Abstract
The calculation of evolutionary distance via models of genome rearrangement has an inherent combinatorial complexity. Various algorithms and estimators have been used to address this; however, many of these set quite specific conditions for the underlying model. A recently proposed technique, applying representation theory to calculate evolutionary distance between circular genomes as a maximum likelihood estimate, reduces the computational load by converting the combinatorial problem into a numerical one. We show that the technique may be applied to models with any choice of rearrangements and relative probabilities thereof; we then investigate the symmetry of circular genome rearrangement models in general. We discuss the practical implementation of the technique and, without introducing any bona fide numerical approximations, give the results of some initial calculations for genomes with up to 11 regions.
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Notes
If we do not require that \(\mathcal {M}\) generates \(\mathcal {S}_N\), then it is certainly trivial!
The result in Serdoz et al. (2017) is stated in terms of likelihoods; however, the likelihoods are for single elements of \(\mathcal {S}_N\), with dihedral symmetry not included in calculations until later in the paper.
We note that the OEIS entry includes a characterisation of this sequence that is equivalent to our definition of genomes (namely, the number of necklaces that may be formed from N distinct beads).
Simply defined via the matrices \(\rho _p(d)\), for \(d\in D_N\).
\(\rho _{p^{*}}(\sigma ):=\mathrm {sgn}(\sigma )\rho _p(\sigma )\).
Underlying code written by Franco Saliola.
Further examples and discussion of this phenomenon are given below.
The errors were easily identified by, for example, summing the projection matrices for a given irreducible representation.
We computed path probabilities \(\alpha _k(\sigma )\) both via partial traces (4) and directly from the irreducible representations (3)—in the latter case, avoiding eigenvalue/eigenvector estimation—and these coincide. Additionally, for the cases predicted theoretically by the results of Sect. 4, we obtained zero partial trace values (within the expected numerical tolerance).
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This work was supported by Australian Research Council Discovery Early Career Research Award DE130100423 to JS and by use of the Nectar Research Cloud, a collaborative Australian research platform supported by the National Collaborative Research Infrastructure Strategy. We would like to thank Andrew Francis for helpful discussions and for providing the inspiration to follow this line of research. We also thank the anonymous reviewers, whose comments assisted us in making substantial improvements to the manuscript.
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Terauds, V., Sumner, J. Maximum Likelihood Estimates of Rearrangement Distance: Implementing a Representation-Theoretic Approach. Bull Math Biol 81, 535–567 (2019). https://doi.org/10.1007/s11538-018-0511-6
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DOI: https://doi.org/10.1007/s11538-018-0511-6