MUSCLE: Multiple Sequence Alignment with Improved Accuracy and Speed
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Two-Phase Quantum Based Evolutionary Algorithm for Multiple Sequence Alignment
Computational Intelligence and Security
Learning Models for Aligning Protein Sequences with Predicted Secondary Structure
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
Multiple Sequence Alignment Based Upon Statistical Approach of Curve Fitting
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Alignment of multiple proteins with an ensemble of Hidden Markov Models
International Journal of Data Mining and Bioinformatics
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
A novel approach to multiple sequence alignment using hadoop data grids
Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud
A novel approach to Multiple Sequence Alignment using hadoop data grids
International Journal of Bioinformatics Research and Applications
A polynomial time solvable formulation of multiple sequence alignment
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
GLProbs: Aligning multiple sequences adaptively
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Motivation: Multiple alignment of highly divergent sequences is a challenging problem for which available programs tend to show poor performance. Generally, this is due to a scoring function that does not describe biological reality accurately enough or a heuristic that cannot explore solution space efficiently enough. In this respect, we present a new program, Align-m, that uses a non-progressive local approach to guide a global alignment. Results: Two large test sets were used that represent the entire SCOP classification and cover sequence similarities between 0 and 50% identity. Performance was compared with the publicly available algorithms ClustalW, T-Coffee and DiAlign. In general, Align-m has comparable or slightly higher accuracy in terms of correctly aligned residues, especially for distantly related sequences. Importantly, it aligns much fewer residues incorrectly, with average differences of over 15% compared with some of the other algorithms. Availability: Align-m and the test sets are available at http://bioinformatics.vub.ac.be