Aligning sequences via an evolutionary tree: complexity and approximation
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
The deferred path heuristic for the generalized tree alignment problem
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
Journal of the ACM (JACM)
A Probabilistic Learning Approach to Whole-Genome Operon Prediction
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Read Mapping Algorithms for Single Molecule Sequencing Data
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Hi-index | 0.00 |
We present a novel technique for improving a fundamental aspect of iterated dynamic programmmg procedures on sequences, such as progressive sequence alignment. Instead of relying on the unrealistic assumption that each iteration can be performed accurately without including information from other sequences, our technique employs the combinatorial data structure of weighted sequence graphs to represent an exponential number of optimal and suboptimal sequences. The usual dynamic programming algorithm on linear sequences can be generalized to weighted sequence graphs, and therefore allows to align sequence graphs instead of individual sequences in subsequent stages. Thus, locally suboptimal, but globally correct solutions can for the first time be identified through iterated sequence alignment. We demonstrate the utility of our technique by applying it to the benchmark alignment problem of Sankoff et al. (J. Mol. Evol. 7 (1976) 133). Although a recent effort could improve on the original solution from 1976 slightly, our technique leads to even more significant improvements.