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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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Understanding sets of trees

Understanding sets of trees

CS 394C

September 10, 2009


Basic challenge
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”

  • Each matrix (each dataset) can result in many trees (almost no matter how you analyze the matrix)

    What to do with huge numbers of trees?


What to do
What to do?

  • How to estimate evolutionary history from many trees

  • How to efficiently store large sets of trees

  • How to enable efficient queries of the set of trees


What to do1
What to do?

  • How to estimate evolutionary history from many trees

  • How to efficiently store large sets of trees

  • How to enable efficient queries of the set of trees


First a few questions
First, a few questions:

  • Why are gene trees different from the species tree?

  • Why are estimated gene trees different from the true gene tree?

  • Under what conditions is the true evolutionary history not a tree? (i.e., what is “reticulation”?)


Reticulation
Reticulation

  • Evolutionary histories can be reticulate (meaning non-treelike):

    • Horizontal Gene Transfer (HGT)

    • Hybrid speciation

    • Recombination

  • Most phylogeny estimation methods produce trees.

  • Good resource about reticulate phylogenies: book chapter by Luay Nakhleh (see 394C webpage for the link)



Estimated gene trees can differ from species trees
Estimated Gene Trees can differ from Species Trees for the remainder of today’s presentation.

  • Biological reasons:

    • Deep coalescent events (alleles)

    • Gene duplication and loss (gene families)

  • Computational reasons:

    • Insufficient time

    • Poor methods (e.g., UPGMA)

    • Poor models (e.g., ML using Jukes-Cantor)

  • Data issues:

    • Insufficient data (meaning not enough sites)

    • Poor alignments


Examples of problems
Examples of problems for the remainder of today’s presentation.

When true gene trees can differ from species tree:

  • Given a collection of gene trees, find a species tree that minimizes the number of “deep coalescent” events

    When true gene trees should equal the species tree:

  • Given a collection of gene trees, find a species tree that minimizes the total distance to the gene trees


When gene trees can differ from species tree
When gene trees can differ from species tree for the remainder of today’s presentation.

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

NOTUNG (Durand)

Duptree (Bansal et al.)

Hallet and Lagergren - algorithms/complexity


When gene trees should equal the species tree
When gene trees should equal the species tree for the remainder of today’s presentation.

  • The problem here is that estimated gene trees can differ from the true gene trees.

  • Although the problem is “simple”, it is still interesting -- computationally and mathematically.

  • Plus, we can still make novel contributions.


The very simplest problem
The very simplest problem for the remainder of today’s presentation.

Easiest case:

  • One species tree, true gene trees will agree with the species tree,

  • Estimated trees are on the full set of taxa

    Approaches:

    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


Consensus methods
Consensus methods for the remainder of today’s presentation.

  • These are the most usual ways of analyzing datasets of trees

  • Examples:

    • Strict consensus

    • Majority consensus

    • Greedy consensus (aka “extended majority”)

    • Others less frequently used include: Gordon’s, Adams, the Strict Consensus Supertree, Local Consensus methods, and more.

  • Survey paper by David Bryant for some of these


Simplest problems cont
Simplest problems, cont. for the remainder of today’s presentation.

  • “Agreement” methods return trees on subsets of S, on which the trees are the same (or compatible)

    • MAST: maximum agreement subtree (used in practice, sometimes)

    • MCST: maximum compatible subtree (Ganapathy et al., not used in practice)

  • The difference between these is how polytomies are handled


Soft vs hard polytomies
Soft vs. hard polytomies for the remainder of today’s presentation.

  • Polytomy: node of high degree (greater than three for an unrooted tree)

  • Polytomies arise in estimations when consensus methods are used

  • Polytomies also arise when contracting short branches in estimated trees

  • Polytomies can be “hard” (representing true radiations) or “soft” (representing lack of information)


Compatible source trees
Compatible source trees for the remainder of today’s presentation.

  • Estimated trees can be “compatible” when we interpret polytomies as “soft”

  • “Compatible” means that there is a tree which is a common refinement.

  • Example: 123|456, 12|3456, 1235|46.

  • We can compute the compatibility tree (when it exists) in O(nk) time, where n=|S| and there are k source trees


Computational complexity
Computational complexity for the remainder of today’s presentation.

  • Most consensus methods (which return a tree on the entire set S of taxa) are polynomial time.

  • Most “agreement methods” (which return a tree on the largest subset of the taxa on which the source trees “agree”) are based upon NP-hard problems. Some (e.g., MAST) have fixed-parameter polynomial time solutions.


Supertree problems
Supertree problems for the remainder of today’s presentation.

  • Realistic complexity: not all the source trees are on the same set of taxa.

  • Obvious problems:

    • Find the tree on which all the source trees agree (if it exists).

    • Find the tree on which a maximum number of the source trees agree.

  • Both are NP-hard.


Quartet compatibility
Quartet compatibility for the remainder of today’s presentation.

  • Simple case: all the source trees are on four taxa.

  • We ask: does there exist a tree which agrees with all the source trees?

  • NP-hard!


Quartet tree amalgamation
Quartet tree amalgamation for the remainder of today’s presentation.

  • Given collection of quartet trees, find a tree which agrees with a maximum number of these quartet trees

    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.)


Quartet amalgamation algorithms
Quartet amalgamation algorithms for the remainder of today’s presentation.

  • Quartet Puzzling (Strimmer and von Haeseler)

  • Q* (Berry et al.)

  • Quartet Cleaning (Berry et al.)

  • Weight Optimization (Ranwez and Gascuel)

  • Quartets MaxCut (Snir and Rao)

    But see also the paper (St. John et al.) evaluating early quartet methods on the CS 394C webpage


What about rooted trees
What about rooted trees? for the remainder of today’s presentation.

Given set of rooted source trees, we ask:

  • Is there a tree on which all the rooted source trees are correct?


Rooted tree compatibility
Rooted tree compatibility for the remainder of today’s presentation.

  • Aho, Sagiv, Szymanski, and Ullman: polynomial time, recursive algorithm:

    • If n=1, return the singleton tree.

    • If n>1, then compute an equivalence relation on the set of taxa as follows.

      • For each rooted triple ((a,b),c) in the set, put a and b in the same equivalence class.

      • Compute transitive closure.

    • If only one equivalence class, reject (set is incompatible). Otherwise, recurse on each subset, and return tree obtained by making all recursively computed trees sibling subtrees.


Subtree compatibility
Subtree compatibility for the remainder of today’s presentation.

  • If source trees are rooted, then compatibility can be tested in polynomial time. Optimization problems are NP-hard, however.

  • If source trees are unrooted, then compatibility is NP-hard. And so optimization problems are also NP-hard.


Supertree problems in practice
Supertree problems, in practice for the remainder of today’s presentation.

  • In practice, the most frequently used supertree method is MRP, for “Matrix Representation with Parsimony”.

  • There are, however, many other supertree methods!


Many supertree methods

MRP for the remainder of today’s presentation.

weighted MRP

Min-Cut

Modified Min-Cut

Semi-strict Supertree

MRF

MRD

QILI

SDM

Q-imputation

PhySIC

Majority-Rule Supertrees

Maximum Likelihood Supertrees

and many more ...

Matrix Representation with Parsimony

(Most commonly used)

Many Supertree Methods


MRP for the remainder of today’s presentation.

  • Idea: take every sourcetree, and replace it with a matrix of 0,1,?.

  • Concatenate the matrices.

  • Apply Maximum Parsimony.

    If all the source trees are compatible, then an exact solution to MRP will return the compatibility trees.


Homework due 9 15
Homework, due 9/15 for the remainder of today’s presentation.

  • Read two papers (linked on the webpage):

    • St. John et al., about quartet-based methods

    • Moret et al., about sequence-length requirements

  • Pick one, write summary, and include questions


Question
Question! for the remainder of today’s presentation.

  • How do you feel about occasionally having class on some Monday or Friday, so we can have guest lectures?


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