Sampling and counting genome rearrangement scenarios

István Miklós, Heather Smith

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Background: 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 about 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 http://www.renyi.hu/~miklosi/SCJ-Gibbs/.

Original languageEnglish
Article numberS6
JournalBMC bioinformatics
Volume16
Issue number14
DOIs
Publication statusPublished - Oct 2 2015

    Fingerprint

Keywords

  • Computational complexity
  • Genome rearrangement
  • Gibbs sampling
  • Single cut or join

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this