Abstract
Reconstructing the phylogeny of large groups of large divergent genomes remains a difficult problem to solve, whatever the methods considered. Methods based on distance matrices are blocked due to the calculation of these matrices that is impossible in practice, when Bayesian inference or maximum likelihood methods presuppose multiple alignment of the genomes, which is itself difficult to achieve if precision is required. In this paper, we propose to calculate new distances for randomly selected couples of species over iterations, and then to map the biological sequences in a space of small dimension based on the partial knowledge of this genome similarity matrix. This mapping is then used to obtain a complete graph from which a minimum spanning tree representing the phylogenetic links between species is extracted. This new online Newton method for the computation of eigenvectors that solves the problem of constructing the Laplacian eigenmap for molecular phylogeny is finally applied on a set of more than two thousand complete chloroplasts.
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A A Python Implementation
A A Python Implementation
The following code gives the Python implementation of the method for the more general case of the Stiefel manifold, a generalisation of the sphere. (The case of the sphere corresponds to taking \(r=1\).)
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Chrétien, S., Guyeux, C. (2019). Efficient Online Laplacian Eigenmap Computation for Dimensionality Reduction in Molecular Phylogeny via Optimisation on the Sphere. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11465. Springer, Cham. https://doi.org/10.1007/978-3-030-17938-0_39
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DOI: https://doi.org/10.1007/978-3-030-17938-0_39
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