Sampling and counting genome rearrangement scenarios

István Miklós, Heather Smith
2015 BMC Bioinformatics  
Even for moderate size inputs, there are a tremendous number of optimal rearrangement scenarios, regardless what the model is and which specific question is to be answered. Therefore giving one optimal solution might be misleading and cannot be used for statistical inferring. Statistically well funded methods are necessary to sample uniformly from the solution space and then a small number of samples are sufficient for statistical inferring. Contribution: In this paper, we give a mini-review
more » ... ut the state-of-the-art of sampling and counting rearrangement scenarios, focusing on the reversal, DCJ and SCJ models. Above that, we also give a Gibbs sampler for sampling most parsimonious labeling of evolutionary trees under the SCJ model. The method has been implemented and tested on real life data. The software package together with example data can be downloaded from
doi:10.1186/1471-2105-16-s14-s6 pmid:26452124 pmcid:PMC4603625 fatcat:prugvnwdyffvtojt5l3m5lwwfm