Fast Heuristics for Resolving Weakly Supported Branches Using Duplication, Transfers, and Losses

  • Han Lai
  • Maureen Stolzer
  • Dannie DurandEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10562)


Weak branch supports in a gene tree suggest that the signal in sequence data is insufficient to resolve a particular branching order. One approach to reduce uncertainty takes the topology of the species tree into account. Under a maximum parsimony model, the best resolution of the weak branches is the binary tree that minimizes the cost of duplications, transfers, and losses. However, this problem is NP-hard, and the exact algorithm is limited to small, weakly supported areas.

We present an exact algorithm and several heuristic methods to resolve weak or non-binary gene trees given an undated species tree. These methods generate a set of optimal, binary resolutions that are temporally feasible, as well as event histories corresponding to each binary resolution. We compared the accuracy and runtime of these methods on simulated and biological datasets. The best of these heuristics provide close approximation to the event cost of the exact method and are much faster in practice. Surprisingly, a heuristic based on duplications and losses provides a good initialization for tree searching methods, even when transfers are present. Comparing event costs with RF distance, we observed that the two measures of distance captured very different information and are poorly correlated.

All methods are implemented in a new release of Notung, a Java-based, cross-platform software for reconciling and resolving gene trees. Notung is available at:


Transfers Resolve Rearrange Non-binary gene tree Weak branches Reconciliation Gene tree corrections 



We thank Annette McLeod for help with figures.


This material is based upon work supported by the National Science Foundation under Grant No. DBI-1262593. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Biological SciencesCarnegie Mellon UniversityPittsburghUSA
  2. 2.Department of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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