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Detection of Copy Number Alterations Using Single Cell Sequencing
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Sampling and counting genome rearrangement scenarios
BMC Bioinformatics
|October 10, 2015
Summary
Sampling rearrangement scenarios is crucial for evolutionary biology. This study reviews methods for reversal, DCJ, and SCJ models, introducing a new Gibbs sampler for SCJ evolutionary tree labeling to enable statistical inference.
Area of Science:
- Computational Biology
- Bioinformatics
- Evolutionary Genetics
Background:
- Optimal rearrangement scenarios are numerous, making single solutions misleading for statistical inference.
- Statistically sound methods are needed to sample uniformly from the solution space.
Purpose of the Study:
- To review state-of-the-art sampling and counting methods for rearrangement scenarios.
- To introduce a novel Gibbs sampler for the SCJ model.
Main Methods:
- Mini-review of reversal, DCJ, and SCJ models.
- Development and implementation of a Gibbs sampler for sampling most parsimonious labeling of evolutionary trees under the SCJ model.
Main Results:
- The developed Gibbs sampler has been implemented and tested on real-life data.
- The software package is available for download.
Conclusions:
- The new Gibbs sampler provides a statistically robust method for analyzing evolutionary trees under the SCJ model.
- Uniform sampling from solution spaces is essential for accurate statistical inference in rearrangement scenarios.
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