Learning Scoring Schemes for Sequence Alignment from Partial Examples
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Clustering sequences by overlap
International Journal of Data Mining and Bioinformatics
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
On the bottleneck tree alignment problems
Information Sciences: an International Journal
Multiple sequence alignment based on dynamic weighted guidance tree
International Journal of Bioinformatics Research and Applications
Superposition and Alignment of Labeled Point Clouds
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
PAAA: a progressive iterative alignment algorithm based on anchors
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Estimating the accuracy of multiple alignments and its use in parameter advising
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
Inverse sequence alignment from partial examples
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
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Motivation: Multiple sequence alignment is a fundamental task in bioinformatics. Current tools typically form an initial alignment by merging subalignments, and then polish this alignment by repeated splitting and merging of subalignments to obtain an improved final alignment. In general this form-and-polish strategy consists of several stages, and a profusion of methods have been tried at every stage. We carefully investigate: (1) how to utilize a new algorithm for aligning alignments that optimally solves the common subproblem of merging subalignments, and (2) what is the best choice of method for each stage to obtain the highest quality alignment. Results: We study six stages in the form-and-polish strategy for multiple alignment: parameter choice, distance estimation, merge-tree construction, sequence-pair weighting, alignment merging, and polishing. For each stage, we consider novel approaches as well as standard ones. Interestingly, the greatest gains in alignment quality come from (i) estimating distances by a new approach using normalized alignment costs, and (ii) polishing by a new approach using 3-cuts. Experiments with a parameter-value oracle suggest large gains in quality may be possible through an input-dependent choice of alignment parameters, and we present a promising approach for building such an oracle. Combining the best approaches to each stage yields a new tool we call Opal that on benchmark alignments matches the quality of the top tools, without employing alignment consistency or hydrophobic gap penalties. Availability:Opal, a multiple alignment tool that implements the best methods in our study, is freely available at http://opal.cs.arizona.edu Contact: twheeler@cs.arizona.edu