Trees, stars, and multiple biological sequence alignment
SIAM Journal on Applied Mathematics
Aligning sequences via an evolutionary tree: complexity and approximation
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
A linear space algorithm for computing maximal common subsequences
Communications of the ACM
Approximation Algorithms for Multiple Sequence Alignment
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Improved Approximation Algorithms for Tree Alignment
CPM '96 Proceedings of the 7th Annual Symposium on Combinatorial Pattern Matching
Lower bounds for maximum parsimony with gene order data
RCG'05 Proceedings of the 2005 international conference on Comparative Genomics
Linear programming for phylogenetic reconstruction based on gene rearrangements
CPM'05 Proceedings of the 16th annual conference on Combinatorial Pattern Matching
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We describe GESTALT (GEnomic sequences STeiner ALignmenT), a public-domain suite of programs for generating multiple alignments of a set of biosequences.We allow the use of either of the two popular objectives, Tree Alignment or Sum-of-Pairs. The main distinguishing feature of our method is that the alignment is obtained via a tree in which the internal nodes (ancestors) are labeled by Steiner sequences for triples of the input sequences. Given lists of candidate labels for the ancestral sequences, we use dynamic programming to choose an optimal labeling under either objective function. Finally, the fully labeled tree of sequences is turned into into a multiple alignment. Enhancements in our implementation include the traditional space-saving ideas of Hirschberg as well as new data-packing techniques. The running-time bottleneck of computing exact Steiner sequences is handled by a highly effective but much faster heuristic alternative. Finally, other modules in the suite allow automatic generation of linear-program input files that can be used to compute new lower bounds on the optimal values. We also report on some preliminary computational experiments with GESTALT.