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TREE-PUZZLE

TREE-PUZZLE

TREE-PUZZLE (previously known as PUZZLE) is an interactive console program that implements a fast tree search algorithm, quartet puzzling, that allows analysis of large data sets and automatically assigns estimations of support to each internal branch.

TREE-PUZZLE also computes pairwise maximum likelihood distances as well as branch lengths for user specified trees. Branch lengths can also be calculated under the clock-assumption. In addition, TREE-PUZZLE offers a novel method, likelihood mapping, to investigate the support of a hypothesized internal branch without computing an overall tree and to visualize the phylogenetic content of a sequence alignment.

The main purpose of the TREE-PUZZLE package is to reconstruct trees. It uses the quartet puzzling algorithm as described by Strimmer and von Haeseler.

 TREE-PUZZLE 5.2

Price
Free to download

Size
0.8MB
License

GNU GPL v2

Developer
Heiko A. Schmidt, Korbinian Strimmer, Martin Vingron, Arndt von Haeseler, John-von-Neumann

Website
www.tree-puzzle.de

System Requirements
libsprng2
tree-puzzle-doc

Support:
Manual

Selected Reviews:

Features include:

  • Fast tree search algorithm
  • Quartet puzzling
  • Automatically assigns estimations of support to each internal branch
  • Incorporates rate heterogeneity in all models of substitution (nucleotides: SH, GTR, TN, HKY, F84, and submodels; amino acids: Dayhoff, JTT, mtREV24, BLOSUM 62, VT, and WAG; two-state data: F81)
  • GTR model
  • Analysis:
    • Tree Reconstruction using quartet puzzling
    • Likelihood mapping
    • Usertree Evaluation and testing
    • Consensus tree construction
    • Parameter estimation and pairwise distances
  • Consensus tree construction for user given sets of trees
  • Parallel parameter estimation
  • Conducts a number of statistical tests on the data set:
    • Chi-square test for homogeneity of base composition
    • Likelihood ratio to test the clock hypothesis
    • Kishino-Hasegawa test
  • Weighting scheme of quartets
  • Detects sequences that occur more than once in the data and that therefore can be removed from the data set to speed up analysis

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Last Updated Monday, May 25 2015 @ 12:28 PM EDT


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