Biomedical subjects

B M Moret

Publications and source records attributed to B M Moret.

4 recordsLinked to original sources

PubMed2001

A linear-time algorithm for computing inversion distance between signed permutations with an experimental study.

Hannenhalli and Pevzner gave the first polynomial-time algorithm for computing the inversion distance between two signed permutations, as part of the larger task of determining the shortest sequence of inversions needed to transform one permutation into the other. Their algorithm (restricted to distance calculation) proceeds in two stages: in the first stage, the overlap graph induced by the permutation is decomposed into connected components; then, in the second stage, certain graph structures (hurdles and others) are identified. Berman and Hannenhalli avoided the explicit computation of the overlap graph and gave an O(nalpha(n)) algorithm, based on a Union-Find structure, to find its connected components, where alpha is the inverse Ackerman function. Since for all practical purposes alpha(n) is a constant no larger than four, this algorithm has been the fastest practical algorithm to date. In this paper, we present a new linear-time algorithm for computing the connected components, which is more efficient than that of Berman and Hannenhalli in both theory and practice. Our algorithm uses only a stack and is very easy to implement. We give the results of computational experiments over a large range of permutation pairs produced through simulated evolution; our experiments show a speed-up by a factor of 2 to 5 in the computation of the connected components and by a factor of 1.3 to 2 in the overall distance computation.

Algorithms↗

PubMed2001

New approaches for reconstructing phylogenies from gene order data.

We report on new techniques we have developed for reconstructing phylogenies on whole genomes. Our mathematical techniques include new polynomial-time methods for bounding the inversion length of a candidate tree and new polynomial-time methods for estimating genomic distances which greatly improve the accuracy of neighbor-joining analyses. We demonstrate the power of these techniques through an extensive performance study based on simulating genome evolution under a wide range of model conditions. Combining these new tools with standard approaches (fast reconstruction with neighbor-joining, exploration of all possible refinements of strict consensus trees, etc.) has allowed us to analyze datasets that were previously considered computationally impractical. In particular, we have conducted a complete phylogenetic analysis of a subset of the Campanulaceae family, confirming various conjectures about the relationships among members of the subset and about the principal mechanism of evolution for their chloroplast genome. We give representative results of the extensive experimentation we conducted on both real and simulated datasets in order to validate and characterize our approaches. We find that our techniques provide very accurate reconstructions of the true tree topology even when the data are generated by processes that include a significant fraction of transpositions and when the data are close to saturation.

Biological Evolution↗

PubMed2001

A new implementation and detailed study of breakpoint analysis.

Phylogenies derived from gene order data may prove crucial in answering some fundamental open questions in biomolecular evolution. Yet very few techniques are available for such phylogenetic reconstructions. One method is breakpoint analysis, developed by Blanchette and Sankoff for solving the "breakpoint phylogeny." Our earlier studies confirmed the usefulness of this approach, but also found that BPAnalysis, the implementation developed by Sankoff and Blanchette, was too slow to use on all but very small datasets. We report here on a reimplementation of BPAnalysis using the principles of algorithmic engineering. Our faster (by 2 to 3 orders of magnitude) and flexible implementation allowed us to conduct studies on the characteristics of breakpoint analysis, in terms of running time, quality, and robustness, as well as to analyze datasets that had so far been considered out of reach. We report on these findings and also discuss future directions for our new implementation.

Algorithms↗

PubMed2000

A new fast heuristic for computing the breakpoint phylogeny and experimental phylogenetic analyses of real and synthetic data.

The breakpoint phylogeny is an optimization problem proposed by Blanchette et al. for reconstructing evolutionary trees from gene order data. These same authors also developed and implemented BPAnalysis [3], a heuristic method (based upon solving many instances of the travelling salesman problem) for estimating the breakpoint phylogeny. We present a new heuristic for this purpose; although not polynomial-time, our heuristic is much faster in practice than BPAnalysis. We present and discuss the results of experimentation on synthetic datasets and on the flowering plant family Campanulaceae with three methods: our new method, BPAnalysis, and the neighbor-joining method [25] using several distance estimation techniques. Our preliminary results indicate that, on datasets with slow evolutionary rates and large numbers of genes in comparison with the number of taxa (genomes), all methods recover quite accurate reconstructions of the true evolutionary history (although BPAnalysis is too slow to be practical), but that on datasets where the rate of evolution is high relative to the number of genes, the accuracy of all three methods is poor.

Algorithms↗