Methods for reconstructing the history of tandem repeats and their application to the human genome
Journal of Computer and System Sciences - Computational biology 2002
Parallel Multiple Sequences Alignment in SMP Cluster
HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
Approximation algorithms for labeling hierarchical taxonomies
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Packing multiway cuts in capacitated graphs
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Barking Up The Wrong Treelength: The Impact of Gap Penalty on Alignment and Tree Accuracy
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Approximation algorithms for constrained generalized tree alignment problem
Discrete Applied Mathematics
Improved Approximation Algorithms for Reconstructing the History of Tandem Repeats
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On the bottleneck tree alignment problems
Information Sciences: an International Journal
Evolving consensus sequence for multiple sequence alignment with a genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
The bottleneck tree alignment problems
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
| Hi-index | 0.00 |
We present a new polynomial time approximation scheme (PTAS) for tree alignment, which is an important variant of multiple sequence alignment. As in the existing PTASs in the literature, the basic approach of our algorithm is to partition the given tree into overlapping components of a constant size and then apply local optimization on each such component. But the new algorithm uses a clever partitioning strategy and achieves a better efficiency for the same performance ratio. For example, to achieve approximation ratios 1.6 and 1.5, the best existing PTAS has to spend time O(kdn5) and O(kdn9), respectively, where n is the length of each leaf sequence and d,k are the depth and number of leaves of the tree, while the new PTAS only has to spend time O(kdn4) and O(kdn5). Moreover, the performance of the PTAS is more sensitive to the size of the components, which basically determines the running time, and we obtain an improved approximation ratio for each size. Some experiments of the algorithm on simulated and real data are also given.