Hybroscale 1.5


There exist some programs for visualizing rooted trees or networks, only few programs for both computing and studying hybridization networks displaying two bifurcating input trees, and no program, however, computing all hybridization networks for an arbitrary number (>2) of non-binary input trees sharing just an overlapping set of taxa. Hybroscale is specifically designed to combine all of those functionalities that are, obviously, of high interest for the research of retiuculate evolution. This program contains a graphical user interface, which enables an easy handling of its algorithms and visualization methods.


  • The program is offered as runnable jar file requiring Java 1.7 or higher. For a detailed description of how to use the program please have a look at the manual.
  • Hybroscale hybroscale_1.5.jar
    Manual manual.pdf
    Latest Changes
    • Performance improved by working on bottlenecks in terms of time and space comsumption. Redundancy of underlying algorithms reduced. Method for re-rooting trees by hybridization number added. New design and new features ensuring a better useability.
    • Constraints added for pruning network search spaces or for filtering a set of calculated networks. The work is motivated by the paper "Fighting network space: it is time for an SQL-type language to filter phylogenetic networks" (arXiv:1310.6844).
    • Modification to our algorithm such that now all networks for multiple multifurcating trees sharing just an overlapping set of taxa can be computed.

    If you have any questions or suggestions how to improve Hybroscale, please contact Benjamin Albrecht.

      To show the efficiency of its algorithm computing all hybridization networks for multiple input trees, we have generated a synthetic dataset and performed a simulation study comparing Hybroscale against PIRN v.2.0.1 which is, to our knowledge, the best available software for computing hybridization numbers for more than two input trees. The synthetic dataset contains tree sets of four different parameters, namely the number of input trees n, the number of leaves l, an upper bound for the hybridization number k, and the tangling degree t. Each input tree of a tree set of size n is an embedded tree of a certain bicombining network that is computed in respect to these five different parameters as follows: Firstly, a random binary tree containing l leaves is computed and, secondly, k edges are inserted in respect to the parameter t such that the resulting network contains k reticulation nodes with indegree two.
      Both software packages have been run on a grid computer containing 16 cores and 40GB RAM for our synthetic dataset consisting of 2430 tree sets with parameters n in {3,4,5}, l in {10,25,50}, k in {5,10,15}, and t in {1,3,5}. The result of our simulation study as well as the synthetic dataset can be downloaded from below:
    Results results.ods