BMC bioinformatics · 2011
A method for computing an inventory of metazoan mitochondrial gene order rearrangements.
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Bernt Matthias, Middendorf Martin
Аннотация
BACKGROUND: Changes in the order of mitochondrial genes are a good source of information for phylogenetic investigations. Phylogenetic hypotheses are often supported by parsimonious mitochondrial gene order rearrangement scenarios. CREx is a heuristic for computing short pairwise rearrangement scenarios for metazoan mitochondrial gene orders. Different from other methods, CREx considers four types of rearrangement operations: inversions, transpositions, inverse transpositions, and tandem duplication random loss operations.
RESULTS: An extensive analysis of the CREx reconstructions for artificial data has been presented and it is shown how the quality of the reconstructed rearrangement scenarios depends on the type of rearrangement model and additional parameter values. Moreover, a fast method is proposed to apply CREx to a large number of gene orders to find likely rearrangement scenarios and store them in a graph structure called RI-Graph. This method is applied to analyse all known metazoan mitochondrial gene orders. It is shown that the obtained RI-Graph contains many rearrangement scenarios that are described in the literature.
CONCLUSIONS: The prospects and limitations of CREx have been analysed empirically and a comparison with the literature on gene order evolution highlights its benefits. The newly developed method to apply CREx to a large number of gene orders is successful in computing an RI-graph that contains many rearrangement scenarios for metazoan gene orders that have also been described in the literature. This shows that the new method is very helpful for a fast analysis of a large number of gene orders which is relevant due to the strongly increasing number of known gene orders.
RESULTS: An extensive analysis of the CREx reconstructions for artificial data has been presented and it is shown how the quality of the reconstructed rearrangement scenarios depends on the type of rearrangement model and additional parameter values. Moreover, a fast method is proposed to apply CREx to a large number of gene orders to find likely rearrangement scenarios and store them in a graph structure called RI-Graph. This method is applied to analyse all known metazoan mitochondrial gene orders. It is shown that the obtained RI-Graph contains many rearrangement scenarios that are described in the literature.
CONCLUSIONS: The prospects and limitations of CREx have been analysed empirically and a comparison with the literature on gene order evolution highlights its benefits. The newly developed method to apply CREx to a large number of gene orders is successful in computing an RI-graph that contains many rearrangement scenarios for metazoan gene orders that have also been described in the literature. This shows that the new method is very helpful for a fast analysis of a large number of gene orders which is relevant due to the strongly increasing number of known gene orders.