowler G, Jhangiani S, Joshi V, Lee S, Lewis LR, Nazareth LV, Okwuonu G, Santibanez J, Warren WC, Mardis ER, Weinstock GM, Wilson RK, Genome Institute at Washington University, Delehaunty K, Dooling D, Fronik C, Fulton L, Fulton B, Graves T, Minx P, Sodergren E, Birney E, Margulies EH, Herrero J, Green ED, Haussler D, Siepel A, Goldman N, Pollard KS, Pedersen JS, Lander ES, Kellis M (2011) A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478:476–482

    Google Scholar 

  56. Mi H, Dong Q, Muruganujan A, Gaudet P, Lewis S, Thomas PD (2010) PANTHER version 7: improved phylogenetic trees, orthologs and collaboration with the gene ontology consortium. Nucleic Acids Res 38:D204–D210

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  57. Essien K, Hannenhalli S, Stoeckert CJ (2008) Computational analysis of constraints on noncoding regions, coding regions and gene expression in relation to Plasmodium phenotypic diversity. PLoS One 3:e3122

    Article  PubMed Central  PubMed  Google Scholar 

  58. Holm, S. (1979). “A simple sequentially rejective multiple test procedure”. Scandinavian Journal of Statistics 6 (2): 65–70

    Google Scholar 

  59. Abdi H (2007) Chapter Bonferroni and Sidak corrections for multiple comparisons. In: Salkind NJ (ed) Encyclopedia of measurement and statistics. Sage, Thousand Oaks, CA

    Google Scholar 

  60. Nielsen R (2005) Molecular signatures of natural selection. Annu Rev Genet 39:197–218

    Article  CAS  PubMed  Google Scholar 

  61. Nielsen R (2005) Statistical methods in molecular evolution. Springer, New York, NY

    Book  Google Scholar 

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Acknowledgements

We thank Ziheng Yang, Caroline Biagosch, and Sanne Nygaard for comments on the manuscript.

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Correspondence to Daniel C. Jeffares .

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A worked example of estimating ω and testing for adaptive evolution in six (PDF 223 kb)

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Jeffares, D.C., Tomiczek, B., Sojo, V., dos Reis, M. (2015). A Beginners Guide to Estimating the Non-synonymous to Synonymous Rate Ratio of all Protein-Coding Genes in a Genome. In: Peacock, C. (eds) Parasite Genomics Protocols. Methods in Molecular Biology, vol 1201. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1438-8_4

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