The multiple sequence alignment problem in biology
SIAM Journal on Applied Mathematics
A time-efficient, linear-space local similarity algorithm
Advances in Applied Mathematics
Multiple sequence alignment based on profile alignment of intermediate sequences
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Multiple sequence alignment based on dynamic weighted guidance tree
International Journal of Bioinformatics Research and Applications
Information Sciences: an International Journal
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A common and cost-effective mechanism to identify the functionalities, structures, or relationships between species is multiple-sequence alignment, in which DNA/RNA/protein sequences are arranged and aligned so that similarities between sequences are clustered together. Correctly identifying and aligning these sequence biological similarities help from unwinding the mystery of species evolution to drug design. We present our knowledge-based multiple sequence alignment (KB-MSA) technique that utilizes the existing knowledge databases such as SWISSPROT, GENBANK, or HOMSTRAD to provide a more realistic and reliable sequence alignment. We also provide a modified version of this algorithm (CB-MSA) that utilizes the sequence consistency information when sequence knowledge databases are not available. Our benchmark tests on BAliBASE, PREFAB, HOMSTRAD, and SABMARK references show accuracy improvements up to 10 percent on twilight data sets against many leading alignment tools such as ISPALIGN, PADT, CLUSTALW, MAFFT, PROBCONS, and T-COFFEE.