Search PubMed⌕ Search

Biomedical subjects

Katharina T Huber

Publications and source records attributed to Katharina T Huber.

4 recordsLinked to original sources

Polyploidy Arithmetic.

Polyploidy occurs in plants and animals, and is an important force in speciation and genome evolution. The main focus of this paper is the following fundamental question that was recently posed by Huber and Maher: Given the ploidy numbers of a collection of extant species, or their ploidy profile, what is the smallest number of hybridizations needed in any evolutionary history for these species to completely represent these numbers? In this paper, we shall show that this question can be rephrased in terms of addition chains and the closely related addition sequences, which have been studied for over a century in mathematics and computer science. These are sequences of natural numbers that start with 1, so that each number in the sequence larger than 1 is the sum of two other numbers arising earlier in the sequence. In our first main result, we show that finding the smallest number of hybridization events to explain a ploidy profile, or the hybrid number, is equivalent to solving the so-called addition sequence problem. This immediately implies that computing the hybridization number is computationally intractable. Even so, it also leads to new connections to representing polyploid evolution using networks. More specifically, in our second main result we show that ploidy profiles representable by tree-child networks are exactly the addition chains, implying a polynomial-time algorithm for identifying these profiles. We then consider beaded tree-child networks, which permit the representation of autopolyploidy events, and in our third main result we provide a greedy polynomial-time algorithm to decide whether a given profile can be realized by such a network. We expect that our results can be leveraged in future work through, for example, making use of known algorithms for computing short addition sequences to give bounds for the hybrid number, and in guiding network reconstruction for polyploid species.

Polyploidy↗

Reconstructing the evolutionary history of polyploids from multilabeled trees.

In recent studies, phylogenetic networks have been derived from so-called multilabeled trees in order to understand the origins of certain polyploids. Although the trees used in these studies were constructed using sophisticated techniques in phylogenetic analysis, the presented networks were inferred using ad hoc arguments that cannot be easily extended to larger, more complicated examples. In this paper, we present a general method for constructing such networks, which takes as input a multilabeled phylogenetic tree and outputs a phylogenetic network with certain desirable properties. To illustrate the applicability of our method, we discuss its use in reconstructing the evolutionary history of plant allopolyploids. We conclude with a discussion concerning possible future directions. The network construction method has been implemented and is freely available for use from http://www.uea.ac.uk/ approximately a043878/padre.html.

Algorithms↗

Using consensus networks to visualize contradictory evidence for species phylogeny.

Building species phylogenies from genome data requires the evaluation of phylogenetic evidence from independent gene loci. We propose an approach to do this using consensus networks. We compare gene trees for eight yeast genomes and show that consensus networks have potential for helping to visualize contradictory evidence for species phylogenies.

Genome, Fungal↗

Spectronet: a package for computing spectra and median networks.

Spectronet is a package that uses various methods for exploring and visualising complex evolutionary signals. Given an alignment in NEXUS format, the package works by computing a collection of weighted splits or bipartitions of the taxa and then allows the user to interactively analyse the resulting collection using tools such as Lento-plots and median networks. The package is highly interactive and available for PCs.

Computational Biology↗