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Simulation of species and gene tree scenarios with asymmetric evolution rates.

Project description



license pypi version

AsymmeTree is an open-source Python library for the simulation and analysis of phylogenetic scenarios. It includes a simulator for species and gene trees with asymmetric evolution rates, tools for the inference and analysis of phylogenetic Best Matches (resp. best hits) from known gene trees or evolutionary distances. Moreover, it includes an algorithm to compute supertrees and a method to estimate rooted species trees from an ensemble of orthology/paralogy relations.

The library, and especially the simulator, was primarily designed to be able to validate mathematical concepts and test inference methods for various steps on the way to more realistically available data, i.e., dated gene trees, additive distances of gene sets, noisy distances and finally sequences. At the moment, AsymmeTree does not include a sequence simulator by itself, but third-party software such as Pyvolve can easily be incorporated into a simulation pipeline based on AsymmeTree.


AsymmeTree requires Python 3.5 or higher. Python 2 is not supported.

Easy installation with pip

The asymmetree package is available on PyPI:

pip install asymmetree

For details about how to install Python packages see here.


AssymmeTree has several dependencies (which are installed automatically when using pip):

Furthermore, to use functions involving sequence simulation and alignment, the following packages must be installed (i.e., they are not installed automatically!):

To use the tree reconstruction method for best match inference and the C++ implementation of the quartet method, resp., the following software must be installed (I recommend that you compile these tools on your machine, place the binaries into a persistent location and add this location to your PATH environment variable):

Usage and Description

For a more detailed description of the usage and the implementation of the simulator please read the manual.

Tree data structures

The two classes Tree (in and PhyloTree (in, inherits from Tree) implement tree data structures which are essential for most of the modules in the package. The latter contains converters and parsers for the Newick format and a NetworkX graph format.

Simulator for species and gene trees

The subpackage asymmetree.simulator contains modules for the simulation and manipulation of species trees and gene trees.

The following steps are implemented:

  • species tree simulation ('innovation model')
  • gene tree simulation (Gillespie algorithm)
  • gene tree imbalancing (asymmetric evolution rates of paralogous genes)
  • computation of a (noisy) distance matrix from the gene tree

Best Match Inference

Phylogenetic best matches of a gene x of species X are defined as those genes y of another species Y that share the lowest common ancestor with x in the gene tree among all genes in that species. In contrast, two genes are orthologs if their last common ancestor was a speciation event. Orthology and reciprocal best matches are closely related.

The subpackage asymmetree.best_matches contains functions to compute both relations from a given gene tree or to estimate them from distance data on a set of genes \citep{stadler2020}.

If the true (observable) gene tree is known (as e.g. the case in simulations), best matches and orthologs can be computed using the module TrueBMG. If only distance data is available, best matches have to be estimated. AsymmeTree currently implements three different methods that are described by Stadler et al. (2020).

Supertree Computation

Implementation of the BuildST algorithm described by Deng & Fernández-Baca (2016) to compute a supertree from a given list of tree based on the leaf labels. The algorithm uses the dynamic graph data structure described by Holm, de Lichtenberg and Thorup in 2001 (HDT algorithm).


Cograph editing and ParaPhylo

The subpackages asymmetree.cograph and asymmetree.paraphylo contain heuristics for cograph editing and a method to compute rooted species tree from orthology/paralogy relations. The latter is a reimplementation of ParaPhylo which uses heuristics for the NP-hard steps instead of exact ILP solutions.


If you use AsymmeTree in your project or code from it, please cite:

  • Stadler, P. F., Geiß, M., Schaller, D., López Sánchez, A., González Laffitte, M., Valdivia, D., Hellmuth, M., and Hernández Rosales, M. (2019) From Best Hits to Best Matches. Submitted to Algorithms for Molecular Biology.

Other references for concepts and algorithms that were implemented:

  • Deng, Y. and Fernández-Baca, D. (2016) Fast Compatibility Testing for Rooted Phylogenetic Trees. 27th Annual Symposium on Combinatorial Pattern Matching (CPM 2016). doi: 10.4230/LIPIcs.CPM.2016.12.

  • Geiß, M., Chávez, E., González Laffitte, M., López Sánchez, A., Stadler, B. M. R., Valdivia, D. I., Hellmuth, M., Hernández Rosales, M., and Stadler, P. F. (2019) Best match graphs. Journal of Mathematical Biology, 78(7):2015-2057. ISSN 0303-6812, 1432-1416. doi: 10.1007/s00285-019-01332-9.

  • Hellmuth, M., Wieseke, N., Lechner, M., Lenhof, H.-P., Middendorf, M., and Stadler, P. F. (2015) Phylogenomics with paralogs. PNAS, 112(7):2058-2063. doi: 10.1073/pnas.1412770112.

  • Holm, J., de Lichtenberg, K., and Thorup, M. (2001) Poly-logarithmic deterministic fully-dynamic algorithms for connectivity, minimum spanning tree, 2-edge, and biconnectivity. J. ACM, 48(4):723–760. doi: 10.1145/502090.502095.

  • Lechner, M., Findeiß, S., Steiner, L., Marz, M., Stadler, P. F., and Prohaska, S. J. (2011). Proteinortho: detection of (co-) orthologs in large-scale analysis. BMC bioinformatics, 12(1), 124. ISSN 1471-2105. doi: 10.1186/1471-2105-12-124.

  • Rauch Henzinger, M. and King, V. (1999) Randomized fully dynamic graph algorithms with polylogarithmic time per operation. J. ACM 46(4):502–536. doi: 10.1145/225058.225269.

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