A practical approximation algorithm for solving massive instances of hybridization number for binary and nonbinary trees
 Leo van Iersel^{1}Email author,
 Steven Kelk^{2},
 Nela Lekić^{2} and
 Celine Scornavacca^{3}
DOI: 10.1186/1471210515127
© van Iersel et al.; licensee BioMed Central Ltd. 2014
Received: 2 December 2013
Accepted: 24 April 2014
Published: 5 May 2014
Abstract
Background
Reticulate events play an important role in determining evolutionary relationships. The problem of computing the minimum number of such events to explain discordance between two phylogenetic trees is a hard computational problem. Even for binary trees, exact solvers struggle to solve instances with reticulation number larger than 4050.
Results
Here we present CycleKiller and NonbinaryCycleKiller, the first methods to produce solutions verifiably close to optimality for instances with hundreds or even thousands of reticulations.
Conclusions
Using simulations, we demonstrate that these algorithms run quickly for large and difficult instances, producing solutions that are very close to optimality. As a spinoff from our simulations we also present TerminusEst, which is the fastest exact method currently available that can handle nonbinary trees: this is used to measure the accuracy of the NonbinaryCycleKiller algorithm. All three methods are based on extensions of previous theoretical work (SIDMA 26(4):16351656, TCBB 10(1):1825, SIDMA 28(1):4966) and are publicly available. We also apply our methods to real data.
Keywords
Hybridization number Phylogenetic networks Approximation algorithms Directed feedback vertex setBackground
Phylogenetic trees are used in biology to represent the evolutionary history of a set $\mathcal{X}$ of species (or taxa) [1, 2]. They are trees whose leaves are bijectively labeled by $\mathcal{X}$ and whose internal vertices represent the ancestors of the species set; they can be rooted or unrooted. Since in a rooted tree edges have a direction, the concepts of indegree and outdegree of a vertex are well defined. Binary rooted (phylogenetic) trees are rooted (phylogenetic) trees whose internal vertices have outdegree 2. Nonbinary rooted (phylogenetic) trees have no restriction on the outdegree of inner vertices.
Biological events in which a species derives its genes from different ancestors, such as hybridization, recombination and horizontal gene transfer events, cannot be modelled by a tree. To be able to represent such events, a generalization of trees is considered which allows vertices with indegree two or higher, known as reticulations. This model, which is called a rooted phylogenetic network, is of growing importance to biologists [3]. For detailed background information we refer the reader to [4–6].
Although phylogenetic networks are more general than phylogenetic trees, trees are still often the basic building blocks from which phylogenetic networks are constructed. Specifically, there are many techniques available for constructing gene trees. However, when more genes are analyzed, topological conflicts between individual gene phylogenies can arise for methodological or biological reasons (e.g. aforementioned reticulate phenomena such as hybridization). This has led computational biologists to try and quantify the amount of reticulation that is needed to simultaneously explain two trees.
To state this problem more formally, we have that a phylogenetic tree T on is a refinement of a phylogenetic tree T^{′} on the same set if T can be obtained from T^{′} by deleting edges and identifying their incident vertices. Then, we say that a phylogenetic network N on displays a phylogenetic tree T on if T can be obtained from a subgraph of N by contracting edges. Informally, this means that (a refinement of) T can be obtained from N by, for each reticulation vertex of N, “switching off” all but one of its incoming edges and then suppressing all indegree1 outdegree1 vertices (i.e. replacing paths of these vertices by one edge). Given two rooted phylogenetic trees T_{1} and T_{2} on , the problem then becomes to determine the minimum number of reticulation events contained in a phylogenetic network N on displaying both trees (where an indegreed reticulation counts as d−1 reticulation events). The value we are minimizing is often called the hybridization number and instead of the term phylogenetic network, the term hybridization network is often used. It is known that the problem of computing hybridization numbers is both NPhard and APXhard [7], but it is not known whether it is in APX (i.e. whether it admits a polynomialtime approximation algorithm that achieves a constant approximation ratio).
Until recently, most research on the hybridization number of two phylogenetic trees had focused on the question of how to exactly compute this value using fixed parameter tractable (FPT) algorithms, where the parameter in question is the hybridization number r of the two trees. For an introduction to FPT we refer to [8, 9].
For binary trees, algorithmic progress has been considerable in this area, with various authors reporting increasingly sophisticated FPT algorithms [10–13]. The fastest algorithms currently implemented are the algorithm available inside the package DENDROSCOPE[14], based on [15], and the sequence of progressively faster algorithms in the HYBRIDNET family [11, 16, 17]. The fastest theoretical FPT algorithm has running time O(3.18^{ r }n) [13], where n is the number of taxa in the trees.
Even though in practice it rarely happens that trees are binary, the nonbinary variant of the problem has been less studied. The nonbinary version is also FPT [18, 19] and a (nonFPT) algorithm has recently been implemented in DENDROSCOPE[14].
Such (FPT) algorithms do, however, have their limits. The running time still grows exponentially in r, albeit usually at a slower rate than algorithms that have a running time of the form n^{f(r)}, where f is some function of r. In practice this means that existing algorithms can only handle instances of binary trees when r is at most 4050 and instances of nonbinary trees when r is at most 510.
These limitations are problematic. Due to ongoing advances in DNA sequencing, more and more species and strains are being sequenced. Consequently, biologists use trees with more and more taxa and software that can handle large trees is required. For such large and/or difficult trees one can try to generate heuristic or approximate solutions, but how far are such solutions from optimality? In [20] we showed that the news is worrying. Indeed, we showed that polynomialtime constantratio approximation algorithms exist if and only if such algorithms exist for the problem Directed Feedback Vertex Set (DFVS). However, DFVS is a wellstudied problem in combinatorial optimization and to this day it is unknown if it permits such an algorithm. Pending a major breakthrough in computer science, it therefore seems difficult to build polynomialtime algorithms which approximate hybridization number well. On the positive side, we showed that in polynomial time an algorithm with approximation ratio O(logr log logr) is possible. However, this algorithm is purely of theoretical interest and is not useful in practice.
New algorithms: CYCLEKILLER and NONBINARYCYCLEKILLER
In this article we extend the theoretical work of [20] slightly and give it a practical twist to yield a fast approximation algorithm which we have made publicly available as the program CYCLEKILLER. Furthermore, we give an implementation of the algorithm presented in [21], available as NONBINARYCYCLEKILLER.
The worstcase running time of these approximation algorithms is exponential. However, as we demonstrate with experiments, the running time of our algorithms is in practice extremely fast. For large and/or massively discordant binary trees, CYCLEKILLER is typically orders of magnitude faster than the HYBRIDNET algorithms and the algorithm in DENDROSCOPE. The performance gap between NONBINARYCYCLEKILLER and its exact counterparts is less pronounced, but still significant, especially in its fastest mode of operation.
Of course, exact algorithms attempt to compute optimum solutions, whereas our algorithms only give approximate solutions. Nevertheless, our experiments show that when CYCLEKILLER and NONBINARYCYCLEKILLER are run in their most accurate mode of operation, an approximation ratio very close to 1 is not unusual, suggesting that the algorithms often produce solutions close to optimality and well within the worstcase approximation guarantee.
The idea behind the binary and nonbinary algorithm is similar. Specifically, we describe an algorithm with approximation ratio d(c+1) for the hybridization number problem on two binary trees and an algorithm with approximation ratio d(c+3) for the hybridization number problem on two nonbinary trees by combining a capproximation for the problem MAF (Maximum Agreement Forest) with a dapproximation for the problem DFVS. Both these problems are NPhard so polynomialtime algorithms attaining c=1 or d=1 are not realistic. Nevertheless, there exist extremely fast FPT algorithms for solving MAF on binary trees exactly (i.e. c=1), the fastest is RSPR by Whidden, Beiko and Zeh [22, 23] although the MAF algorithm inside [17] is also competitive. Moreover, we observe that the type of DFVS instances that arise in practice can easily be solved using Integer Linear Programming (ILP) (and freelyavailable ILP solver technology such as GLPK), so d=1 is also often possible.
Combining these two exact approaches gives us, in the binary case, an exponentialtime approximation algorithm with worstcase approximation ratio 2 that for large instances still runs extremely quickly; this is the 2approx option of CYCLEKILLER. In practice, we have observed that the upper bound of 2 is often pessimistic, with much better approximation ratios observed in experiments (1.003 on average for the simulations presented in this article). We find that this algorithm already allows us to cope with much bigger trees than the HYBRIDNET algorithms or the algorithm in DENDROSCOPE.
Nevertheless, for truly massive trees it is often not feasible to have c=1. Fortunately there exist lineartime algorithms which achieve c=3 [13]. This, coupled with the fact that (even for such trees) it remains feasible to use an exact (d=1) solver for DFVS, means that in practice we achieve a 4approximation for gigantic binary trees; this is the 4approx option of CYCLEKILLER. Again, the ratio of 4 is a worstcase bound and we suspect that in practice we are doing much better than 4. However, this cannot be experimentally verified due to the lack of good lower bounds for such massive instances. In any case, the main advantage of this option is that it can, without too much effort, cope with trees with hundreds or thousands of taxa and hybridization number of a similar order of magnitude. An implementation of CYCLEKILLER and accompanying documentation can be downloaded from http://skelk.sdfeu.org/cyclekiller. Networks created by the algorithm can be viewed in DENDROSCOPE.
For the nonbinary case, there also exist exact and approximation algorithms for MAF [13, 21, 24]. In case when one of the input trees is binary we can still use the exact (thus c=1) and approximate (c=3) algorithms given in [13] (referred to as RSPR) to obtain respectively a 4approximation and a 6approximation of the hybridization number problem for nonbinary trees. When both input trees are nonbinary, then we must use the somewhat less optimized exact (c=1) and approximate (c=4) algorithms described in [21]. We then obtain 4 and 7approximations (because in the nonbinary case d=1 is still easily attainable using ILP).
To measure the approximation ratios attained by NONBINARYCYCLEKILLER in practice we have also implemented and made publicly available the exact nonbinary algorithm TERMINUSEST, based on the theoretical results in [18]. TERMINUSEST will be of independent interest because it is currently the fastest exact nonbinary solver available.
CYCLEKILLER, NONBINARYCYCLEKILLER and TERMINUSEST can be downloaded respectively from http://skelk.sdfeu.org/cyclekiller[25], from http://homepages.cwi.nl/~iersel/cyclekiller[26], and from http://skelk.sdfeu.org/terminusest[27].
Theoretical and practical significance
We have described, implemented and made publicly available two algorithms with two desirable qualities: they terminate quickly even for massive instances of hybridization number and give a nontrivial guarantee of proximity to optimality. These are the first algorithms with such properties. Both algorithms are based on a nontrivial marriage of MAF and DFVS solvers (both exact and approximate), meaning that further advances in solving MAF and DFVS will directly lead to improvements in CYCLEKILLER and NONBINARYCYCLEKILLER.
This article also improves the theoretical work given in [20], which also proposed using DFVS but beginning from a trivial Agreement Forest (AF) known as a chain forest. Here we use a smarter starting point: an (approximate) MAF, and it is this insight which makes a 2approximation (rather than the 6approximation implied by [20]) possible when using an exact DFVS solver. Other articles have also had the idea of cyclebreaking in AFs: the advanced FPT algorithm of Whidden et al. [13] – which has not been implemented – and the algorithms in the aforementioned HYBRIDNET family. However, both algorithms start the cyclebreaking from many starting points. In contrast, our algorithm requires only a single starting point, i.e. a single (approximate) solution to MAF.
Here, we only present the theory behind the binary algorithm. The nonbinary case is more involved and we refer the reader to [21] in which we introduce it. Note that our results for the binary case do not follow from the results for the nonbinary case in [21] because here we obtain a better constant in the approximation ratio. After a presentation of the binary algorithm in Section “??”, we will show the results of some experiments with binary trees in Section “Practical experiments with binary trees” and nonbinary trees in Section “Practical experiments with nonbinary trees”. Finally, in Section “Practical experiments on biologically relevant trees” we demonstrate that both TERMINUSEST and NONBINARYCYCLEKILLER are easily capable of generating optimal (respectively, nearly optimal) solutions on a real biological dataset originally obtained from the GreenPhylDB database.
Technical note
At the time the experiments on binary trees were conducted (i.e. for the preliminary version of this article [28]) HYBRIDNET was the fastest algorithm available in its family. It has recently been superceded by the faster ULTRANET[17]. We believe, however, that it is neither necessary nor desirable to rerun the binary experiments, for the following reasons. In the same period the solver RSPR has also increased dramatically in speed (it is now at v1.2), leading to a corresponding speedup in CYCLEKILLER. In fact, both RSPR and the algorithms in the HYBRIDNET family are constantly in flux and are always being improved, so any experimental setup is prone to age extremely quickly. However, the conclusions that we can derive from these experiments are unlikely to change much over time. Given that the algorithms in the HYBRIDNET family (and the theoretical algorithm in [13]) implicitly have to explore exponentially many optimal and suboptimal solutions to the MAF problem, the running time of MAF solvers (and thus also CYCLEKILLER) is likely for the foreseeable future to remain much better than the running time of solvers for hybridization number. The central message is stable: approximating hybridization number by splitting it into MAF and DFVS instances yields extremely competitive approximation ratios for instances that exact hybridization number solvers will probably never be able to cope with.
Methods
Preliminaries
Let be a finite set (e.g. of species). A rooted phylogenetic tree is a rooted tree with no vertices with indegree 1 and outdegree 1, a root with indegree 0 and outdegree at least 2, and leaves bijectively labelled by the elements of . We identify each leaf with its label and use L(T) to refer to the leaf set (or label set) of T. A rooted phylogenetic tree is called binary if each nonleaf vertex has outdegree two. We henceforth call a rooted, binary phylogenetic tree a tree for short. For a tree T and a set ${\mathcal{X}}^{\prime}\subset \mathcal{X}$, we use the notation $T\left({\mathcal{X}}^{\prime}\right)$ to denote the minimal subtree of T that contains all elements of ${\mathcal{X}}^{\prime}$ and $T{\mathcal{X}}^{\prime}$ denotes the result of suppressing all indegree1 outdegree1 vertices in $T\left({\mathcal{X}}^{\prime}\right)$.
The following definitions apply only to binary trees. Definitions for nonbinary trees are analogous but slightly more technical [21].
We define a forest as a set of trees. Each element of a forest is called a component. Let T be a tree and a forest. We say that is a forest for T if, for all $F\in \mathcal{F}$, TL(F) is isomorphic to F and the trees $\left\{T\right(L\left(F\right)),F\in \mathcal{F}\}$ are vertexdisjoint subtrees of T whose leafset union equals L(T). If T_{1} and T_{2} are two trees, then a forest is an agreement forest of T_{1} and T_{2} if it is a forest for T_{1} and T_{2}. The number of components of is denoted $\left\mathcal{F}\right$.
We define cleaning up a directed graph as repeatedly suppressing indegree1 outdegree1 vertices, removing indegree0 outdegree1 vertices and removing unlabelled outdegree0 vertices until no such operation is possible. Observe that, if is a forest for T, can be obtained from T by removing $\left\mathcal{F}\right1$ edges and cleaning up. From now on we consider T_{1},T_{2} as trees on the same taxon set.
Problem: Maximum Agreement Forest (MAF)Instance: Two rooted, binary phylogenetic trees T_{1} and T_{2}.Solution: An agreement forest of T_{1} and T_{2}.Objective: Minimize $\left\mathcal{F}\right1$.
The directed graph $\mathit{\text{IG}}({T}_{1},{T}_{2},\mathcal{F})$, called the inheritance graph, is the directed graph whose vertices are the components of and which has an edge (F,F^{′}) precisely if either

there is a directed path in T_{1} from the root of T_{1}(L(F)) to the root of T_{1}(L(F^{′})) or;

there is a directed path in T_{2} from the root of T_{2}(L(F)) to the root of T_{2}(L(F^{′})).
An agreement forest of T_{1} and T_{2} is called an acyclic agreement forest if the graph $\mathit{\text{IG}}({T}_{1},{T}_{2},\mathcal{F})$ is acyclic. A maximum acyclic agreement forest (MAAF) of T_{1} and T_{2} is an acyclic agreement forest of T_{1} and T_{2} with a minimum number of components.
Problem: Maximum Acyclic Agreement Forest (MAAF)Instance: Two rooted, binary phylogenetic trees T_{1} and T_{2}. Solution: An acyclic agreement forest of T_{1} and T_{2}.Objective: Minimize $\left\mathcal{F}\right1$.
We use MAF (T_{1},T_{2}) and MAAF (T_{1},T_{2}) to denote the optimal solution value of the problem MAF and MAAF respectively, for an instance T_{1},T_{2}.
It was shown that the optimum to MAAF is equal to the optimum of the following problem [29].
Problem: MINIMUMHYBRIDIZATIONInstance: Two rooted binary phylogenetic trees T_{1} and T_{2}.
Solution: A rooted phylogenetic network N that displays T_{1} and T_{2}.Objective: Minimize r(N).
Moreover, it was shown that, for two trees T_{1},T_{2}, any acyclic agreement forest for T_{1} and T_{2} with k+1 components can be turned into a phylogenetic network that displays T_{1} and T_{2} and has reticulation number k, and vice versa. Thus, any approximation for MAAF gives an approximation for MINIMUMHYBRIDIZATION.
Finally, a feedback vertex set of a directed graph is a subset of the vertices that contains at least one vertex of each directed cycle. Equivalently, a subset of the vertices of a directed graph is a feedback vertex set if removing these vertices from the graph makes it acyclic.
Problem: Directed Feedback Vertex Set (DFVS)Instance: A directed graph D. Goal: Find a feedback vertex set of D of minimum size.
We note that the definition of MINIMUMHYBRIDIZATION easily generalises to nonbinary trees, since the definition of display allows the image of each input tree in the network to be more “resolved” than the original tree. However, the definitions of (acyclic) agreement forests are different in the nonbinary case [21].
The algorithm for binary trees
We show how MAAF can be approximated by combining algorithms for MAF and DFVS. In particular, we will prove the following theorem.
Theorem 1
If there exists a capproximation for MAF and a dapproximation for DFVS, then there exists a d(c+1)approximation for MAAF (and thus for MINIMUMHYBRIDIZATION).
Note that this theorem does not follow from Theorem 2.1 of [21], since there the approximation ratio for MAAF is a d(c+3)approximation.
An Msplitting is an acyclic agreement forest that can be obtained from M by removing edges and cleaning up.
Lemma 2
Let T_{1} and T_{2} be two trees and M an agreement forest of T_{1} and T_{2}. Then, there exists an Msplitting of size at most MAAF(T_{1},T_{2})+M.
Proof
Consider a maximum acyclic agreement forest F of T_{1} and T_{2}. For i∈{1,2}, F can be obtained from T_{ i } by removing a set of edges, say ${E}_{F}^{i}$, and cleaning up. Moreover, also M can be obtained from T_{ i } by removing a set of edges, say ${E}_{M}^{i}$, and cleaning up.
Now consider the forest S obtained from T_{1} by removing ${E}_{M}^{1}\cup {E}_{F}^{1}$and cleaning up. Then,

S is an agreement forest of T_{1} and T_{2} because it can be obtained from T_{2} by removing edges ${E}_{M}^{2}\cup {E}_{F}^{2}$ and cleaning up;

S is acyclic because it can be obtained by removing edges from F, which is acyclic, and cleaning up;

S can be obtained from M by removing edges and cleaning up.
Hence, S is an Msplitting. Furthermore, $\leftS\right\le \left{E}_{F}^{1}\right+\left{E}_{M}^{1}\right+1$. The lemma follows since $\left{E}_{F}^{1}\right=\text{MAAF}({T}_{1},{T}_{2})$ and $\leftM\right=\left{E}_{M}^{1}\right+1$.
Lemma 3.
A subset V^{′} of the vertices of D is a feedback vertex set of D if and only if removing V^{′} from M makes it an acyclic agreement forest.
Proof.
We show that D∖V^{′} has a directed cycle if and only if the inheritance graph of M∖V^{′} has a directed cycle.
To prove this, first suppose that there is a cycle v_{1},v_{2},…,v_{ k }=v_{1} in the inheritance graph of M∖V^{′}. The vertices in the inheritance graph of M∖V^{′} correspond to the roots of the components of M∖V^{′}. Since these roots have outdegree 2 in M∖V^{′}, they had outdegree 2 in M, and are thus vertices of D. So the vertices v_{1},v_{2},…,v_{ k } that form the cycle are vertices of D. Since these vertices are in the inheritance graph of M∖V^{′}, they can not be in V^{′} and so they are vertices of D∖V^{′}. The reachability relation between these vertices in D∖V^{′} is the same as in the inheritance graph of M∖V^{′}. So, the vertices v_{1},v_{2},…,v_{ k } form a cycle in D∖V^{′}.
Now suppose that there is a cycle w_{1},w_{2},…,w_{ k }=w_{1} in D∖V^{′}. Each of the vertices w_{1},w_{2},…,w_{ k } is a vertex with outdegree2 in M. Some of them might be roots of components, while others are not. However, observe that if there is a directed path from a vertex u to a vertex v in T_{1} (or in T_{2}) then there is also a directed path from the root of the component of M∖V^{′} that contains u to the root of the component of M∖V^{′} that contains v. Hence, there is a directed cycle in the inheritance graph of M∖V^{′}, formed by the roots of the components of M∖V^{′} that contain w_{1},w_{2},…,w_{ k }.
Proof of Theorem 1
Suppose that there exists a dapproximation for DFVS. Let FVS be a feedback vertex set returned by this algorithm and let MFVS be a minimum feedback vertex set. Then, removing the vertices of MFVS from M gives an optimal Msplitting. Furthermore, OptSplitting${}_{{T}_{1},{T}_{2}}\left(M\right)=\leftM\right+\left\text{MFVS}\right$. This is because for every vertex in a cycle C, its parent in M must participate in some cycle that contains elements of C. So if we start by removing the root of the component we are splitting and subsequently remove only those vertices whose parents have already been removed we see that we add at most one component per vertex. In fact, because vertices of D all have outdegree 2 in M, we add exactly one component per vertex.
where the last inequality follows from Eq. 2. Thus, is a d(c+1)approximation to MAAF, which concludes the proof of Theorem 1.
Theorem 1 implies that a solution to the MAAF problem for a given instance can be constructed by (i) finding a solution to the MAF problem for the same instance (ii) constructing the extended inheritance graph D for (iii) finding a solution V for the DFVS problem on the graph D and (iv) modifying accordantly to V.
Results and discussion
Practical experiments with binary trees
To assess the performance of CYCLEKILLER, a simulation study was undertaken. We generated 3 synthetic datasets, an easy, a medium and a hard one, containing respectively 800, 640 and 640 pairs of rooted binary phylogenetic trees.
The easy data set was created by varying two parameters, namely the number of taxa n and the number of rSPRmoves k used to obtain the second tree from the first (note that this number is an upper bound on the actual rSPR distance). The 800 pairs of rooted binary phylogenetic trees were created by varying n in {20,50,100,200} and k in {5,10,...,25}, and then creating 40 different instances per each combination of parameters. Each pair (T_{1},T_{2}) of rooted binary phylogenetic trees for a given set of parameters n and k is created as follows: The first tree T_{1} on $\mathcal{X}=\{{x}_{1},\dots ,{x}_{n}\}$ is generated by first creating a set of n leaf vertices bijectively labeled by the set . Then, two vertices u and v, both with indegree 0, are randomly picked and a new vertex w, along with two new edges (w,u) and (w,v), is created. This is done until only one vertex with no ancestor, the root, is present. The second tree T_{2} is obtained from T_{1} by applying k rSPRmoves. The medium and the hard data sets were generated in the same way as the easy one, but for different choices of the parameters: n in {50,100,200,300} and k in {15,25,40,55} for the medium one and n in {100,200,400,500} and k in {40,60,80,100} for the hard one.
The exact hybridization number has been computed by HYBRIDNET[11], available from http://www.cs.cityu.edu.hk/~lwang/software/Hn/treeComp.html or with DENDROSCOPE[14], available from http://dendroscope.org/. We will refer to these algorithms as the exact algorithms. Each instance has been run on a single core of an Intel Xeon E5506 processor.
Given a directed graph D=(V,E), the binary variables x_{ v } model whether a vertex is in the feedback vertex set, and the integer variables ℓ_{ v } model the positions of the surviving vertices in the induced topological order. The edge constraints enforce the topological order. Note that an edge constraint is essentially eliminated if one or both endpoints of the edge are in the feedback vertex set.
Experimental results for instances with two binary trees
Exact algorithms  CYCLEKILLER  

Dataset  Total runs  Completed  Time  2approx  4approx  Ratio  Opt. found  
Compl.  Time  Compl.  Time  
Easy  800  767  798  800  3      1.003  96.6% 
Medium  640  199  2572  613  212  27  <1  1.002  97.5% 
Hard  640  0  3600  440  1271  200  1.5     
For the medium data set, CYCLEKILLER finished with the 2approx option for 613 instances, and for the remaining ones with the 4approx option. The exact algorithms could compute the hybridization number for only 199 instances (out of 640). For 97.5% of these instances, CYCLEKILLER also found an optimal solution, but with a much better running time. Regarding the hard data set, 444 runs were completed with the 2 approx option and for the remaining ones we were able to use the 4approx option within the given time constraint. Unfortunately, the exact algorithms were unable to compute the hybridization number for any treepair of this data set and hence we could not compute the average approximation ratios. Over all our experiments, the maximum hybridization number that the exact algorithms could handle was 25^{a}. In contrast, the 2approx option of CYCLEKILLER could be used for instances for which the size of a MAF was up to 97, and thus for instances for which the hybridization number was at least 97.
To find the limits of the 4approx option of CYCLEKILLER, we also tested it on randomly generated trees. On a normal laptop, it could construct networks with up to 10,000 leaves and up to 10,000 reticulations within 10 minutes. Since the number of reticulations found is at most four times the optimal hybridization number, this implies that the 4approx option of CYCLEKILLER can handle hybridization numbers up to at least 2,500. These randomly generated trees are, however, biologically meaningless and, therefore, we conducted the extensive experiment described above on trees generated by rSPR moves. Finally we note that over all experiments the worst approximation ratio we encountered was 1.2.
Practical experiments with nonbinary trees
To run the simulations with NONBINARYCYCLEKILLER, we used a subset of the trees from the easy set of binary experiments. We then applied random edge contractions in order to obtain nonbinary trees. Hence, we have the same two parameters as before, namely the number of taxa n∈{20,50,100} and the number of rSPRmoves k∈{5,10,15,20}, and an additional parameter ρ∈{25,50,75} which measures the percentage of the edges of an original binary tree that were contracted in order to obtain a nonbinary tree. We could only use smaller values of n and k from the easy set of experiments because exact solvers for nonbinary MAF (upon which NONBINARYCYCLEKILLER is built) and exact solvers for nonbinary MINIMUMHYBRIDIZATION (which is important to measure the accuracy of NONBINARYCYCLEKILLER in practice) are slower than their binary counterparts.
We performed two runs of experiments^{b}. One run with instances consisting of one binary and one nonbinary tree, and one run with instances consisting of two nonbinary trees.
Summary of results for instances with one binary and one nonbinary tree
TERMINUSEST  NONBINARYCYCLEKILLER  

4approx  6approx  
Contr.  Dataset  opt  Time  r ( N )  Time  Ratio  r ( N )  Time  Ratio 
25%  Simple  7.504  8.004  7.567  0.967  1.007  11.421  0.996  1.532 
Tricky  17.000  203.650  17.288  3.675  1.003  27.238  3.638  1.600  
50%  Simple  6.736  9.896  6.829  0.942  1.008  10.900  0.925  1.639 
Tricky  14.976  374.263  16.288  3.388  1.006  26.413  3.438  1.640  
75%  Simple  5.139  12.304  5.263  0.867  1.011  8.692  0.963  1.659 
Tricky  10.500  391.575  13.475  3.263  1.006  23.200  3.275  1.633  
Worst case  20  600  22  15  1.75  37  13  3 
Summary of results for instances with two nonbinary trees
TERMINUSEST  NONBINARYCYCLEKILLER  

4approx  7approx  
Contr.  Dataset  opt  Time  r ( N )  Time  Ratio  r ( N )  Time  Ratio 
25%  Simple  7.168  12.971  7.240  43.967  1.032  16.338  2.463  2.343 
Tricky  16.148  279.100        35.638  7.000  2.193  
50%  Simple  5.933  11.150  5.900  41.325  1.030  13.721  2.004  2.405 
Tricky  13.216  379.238        32.363  7.200  2.331  
75%  Simple  3.654  1.121  3.729  4.208  1.015  9.075  1.483  2.590 
Tricky  8.672  183.150        21.950  5.800  2.294  
Worst case  20  600  29  600  1.5  56  22  4 
The exact hybridization number in both runs was computed by TERMINUSEST[27].
Each instance that took longer than 10 minutes to compute was aborted and the running time was set to 600 seconds. The averages of the runningtimes are taken over all instances, with runningtime taken to be 600 if the program timed out for that instance. (We used a shorter timeout than in the binary experiments because of the observation that, in the nonbinary case, exact algorithms running longer than 10 minutes almost always took longer than 60 minutes too.)
Note that we did not compare the performance of NONBINARYCYCLEKILLER to DENDROSCOPE because TERMINUSEST has better running times than the exact nonbinary MINIMUMHYBRIDIZATION solver inside DENDROSCOPE (data not shown).
To enable a clearer analysis we divided the trees into representative “simple” and “tricky” ones based on two parameters, n and k. Parameter values for the simple set were n∈{20,50}, k∈{5,10,15} and for the tricky set n∈{50,100}, k=20. In addition we varied the percentage of contracted edges (in a single tree in the first run and in both trees in the second run).
In Table 2 we show running times and solution quality of our algorithm when one of the input trees is binary. For the simple set of instances (regardless of the percentage of edgecontractions) we see that the more accurate version of our algorithm, the 4approximation, had a better running time than the exact algorithm, and at the same time had an average approximation ratio very close to 1. Far more interesting is to see what happens with tricky instances. As predicted, the running time of the exact algorithm is much higher for tricky instances due to the higher hybridization numbers. On the other hand, the running time of the 4approximation does not rise significantly at all, whilst still attaining an approximation ratio again very close to 1. Another thing to note is that the percentage of contraction only seems to affect the running time of the exact algorithm. The practical worstcase approximation ratio observed in these experiments was 1.75 for the 4approximation and 3 for the 6approximation.
Table 3 shows our results on instances with two nonbinary trees. The exact algorithm for MAF is in this case much slower and this affects the running times even for the simple set. While the 4approximation version has an average approximation ratio very close to 1 again, the running time is in this case worse than that of TERMINUSEST. For the tricky set the situation is even more significant; the exact MAF algorithm cannot deal with reticulation numbers above 15, while TERMINUSEST can get slightly further. On the other hand, the 7approximation still runs much faster than TERMINUSEST, both for simple and tricky instances, while having an average approximation ratio of less than 2.6. The practical worstcase approximation ratio observed in these experiments was 1.5 for the 4approximation and 4 for the 7approximation.
It is worth noting that, for the 4approximation, the running time for the 75%contraction trees is considerably lower than the one for the 50%contraction trees. This is due to the fact that a high contraction in both trees causes the hybridization number of the instance to drop, and a lower hybridization number leads to a better running time. Also note that the exact solver TERMINUSEST seems more able to cope with the tricky 25%contraction instances than the tricky 50%contraction instances. This is probably because, although low contraction rates yield a higher hybridization number, the trees remain “relatively binary” and this can induce more efficient branching in the underlying FPT algorithm [18]. It is plausible that with 50%contraction the instances suffer from the disadvantage of relatively high hybridization number without the branching advantages associated with (relatively) binary trees.
To find the limits of the 7approx option of NBCK, we also tested it on huge, biologically meaningless, randomly generated trees. Below some results:

1000 leaves, 25%contraction, on average 995 reticulations in 63 sec.

1000 leaves, 50%contraction, on average 989 reticulations in 82 sec.

1000 leaves, 75%contraction, on average 840 reticulations, in 656 sec.
Computation times of this last run of experiments do not include the network construction.
Practical experiments on biologically relevant trees
Summary of results for dataset F _{ 1 } (204 gene trees) originally obtained from GreenPhylDB database
MIN  AVG  MAX  

Common taxa  3  5.235  20 
opt  0  0.873  7 
Ratio 4approx  1  1.002  1.2 
Ratio 6approx  1  1.088  3 
Gap (TEST  MAF)  0  0.010  1 
Gap (4approx  MAF)  0  0.020  2 
Time TEST  0  0.221  3 
Time 4approx  0  0.270  1 
Summary of results for dataset F _{ 2 } (1003 gene trees) originally obtained from GreenPhylDB database
MIN  AVG  MAX  

Common taxa  3  11.704  22 
opt  0  2.854  10 
Ratio 4approx  1  1.025  2 
Ratio 6approx  1  1.264  3 
Gap (TEST  MAF)  0  0.048  1 
Gap (4approx  MAF)  0  0.165  3 
Time TEST  0  0.576  7 
Time 4approx  0  0.605  3 
Summary of results for dataset ${F}_{3}^{p}$ (5924 gene trees) originally obtained from GreenPhylDB database
MIN  AVG  MAX  

Common taxa  2  14.206  22 
opt  0  3.613  12 
Ratio 4approx  1  1.027  2 
Ratio 6approx  1  1.277  3 
Gap (TEST  MAF)  0  0.065  2 
Gap (4approx  MAF)  0  0.195  4 
Time TEST  0  0.689  21 
Time 4approx  0  0.729  3 
Summary of results for dataset ${F}_{3}^{s}$ (5789 gene trees) originally obtained from GreenPhylDB database
MIN  AVG  MAX  

Common taxa  3  17.319  22 
opt  0  1.560  12 
Ratio 4approx  1  1.021  2 
Ratio 7approx  1  1.704  4 
Gap (TEST  MAF)  0  0.053  4 
Gap (4approx  MAF)  0  0.132  5 
Time TEST  0  0.422  15 
Time 4approx  0  1.182  14 
Conclusions
Our experiments with binary trees show that CYCLEKILLER is much faster than available exact methods once the input trees become sufficiently large and/or discordant. In over 96% of the cases CYCLEKILLER finds the optimal solution and in the remaining cases it finds a solution very close to the optimum. We have shown that the most accurate mode of the program produces solutions that are at most a factor 2 from the optimum. In practice, the averagecase approximation ratio that we observed was 1.003. The fastest mode of the algorithm can be used on trees with thousands of leaves and provably constructs networks that are at most a factor of 4 from the optimum.
Our experiments with nonbinary trees highlight once again that the cyclebreaking technique described in this article is intrinsically linked to the current stateoftheart in MAF algorithms. TERMINUSEST is faster than the most accurate mode of NONBINARYCYCLEKILLER when both trees are nonbinary due to the fact that MAF solvers for two nonbinary trees have not yet been optimized to the same extent as their binary counterparts. In fact, TERMINUSEST is the best avaible exact method for nonbinary trees and can handle instances for which the optimum is up to 1520. For other instances, NONBINARYCYCLEKILLER in its fastest mode is much faster than TERMINUSEST and produces solutions that are at most a factor 4 from the optimum (less than 2.6 on average).
Finally, for instances with one binary and one nonbinary tree, the most accurate mode of NONBINARYCYCLEKILLER is again much faster than TERMINUSEST and produces solutions that are at most a factor 1.75 from the optimum (less than 1.011 on average).
Endnotes
^{a} In [15], it has been shown that this number can go up to 40 when running Dendroscope on a similar processor but allocating all cores for one instance, i.e. exploiting the possibilities of parallel computation of this implementation.
^{b} We note that NONBINARYCYCLEKILLER uses a rowgeneration ILP formulation  based on [33]  to solve DFVS, rather than the polynomialsize formulation used by CYCLEKILLER. ILP is in neither case a bottleneck for the running time.
Authors’ information
Leo van Iersel was supported by a Veni grant of The Netherlands Organisation for Scientific Research (NWO). Nela Lekić was supported by a Vrije Competitie grant of of The Netherlands Organisation for Scientific Research (NWO).
Declarations
Acknowledgements
A preliminary version of this paper (restricted to the binary case) appeared in the proceedings of the 12th Workshop on Algorithms in Bioinformatics (WABI 2012) [28]. We thank Simone Linz and Leen Stougie for fruitful discussions. This publication is the contribution no. 2014040 of the Institut des Sciences de l’Evolution de Montpellier (ISEM, UMR 5554). This work has been partially funded by the French Agence Nationale de la Recherche, Investissements d’avenir/Bioinformatique (ANR10BINF0102, Ancestrome), and it has benefited from the ISEM computing facilities.
Authors’ Affiliations
References
 Gascuel O, (ed.): Mathematics of Evolution and Phylogeny. 2005, UK: Oxford University Press Inc.Google Scholar
 Gascuel O, Steel M, (eds.): Reconstructing Evolution: New Mathematical and Computational Advances. 2007, UK: Oxford University PressGoogle Scholar
 Bapteste E, van Iersel LJJ, Janke A, Kelchner S, Kelk SM, McInerney JO, Morrison DA, Nakhleh L, Steel M, Stougie L, Whitfield J: Networks: expanding evolutionary thinking. Trends Genet. 2013, 29 (8): 439441. 10.1016/j.tig.2013.05.007.View ArticlePubMedGoogle Scholar
 Huson DH, Rupp R, Scornavacca C: Phylogenetic Networks: Concepts, Algorithms and Applications. 2011, UK: Cambridge University Press,Google Scholar
 Huson DH, Scornavacca C: A survey of combinatorial methods for phylogenetic networks. Genome Biol Evol. 2011, 3: 2335. 10.1093/gbe/evq077.View ArticlePubMed CentralPubMedGoogle Scholar
 Nakhleh L: Evolutionary phylogenetic networks: models and issues. The Problem Solving Handbook for Computational Biology and Bioinformatics. Edited by: Heath L, Ramakrishnan N. 2009, Berlin: Springer,Google Scholar
 Bordewich M, Semple C: Computing the minimum number of hybridization events for a consistent evolutionary history. Discrete Appl Math. 2007, 155 (8): 914928. 10.1016/j.dam.2006.08.008.View ArticleGoogle Scholar
 Flum J, Grohe M: Parameterized Complexity Theory. 2006, Berlin: SpringerGoogle Scholar
 Downey RG, Fellows MR: Parameterized Complexity (Monographs in Computer Science). 1999, Berlin: SpringerView ArticleGoogle Scholar
 Bordewich M, Linz S, John KS, Semple C: A reduction algorithm for computing the hybridization number of two trees. Evol Bioinform. 2007, 3: 8698.Google Scholar
 Chen ZZ, Wang L: Hybridnet: a tool for constructing hybridization networks. Bioinformatics. 2010, 26 (22): 29122913. 10.1093/bioinformatics/btq548.View ArticlePubMedGoogle Scholar
 Collins J, Linz S, Semple C: Quantifying hybridization in realistic time. J Comp Biol. 2011, 18: 13051318. 10.1089/cmb.2009.0166.View ArticleGoogle Scholar
 Whidden C, Beiko RG, Zeh N: Fixedparameter algorithms for maximum agreement forests. SIAM J Comput. 42 (4): 14311466.Google Scholar
 Huson DH, Scornavacca C: Dendroscope 3: An interactive tool for rooted phylogenetic trees and networks. Syst Biol. 2012, 61 (6): 10611067. 10.1093/sysbio/sys062.View ArticlePubMedGoogle Scholar
 Albrecht B, Scornavacca C, Cenci A, Huson DH: Fast computation of minimum hybridization networks. Bioinformatics. 2012, 28 (2): 191197. 10.1093/bioinformatics/btr618.View ArticlePubMedGoogle Scholar
 Chen ZZ, Wang L: Algorithms for reticulate networks of multiple phylogenetic trees. IEEE/ACM Trans Comput Biol Bioinf. 2012, 9 (2): 372384.View ArticleGoogle Scholar
 Chen ZZ, Wang L: An ultrafast tool for minimum reticulate networks. J Comput Biol. 2013, 20 (1): 3841. 10.1089/cmb.2012.0240.View ArticlePubMedGoogle Scholar
 Piovesan T, Kelk S: A simple fixed parameter tractable algorithm for computing the hybridization number of two (not necessarily binary) trees. IEEE/ACM Trans Comput Biol Bioinf. 2013, 10 (1): 1825.View ArticleGoogle Scholar
 Linz S, Semple C: Hybridization in nonbinary trees. IEEE/ACM Trans Comput Biol Bioinf. 2009, 6 (1): 3045.View ArticleGoogle Scholar
 Kelk SM, van Iersel LJJ, Lekic N, Linz S, Scornavacca C, Stougie L: Cycle killer...qu’estce que c’est? on the comparative approximability of hybridization number and directed feedback vertex set. SIAM J Discr Math. 2012, 26 (4): 16351656. 10.1137/120864350.View ArticleGoogle Scholar
 van Iersel LJJ, Kelk SM, Stougie L, Lekić N: Approximation algorithms for nonbinary agreement forests. SIAM J Discrete Math. 2014, 28 (1): 4966. 10.1137/120903567.View ArticleGoogle Scholar
 Whidden C: rSPR. http://kiwi.cs.dal.ca/Software/RSPR,
 Whidden C, Beiko RG, Zeh N: Fast FPT algorithms for computing rooted agreement forests: Theory and experiments. Proceedings of the 9th International Symposium on Experimental Algorithms (SEA). Lect Notes Comput Sc, vol. 6049, pp. 141–153 SpringerL: Berlin; 2010,Google Scholar
 Whidden C, Beiko RG, Zeh N: FixedParameter and Approximation Algorithms for Maximum Agreement Forests of Multifurcating Trees. ArXiv preprint: http://arxiv.org/abs/1305.0512 (2013),
 Kelk SM: CYCLEKILLER. http://skelk.sdfeu.org/cyclekiller,
 van Iersel LJJ: NONBINARYCYCLEKILLER. http://homepages.cwi.nl/~iersel/cyclekiller,
 Kelk SM: TERMINUSEST. http://skelk.sdfeu.org/terminusest,
 van Iersel LJJ, Kelk SM, Lekic N, Scornavacca C: A practical approximation algorithm for solving massive instances of hybridization number. Algorithms in Bioinformatics. Lect Notes Comput Sc, vol. 7534, pp. 430–440.Edited by: Raphael B, Tang J. 2012, Berlin: Springer,Google Scholar
 Baroni M, Grünewald S, Moulton V, Semple C: Bounding the number of hybridisation events for a consistent evolutionary history. J Math Biol. 2005, 51: 171182. 10.1007/s0028500503159.View ArticlePubMedGoogle Scholar
 Rouard M, Guignon V, Aluome C, Laporte MA, Droc G, Walde C, Zmasek CM, Périn C, Conte MG: Greenphyldb v2.0: comparative and functional genomics in plants. Nucleic Acids Res. 2010, doi:10.1093/nar/gkq811. Epub 2010 Sep 22,Google Scholar
 Scornavacca C, Berry V, Ranwez V: Building species trees from larger parts of phylogenomic databases. Inform Comput. 2011, 209 (3): 590605. 10.1016/j.ic.2010.11.022.View ArticleGoogle Scholar
 Scornavacca C: SSIMUL. http://www.atgcmontpellier.fr/ssimul/,
 Even G, Naor J, Schieber B, Sudan M: Approximating minimum feedback sets and multicuts in directed graphs. Algorithmica. 1998, 20 (2): 151174. 10.1007/PL00009191.View ArticleGoogle Scholar
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