Comparing and Aggregating Partially Resolved Trees

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Comparing and Aggregating Partially Resolved Trees

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Comparing and Aggregating Partially Resolved Trees
Mukul S. Bansal, Jianrong Dong, and David Ferna´ndez-Baca
Department of Computer Science, Iowa State University, Ames, IA, USA {bansal, jrdong, fernande}@cs.iastate.edu
Abstract. We define, analyze, and give efficient algorithms for two kinds of distance measures for rooted and unrooted phylogenies. For rooted trees, our measures are based on the topologies the input trees induce on triplets; that is, on three-element subsets of the set of species. For unrooted trees, the measures are based on quartets (four-element subsets). Triplet and quartet-based distances provide a robust and fine-grained measure of the similarities between trees. The distinguishing feature of our distance measures relative to traditional quartet and triplet distances is their ability to deal cleanly with the presence of unresolved nodes, also called polytomies. For rooted trees, these are nodes with more than two children; for unrooted trees, they are nodes of degree greater than three. Our first class of measures are parametric distances, where there is parameter that weighs the difference between an unresolved triplet/quartet topology and a resolved one. Our second class of measures are based on Hausdorff distance. Each tree is viewed as a set of all possible ways in which the tree could be refined to eliminate unresolved nodes. The distance between the original (unresolved) trees is then taken to be the Hausdorff distance between the associated sets of fully resolved trees, where the distance between trees in the sets is the triplet or quartet distance, as appropriate.
1 Introduction
Evolutionary trees, also known as phylogenetic trees or phylogenies, represent the evolutionary history of sets of species. Such trees have uniquely labeled leaves, corresponding to the species, and unlabeled internal nodes, representing hypothetical ancestors. The trees may be rooted, if the evolutionary origin is known, or unrooted, otherwise.
This paper addresses two related questions: (1) How does one measure how close two evolutionary trees are to each other? (2) How does one combine or aggregate the phylogenetic information from conflicting trees into a single consensus tree? Among the motivations for the first question is the growth of phylogenetic databases, such as TreeBase [19], with the attendant need for sophisticated querying mechanisms and for means to assess the quality of answers to queries. The second question arises from the fact that phylogenetic analyses — e.g., by parsimony — typically produce multiple evolutionary trees (often in the thousands) for the same set of species.
We address the above questions by defining appropriate distance measures between trees. While several such measures have been proposed before (see below), ours provide a feature that previous ones do not: The ability to deal elegantly with the presence
This work was supported in part by National Science Foundation AToL grant EF-0334832.

of unresolved nodes, also called polytomies. For rooted trees these are nodes with more than two children; for unrooted trees, they are nodes of degree greater than three. Polytomies cannot simply be ignored, since they arise naturally in phylogenetic analysis. Furthermore, they must be treated with care: A node may be unresolved because it truly must be so or because there is not enough evidence to break it up into resolved nodes — that is, the polytomies are either “hard” or “soft” [17].
Our contributions. We define and analyze two kinds of distance measures for phylogenies. For rooted trees, our measures are based on the topologies the input trees induce on triplets; that is, on three-element subsets of the set of species. For unrooted trees, the measures are based on quartets (four-element subsets). Our approach is motivated by the observation that triplet and quartet topologies are the basic building blocks of rooted and unrooted trees, in the sense that they are the smallest topological units that completely identify a phylogenetic tree [21]. Triplet and quartet-based distances thus provide a robust and fine-grained measure of the differences and similarities between trees1. In contrast with traditional quartet and triplet distances, our two classes of distance measures deal cleanly with the presence of unresolved nodes.
The first kind of measures we propose are parametric distances: Given a triplet (quartet) X, we compare the topologies that each of the two input trees induces on X. If they are identical, the contribution of X to the distance is zero. If both topologies are fully resolved but different, then the contribution is one. Otherwise, the topology is resolved in one of the trees, but not the other. In this case, X contributes p to the distance, where p is a real number between 0 and 1. Parameter p allows one to make a smooth transition between hard and soft views of polytomy. At one extreme, if p = 1, an unresolved topology is viewed as different from a fully resolved one. At the other, when p = 0, unresolved topologies are viewed as identical to resolved ones. Intermediate values of p allow one to adjust for the degree of certainty one has about a polytomy.
The second kind of measures proposed here are based on viewing each tree as a set of all possible fully resolved trees that can be obtained from it by refining its unresolved nodes. The distance between two trees is defined as the Hausdorff distance between the corresponding sets, where the distance between trees in the sets is the triplet or quartet distance, as appropriate.
After defining our distance measures, we proceed to study their mathematical and algorithmic properties. We obtain exact and asymptotic bounds on expected values of parametric triplet distance and parametric quartet distance. We also study for which values of p, parametric triplet and quartet distances are metrics, near-metrics (in the sense of [15]), or non-metrics.
Aside from the mathematical elegance that metrics and near-metrics bring to tree comparison, there are also algorithmic benefits. We formulate phylogeny aggregation as a median problem, in which the objective is to find a consensus tree whose total distance to the given trees is minimized. We do not know whether finding the median tree relative to parametric (triplet or quartet) distance is NP-hard, but conjecture that it is. This is suggested by the NP-completeness of the maximum triplet compatibility problem (see [8]). However, by the results mentioned above and well-known facts about
1 Biologically-inspired arguments in favor of triplet-based measures can be found in [11].

the median problem [26], there are simple constant-factor approximation algorithms for the aggregation of rooted and unrooted trees relative to parametric distance: Simply return the input tree with minimum distance to the remaining input trees. We show that there are values of p for which parametric distance is a metric, but the median tree may not be fully resolved even if all the input trees are. However, beyond a threshold, the median tree is guaranteed to be fully resolved if the input trees are fully resolved.
We suspect that computing Hausdorff triplet (quartet) distance between two trees is NP-hard. However, we show that one can partially circumvent the issue by proving that, under a certain density assumption, Hausdorff distance is within a constant factor of parametric distance — that is, the measures are equivalent in the sense of [15].
Finally, we present a O(n2)-time algorithm to compute parametric triplet distance and a O(n2) 2-approximate algorithm for parametric quartet distance. To our knowledge, there was no previous algorithm for computing the parametric triplet distance between two rooted trees, other than by enumerating all Θ(n3) triplets. Two algorithms exist that can be directly applied to compute the parametric quartet distance. One runs in time O(n2 min{d1, d2}), where, for i ∈ {1, 2}, di is the maximum degree of a node in Ti [10]; the other takes O(d9n log n) time, where d is the maximum degree of a node in T1 and T2 [24].2 Our faster O(n2) algorithm offers a 2-approximate solution when an exact value of the parametric quartet distance is not required. Additionally, our algorithm gives the exact answer when p = 12 .
Related work. Several other measures for comparing trees have been proposed; we mention a few. A popular class of distances are those based on symmetric distance between sets of clusters (that is, on sets of species that descend from the same internal node in a rooted tree) or of splits (partitions of the set of species induced by the removal of an edge in an unrooted tree); the latter is the well-known Robinson-Foulds (RF) distance [20]. It is not hard to show that two rooted (unrooted) trees can share many triplet (quartet) topologies but not share a single cluster (split). Cluster- and split-based measures are also coarser than triplet and quartet distances.
One can also measure the distance between two trees by counting the number of branch-swapping operations needed to convert one of the trees into the other [2]. However, the associated measures can be hard to compute, and they fail to distinguish between operations that affect many species and those that affect only a few. An alternative to distance measures are similarity methods such as maximum agreement subtree (MAST) approach [16]. While there are efficient algorithms for computing the MAST, the measure is coarser than triplet-based distances.
There is an extensive literature on consensus methods for phylogenetic trees. A non-exhaustive list of methods based on splits or clusters includes strict consensus trees [18], majority-rule trees [3], and the Adams consensus [1]. For a thorough survey on the subject, see [9].
The fact that consensus methods tend to produce unresolved trees, with an attendant loss of information, has been observed before. An alternative approach is to cluster the
2 Note that unresolved nodes seem to complicate distance computation: The quartet distance between a pair of fully resolved unrooted trees can be obtained in O(n log n) time [7].

input trees into groups using some distance measure, each of which is represented by a consensus tree, in such a way as to minimize some measure of information loss [25].
In addition to consensus methods, there are techniques that take as input sets of quartet trees or triplet trees and try to find large compatible subsets or subsets whose removal results in a compatible set [5, 22]. These problems are related to the supertree problem, which generalizes the consensus problem by allowing the leaves of the input trees to overlap only partially [6].
The consensus problem on trees exhibits parallels with the rank aggregation problem [14, 15]. Here we are given a collection of rankings (that is, permutations) of n objects, and the goal is to find a ranking of minimum total distance to the input rankings. A distance between rankings of particular interest is Kendall’s tau, defined as the number of pairwise disagreements between the two rankings. Like triplet and quartet distances, Kendall’s tau is based on elementary ordering relationships. Rank aggregation under Kendall’s tau is NP-complete even for four lists [14].
A permutation is the analog of a fully resolved tree, since every pairwise relationship between elements is given. The analog to a partially-resolved tree is a partial ranking, in which the elements are grouped into an ordered list of buckets, such that elements in different buckets have known ordering relationships, but elements within a bucket are not ranked [15]. Our definitions of parametric distance and Hausdorff distance are inspired by Fagin et al.’s Kendall tau with parameter p and their Hausdorff version of Kendall’s tau, respectively [15]. We note, however, that aggregating partial rankings seems computationally easier than the consensus problem on trees. For example, while the Hausdorff version of Kendall’s tau is easily computable [15], it is unclear whether the Hausdorff triplet or quartet distances are polynomially-computable for trees.
Organization of the paper. Section 2 reviews basic notions in phylogenetics and distances. Our distance measures and the consensus problem are formally defined in Section 3. The basic properties of parametric distance are proved in Section 4. Section 5 studies the connection between Hausdorff and parametric distances. Section 6 gives efficient algorithms for computing parametric distance.
2 Preliminaries
Phylogenies. By and large, we follow standard terminology (i.e., similar to [21]). We write [N ] to denote the set {1, 2, . . . , N }, where N is a positive integer.
Let T be a rooted or unrooted tree. We write V(T ), E(T ), and L(T ) to denote, respectively, the node set, edge set, and leaf set of T . A taxon (plural taxa) is some basic unit of classification; e.g., a species. Let S be a set of taxa. A phylogenetic tree or phylogeny for S is a tree T such that L(T ) = S. Furthermore, if T is rooted, we require that every internal node have at least two children; if T is unrooted, every internal node is required to have degree at least three. We write RP (n) and P (n) to denote, respectively, the sets of all rooted and unrooted phylogenetic trees over S = [n].
An internal node in a rooted phylogeny is resolved if it has exactly two children; otherwise it is unresolved. Similarly, an internal node in an unrooted phylogeny is resolved if it has degree three, and unresolved otherwise. Unresolved nodes in rooted

and unrooted trees are also referred to as polytomies or multifurcations. A phylogeny (rooted or unrooted) is fully resolved if all its internal nodes are resolved.
A contraction of a phylogeny T is obtained by deleting an internal edge and identifying its endpoints. A phylogeny T2 refines phylogeny T1 if and only if T1 can be obtained from T2 through 0 or more contractions. T2 is a full refinement of T1 if T2 is a fully resolved tree that refines T1. F(T ) denotes the set of all full refinements of T .
Let X be a subset of L(T ) and let T [X] denote the minimal subtree of T having X as its leaf set. The restriction of T to X, denoted T |X, is the phylogeny for X defined as follows. If T is unrooted, then T |X is the tree obtained from T [X] by suppressing all degree-two nodes. If T is rooted, T |X is obtained from T [X] by suppressing all degree-two nodes except for the root.
A triplet is a three-element subset of S; a quartet is a four-element subset of S. A triplet (quartet) X is said to be resolved in a phylogenetic tree T over S if T |X is fully resolved; otherwise, X is unresolved.
Finally, we need some special notation for rooted trees T . We write rt(T ) to denote the root node of T . Let v be a node in T . Then, pa(v) denotes the parent of v in T and Ch(v) is the set of children of v. Furthermore, T (v) denotes the subtree of T rooted at v and T (v) denotes the tree obtained by deleting T (v) from T , as well as the edge from v to its parent, if such an edge exists.
Distance measures, metrics, and near-metrics. A distance measure on a set D is a binary function d on D satisfying the following three conditions: (i) d(x, y) ≥ 0 for all x, y ∈ D; (ii) d(x, y) = d(y, x) for all x, y ∈ D; and (iii) d(x, y) = 0 if and only if x = y. Function d is a metric if, in addition to being a distance measure, it satisfies the triangle inequality; i.e., d(x, z) ≤ d(x, y) + d(y, z) for all x, y, z ∈ D. Distance measure d is a near-metric if there is a constant c, independent of the size of D, such that d satisfies the relaxed polygonal inequality: d(x, z) ≤ c(d(x, x1) + d(x1, x2) + · · · + d(xn−1, z)) for all n > 1 and x, z, x1, . . . , xn−1 ∈ D [15]. Two distance measures d and d with domain D are equivalent if there are constants c1, c2 > 0 such that c1d (x, y) ≤ d(x, y) ≤ c2d (x, y) for every pair x, y ∈ D [15].
3 Distance measures for phylogenies
Let T1 and T2 be any two rooted (respectively, unrooted) phylogenies over the same taxon set S. We partition the set of triplets (quartets) over S into the following five sets.3
1. S(T1, T2): triplets (quartets) X that are resolved in T1 and T2, and T1|X = T2|X. 2. D(T1, T2): triplets (quartets) X that are resolved in T1 and T2, but T1|X = T2|X. 3. R1(T1, T2): triplets (quartets) X that are resolved in T1, but not in T2. 4. R2(T1, T2): triplets (quartets) X that are resolved in T2, but not in T1. 5. U (T1, T2): triplets (quartets) X that are unresolved in both T1 and T2.
3 Note that the sets S(T1, T2) and U (T1, T2) are not used in this section, but are needed in later ones.

Let p be a real number in the interval [0, 1]. The parametric triplet (quartet) distance between T1 and T2 is defined as

d(p)(T1, T2) = |D(T1, T2)| + p (|R1(T1, T2)| + |R2(T1, T2)|) .

(1)

Parameter p allows one to make a smooth transition from soft to hard views of polytomy: When p = 0, resolved triplets (quartets) are treated as equal to unresolved ones, while when p = 1, they are treated as being completely different. Intermediate values of p allow one to adjust for the amount of evidence required to resolve a polytomy.
Let d be a metric over fully resolved trees. Metric d can be extended to partially
resolved trees via Hausdorff distance, as follows.

dHaus(T1, T2) = max max min d(t1, t2), max min d(t1, t2) (2)

t1∈F (T1) t2∈F (T2)

t2∈F (T2) t1∈F (T1)

When d is the triplet (quartet) distance, dHaus is called the Hausdorff triplet (quartet) distance. Observe that, in Equation (2), maxt1∈F(T1) mint2∈F(T2) d(t1, t2) gives the maximum distance between a full refinement of T1 and the set of full refinements of T2. Similarly, maxt2∈F(T2) mint1∈F(T1) d(t1, t2) is the maximum distance between a full refinement of T2 and the set of full refinements of T1. Therefore, T1 and T2 are at Hausdorff distance r of each other if every full refinement of T1 is within distance r of a full refinement of T2 and vice-versa.

Aggregating phylogenies. Let k be a positive integer and S be a set of taxa. A profile of

length k (or simply a profile) is a mapping P that assigns each i ∈ [k] a phylogenetic

tree P(i) over S. We refer to these trees as input trees. A consensus rule is a function

that maps a profile P to some phylogenetic tree T over S called a consensus tree.

Let d be a distance measure whose domain is the set of phylogenies over S. We ex-

tend d to define a distance measure from profiles to phylogenies as d(T, P) =

k i=1

d(T

,

P

(i)).

A

consensus

rule

is

a

median

rule

for

d

if

for

every

profile

P

it

returns a phylogeny T ∗ of minimum distance to P; such a T ∗ is called a median. The

problem of finding a median for a profile with respect to a distance measure d is referred

to as the median problem (relative d), or as the aggregation problem.

4 Properties of parametric distance
In what follows, unless mentioned explicitly, whenever we refer to parametric distance, we mean both its triplet and quartet varieties. We begin with a useful observation.
Proposition 1. For every p, q such that p, q ∈ (0, 1], d(p) and d(q) are equivalent.
The proof of the next theorem is along the lines of an analogous result for aggregating partial rankings by Fagin et al. [15] and is omitted from this extended abstract.
Theorem 1. (1) For p = 0, d(p) is not a distance measure. (2) For 0 < p < 1/2, d(p) is a near-metric, but not a metric. (3) For p ≥ 1/2, d(p) is a metric.

Part (3) of Theorem 1 leads directly to approximation algorithms. Part (2) indicates that the measure degrades nicely, since constant factor approximation ratios are also achievable with near-metrics [15].
The next result establishes a threshold for p beyond which a collection of fully resolved trees give enough evidence to produce a fully resolved tree.

Theorem 2. Let P be a profile of length k, such that for all i ∈ [k], tree P(i) is fully resolved. Then, if p ≥ 2/3, there exists median tree T for P relative to d(p) such that T
is fully resolved.

Proof (sketch). Suppose T is a median tree that contains an unresolved node v. The key idea is to show that there is a way to refine v into two nodes such that the number of input triplet (quartet) topologies with which the resulting tree disagrees is at most twice the number with which it agrees. The theorem follows by applying this refinement step repeatedly, until a fully resolved tree is obtained.

We can, in fact, show that if p > 2/3 and the input trees are fully resolved, the median tree relative to d(p) must be fully resolved. On the other hand, it is easy to show that when p ∈ [1/2, 2/3), there are profiles of fully resolved trees whose median tree is
only partially resolved.
It is interesting to compare Theorem 2 with analogous results for partial rankings.
Consider the variation of Kendall’s tau for partial rankings in which a pair of items that is ordered in one ranking but is in the same bucket in the other contributes p to the distance, where p ∈ [0, 1]. This distance measure is a metric when p ≥ 1/2 [15]. Furthermore, if p ≥ 1/2 the median ranking relative to this distance is a full ranking if
the input consists of full rankings [4]. In contrast, Proposition 1 and Theorem 2 show that, for p ∈ [1/2, 2/3], parametric triplet or quartet distance are metrics, but the median
tree is not guaranteed to be fully resolved even if the input trees are. This opens up a range of values for p wherein parametric triplet/quartet distance is a metric, but where
one can adjust for the degree of evidence needed to resolve a node.
We now consider the expected value of parametric triplet and quartet distances.

Theorem 3. Let u(n) and r(n) denote the probabilities that a given quartet is, respec-
tively, unresolved or resolved in an unrooted phylogeny chosen uniformly at random from P (n). Then,

(i) E(d(p)(T1, T2)) =

n 4

·

2 3

·

r(n)2

+

2

·

p

·

r(n)

·

u(n)

, if T1 and T2 are un-

rooted phylogenies chosen uniformly at random with replacement from P (n), and

(ii) E(d(p)(T1, T2)) =

n 3

·

2 3

·

r(n

+

1)2

+

2

·

p

·

r(n

+

1)

·

u(n

+

1)

, if T1 and

T2 are rooted phylogenies chosen uniformly at random with replacement from

RP (n).

Part (i) of Theorem 3 follows directly from [13, 23]. Part (ii) follows from part (i)

and the relationship between rooted and unrooted trees [21]. Since u(n) ∼

π(2 ln 2−1) 4n

[23] and r(n) = 1 − u(n), Theorem 3 implies that E(d(p)(T1, T2)) is asymptotically

2 3

·

n 4

for

unrooted

trees

and

2 3

·

n 3

for rooted trees.

5 Relationships among the metrics

We do not know whether the Hausdorff triplet or quartet distances are computable in polynomial time. Indeed, we suspect that, unlike its counterpart for partial rankings, this may not be possible. On the positive side, we show here that, in a broad range of cases, it is possible to obtain an approximation to the Hausdorff distance by exploiting its connection with parametric distance. As in the previous section, our results apply to both triplet and quartet distances.

Lemma 1.

For

every

two

phylogenies

T1

and

T2

over

S

,

|D(T1

,

T2

)|

+

2 3

·max{|R1

(T1

,

T2

)|,

|R2(T1, T2)|} ≤ dHaus(T1, T2) ≤ |D(T1, T2)| +|R1(T1, T2)| +|R2(T1, T2)| +|U (T1, T2)|.

Proof (sketch). The proof of the lower bound on dHaus is in two steps. We first show that T1 can be refined so that it disagrees with T2 in at least two thirds of the triplets (quartets) in R2(T1, T2). Next, we show the existence of an analogous refinement of T2. Note that the triplets (quartets) in D(T1, T2) are resolved differently in any refinements of T1 and T2. This gives lower bounds for both arguments in the outer max of the definition of dHaus(T1, T2) (Equation 2) and yields the lemma.
The upper bound follows by assuming that T1 and T2 are refined so that the triplets (quartets) in R1(T1, T2), R2(T1, T2), and U (T1, T2) are resolved differently.

It is instructive to compare Lemma 1 with the situation for partial rankings. In the

Hausdorff version of Kendall’s tau, each partial ranking is viewed as the set of all pos-

sible full rankings that can be obtained by refining it (that is, ordering elements within

buckets). The distance is then the Hausdorff distance between the two sets, where the

distance between two elements is Kendall’s tau. Let L1 and L2 be two partial rankings. Re-using notation, let D(L1, L2) be the set of all pairs that are ordered differently in L1 and L2, R1(L1, L2) be the set of pairs that are ordered in L1 but in the same bucket in L2, and R2(L1, L2) be the set of pairs that are ordered in L2 but in the same bucket in L1. Then, dHaus(L1, L2) = |D(L1, L2)| + max{|R1(L1, L2)|, |R2(L1, L2)|} [12, 15]. This expression leads to an efficient way to compute dHaus(L1, L2) and establishes an

equivalence with the parametric version of Kendall’s tau defined in Section 4 [15]. It

seems unlikely that a similar simple expression can be obtained for Hausdorff triplet

or quartet distance. There are at least two reasons for this. Let L1 and L2 be partial

rankings. Then, it is possible to resolve L1 so that it disagrees with L2 in any pair in

R2(L1, L2). Similarly, there is a way to resolve L2 so that it disagrees with L1 in any

pair in R1(L1, L2). An analog for trees cannot be established for this property; hence,

the

2 3

factor

in

the

lower

bound

of

Lemma

1.

The

second

reason

is

due

to

the

properties

of the set U (L1, L2). It can be shown that is one can refine L1 and L2 in such a way

that pairs of elements that are unresolved in both rankings are resolved the same way

in the refinements. This is, in general, impossible for trees and leads to the presence of

|U (T1, T2)| in the upper bound of Lemma 1.

While the above observations are an obstacle to establishing equivalence between dHaus and d(p), we can show equivalence when the number of triplets (quartets) that are

unresolved in both trees is suitably small. The result below follows from Lemma 1.

Theorem 4. Let β be a positive real number. Suppose we restrict ourselves to pairs of trees (T1, T2) such that |U (T1, T2)| ≤ β(|D(T1, T2)| + |R1(T1, T2)| + |R2(T1, T2)|).
Then, Hausdorff distance and parametric distance are equivalent.

6 Computing parametric distance

Let R(T ) and U (T ) denote the sets of all triplets (quartets) that are, respectively resolved and unresolved in T . We need the following fact, which holds for rooted and
unrooted trees.

Proposition 2. For any two phylogenies T1, T2 over the same set of taxa,

d(p)(T1, T2) = |R(T1)| − |S(T1, T2)| + p · (|U (T1)| − |U (T2)|)

+ (2p − 1) · |R1(T1, T2)|.

(3)

Proof. It can be shown that |R1(T1, T2)| + |U (T1, T2)| = |U (T2)|, |R2(T1, T2)| + |U (T1, T2)| = |U (T1)|, and |S(T1, T2)|+|D(T1, T2)|+|R1(T1, T2)| = |R(T1)|. These
relationships, along with Equation (1), establish Equation (3).

6.1 Computing the parametric triplet distance
Theorem 5. The parametric triplet distance d(p)(T1, T2) for two rooted phylogenetic trees T1 and T2 over the same set of n taxa can be computed in O(n2) time.
Proof (sketch). Our algorithm computes d(p)(T1, T2) via Equation (3). For this, it needs |R(T1)|, |U (T1)|, |U (T2)|, |S(T1, T2)| and |R1(T1, T2)|. The first three values can easily be obtained in O(n) time. Below we outline an algorithm that computes the remaining two values in O(n2) time. This gives a O(n2) parametric triplet distance algorithm.
Our algorithm relies on a preprocessing step that calculates and stores the following four quantities for every pair u, v such that u, v are internal nodes of T1 and T2, respectively: |L(T1(u)) ∩ L(T2(v))|, |L(T1(u)) ∩ L(T2(v))|, |L(T1(u)) ∩ L(T2(v))|, and |L(T1(u)) ∩ L(T2(v))|. All these O(n2) values can be computed in O(n2) time by visiting the pairs according to interleaved postorder traversals of T1 and T2, in which the set intersection sizes for each pair of nodes are computed by using the set intersection
sizes computed for their children. We omit the details. We need two definitions. Let T be a rooted phylogenetic tree. Let X = {x, y, z} be
a triplet. Suppose X is resolved in T . We say that X is induced by edge (pa(v), v) in T if x, y are in L(T (v)), and z is in L(T (v)). Note that X may be induced by multiple edges in T . Now suppose X is unresolved in T . We say that X is associated with the least common ancestor (lca) v of X in T . Observe that node v is unique and that it must
be unresolved. To compute |S(T1, T2)| we enumerate all pairs of internal edges (pa(u), u) ∈ E(T1)
and (pa(v), v) ∈ E(T2) according to an order obtained by interleaving postorder traversals of T1 and T2. For each pair, we compute the number of common triplet topologies induced by the pair in O(1) time by using the values |L(T1(u)) ∩ L(T2(v))|, and

|L(T1(u)) ∩ L(T2(v))| computed in the preprocessing step. Thus, each identically re-

solved triplet is counted at least once. Since a triplet may be induced by multiple edges,

it is necessary to adjust for over counting. Indeed, among the triplets induced by the

edges (pa(u), u) ∈ T1 and (pa(v), v) ∈ T2, the ones that have already been counted at an earlier step are exactly those that are either (i) induced by both edges (pa(u), u) and

(u, y) in T1, for some y ∈ Ch(u), and are induced by the edge (pa(v), v) in T2, or, (ii) induced by both edges (pa(v), v) and (v, y) in T2, for some y ∈ Ch(v), and are induced by the edge (pa(u), u) in T1. Both the counting and the correction for over counting can be done in O(| Ch(u)| + | Ch(v)|) per pair, for a total of O(n2) time

To compute the value of |R1(T1, T2)| we enumerate all pairs formed by picking an

edge e = (pa(u), u) ∈ E(T1) and an internal unresolved node v ∈ V(T2) according

to interleaved postorder traversals of T1 and T2. At each step, we count the number of

triplets that are induced by e in T1 and associated with v in T2. Such triplets must be

resolved in T1 but unresolved in T2. Let us say that a triplet X is relevant if it is induced

by edge (pa(u), u) in T1, and T2[X] is a subtree of T2(v). There are m =

|P | 2

· |Q|

relevant triplets, where P = L(T2(v)) ∩ L(T1(u)) and Q = L(T2(v)) ∩ L(T1(u)).

Out of these, we are interested in counting the number of triplets X whose lca in T2

is v, and X is unresolved in T2. Any such triplet X falls into one of three categories:

(i) the lca of X in T2 is not v, (ii) the lca of X in T2 is v, X is resolved in T1 and

T2, and T1|X = T2|X, (iii) the lca of X in T2 is v, X is resolved in T1 and T2, but

T1|X = T2|X. The sizes of these sets can be obtained in O(| Ch(u)| · | Ch(v)|) time

— details are omitted. The number thus computed is then subtracted from m to get the

quantity we need. The total time over all pairs u, v is O(n2). As in the computation

of |S(T1, T2)|, we must correct for over counting. Indeed any triplet induced by edge (pa(u), u) and edge (u, y) in T1, for some y ∈ Ch(u), has already been counted in an earlier step of the interleaved traversals of T1 and T2. It can be shown that one can

adjust for this over counting while keeping within the required time bound.

6.2 An approximation algorithm for parametric quartet distance

Theorem 6. Let T1 and T2 be two unrooted phylogenetic trees on the same n leaves.

Then,

for

p

=

12 ,

d(p)(T1, T2)

can

be

computed

in

O(n2)

time.

For

p



(

1 2

,

1]

,

a

value

x such that d(p)(T1, T2) ≤ x ≤ 2 · d(p)(T1, T2) can be computed in O(n2) time.

Proof (sketch). Our algorithm first computes the values of |S(T1, T2)|, |R(T1)|, |U (T1)|, and |U (T2)| — this can be done in O(n2) time [10]. If p = 12 , these values suffice to obtain d(p)(T1, T2) exactly, since the term involving |R1(T1, T2)| in Equation (3) vanishes. For p > 21 , we also use Equation (3), but instead of |R1(T1, T2)| we use a 2approximation y to |R1(T1, T2)|; that is, y satisfies |R1(T1, T2)| ≤ y ≤ 2|R1(T1, T2)|. Below, we outline how to compute such a y in O(n2) time. As a result, we obtain a 2-approximation to d(p)(T1, T2) in O(n2) time.
Let (u, v) be an edge in tree T . We denote the subtree of T − (u, v) that contains node u by T (u ← v), and the other subtree by T (v ← u). Quartet {a, b, c, d} is induced
by edge (u, v) if {a, b} ∈ L(T (u ← v)) and {c, d} ∈ L(T (v ← u)). Every resolved quartet is induced by at least one edge. Quartet {a, b, c, d} is associated with node v in
TreesDistanceTripletTreeNodes