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GramAlign: Fast alignment driven by grammar-based phylogeny

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Multiple Sequence Alignment Methods

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1079))

Abstract

Multiple sequence alignment involves identifying related subsequences among biological sequences. When matches are found, the associated pieces are shifted so that when sequences are presented as successive rows—one sequence per row—homologous residues line-up in columns. Exact alignment of more than a few sequences is known to be computationally prohibitive. Thus many heuristic algorithms have been developed to produce good alignments in an efficient amount of time by determining an order by which pairs of sequences are progressively aligned and merged. GramAlign is such a progressive alignment algorithm that uses a grammar-based relative complexity distance metric to determine the alignment order. This technique allows for a computationally efficient and scalable program useful for aligning both large numbers of sequences and sets of long sequences quickly. The GramAlign software is available at http://bioinfo.unl.edu/gramalign.php for both source code download and a web-based alignment server.

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References

  1. Clote P, Backofen R (1998) Computational molecular biology, an introduction. Cambridge University Press, New York

    Google Scholar 

  2. Durbin R, Eddy S, Krogh A, Mitchison G (1998) Biological sequence analysis, probabilistic models of proteins and nucleic acids. Cambridge University Press, New York

    Book  Google Scholar 

  3. Sundquist A, Ronaghi M, Tang H, Pevzner P, Batzoglou S (2007) Whole-genome sequencing and assembly with high-throughput, short-read technologies. PLoS ONE 2, pp 1–14

    Google Scholar 

  4. Mitrophanov AY, Borodovsky M (2006) Statistical significance in biological sequence analysis. Brief Bioinform 7:2–24

    Article  PubMed  CAS  Google Scholar 

  5. Lipman DJ, Altschul SF, Kececioglu JD (1989) A tool for multiple sequence alignment. Proc Natl Acad Sci USA 86:4412–4415

    Article  PubMed  CAS  Google Scholar 

  6. Notredame C (2007) Recent evolutions of multiple sequence alignment algorithms. PLoS Comput Biol 3:1405–1408

    Article  CAS  Google Scholar 

  7. Jukes TH, Cantor CR (1969) Evolution of protein molecules. In: Munro HN (ed) Mammalian protein metabolism. Academic, New York, pp 21–132

    Google Scholar 

  8. Kimura M (1980) A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol 16:111–120

    Article  PubMed  CAS  Google Scholar 

  9. Barry D, Hartigan JA (1987) Asynchronous distance between homologous DNA sequences. Biometrics 43:261–276

    Article  PubMed  CAS  Google Scholar 

  10. Kishino H, Hasegawa M (1989) Evaluation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in Hominoidea. J Mol Evol 29:170–179

    Article  PubMed  CAS  Google Scholar 

  11. Lake JA (1994) Reconstructing evolutionary trees from DNA and protein sequences: paralinear distances. Proc Natl Acad Sci USA 91:1455–1459

    Article  PubMed  CAS  Google Scholar 

  12. Camin JH, Sokal RR (1965) A method for deducing branching sequences in phylogeny. Evolution 19:311–326

    Article  Google Scholar 

  13. Cavalli-Sforza LL, Edwards AWF (1967) Phylogenetic analysis: models and estimation procedures. Evolution 21:550–570

    Article  Google Scholar 

  14. Fitch WM (1971) Toward defining the course of evolution: minimum change for a specific tree topology. Syst Zool 20:406–416

    Article  Google Scholar 

  15. Adachi J, Hasegawa M (1996) MOLPHY version 2.3: programs for molecular phylogenetics based on maximum likelihood. Number 28 in computer science monographs. Institute of Statistical Mathematics, Tokyo

    Google Scholar 

  16. Guindon S, Gascuel O (2003) A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52:696–704

    Article  PubMed  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  18. Felsenstein J (1989) PHYLIP—phylogeny inference package (version 3.2). Cladistics 5:164–166

    Google Scholar 

  19. Swofford DL (1998) PAUP: phylogenetic analysis using parsimony (*and other methods). Sinauer Associates, Sunderland

    Google Scholar 

  20. Sankoff D, Leduc G, Antoine N, Paquin B, Lang BF, Cedergren R (1992) Gene order comparisons for phylogenetic inference: evolution of the mitochondrial genome. Proc Natl Acad Sci USA 89:6575–6579

    Article  PubMed  CAS  Google Scholar 

  21. Gramm J, Niedermeier R (2002) Breakpoint medians and breakpoint phylogenies: a fixed-parameter approach. Bioinformatics 18(Suppl 2):S128–S139

    Article  PubMed  Google Scholar 

  22. Lin Y, Rajan V, Swenson KM, Moret BME (2010) Estimating true evolutionary distances under rearrangements, duplications, and losses. BMC Bioinformatics 11(Suppl 1):1–11

    Google Scholar 

  23. Moret BME, Tang J, Wang L, Warnow T (2002) Steps toward accurate reconstructions of phylogenies from gene-order data. J Comput Syst Sci 65:508–525

    Article  Google Scholar 

  24. Hannenhalli S, Pevzner PA (1995) Towards a computational theory of genome rearrangements. Lect Notes Comput Sci 1000:184–202

    Article  Google Scholar 

  25. Kececioglu J, Sankoff D (1995) Exact and approximation algorithms for sorting by reversals, with application to genome rearrangement. Algorithmica 13:180–210

    Article  Google Scholar 

  26. Kececioglu J, Gusfield D (1998) Reconstructing a history of recombinations from a set of sequences. Discrete Appl Math 88:239–260

    Article  Google Scholar 

  27. Kececioglu J, Ravi R (1995) Of mice and men: algorithms for evolutionary distances between genomes with translocation. In: Proceedings of the 6th ACM-SIAM symposium on discrete algorithms, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp 604–613

    Google Scholar 

  28. Boore JL, Brown WM (1998) Big trees from little genomes: mitochondrial gene order as a phylogenetic tool. Curr Opin Genet Dev 8:668–674

    Article  PubMed  CAS  Google Scholar 

  29. Sankoff D, Blanchette M (1998) Multiple genome rearrangement and breakpoint phylogeny. J Comput Biol 5:555–570

    Article  PubMed  CAS  Google Scholar 

  30. Sankoff D (1999) Genome rearrangement with gene families. Bioinformatics 15:909–917

    Article  PubMed  CAS  Google Scholar 

  31. Otu HH, Sayood K (2003) A new sequence distance measure for phylogenetic tree construction. Bioinformatics 19:2122–2130

    Article  PubMed  CAS  Google Scholar 

  32. Bastola DR, Otu HH, Doukas SE, Sayood K, Hinrichs SH, Iwen PC (2004) Utilization of the relative complexity measure to construct a phylogenetic tree for fungi. Mycol Res 108:117–125

    Article  PubMed  CAS  Google Scholar 

  33. Russell DJ, Otu HH, Sayood K (2008) Grammar-based distance in progressive multiple sequence alignment. BMC Bioinformatics 9:1–13

    Google Scholar 

  34. Russell DJ, Way SF, Benson AK, Sayood K (2010) A grammar-based distance metric enables fast and accurate clustering of large sets of 16S sequences. BMC Bioinformatics 11:1–14

    Google Scholar 

  35. Boutros R, Stokes N, Bekaert M, Teeling EC (2009) UniPrime2: a web service providing easier universal primer design. Nucleic Acids Res 37(Web Server issue):W209–W213

    Google Scholar 

  36. Albayrak A, Otu HH, Sezerman UO (2010) Clustering of protein families into functional subtypes using relative complexity measure with reduced amino acid alphabets. BMC Bioinformatics 11:1–10

    Google Scholar 

  37. Li M, Vitanyi P (1997) An introduction to Kolmogorov complexity and its applications. Springer, Berlin

    Book  Google Scholar 

  38. Lempel A, Ziv J (1976) On the complexity of finite sequences. IEEE Trans Inf Theory 22:75–81

    Article  Google Scholar 

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Russell, D.J. (2014). GramAlign: Fast alignment driven by grammar-based phylogeny. In: Russell, D. (eds) Multiple Sequence Alignment Methods. Methods in Molecular Biology, vol 1079. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-646-7_11

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  • DOI: https://doi.org/10.1007/978-1-62703-646-7_11

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-645-0

  • Online ISBN: 978-1-62703-646-7

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