Understanding sets of trees. CS 394C September 10, 2009. Basic challenge. Phylogenetic analyses are sometimes based upon a single marker, but often based upon many markers Each marker can be analyzed separately, or the entire set can be combined into one “super-matrix”
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September 10, 2009
What to do with huge numbers of trees?
When true gene trees can differ from species tree:
When true gene trees should equal the species tree:
Software/Algorithms for deep-coalescent (see PhyloNet from Nakhleh’s webpage at Rice)
GLASS (Roch and Mossel) - distance-based
MDC (Than and Nakhleh) - parsimony
STEM (Kubatko) - ML
BEST (Liu et al.) - Bayesian
BUCKy (Ané et al.) - Bayesian
Software/Algorithms for duplication-loss
Duptree (Bansal et al.)
Hallet and Lagergren - algorithms/complexity
Consensus methods: return a tree on the entire set S of taxa summarizing the input trees
Agreement methods: return a tree on a subset of the taxa on which the trees agree
Clustering, then consensus/agreement
NP-hard, since compatibility is NP-hard
Hard to approximate, but PTAS if you have a tree on every quartet of taxa (Jiang et al.)
But see also the paper (St. John et al.) evaluating early quartet methods on the CS 394C webpage
Given set of rooted source trees, we ask:
MRP for the remainder of today’s presentation.
Maximum Likelihood Supertrees
and many more ...
Matrix Representation with Parsimony
(Most commonly used)Many Supertree Methods
If all the source trees are compatible, then an exact solution to MRP will return the compatibility trees.