The multiple sequence alignment problem in biology
SIAM Journal on Applied Mathematics
A time-efficient, linear-space local similarity algorithm
Advances in Applied Mathematics
The Maximum Weight Trace Problem in Multiple Sequence Alignment
CPM '93 Proceedings of the 4th Annual Symposium on Combinatorial Pattern Matching
Multiple sequence alignment by ant colony optimization and divide-and-conquer
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
GLProbs: Aligning multiple sequences adaptively
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Obtaining an accurate multiple alignment of protein sequences is a difficult computational problem for which many heuristic techniques sacrifice optimality to achieve reasonable running times. The most commonly used heuristic is progressive alignment, which merges sequences into a multiple alignment by pairwise comparisons along the nodes of a guide tree. To improve accuracy, consistency-based methods take advantage of conservation across many sequences to provide a stronger signal for pairwise comparisons. In this paper, we introduce the concept of probabilistic consistency for multiple sequence alignments. 'We also present PROBCONS, an HMM-based protein muhiple sequence aligner, based on an approximation of the probabilistic consistency objective function. On the BAliBASE benchmark alignment database, PROBCONS demonstrates a statistically significant improvement in accuracy compared to several leading alignment programs while maintaining practical running times. Source code and program updates are freely available under the GNU Public License at http://probcons.stanford.edu/.