SIAM Journal on Discrete Mathematics
A 2-approximation algorithm for genome rearrangements by reversals and transpositions
Theoretical Computer Science - Special issue: Genome informatics
Transforming cabbage into turnip: polynomial algorithm for sorting signed permutations by reversals
Journal of the ACM (JACM)
A Faster and Simpler Algorithm for Sorting Signed Permutations by Reversals
SIAM Journal on Computing
An algorithm to enumerate all sorting reversals
Proceedings of the sixth annual international conference on Computational biology
A Very Elementary Presentation of the Hannenhalli-Pevzner Theory
CPM '01 Proceedings of the 12th Annual Symposium on Combinatorial Pattern Matching
1.375-Approximation Algorithm for Sorting by Reversals
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
(1+epsilon)-Approximation of Sorting by Reversals and Transpositions
WABI '01 Proceedings of the First International Workshop on Algorithms in Bioinformatics
ParIS Genome Rearrangement server
Bioinformatics
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
On the tandem duplication-random loss model of genome rearrangement
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Parking Functions, Labeled Trees and DCJ Sorting Scenarios
RECOMB-CG '09 Proceedings of the International Workshop on Comparative Genomics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Efficient sampling of transpositions and inverted transpositions for bayesian MCMC
WABI'06 Proceedings of the 6th international conference on Algorithms in Bioinformatics
Hi-index | 0.00 |
In the first part of this paper, we investigate gene orders of closely related mitochondrial genomes for studying the properties of mutations rearranging genes in mitochondria. Our conclusions are that the evolution of mitochondrial genomes is more complicated than it is considered in recent methods, and stochastic modelling is necessary for its deeper understanding and more accurate inferring. The second part is a review on the Markov chain Monte Carlo approaches for the stochastic modelling of genome rearrangement, which seem to be the only computationally tractable way to this problem. We introduce the concept of partial importance sampling, which yields a class of Markov chains being efficient both in terms of mixing and computational time. We also give a list of open algorithmic problems whose solution might help improve the efficiency of partial importance samplers.