Multiple Protein Sequence Alignment with MSAProbs

  • Yongchao Liu
  • Bertil Schmidt
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1079)

Abstract

Multiple sequence alignment (MSA) generally constitutes the foundation of many bioinformatics studies involving functional, structural, and evolutionary relationship analysis between sequences. As a result of the exponential computational complexity of the exact approach to producing optimal multiple alignments, the majority of state-of-the-art MSA algorithms are designed based on the progressive alignment heuristic. In this chapter, we outline MSAProbs, a parallelized MSA algorithm for protein sequences based on progressive alignment. To achieve high alignment accuracy, this algorithm employs a hybrid combination of a pair hidden Markov model and a partition function to calculate posterior probabilities. Furthermore, we provide some practical advice on the usage of the algorithm.

Key words

Multiple sequence alignment Progressive alignment Hidden Markov models Partition function Consistency-based scheme 

References

  1. 1.
    Feng DF, Doolittle RF (1987) Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J Mol Evol 25:351–361PubMedCrossRefGoogle Scholar
  2. 2.
    Liu Y, Schmidt B, Maskell DL (2010) MSAProbs: multiple sequence alignment based on pair hidden Markov models and partition function posterior probabilities. Bioinformatics 26:1958–1964PubMedCrossRefGoogle Scholar
  3. 3.
    Durbin R, Eddy SR, Krogh A, Mitchison G (1998) Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  4. 4.
    Miyazawa S (1995) A reliable sequence alignment method based on probabilities of residue correspondences. Protein Eng 8:999–1009PubMedCrossRefGoogle Scholar
  5. 5.
    Thompson JD, Koehl P, Ripp R, Poch O (2005) BAliBASE 3.0: latest developments of the multiple sequence alignment benchmark. Proteins 61:127–136PubMedCrossRefGoogle Scholar
  6. 6.
    Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797PubMedCrossRefGoogle Scholar
  7. 7.
    Sievers F, Wilm A, Dineen D et al (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 7:539PubMedCrossRefGoogle Scholar
  8. 8.
    Chang JM, Di Tommaso P, Taly JF et al (2012) Accurate multiple sequence alignment of transmembrane proteins with PSI-Coffee. BMC Bioinformatics 13:S1PubMedCrossRefGoogle Scholar
  9. 9.
    Deng X, Cheng J (2011) MSACompro: protein multiple sequence alignment using predicted secondary structure, solvent accessibility, and residue–residue contacts. BMC Bioinformatics 12:472PubMedCrossRefGoogle Scholar
  10. 10.
    Vingron M, Argos P (1989) A fast and sensitive multiple sequence alignment algorithm. Comput Appl Biosci 5:115–121PubMedGoogle Scholar
  11. 11.
    Gotoh O (1990) Consistency of optimal sequence alignments. Bull Math Biol 52:509–525PubMedGoogle Scholar
  12. 12.
    Notredame C, Holm L, Higgins DG (1998) COFFEE: an objective function for multiple sequence alignments. Bioinformatics 14:407–422PubMedCrossRefGoogle Scholar
  13. 13.
    Notredame C, Higgins DG, Heringa J (2000) T-coffee: a novel method for fast and accurate multiple sequence alignment. J Mol Biol 302:205–217PubMedCrossRefGoogle Scholar
  14. 14.
    Do CB, Mahabhashyam MS, Brudno M et al (2005) ProbCons: probabilistic consistency-based multiple sequence alignment. Genome Res 15:330–340PubMedCrossRefGoogle Scholar
  15. 15.
    Liu Y, Schmidt B, Maskell DL (2009) MSA-CUDA: multiple sequence alignment on graphics processing units with CUDA. 20th IEEE international conference on application-specific systems, architectures and processors, pp 121–128Google Scholar
  16. 16.
    Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2014

Authors and Affiliations

  • Yongchao Liu
    • 1
  • Bertil Schmidt
    • 1
  1. 1.Institut für InformatikJohannes Gutenberg Universitat MainzMainzGermany

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