IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Phylogenetic Super-Networks from Partial Trees
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Understanding and Using Linear Programming (Universitext)
Understanding and Using Linear Programming (Universitext)
Constructing Phylogenetic Supernetworks from Quartets
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
The minimum evolution problem: Overview and classification
Networks - Special Issue on Trees
Consistency of the QNet algorithm for generating planar split networks from weighted quartets
Discrete Applied Mathematics
Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities
Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities
Reducing distortion in phylogenetic networks
WABI'06 Proceedings of the 6th international conference on Algorithms in Bioinformatics
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Supertrees are a commonly used tool in phylogenetics to summarize collections of partial phylogenetic trees. As a generalization of supertrees, phylogenetic supernetworks allow, in addition, the visual representation of conflict between the trees that is not possible to observe with a single tree. Here, we introduce SuperQ, a new method for constructing such supernetworks (SuperQ is freely available at www.uea.ac.uk/computing/superq.). It works by first breaking the input trees into quartet trees, and then stitching these together to form a special kind of phylogenetic network, called a split network. This stitching process is performed using an adaptation of the QNet method for split network reconstruction employing a novel approach to use the branch lengths from the input trees to estimate the branch lengths in the resulting network. Compared with previous supernetwork methods, SuperQ has the advantage of producing a planar network. We compare the performance of SuperQ to the Z-closure and Q-imputation supernetwork methods, and also present an analysis of some published data sets as an illustration of its applicability.