Incorporating uncertainty in distance-matrix phylogenetic reconstruction
Seminar Room 1, Newton Institute
One approach to estimating phylogenetic trees supposes that a matrix of estimated evolutionary distances between taxa is available. Agglomerative methods have been proposed in which closely related taxon-pairs are successively combined to form ancestral taxa. Several of these computationally efficient agglomerative algorithms involve steps to reduce the variance in estimated distances. However, formal statistical models and methods for agglomerative distance-based phylogenetic reconstruction have not previously been published.
We propose a statistical agglomerative phylogenetic method which formally considers uncertainty in distance estimates and how it propagates during the agglomerative process. It simultaneously produces two topologically identical rooted trees, one tree having branch lengths proportional to elapsed time, and the other having branch lengths proportional to underlying evolutionary divergence. The method models two major sources of variation which have been separately discussed in the literature: noise, reflecting inaccuracies in measuring divergences, and distortion, reflecting randomness in the amounts of divergence in different parts of the tree. The methodology is based on successive hierarchical generalised least-squares regressions. It involves only means, variances and covariances of distance estimates, thereby avoiding full distributional assumptions. Exploitation of the algebraic structure of the estimation leads to an algorithm with computational complexity comparable to the leading published agglomerative methods. A parametric bootstrap procedure allows full uncertainty in the phylogenetic reconstruction to be assessed.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.