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Multiple Sequence Alignment

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Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1525))

Abstract

The increasing importance of Next Generation Sequencing (NGS) techniques has highlighted the key role of multiple sequence alignment (MSA) in comparative structure and function analysis of biological sequences. MSA often leads to fundamental biological insight into sequence–structure–function relationships of nucleotide or protein sequence families. Significant advances have been achieved in this field, and many useful tools have been developed for constructing alignments, although many biological and methodological issues are still open. This chapter first provides some background information and considerations associated with MSA techniques, concentrating on the alignment of protein sequences. Then, a practical overview of currently available methods and a description of their specific advantages and limitations are given, to serve as a helpful guide or starting point for researchers who aim to construct a reliable MSA.

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References

  1. Gribskov M, McLachlan AD, Eisenberg D (1987) Profile analysis: detection of distantly related proteins. Proc Natl Acad Sci U S A 84:4355–4358

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Haussler D, Krogh A, Mian IS et al (1993) Protein modeling using hidden Markov models: analysis of globins. In: Proceedings of the Hawaii international conference on system sciences. IEEE Computer Society Press, Los Alamitos, CA

    Google Scholar 

  3. Bucher P, Karplus K, Moeri N et al (1996) A flexible motif search technique based on generalized profiles. Comput Chem 20:3–23

    Article  CAS  PubMed  Google Scholar 

  4. Dayhoff MO, Schwart RM, Orcutt BC (1978) A model of evolutionary change in proteins. In: Dayhoff M (ed) Atlas of protein sequence and structure. National Biomedical Research Foundation, Washington, DC

    Google Scholar 

  5. Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A 89:10915–10919

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48:443–453

    Article  CAS  PubMed  Google Scholar 

  7. Carillo H, Lipman DJ (1988) The multiple sequence alignment problem in biology. SIAM J Appl Math 48:1073–1082

    Article  Google Scholar 

  8. Stoye J, Moulton V, Dress AW (1997) DCA: an efficient implementation of the divide-and-conquer approach to simultaneous multiple sequence alignment. Comput Appl Biosci 13:625–626

    CAS  PubMed  Google Scholar 

  9. Feng DF, Doolittle RF (1987) Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J Mol Evol 25:351–360

    Article  CAS  PubMed  Google Scholar 

  10. Hogeweg P, Hesper B (1984) The alignment of sets of sequences and the construction of phyletic trees: an integrated method. J Mol Evol 20:175–186

    Article  CAS  PubMed  Google Scholar 

  11. Gotoh O (1996) Significant improvement in accuracy of multiple protein sequence alignments by iterative refinement as assessed by reference to structural alignments. J Mol Biol 264:823–838

    Article  CAS  PubMed  Google Scholar 

  12. Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    Article  CAS  PubMed  Google Scholar 

  13. Pearson WR (1990) Rapid and sensitive sequence comparison with FASTP and FASTA. Methods Enzymol 183:63–98

    Article  CAS  PubMed  Google Scholar 

  14. Heringa J, Taylor WR (1997) Three-dimensional domain duplication, swapping and stealing. Curr Opin Struct Biol 7:416–421

    Article  CAS  PubMed  Google Scholar 

  15. Smith TF, Waterman MS (1981) Identification of common molecular subsequences. J Mol Biol 147:195–197

    Article  CAS  PubMed  Google Scholar 

  16. Waterman MS, Eggert M (1987) A new algorithm for best subsequence alignments with application to tRNA-rRNA comparisons. J Mol Biol 197:723–728

    Article  CAS  PubMed  Google Scholar 

  17. Thompson JD, Plewniak F, Poch O (1999) BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs. Bioinformatics 15:87–88

    Article  CAS  PubMed  Google Scholar 

  18. Heringa J (1999) Two strategies for sequence comparison: profile-preprocessed and secondary structure-induced multiple alignment. Comput Chem 23:341–364

    Article  CAS  PubMed  Google Scholar 

  19. Heringa J (2002) Local weighting schemes for protein multiple sequence alignment. Comput Chem 26:459–477

    Article  CAS  PubMed  Google Scholar 

  20. Simossis VA, Heringa J (2005) PRALINE: a multiple sequence alignment toolbox that integrates homology-extended and secondary structure information. Nucleic Acids Res 33:W289–W294

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Altschul SF, Madden TL, Schaffer AA et al (1997) Gapped BLAST and PSIBLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637

    Article  CAS  PubMed  Google Scholar 

  23. Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292:195–202

    Article  CAS  PubMed  Google Scholar 

  24. Rost B, Sander C (1993) Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 232:584–599

    Article  CAS  PubMed  Google Scholar 

  25. Lin K, Simossis VA, Taylor WR et al (2005) A simple and fast secondary structure prediction method using hidden neural networks. Bioinformatics 21:152–159

    Article  CAS  PubMed  Google Scholar 

  26. Edgar RC (2004) MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5:113

    Article  PubMed  PubMed Central  Google Scholar 

  27. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Edgar RC (2004) Local homology recognition and distance measures in linear time using compressed amino acid alphabets. Nucleic Acids Res 32:380–385

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Notredame C, Higgins DG, Heringa J (2000) T-Coffee: A novel method for fast and accurate multiple sequence alignment. J Mol Biol 302:205–217

    Article  CAS  PubMed  Google Scholar 

  30. Huang X, Miller W (1991) A time-efficient, linear-space local similarity algorithm. Adv Appl Math 12:337–357

    Article  Google Scholar 

  31. 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 22:4673–4680

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. O’Sullivan O, Suhre K, Abergel C et al (2004) 3DCoffee: combining protein sequences and structures within multiple sequence alignments. J Mol Biol 340:385–395

    Article  PubMed  Google Scholar 

  33. Taylor WR, Orengo CA (1989) Protein structure alignment. J Mol Biol 208:1–22

    Article  CAS  PubMed  Google Scholar 

  34. Shi J, Blundell TL, Mizuguchi K (2001) FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J Mol Biol 310:243–257

    Article  CAS  PubMed  Google Scholar 

  35. Wallace IM, O’Sullivan O, Higgins DG et al (2006) M-Coffee: combining multiple sequence alignment methods with T-Coffee. Nucleic Acids Res 34:1692–1699

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Katoh K, Misawa K, Kuma K et al (2002) MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res 30:3059–3066

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Katoh K, Kuma K, Toh H et al (2005) MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res 33:511–518

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Gotoh O (1995) A weighting system and algorithm for aligning many phylogenetically related sequences. Comput Appl Biosci 11:543–551

    CAS  PubMed  Google Scholar 

  39. Altschul SF (1998) Generalized affine gap costs for protein sequence alignment. Proteins 32:88–96

    Article  CAS  PubMed  Google Scholar 

  40. Zachariah MA, Crooks GE, Holbrook SR et al (2005) A generalized affine gap model significantly improves protein sequence alignment accuracy. Proteins 58:329–338

    Article  CAS  PubMed  Google Scholar 

  41. Do CB, Mahabhashyam MS, Brudno M et al (2005) ProbCons: probabilistic consistency-based multiple sequence alignment. Genome Res 15:330–340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Holmes I, Durbin R (1998) Dynamic programming alignment accuracy. J Comput Biol 5:493–504

    Article  CAS  PubMed  Google Scholar 

  43. Lassmann T, Sonnhammer ELL (2005) Kalign: an accurate and fast multiple sequence alignment algorithm. BMC Bioinformatics 6(1):298

    Article  PubMed  PubMed Central  Google Scholar 

  44. Wu S, Manber U (1992) Fast text searching allowing errors. Commun ACM 35:83–91

    Article  Google Scholar 

  45. Liu Y, Schmidt B, Maskell DL (2010) MSAProbs: multiple sequence alignment based on pair hidden Markov models and partition function posterior probabilities. Bioinformatics 26(16):1958–1964

    Article  CAS  PubMed  Google Scholar 

  46. Sievers F, Wilm A, Dineen D, 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(1):539

    Article  PubMed  PubMed Central  Google Scholar 

  47. Söding J (2005) Protein homology detection by HMM–HMM comparison. Bioinformatics 21(7):951–960

    Article  PubMed  Google Scholar 

  48. Blackshields G, Sievers F, Shi W, Wilm A, Higgins DG (2010) Sequence embedding for fast construction of guide trees for multiple sequence alignment. Algorithms Mol Biol 5:21

    Article  PubMed  PubMed Central  Google Scholar 

  49. Rost B (1999) Twilight zone of protein sequence alignments. Protein Eng 12:85–94

    Article  CAS  PubMed  Google Scholar 

  50. Morgenstern B, Dress A, Werner T (1996) Multiple DNA and protein sequence alignment based on segment-to-segment comparison. Proc Natl Acad Sci U S A 93:12098–12103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Morgenstern B (2004) DIALIGN: multiple DNA and protein sequence alignment at BiBiServ. Nucleic Acids Res 32:W33–W36

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Sammeth M, Heringa J (2006) Global multiple-sequence alignment with repeats. Prot Struct Funct Bioinf 64:263–274

    Article  CAS  Google Scholar 

  53. Phuong TM, Choung BD, Edgar RC, Batzoglou S (2006) Multiple alignment of protein sequences with repeats and rearrangements. Nucleic Acids Res 34:5932–5942

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Krogh A, Larsson B, von Heijne G et al (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567–580

    Article  CAS  PubMed  Google Scholar 

  55. Kall L, Krogh A, Sonnhammer EL (2004) A combined transmembrane topology and signal peptide prediction method. J Mol Biol 338:1027–1036

    Article  CAS  PubMed  Google Scholar 

  56. Clamp M, Cuff J, Searle SM et al (2004) The Jalview Java alignment editor. Bioinformatics 20:426–427

    Article  CAS  PubMed  Google Scholar 

  57. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4:406–425

    CAS  PubMed  Google Scholar 

  58. Galtier N, Gouy M, Gautier C (1996) SEAVIEW and PHYLO_WIN: two graphic tools for sequence alignment and molecular phylogeny. Comput Appl Biosci 12:543–548

    CAS  PubMed  Google Scholar 

  59. Li W-H, Graur D (1991) Fundamentals of molecular evolution. Sinauer, Sunderland, MA

    Google Scholar 

  60. Gille C, Frommel C (2001) STRAP: editor for STRuctural Alignments of Proteins. Bioinformatics 17:377–378

    Article  CAS  PubMed  Google Scholar 

  61. Parry-Smith DJ, Payne AW, Michie AD et al (1998) CINEMA—a novel colour INteractive editor for multiple alignments. Gene 221:GC57–GC63

    Article  CAS  PubMed  Google Scholar 

  62. Attwood TK, Beck ME, Bleasby AJ et al (1997) Novel developments with the PRINTS protein fingerprint database. Nucleic Acids Res 25:212–217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Golubchik T, Wise MJ, Easteal S, Jermiin LS (2007) Mind the gaps: evidence of bias in estimates of multiple sequence alignments. Mol Biol Evol 24(11):2433–2442

    Article  CAS  PubMed  Google Scholar 

  64. Raghava GPS, Searle SMJ, Audley PC, Barber JD, Barton GJ (2003) OXBench: a benchmark for evaluation of protein multiple sequence alignment accuracy. BMC Bioinformatics 4(1):47

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Van Walle I, Lasters I, Wyns L (2005) SABmark—a benchmark for sequence alignment that covers the entire known fold space. Bioinformatics 21(7):1267–1268

    Article  PubMed  Google Scholar 

  66. Cline M, Hughey R, Karplus K (2002) Predicting reliable regions in protein sequence alignments. Bioinformatics 18(2):306–314

    Article  CAS  PubMed  Google Scholar 

  67. Bawono P, van der Velde A, Abeln S, Heringa J (2015) Quantifying the displacement of mismatches in multiple sequence alignment benchmarks. PLoS ONE 10(5):e0127431

    Article  PubMed  PubMed Central  Google Scholar 

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Correspondence to Jaap Heringa .

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Bawono, P., Dijkstra, M., Pirovano, W., Feenstra, A., Abeln, S., Heringa, J. (2017). Multiple Sequence Alignment. In: Keith, J. (eds) Bioinformatics. Methods in Molecular Biology, vol 1525. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6622-6_8

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  • DOI: https://doi.org/10.1007/978-1-4939-6622-6_8

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6620-2

  • Online ISBN: 978-1-4939-6622-6

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