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The inversion history could thus be the reflection of evolutionary history, from which ancient genome structure of H. pylori may be inferred.
The first polynomial-time algorithm to compute inversion history was given by Hannenhalli and Pevzner (1995).
As such, it is clear that going beyond inversion distance estimation to reconstruct actual inversion history is useful to elucidate patterns of natural selection and recombination bias.
Similar(57)
Methods already exist to infer inversion histories among pairs of genomes [57], [58] and multiple genomes [59], [60].
For the present work, we are interested in methods to reconstruct parsimonious inversion histories and, in particular, to sample from the uniform or other prescribed distribution of possible parsimonious inversion histories.
Ajana et al. (2002) introduced an importance sampler for parsimonious inversion histories; however, the sampling distribution might be very far from the uniform one (Mélykúti 2006).
In the present work, we describe a method to efficiently sample from the uniform or other prescribed distribution of all parsimonious inversion histories.
If we are interested in all parsimonious inversion histories and not only the set of sorting inversions that decrease the inversion distance by 1 for a particular genome, the situation gets more complicated.
Some bacterial lineages, particularly host-restricted pathogens like Yersinia pestis, have undergone such extensive genomic rearrangement that numerous possible inversion histories exist which are all equally parsimonious in the total number of inversions.
Methods such as GRAPPA (Tang and Moret, 2003) can infer inversion histories and phylogenies on single chromosomes, while GRIMM/MGR (Tesler, 2002) can be applied to multi-chromosomal genomes.
Yet another method, BADGER (Larget et al., 2005), implements a Bayesian framework to sample likely phylogenetic inversion histories and can report the uncertainty in individual reconstructions (Darling et al., 2008).
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