25,296 research outputs found
Reconstructing (super)trees from data sets with missing distances: Not all is lost
The wealth of phylogenetic information accumulated over many decades of biological research, coupled with recent technological advances in molecular sequence generation, present significant opportunities for researchers to investigate relationships across and within the kingdoms of life. However, to make best use of this data wealth, several problems must first be overcome. One key problem is finding effective strategies to deal with missing data. Here, we introduce Lasso, a novel heuristic approach for reconstructing rooted phylogenetic trees from distance matrices with missing values, for datasets where a molecular clock may be assumed. Contrary to other phylogenetic methods on partial datasets, Lasso possesses desirable properties such as its reconstructed trees being both unique and edge-weighted. These properties are achieved by Lasso restricting its leaf set to a large subset of all possible taxa, which in many practical situations is the entire taxa set. Furthermore, the Lasso approach is distance-based, rendering it very fast to run and suitable for datasets of all sizes, including large datasets such as those generated by modern Next Generation Sequencing technologies. To better understand the performance of Lasso, we assessed it by means of artificial and real biological datasets, showing its effectiveness in the presence of missing data. Furthermore, by formulating the supermatrix problem as a particular case of the missing data problem, we assessed Lasso's ability to reconstruct supertrees. We demonstrate that, although not specifically designed for such a purpose, Lasso performs better than or comparably with five leading supertree algorithms on a challenging biological data set. Finally, we make freely available a software implementation of Lasso so that researchers may, for the first time, perform both rooted tree and supertree reconstruction with branch lengths on their own partial datasets
Uncertainty in phylogenetic tree estimates
Estimating phylogenetic trees is an important problem in evolutionary
biology, environmental policy and medicine. Although trees are estimated, their
uncertainties are discarded by mathematicians working in tree space. Here we
explicitly model the multivariate uncertainty of tree estimates. We consider
both the cases where uncertainty information arises extrinsically (through
covariate information) and intrinsically (through the tree estimates
themselves). The importance of accounting for tree uncertainty in tree space is
demonstrated in two case studies. In the first instance, differences between
gene trees are small relative to their uncertainties, while in the second, the
differences are relatively large. Our main goal is visualization of tree
uncertainty, and we demonstrate advantages of our method with respect to
reproducibility, speed and preservation of topological differences compared to
visualization based on multidimensional scaling. The proposal highlights that
phylogenetic trees are estimated in an extremely high-dimensional space,
resulting in uncertainty information that cannot be discarded. Most
importantly, it is a method that allows biologists to diagnose whether
differences between gene trees are biologically meaningful, or due to
uncertainty in estimation.Comment: Final version accepted to Journal of Computational and Graphical
Statistic
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