Biomedical subjects

Stefan Grünewald

Publications and source records attributed to Stefan Grünewald.

3 recordsLinked to original sources

PubMed2006

QNet: an agglomerative method for the construction of phylogenetic networks from weighted quartets.

We present QNet, a method for constructing split networks from weighted quartet trees. QNet can be viewed as a quartet analogue of the distance-based Neighbor-Net (NNet) method for network construction. Just as NNet, QNet works by agglomeratively computing a collection of circular weighted splits of the taxa set which is subsequently represented by a planar split network. To illustrate the applicability of QNet, we apply it to a previously published Salmonella data set. We conclude that QNet can provide a useful alternative to NNet if distance data are not available or a character-based approach is preferred. Moreover, it can be used as an aid for determining when a quartet-based tree-building method may or may not be appropriate for a given data set. QNet is freely available for download.

Algorithms↗

PubMed2005

Bounding the number of hybridisation events for a consistent evolutionary history.

Evolutionary processes such as hybridisation, lateral gene transfer, and recombination are all key factors in shaping the structure of genes and genomes. However, since such processes are not always best represented by trees, there is now considerable interest in using more general networks instead. For example, in recent studies it has been shown that networks can be used to provide lower bounds on the number of recombination events and also for the number of lateral gene transfers that took place in the evolutionary history of a set of molecular sequences. In this paper we describe the theoretical performance of some related bounds that result when merging pairs of trees into networks.

Evolution, Molecular↗

PubMed2005

Integer linear programming as a tool for constructing trees from quartet data.

The task of the quartet puzzling problem is to find a best-fitting binary X-tree for a finite n-set from confidence values for the 3n4 binary trees with exactly four leaves from X, its fitness being measured by the sum of the confidence values of all "induced" four-leaves subtrees. We describe a method for finding an exact solution of this problem by integer linear programming. Similar procedures can also be used for finding, e.g. best-fitting "circular" networks. A crucial problem in this context is, of course, how to obtain the input confidence values for the quartet trees. We propose to use inner products of rate-matrix diagonals calculated for pairs of taxa and present the trees resulting from applying our approach to two data sets of up to 36 mitochondrial sequences of mammals including an outgroup.

Journal Article↗