Transforming cabbage into turnip: polynomial algorithm for sorting signed permutations by reversals
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Formulations and hardness of multiple sorting by reversals
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Sorting Permutations by Reversals and Eulerian Cycle Decompositions
SIAM Journal on Discrete Mathematics
To cut…or not to cut (applications of comparative physical maps in molecular evolution)
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Inversion Medians Outperform Breakpoint Medians in Phylogeny Reconstruction from Gene-Order Data
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
The Median Problem for Breakpoints in Comparative Genomics
COCOON '97 Proceedings of the Third Annual International Conference on Computing and Combinatorics
Genome Rearrangement Based on Reversals that Preserve Conserved Intervals
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Using median sets for inferring phylogenetic trees
Bioinformatics
Reversal and transposition medians
Theoretical Computer Science
Reactive stochastic local search algorithms for the genomic median problem
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
The transposition median problem is NP-complete
Theoretical Computer Science
| Hi-index | 0.00 |
In the past decade, genome rearrangements have attracted increasing attention fromboth biologists and computer scientists as a newtype of data for phylogenetic analysis.Methods for reconstructing phylogeny fromgenome rearrangements include distance-based methods, MCMC methods and direct optimization methods. The latter, pioneered by Sankoff and extended with the software suite GRAPPA and MGR, is the most accurate approach, but is very limited due to the difficulty of its scoring procedure-it must solvemultiple instances of median problem to compute the score of a given tree. The median problem is known to be NP-hard and all existing solvers are extremely slow when the genomes are distant. In this paper, we present a new inversion median heuristic for unichromisomal genomes. The new method works by applying sets of reversals in a batch where all such reversals both commute and do not break the cycle of any other. Our testing using simulated datasets shows that this method is much faster than the leading solver for difficult datasets with only a slight accuracy penalty, yet retains better accuracy than other heuristics with comparable speed. This new method will dramatically increase the speed of current direct optimization methods and enables us to extend the range of their applicability to organellar and small nuclear genomes with more than 50 inversions along each edge. As a further improvement, this new method can very quickly produce reasonable solutions to problemswith hundreds of genes.