Sensitivity analysis for reversal distance and breakpoint reuse in genome rearrangements
Amit U Sinha1, Jaroslaw Meller
1Department of Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA. sinhaam@ececs.uc.edu
Abstract:
Identifying syntenic regions and quantifying evolutionary relatedness between genomes by interrogating genome rearrangement events is one of the central goals of comparative genomics. However, identification of synteny blocks and the resulting assessment of genome rearrangements are dependent on the choice of conserved markers, the definition of conserved segments, and the choice of various parameters that are used to construct such segments for two genomes. In this work, we performed an extended sensitivity analysis of synteny block generation using alternative sets of markers in multiple genomes. A simple approach to synteny block aggregation is used, which depends on two principle parameters: the maximum gap (max gap) between adjacent blocks to be merged, and the minimum length (min len) of synteny blocks. In particular, the dependence on the choice of conserved markers and max gap/min len aggregation parameters is assessed for two important quantities that can be used to characterize evolutionary relationships between genomes, namely the reversal distance and breakpoint reuse. We observe that the number of synteny blocks depends on both parameters, while the reversal distance depends mostly on min len. On the other hand, we observe that relative reversal distances between mammalian genomes, which are defined as ratios of distances between different pairs of genomes, are nearly constant for both parameters. Similarly, the breakpoint reuse rate was found to be almost constant for different data sets and a wide range of parameters. Breakpoint reuse is also strongly correlated with evolutionary distances, increasing for pairs of more divergent genomes. Finally, we demonstrate that the role of parameters may be further reduced by using a multi-way analysis that involves markers conserved in multiple genomes, which opens a way to guide the choice of a correct parameterization.
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