article · Bioinformatics · 2004

Combining partial order alignment and progressive multiple sequence alignment increases alignment speed and scalability to very large alignment problems

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Abstract

MOTIVATION: Partial order alignment (POA) has been proposed as a new approach to multiple sequence alignment (MSA), which can be combined with existing methods such as progressive alignment. This is important for addressing problems both in the original version of POA (such as order sensitivity) and in standard progressive alignment programs (such as information loss in complex alignments, especially surrounding gap regions). RESULTS: We have developed a new Partial Order-Partial Order alignment algorithm that optimally aligns a pair of MSAs and which therefore can be applied directly to progressive alignment methods such as CLUSTAL. Using this algorithm, we show the combined Progressive POA alignment method yields results comparable with the best available MSA programs (CLUSTALW, DIALIGN2, T-COFFEE) but is far faster. For example, depending on the level of sequence similarity, aligning 1000 sequences, each 500 amino acids long, took 15 min (at 90% average identity) to 44 min (at 30% identity) on a standard PC. For large alignments, Progressive POA was 10-30 times faster than the fastest of the three previous methods (CLUSTALW). These data suggest that POA-based methods can scale to much larger alignment problems than possible for previous methods. AVAILABILITY: The POA source code is available at http://www.bioinformatics.ucla.edu/poa

Cite this paper

APA 7

Grasso, C. S., & Lee, C. J. (2004). Combining partial order alignment and progressive multiple sequence alignment increases alignment speed and scalability to very large alignment problems. Bioinformatics, 20(10), 1546–1556. https://doi.org/10.1093/bioinformatics/bth126

MLA 9

Grasso, Catherine S., and Christopher J. Lee. “Combining partial order alignment and progressive multiple sequence alignment increases alignment speed and scalability to very large alignment problems.” Bioinformatics, vol. 20, no. 10, 2004, pp. 1546–56, https://doi.org/10.1093/bioinformatics/bth126.

Chicago

Grasso, Catherine S., and Christopher J. Lee. 2004. “Combining partial order alignment and progressive multiple sequence alignment increases alignment speed and scalability to very large alignment problems.” Bioinformatics 20 (10): 1546–56. https://doi.org/10.1093/bioinformatics/bth126.

Harvard

Grasso, C.S. and Lee, C.J. (2004) ‘Combining partial order alignment and progressive multiple sequence alignment increases alignment speed and scalability to very large alignment problems’, Bioinformatics, 20(10), pp. 1546–1556. Available at: https://doi.org/10.1093/bioinformatics/bth126.

BibTeX

@article{grasso2004combining,
  title = {{Combining partial order alignment and progressive multiple sequence alignment increases alignment speed and scalability to very large alignment problems}},
  author = {Grasso, Catherine S. and Lee, Christopher J.},
  journal = {Bioinformatics},
  volume = {20},
  number = {10},
  pages = {1546--1556},
  year = {2004},
  doi = {10.1093/bioinformatics/bth126}
}

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