Abstract
BackgroundThe alignment of multiple protein sequences is a fundamental step in the analysis of biological data. It has traditionally been applied to analyzing protein families for conserved motifs, phylogeny, structural properties, and to improve sensitivity in homology searching. The availability of complete genome sequences has increased the demands on multiple sequence alignment (MSA) programs. Current MSA methods suffer from being either too inaccurate or too computationally expensive to be applied effectively in large-scale comparative genomics.ResultsWe developed Kalign, a method employing the Wu-Manber string-matching algorithm, to improve both the accuracy and speed of multiple sequence alignment. We compared the speed and accuracy of Kalign to other popular methods using Balibase, Prefab, and a new large test set. Kalign was as accurate as the best other methods on small alignments, but significantly more accurate when aligning large and distantly related sets of sequences. In our comparisons, Kalign was about 10 times faster than ClustalW and, depending on the alignment size, up to 50 times faster than popular iterative methods.ConclusionKalign is a fast and robust alignment method. It is especially well suited for the increasingly important task of aligning large numbers of sequences.
Highlights
The alignment of multiple protein sequences is a fundamental step in the analysis of biological data
We demonstrate that Kalign is well suited both in terms of speed and accuracy to deal with the challenges posed by large-scale comparative genomics
Only conserved blocks in the Balibase alignments were used for evaluation
Summary
The alignment of multiple protein sequences is a fundamental step in the analysis of biological data. In contrast to pairwise alignment, multiple sequence alignment (MSA) can reveal subtle similarities among large groups of proteins Such information can be used in phylogenetic analysis [2], function prediction [3], HMM building [4], finding consensus sequences and in the identification of residues critical to function. Global methods tend to outperform local methods when sequences are related over their entire length [14], while local methods are superior in multiple domain cases when sequences may only share one common domain [15] Since it is rarely known how sequences are related prior to the alignment, a method attempting to combine both local and global features was proposed by Notredame et al [16].
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