Grammar-based distance in progressive multiple sequence alignment.
BACKGROUND
We propose a multiple sequence alignment (MSA) algorithm and compare the alignment-quality and execution-time of the proposed algorithm with that of existing algorithms. The proposed progressive alignment algorithm uses a grammar-based distance metric to determine the order in which biological sequences are to be pairwise aligned. The progressive alignment occurs via pairwise aligning new sequences with an ensemble of the sequences previously aligned.


RESULTS
The performance of the proposed algorithm is validated via comparison to popular progressive multiple alignment approaches, ClustalW and T-Coffee, and to the more recently developed algorithms MAFFT, MUSCLE, Kalign, and PSAlign using the BAliBASE 3.0 database of amino acid alignment files and a set of longer sequences generated by Rose software. The proposed algorithm has successfully built multiple alignments comparable to other programs with significant improvements in running time. The results are especially striking for large datasets.


CONCLUSIONS
We introduce a computationally efficient progressive alignment algorithm using a grammar based sequence distance particularly useful in aligning large datasets.
Russell
David J
DJ
Department of Electrical Engineering, University of Nebraska-Lincoln, 209N WSEC, Lincoln, NE 68588-0511, USA. drussell@engr.unl.edu
Otu
Hasan H
HH
Sayood
Khalid
K
D000818:Animals; D003205:Computing Methodologies; D030541:Databases, Genetic; D023281:Genomics / Q000379:methods; D006801:Humans; D007257:Information Theory; D009323:Natural Language Processing; D012660:Semantics; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods; D009626:Terminology as Topic