# Phylogenetics: Parsimony

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1 1 Phylogenetics: Parsimony COMP 571 Luay Nakhleh, Rice University he Problem 2 Input: Multiple alignment of a set S of sequences Output: ree leaf-labeled with S Assumptions Characters are mutually independent Following a speciation event, characters continue to evolve independently

2 4 In parsimony-based methods, the inferred tree is fully labeled. 5-1 ACC GGA ACG GAA 5-2 ACC GGA ACC GAA ACG GAA

3 A Simple Solution: ry All rees 6 Problem: (2n-)!! rooted trees (2m-5)!! unrooted trees A Simple Solution: ry All rees 7 Number of axa Number of unrooted trees Number of rooted trees E E E E E E E E E E E E E+17.4E+19 Solution 8 Define an optimization criterion Find the tree (or, set of trees) that optimizes the criterion wo common criteria: parsimony and likelihood

4 9 Parsimony 10 he parsimony of a fully-labeled unrooted tree, is the sum of lengths of all the edges in Length of an edge is the Hamming distance between the sequences at its two endpoints PS() 11-1 ACC GGA ACC GAA ACG GAA

5 11-2 ACC 0 1 ACC GAA 1 0 GGA ACG GAA 11- ACC 0 1 ACC GAA 1 0 GGA ACG GAA Parsimony score = 5 Maximum Parsimony (MP) 12 Input: a multiple alignment S of n sequences Output: tree with n leaves, each leaf labeled by a unique sequence from S, internal nodes labeled by sequences, and PS() is minimized

6 1 AGC 14-1 AGC AGC AGC 14-2 AGC AGC AGC

7 14- AGC AGC AGC 14-4 AGC AGC AGC he three trees are equally good MP trees 14-5 AGC AGC AGC

8

9 MP tree 6

10 Weighted Parsimony 17 Each transition from one character state to another is given a weight Each character is given a weight See a tree that minimizes the weighted parsimony 18 Both the MP and weighted MP problems are NP-hard A Heuristic For Solving the MP Problem 19 Starting with a random tree, move through the tree space while computing the parsimony of trees, and keeping those with optimal score (among the ones encountered) Usually, the search time is the stopping factor

11 wo Issues 20 How do we move through the tree search space? Can we compute the parsimony of a given leaf-labeled tree efficiently? Searching hrough the ree Space Use tree transformation operations (NNI, BR, and SPR) 21 Searching hrough the ree Space 22 Use tree transformation operations (NNI, BR, and SPR) global maximum local maximum

12 Computing the Parsimony Length of a Given ree 2 Fitch s algorithm Computes the parsimony score of a given leaf-labeled rooted tree Polynomial time Fitch s Algorithm 24 Alphabet Σ Character c takes states from Σ vc denotes the state of character c at node v Fitch s Algorithm 25 Bottom-up phase: For each node v and each character c, compute the set Sc,v as follows: If v is a leaf, then Sc,v={vc} If v is an internal node whose two children are x and y, then { Sc,x S S c,v = c,y S c,x S c,y S c,x S c,y otherwise

13 26 Fitch s Algorithm 27 op-down phase: For the root r, let rc=a for some arbitrary a in the set Sc,r For internal node v whose parent is u, { uc u v c = c S c,v arbitrary α S c,v otherwise 28-1

14

15 mutations Fitch s Algorithm 29 akes time O(nkm), where n is the number of leaves in the tree, m is the number of sites, and k is the maximum number of states per site (for DNA, k=4)

16 Informative Sites and Homoplasy 0 Invariable sites: In the search for MP trees, sites that exhibit exactly one state for all taxa are eliminated from the analysis Only variable sites are used Informative Sites and Homoplasy 1 However, not all variable sites are useful for finding an MP tree topology Singleton sites: any nucleotide site at which only unique nucleotides (singletons) exist is not informative, because the nucleotide variation at the site can always be explained by the same number of substitutions in all topologies 2 C,,G are three singleton substitutions non-informative site All trees have parsimony score

17 Informative Sites and Homoplasy For a site to be informative for constructing an MP tree, it must exhibit at least two different states, each represented in at least two taxa hese sites are called informative sites For constructing MP trees, it is sufficient to consider only informative sites Informative Sites and Homoplasy 4 Because only informative sites contribute to finding MP trees, it is important to have many informative sites to obtain reliable MP trees However, when the extent of homoplasy (backward and parallel substitutions) is high, MP trees would not be reliable even if there are many informative sites available Measuring the Extent of Homoplasy 5 he consistency index (Kluge and Farris, 1969) for a single nucleotide site (i-th site) is given by c i =m i /s i, where m i is the minimum possible number of substitutions at the site for any conceivable topology (= one fewer than the number of different kinds of nucleotides at that site, assuming that one of the observed nucleotides is ancestral) s i is the minimum number of substitutions required for the topology under consideration

18 Measuring the Extent of Homoplasy 6 he lower bound of the consistency index is not 0 he consistency index varies with the topology herefore, Farris (1989) proposed two more quantities: the retention index and the rescaled consistency index he Retention Index 7 he retention index, ri, is given by (gi-si)/(gi-mi), where gi is the maximum possible number of substitutions at the i-th site for any conceivable tree under the parsimony criterion and is equal to the number of substitutions required for a star topology when the most frequent nucleotide is placed at the central node he Retention Index 8 he retention index becomes 0 when the site is least informative for MP tree construction, that is, si=gi

19 he Rescaled Consistency Index 9 rc i = g i s i g i m i m i s i Ensemble Indices 40 he three values are often computed for all informative sites, and the ensemble or overall consistency index (CI), overall retention index (RI), and overall rescaled index (RC) for all sites are considered Ensemble Indices RI = CI = i m i i s i i g i i s i i g i i m i RC = CI RI 41 hese indices should be computed only for informative sites, because for uninformative sites they are undefined

20 Homoplasy Index 42 he homoplasy index is HI =1 CI When there are no backward or parallel substitutions, we have HI =0. In this case, the topology is uniquely determined A Major Caveat 4 Maximum parsimony is not statistically consistent! 44 Questions?

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Useful for modeling/making predictions on sequential data E.g., biological sequences, text, series of sounds/spoken words Will return to graphical models that are generative sscott@cse.unl.edu 1 / 27 2

### Stephen Scott.

1 / 27 sscott@cse.unl.edu 2 / 27 Useful for modeling/making predictions on sequential data E.g., biological sequences, text, series of sounds/spoken words Will return to graphical models that are generative

### A (short) introduction to phylogenetics

A (short) introduction to phylogenetics Thibaut Jombart, Marie-Pauline Beugin MRC Centre for Outbreak Analysis and Modelling Imperial College London Genetic data analysis with PR Statistics, Millport Field

### Phylogeny of Mixture Models

Phylogeny of Mixture Models Daniel Štefankovič Department of Computer Science University of Rochester joint work with Eric Vigoda College of Computing Georgia Institute of Technology Outline Introduction

### Improved maximum parsimony models for phylogenetic networks

Improved maximum parsimony models for phylogenetic networks Leo van Iersel Mark Jones Celine Scornavacca December 20, 207 Abstract Phylogenetic networks are well suited to represent evolutionary histories