Skip to main content

Detecting the Presence and Location of Selection in Proteins

  • Protocol
Bioinformatics

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 452))

  • 5880 Accesses

Abstract

Methods to detect the action of selection on proteins can now make strong predictions about its strength and location, but are becoming increasingly technical. The complexity of the methods makes it difficult to determine and interpret the significance of any selection detected. With more information being extracted from the data, the quality of the protein alignment and phylogeny used becomes increasingly important in assessing whether or not a prediction is merely a statistical artifact. Both data quality issues and statistical assessment of the results are considered.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. McDonald, J., Kreitman, M. (1991) Adaptive protein evolution at the adh locus in drosophila. Nature 351, 652–654.

    Article  PubMed  CAS  Google Scholar 

  2. Sokal, R. R., Rohlf, F. J. (1995) Biometry, 3rd ed. W. H. Freeman and Company, New York.

    Google Scholar 

  3. Nielsen, R., Yang, Z. (1998) Likelihood models for detecting positively selected amino acid and applications to the HIV-1 envelope gene. Genetics 148, 929–936.

    PubMed  CAS  Google Scholar 

  4. Whelan, S., Goldman, N. (2004) Estimating the frequency of events that cause multiple nucleotide changes. Genetics 167, 2027–2043.

    Article  PubMed  CAS  Google Scholar 

  5. Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol 17, 368–376.

    Article  PubMed  CAS  Google Scholar 

  6. Self, S. G., Liang, K.-Y. (1987) Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. J Amer Stat Assoc 82,605–610.

    Article  Google Scholar 

  7. Yang, Z., Nielsen, R., Goldman, N., et al. (2000) Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 155, 431–449.

    PubMed  CAS  Google Scholar 

  8. Goldman, N. (1993) Statistical tests of models of DNA substitution. J Mol Evol 36, 182–198.

    Article  PubMed  CAS  Google Scholar 

  9. Felsenstein, J. (1985) Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783–791.

    Article  Google Scholar 

  10. Efron, B., Tibshirani, R. J. (1993) An Introduction to the Bootstrap. Chapman and Hall/CRC, Florida.

    Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  13. Suzuki, Y. (2004) New methods for detecting positive selection at single amino acid sites. J Mol Evol 59, 11–19.

    PubMed  CAS  Google Scholar 

  14. Durbin, R., Eddy, S., Krogh, A., et al. (1998) Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge, England.

    Book  Google Scholar 

  15. Eddy, S. R. (1998) Profile hidden Markov models. Bioinformatics 14, 755–763.

    Article  PubMed  CAS  Google Scholar 

  16. Hsu, J. C. (1996) Multiple Comparisons: Theory and Methods. Chapman and Hall, London.

    Google Scholar 

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

    Google Scholar 

  18. Holm, S. (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6, 65–70.

    Google Scholar 

  19. Hochberg, Y. (1988) A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75, 800–803.

    Article  Google Scholar 

  20. Simes, R. J. (1986) An improved Bonfer-roni procedure for multiple tests of significance. Biometrika 73, 751–754.

    Article  Google Scholar 

  21. Wong, W. S. W., Yang, Z., Goldman, N., et al. (2004) Accuracy and power of statistical methods for detecting positive adaptive evolution in protein coding sequences and for identifying positively selected sites. Genetics 168, 1041–1051.

    Article  PubMed  CAS  Google Scholar 

  22. Sharp, P. M., Li, W.-H. (1987) The codon adaptation index — a measure of directional synonymous usage bias, and its potential applications. Nucl Acids Res 15, 1281–1295.

    Article  PubMed  CAS  Google Scholar 

  23. Wright, F. (1990) The “effective number of codons” used in a gene. Gene 87, 23–29.

    Article  PubMed  CAS  Google Scholar 

  24. Yang, Z. (2000) Phylogenetic Analysis by Maximum Likelihood (PAML), version 3.0. University College London. http://abacus.gene.ucl.ac.uk/software/paml.html.

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

    PubMed  CAS  Google Scholar 

  26. Kosakovsky-Pond, S. L., Frost, S. D. W. (2005) Not so different after all: a comparison of methods for detecting amino acid sites under selection. Mol Biol Evol 22, 1208–1222.

    Article  PubMed  Google Scholar 

  27. Swanson, W. J., Nielsen, R., Yang, Q. (2003) Pervasive adaptive evolution in mammalian fertilization proteins. Mol Biol Evol 20, 18–20.

    PubMed  CAS  Google Scholar 

  28. Chen, H., Chen, J., Kalbfleisch, J. D. (2001) A modified likelihood ratio test for homogeneity in finite mixture models. J Roy Stat Soc B 63, 19–29.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Humana Press, a part of Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Massingham, T. (2008). Detecting the Presence and Location of Selection in Proteins. In: Keith, J.M. (eds) Bioinformatics. Methods in Molecular Biology™, vol 452. Humana Press. https://doi.org/10.1007/978-1-60327-159-2_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-60327-159-2_15

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-707-5

  • Online ISBN: 978-1-60327-159-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics