Volume 18 Supplement 2
Selected articles from the 15th Asia Pacific Bioinformatics Conference (APBC 2017)
A program to compute the soft Robinson–Foulds distance between phylogenetic networks
 Bingxin Lu^{1},
 Louxin Zhang^{2}Email author and
 Hon Wai Leong^{1}
DOI: 10.1186/s1286401735005
© The Author(s) 2017
Published: 14 March 2017
Abstract
Background
Over the past two decades, phylogenetic networks have been studied to model reticulate evolutionary events. The relationships among phylogenetic networks, phylogenetic trees and clusters serve as the basis for reconstruction and comparison of phylogenetic networks. To understand these relationships, two problems are raised: the tree containment problem, which asks whether a phylogenetic tree is displayed in a phylogenetic network, and the cluster containment problem, which asks whether a cluster is represented at a node in a phylogenetic network. Both the problems are NPcomplete.
Results
A fast exponentialtime algorithm for the cluster containment problem on arbitrary networks is developed and implemented in C. The resulting program is further extended into a computer program for fast computation of the Soft Robinson–Foulds distance between phylogenetic networks.
Conclusions
Two computer programs are developed for facilitating reconstruction and validation of phylogenetic network models in evolutionary and comparative genomics. Our simulation tests indicated that they are fast enough for use in practice. Additionally, the distribution of the Soft Robinson–Foulds distance between phylogenetic networks is demonstrated to be unlikely normal by our simulation data.
Keywords
Phylogenetic network Cluster containment problem Tree containment problem (Soft) Robinson–Foulds distance Exponentialtime algorithmBackground
Since Darwin’s The Origin of Species, the evolutionary history of life has been widely depicted as phylogenetic trees. However, the simplified treelike evolutionary models are being challenged by the accumulating amount of evidence of lateral genetic transfer between lineages, particularly in prokaryotes [1–3]. Additionally, other reticulate evolutionary events also cause complications in constructing treelike models, such as hybridization and introgression between species [4, 5], and recombination of various forms [6]. The recognized limitations of phylogenetic trees motivated the adoption of phylogenetic networks to model these reticulation events [7, 8]. Phylogenetic networks can be used to either visualize conflicting phylogenetic information or model reticulation events explicitly. The former are typically unrooted, whereas the latter are rooted, which is the focus of this study. In recent years, phylogenetic networks have been the subject of intensive theoretic studies [9–12]. However, considerable challenges in reconstructing phylogenetic networks still exist [13].
In a phylogenetic tree, the taxa below a node form a unique subset of the taxa, called its cluster. A phylogenetic tree is uniquely determined by the set of “nested" clusters in the tree (see, for example, [10]).
A phylogenetic network is a generalization of a phylogenetic tree in which there are additional reticulation nodes, which are the nodes with an indegree of at least two. Since most gene families have treelike evolutionary histories, the network model of the evolution of a set of genomes is often built and validated by checking its consistency with the available related gene trees and/or clusters [10].
In a phylogenetic network, a nonreticulation node is called a tree node. Each tree node represents a cluster and a set of soft clusters. Similar to the case of phylogenetic tree, a node’s cluster consists of all taxa below it, whereas its soft clusters are the clusters represented by this node in the phylogenetic trees that are displayed in the network. Here, a phylogenetic tree is said to be displayed in a phylogenetic network if it can be obtained by deleting all but one incoming edges from each reticulation node and then contracting all the nodes of degree two.
The tree containment problem (TCP) and the cluster containment problem (CCP) have arisen from reconstructions of phylogenetic networks [14]. The TCP asks whether a phylogenetic tree is displayed in a phylogenetic network. The CCP asks whether a cluster is a soft cluster of some tree node in a phylogenetic network. Both the TCP and CCP are NPcomplete [10, 14], even for restricted networks [15].
A polynomialtime algorithm for the CCP is given for reticulationvisible networks [10]. A network is reticulationvisible if, for each reticulation node, a leaf exists such that every path from the network root to the leaf contains this reticulation node. Recently, a lineartime algorithm is presented for the CCP on this class of networks [16]. Given that a large fraction of phylogenetic networks are not reticulationvisible [17], however, it is necessary to develop an algorithm for the CCP for arbitrary networks for the following reason.
Measuring the dissimilarity between phylogenetic networks is important for assessing a network reconstruction method. One of the metric functions that has been proposed for this purpose is the Robinson–Foulds (RF) distance, which is a generalization of the same metric for phylogenetic trees. Simply put, it is the half of the cardinality of the symmetric difference of the two sets of clusters respectively contained in the two networks [18]. It takes lineartime to compute the RF distance between phylogenetic networks [10].
By replacing clusters with soft clusters, we obtain the Soft Robinson–Foulds (SRF) distance [10]. Since the CCP is NPcomplete, there is unlikely a polynomialtime algorithm for computing the SRF distance. To the best knowledge of the authors, only a straightforward method has been implemented in the software Dendroscope [19]. The method exhaustively searches the clusters that are in a phylogenetic tree displayed in one network but are not in any phylogenetic tree displayed in another.
Recently, Gunawan et al. [20] developed a computer program for solving the TCP on arbitrary networks. Although it has exponentialtime complexity in the worst case, it runs fast enough to be used in practice.
Here, we first develop an algorithm for the CCP by using the decomposition theorem in [16]. We then extend it into an algorithm for computing the SRF distance. We implemented these two algorithms in C and tested them on empirical and simulated network datasets. As an application of the programs, we examined the differences of networks reconstructed for two datasets in the literature. We also conducted a preliminary study of the distributions of the RF and SRF distances in the phylogenetic network space.
Methods
We first introduce the basic concepts and notation, then recap the decomposition technique for arbitrary phylogenetic networks, and finally describe the algorithms for the CCP and the SRF distance.
Concepts and notation
Let X be a set of taxa. A rooted phylogenetic network (network for short) over X is an acyclic digraph in which the leaves (i.e., nodes of outdegree zero) are bijectively mapped to X. A taxon typically represents some extant organism or species. A network has a unique root (of indegree zero).
There can be two types of internal nodes in a network: tree nodes, which include the root and nodes of indegree one and outdegree of at least one, and reticulation nodes, which have outdegree one and indegree of at least two. The tree nodes represent speciation events and the reticulation nodes represent reticulation events. We allow degreetwo nodes in a network.

T(N): the set of tree nodes in N.

L(N): the set of leaves in N.

R(N): the set of reticulation nodes in N.

V(N): the set of all nodes in N, namely T(N)∪L(N)∪R(N).

E(N): the set of edges in N.

ρ(N): the root of N.

N−E: the subnetwork (V(N),E(N)∖E) for a subset E⊆E(N).

N−S: the subnetwork (V(N)∖V(S),E ^{′}), where E ^{′}={(x,y)∈E(N)  {x,y}⊆V(N)∖V(S)} for a subnetwork S of N.
For u,v∈V(N), u is a parent of v and v is a child of u if (u,v)∈E(N). We use c(r) to denote the unique child of r∈R(N). If there is a direct path from u to v, v is called a descendant of u.
We use [ r]_{ N } to denote the subnetwork below r∈V(N), which consists of all the descendants of r and the edges between them in N. For a leaf ℓ below r, we use N−[ r]_{ N }+ℓ to denote the subnetwork obtained by replacing [ r]_{ N } with ℓ so that ℓ becomes the child of r.
If each reticulation node in a network has exactly two parents, the network is bicombining. A bicombining network is binary if each tree node is of outdegree two. A phylogenetic tree is a binary network without reticulation nodes. If the unique child of each reticulation node in a network is a tree node or a leaf, this network is called reduced.
Following Gunawan et al. [16], we allow a network to have dummy nodes (i.e., unlabelled nodes of outdegree zero) because such a network may be generated in a recursive step of our algorithms.
Given the set of taxa X, a cluster is any proper subset of X (excluding the empty set and the full set). A cluster is trivial if it contains only one element.
In a phylogenetic tree T over X, each nonroot node induces a unique set of taxa that consists of the labels of the leaves below the node, which is called the cluster of the node. A cluster is in T if it is the cluster of some node in T.
Given a network N over X and a phylogenetic tree T over X, we say that T is displayed in N if T can be obtained from N by the following operations: removing all but one incoming edges for each reticulation node in N, removing nodes that are not in any path from ρ(N) to a leaf ℓ∈X, and contracting degreetwo nodes (i.e., nodes of indegree one and outdegree one). To contract a degreetwo node w which has two incident edges (u,w) and (w,v), we merge the two edges into one edge (u,v).
A cluster B⊂X is a soft cluster in N if there is a tree T displayed in N such that B is a cluster in T. A tree node in a network may represent multiple soft clusters, which could be obtained from different trees displayed in the network. We use S C(N) to denote the set of soft clusters in N.
Given B⊂X and a network T on X, the CCP asks whether B is a soft cluster in N [10], which is formulated as below:

CLUSTER CONTAINMENT

Instance: A phylogenetic network N over a set of taxa X and B⊂X.

Question: Is B∈S C(N)?
Let N _{1} and N _{2} be two networks over the same set of taxa X. The SRF distance between them is defined to be (S C(N _{1})∖S C(N _{2})+S C(N _{2})S C(N _{1}))/2 denoted by d _{ SRF }(N _{1},N _{2}).
It is worth noting that the SRF distance is not a strict metric, since two distinct networks may represent the same set of soft clusters and hence the SRF distance between them will be zero [10]. Nevertheless, the SRF distance provides a useful measure of network dissimilarity.
A decomposition theorem
The key to solving the CCP and computing the SRF distance is the decomposition theorem, which was first proposed by Gunawan et al. [16] for reticulationvisible networks and used later for arbitrary networks in [20].
The decomposition theorem says that an arbitrary network N can be decomposed into a set of connected tree components which are separated by reticulation nodes. Specifically, there is a tree component C _{ r } for each r∈R(N)∪{ρ(N)}, which is either {c(r)} if r∈R(N) and c(r)∈R(N), or a subtree induced by all the tree nodes and leaves that are reachable from r. A tree component is trivial if it contains only one leaf or if it is empty (for a dummy reticulation node).
A node is visible on a leaf ℓ if it lies on all the paths from ρ(N) to ℓ. If a node r∈R(N)∪{ρ(N)} is visible on a leaf ℓ, its tree component C _{ r } is visible on ℓ as well. Given two tree components \(C_{r'}\phantom {\dot {i}\!}\) and \(\phantom {\dot {i}\!}C_{r^{\prime \prime }}\), r ^{′} and \(\phantom {\dot {i}\!}C_{r^{\prime }}\) are right below \(\phantom {\dot {i}\!}C_{r''}\) if a parent of r ^{′} is in \(\phantom {\dot {i}\!}C_{r^{\prime \prime }}\). A tree component is exposed if it contains only one leaf or if all the tree components right below it are trivial.
Obviously, N contains at least one exposed nontrivial tree component. In addition, an exposed tree component C _{ r } is visible if and only if C _{ r } contains a leaf or if a reticulation node r ^{′} exists right below C _{ r } such that all the parents of r ^{′} are in C _{ r }.
Description of the algorithm
The CCP algorithm
With the aid of the generalized decomposition theorem, we extend the lineartime CCP algorithm for reticulationvisible networks in [16] to arbitrary networks.
To determine whether or not a cluster C is in a phylogenetic network N, the algorithm selects a nontrivial exposed component M of N. If M is visible, we either find the negative answer to the problem by working on M or we obtain an instance of the problem that is simpler than the input instance (C,N) in linear time proportional to the size of M. In the latter, we reduce the original instance of the CCP to a simpler instance.
If M is not visible, there is then a reticulation node which has a unique leaf child and does not have all parents in M. In this case, two phylogenetic networks N _{1} and N _{2} are derived from N, which contain fewer nodes than N. The algorithm is then called on both instances ( C,N _{1} ) and ( C,N _{2} ) recursively.
Although this algorithm seems simple, it has significantly less time complexity when the input network is binary. In the rest of this section, we present a formal description of the algorithm.
Let N be a network over X and B⊂X, respectively. We examine a nontrivial exposed tree component C _{ r } of N.
The reticulation nodes below C _{ r } are divided into innerreticulation nodes for which the parents are all in C _{ r }, and crossreticulation nodes for which some parents are not in C _{ r }. We use I R(C _{ r }) and C R(C _{ r }) to denote the sets of inner and cross reticulation nodes, respectively. For example, in Fig. 1, I R(C _{r5})=∅ and C R(C _{r5})={r4,r7,r8}.
For example, in Fig. 1, L _{r5}=∅ and we can get \(\check {L}_{\text {r5}}=\{\text {leaf1}, \text {leaf2}\}\) when assuming B={leaf1,leaf2,leaf5}.
Suppose that L _{ r } is nonempty. C _{ r } is then visible with respect to a leaf ℓ∈L _{ r }. We first check whether B is a soft cluster in C _{ r }. This can be solved by a lineartime algorithm [16]. If not, we then solve the CCP according to the relationship between L _{ r } and B.
Let \(\bar {B}=X \backslash B\). If L _{ r }∩B≠∅ and \( L_{r} \cap \bar {B} \neq \emptyset \), B must be a soft cluster of a node in C _{ r } if B is a soft cluster in N [16].
The leaves below the root of C _{ r } in N _{ a } (i.e., \(\phantom {\dot {i}\!}L([\rho (C_{r})]_{N_{a}})\)) are then \(\phantom {\dot {i}\!}L_{r} \cup \check {L}_{r}\). We denote \(\phantom {\dot {i}\!}L([\rho (C_{r})]_{N_{a}})\) as \(\hat {B}\) for convenience.
With this notation, we can get Theorem 1 for arbitrary networks, which is similar to a theorem proved for reticulationvisible networks in [16]. Theorem 1 is proved in the Additional file 1.
Theorem 1

(i) If \(\hat {B} \subset B\), B is a soft cluster in N if and only if B ^{′} is a soft cluster in N a′.

(ii) If B is not a soft cluster of a node in C _{ r } and L _{ r }∩B=∅, B is a soft cluster in N if and only if B is a soft cluster in N b′.
Clearly, B is a soft cluster in N if and only if B is a soft cluster in either N ^{′} or N ^{″}.
The SRF distance algorithm
We now use the CCP algorithm to compute the SRF distance between two arbitrary networks on the same taxa set X.
For X, we define a kcluster as a cluster having k taxa. We enumerate all the possible clusters over X by generating all the kclusters of X for each k ranging from 1 to X−1. We then call the CCP algorithm on each cluster to see whether it is a soft cluster in only one network.
The time complexity of this SRF distance algorithm is O(2^{L(N)} T(N)), where T(N) is the time complexity of the CCP algorithm.
The program for computing the SRF distance in Dendroscope first finds trees displayed in each network, then extracts clusters from these trees to get the soft clusters in each network, and finally traverses the two sets of soft clusters to compute their symmetric difference. If the networks are bicombining, the time complexity for this method is O(2L(N)∗2^{R(N)}+2q), where q is the number of the soft clusters in a network. We will compare this approach and our SRF distance program in next section.
Results and discussion
Performance of the CCP program
In this subsection, we first analyze the time complexity of the CCP algorithm. We then report the performance of the CCP program on both simulated and empirical networks. The simulated networks were generated by using a network generator reported by Zhang [17].
Theoretical analysis of the time complexity
According to the analysis in [16], the runtime of Step 3 of the CCP algorithm is O(E(C _{ r })), where E(C _{ r }) is the set of edges in the tree component C _{ r }. Thus the time complexity of the CCP algorithm is O((m+1)E(N)) for a general network N, where m is the number of times Step 3 is executed. Note that m should be an exponential function of R(N) because of the NPcompleteness of the CCP. If N is a bicombining reduced network, the time complexity of the CCP algorithm is (2^{0.694R(N)}E(N)) [20].
We denote log2(m) as b(N,B) and call it the effective reticulation number of the CCP algorithm for the network N and the cluster B [20]. We use b(N)= maxB b(N,B) to represent the effective reticulation number of the CCP algorithm for the network N.
To the best of our knowledge, the only previously known algorithm for solving the CCP on an arbitrary network is the naive algorithm which considers all the trees displayed in a network and checks whether the input cluster is in one of them. The number of possible trees displayed in a network N can be as large as \(\prod _{r\in R(N)} deg^{}(r)\), where d e g ^{−}(r) is the indegree of r. This number equals 2^{R(N)} when N is bicombining. It takes O(L(T)) time to check whether a cluster is in a tree T [10]. Thus the effective reticulation number seems to be a good indicator of the efficiency of the CCP algorithm. If log2(m) is smaller than R(N), the CCP algorithm will be faster than the naive algorithm in theory.
Performance on random networks
We examined the performance of the CCP program on random networks in term of the effective reticulation number. The tests were done on computers each with 32 GB RAM and a 2.1 GHz AMD Opteron 32core CPU.
We tested the CCP program on random networks with 10 to 30 leaves and 10 to 80 reticulation nodes. Given that the number of clusters over 15 leaves is huge, it was hard to conduct the evaluation on the whole space of clusters. We therefore generated random clusters for testing on networks with more than 15 leaves. According to the results, the effective reticulation number for each network–cluster pair was frequently smaller than half the number of reticulation nodes in the network.
Here, we report the performance of the CCP program on five groups of networks with 10 leaves and all the possible 1022 (=2^{10}−2) clusters. Each group contained 20 networks, and the networks in the k ^{th} group had 5(1+k) reticulation nodes for each k from 1 to 5. The wall clock time on 102,200 (=5×20×1022) network–cluster pairs was 15 minutes and 15 seconds, implying that on average, the program took about one centisecond for each network–cluster pair.
Application to a network in the literature
We selected one of the largest networks in the literature to validate the performance of the CCP algorithm. This is a bicombining network (denoted A, Additional file 1: Figure S1) from [21] that has 7 leaves and 32 reticulation nodes. This network is an ancestral recombination graph reconstructed to study the phylogenetic relationships among the M2 doublestranded RNA in the Rhizoctonia species complex.
The distribution of b(A,B) in the space of clusters over the same set of taxa as the network A
b(A,B)  0  2  4  6  8  
#Cluster  8  3  45  49  21 
Performance of the program for the SRF distance on random networks
In this subsection, we first compare the program in Dendroscope and our program for the SRF distance. We then report the performance of a parallel version of our program.
The tests were performed on computers each with 128 GB RAM and a 2.6 GHz Intel Xeon E52690 24core CPU. For the generation of random networks, we considered six cases. In the k ^{th} case, we generated six groups of network pairs. The j ^{th} group consists of 3000 pairs of networks with 4k leaves and k j/4 reticulation nodes, where k was from 1 to 6 and j=1,2,4,5,6.
Our program ran faster than the program in Dendroscope for networks with up to 16 leaves. However, our program became slower than the latter when there were more than 16 leaves. This is reasonable, since the number of clusters increases exponentially with the number of taxa and it takes even long time for our program to merely list all the possible clusters when there were more than 16 leaves.
Additionally, the memory usage of our program was extremely low compared with the program in Dendroscope. The memory usage of the Dendroscope program increased rapidly with the number of reticulation nodes in a network. For example, the average maximum resident memory for networks with 12 leaves and 18 reticulation nodes was around 95 GB, which is approximately six times that for networks with 12 leaves and 15 reticulation nodes. Because of this, the average runtime of the Dendroscope program for networks with 12 leaves and 18 reticulation nodes sharply increased. During test, the Dendroscope program failed to get results for networks with more than 12 leaves and 20 reticulation nodes. Hence, some data points are missing for the Dendroscope program in the two panels at the bottom in Fig. 4. In contrast, our program can run on networks with more than 30 reticulation nodes. Even for networks with 24 leaves and 36 reticulation nodes, the average maximum resident memory of our program was less than 32 MB. Thus the test shows that our program is computationally efficient when the number of reticulation nodes in the input network is large.
Although our program runs slow for networks with many leaves, it can be easily parallelized for speeding up. We used OpenMP to implement a parallel version of it by parallelizing the enumerations of clusters. This parallel version ran at least 20 times faster than the original program with slightly extra memory. For 3000 pairs of networks each with 20 leaves and 25 reticulation nodes, the parallel version finished in about 36 seconds with less than 40 MB memory on average.
Computing the SRF distances on real biological data
In this subsection, we examine the SRF distance between phylogenetic networks reconstructed from two datasets in the literature.
Computing the SRF distance between networks over a set of grass species
The Proaceae dataset, originally from the Grass Phylogeny Working Group [22], has often been used for validating network reconstruction methods. The dataset contains sequences for six loci: ITS (internal transcribed spacer of ribosomal DNA), ndhF (NADH dehydrogenase, subunit F), phyB (phytochrome B), rbcL (ribulose 1,5biphosphate carboxylase/oxygenase, subunit), rpoC (RNA polymerase II, subunit β ^{″}), and waxy (granule bound starch synthase I). Rooted binary gene trees were built for these loci previously by Schmidt [23]. From the six trees, van Iersel et al. [24] constructed 57 subsets of gene trees for comparisons of network reconstruction methods.
The average pairwise SRF distances between the output networks from Hybroscale on three sets of gene trees reported by van Iersel et al. [24]
Gene trees  #Taxa  #Ret  #Networks  Average pairwise 

SRF distance  
rbcL, waxy, ITS  11  6  63  12.2 
ndhF, rbcL, waxy  12  5  123  8.0 
phyB, rbcL, rpoC  15  6  40  1.4 
On the other hand, different network reconstruction methods on the same data could produce very different networks. Using five gene trees (ITS, ndhF, phyB, rbcL, rpoC2), we constructed three networks: a cluster network (Additional file 1: Figure S2) obtained from a program in [26], a galled network (Additional file 1: Figure S3) obtained from a program in [27], and a reticulate network (Additional file 1: Figure S4) obtained from PIRN [28]. Since the original network reconstructed by PIRN had reticulation nodes with more than one child and leaves with more than one parent, it was transformed into an equivalent one satisfying our definition in this paper. The three networks have 18, 7, and 13 reticulation nodes and contain 445, 261, and 209 soft clusters, respectively. The SRF distance between the cluster network and the galled network is 199. The SRF distance between the galled network and the reticulate network is 118. The SRF distance between the reticulate network and the cluster network is 185. This suggests that the galled network is more similar to the reticulate network than to the cluster network. This also reflects that the SRF distance is sensitive to the structural properties of phylogenetic networks.
Computing the SRF distance between networks over six mosquito species
To study phylogenetic relationships and introgression among six mosquito species in the Anopheles gambiae species complex, Fontaine et al. [5] constructed a network (denoted M1) by employing treebased methods on the wholegenome sequences. Later, Wen et al. [29] rebuilt a similar network (denoted M2) for the six species by directly applying a network inference method on the gene trees. The two networks are shown in Additional file 1: Figure S5. M1 has three reticulation nodes and M2 has four reticulation nodes. There are 18 and 24 soft clusters in M1 and M2, respectively. The SRF distance between M1 and M2 is 7, implying that the two networks are still quite different in the embedded soft clusters.
Comparison of the RF distance and the SRF distance
Although the RF and SRF distances were proposed to measure the dissimilarity of networks, their relationship is unclear [10]. In this subsection, we present our preliminary comparison of these two measures.
 (i.)
There are at least as many soft clusters as clusters in a network. Therefore, as expected, the SRF distance has larger range than the RF distance.
 (ii.)
The RF distance seems to have a normal distribution of small mean and small variance.
 (iii.)
The distribution of the SRF distances seems not to be normal. It is skewed towards small distances (especially for networks with more leaves) and a small fraction of network pairs had much larger SRF distances than the average SRF distance.
Taken altogether, these three facts indicate that the SRF distance is a fine metric for networks and hence more suitable than the RF distance for measuring the dissimilarity of networks.
Conclusions
The generalized decomposition technique developed in [20] was shown to be powerful for solving the TCP on arbitrary networks. In this work, by applying this technique, we have developed efficient algorithms for solving the CCP and computing the SRF distance for arbitrary networks. These two algorithms were implemented in C.
Both programs facilitate reconstructing and validating network models in evolutionary and comparative genomics. Our simulation experiments showed that the SRF distance program ran fast for networks with an intermediate number of leaves and reticulation nodes. Therefore, the SRF distance program is ready for assessing a network reconstructed by a new method via comparing it with other networks.
Notes
Declarations
Acknowledgements
The authors thank Andreas DM Gunawan for discussion on the topic and anonymous reviewers for helpful comments on the first submission of this work.
Funding
The work in this paper and the publication costs were funded by the Singapore Ministry of Education Academic Research Fund MOE2014T21155.
Availability of data and material
The programs and related data are available for download on https://github.com/icelu/PhyloNetwork.
Authors’ contributions
Project design: LXZ. Program implementation and experiments: BXL. Manuscript preparation: BXL, LXZ and HWL. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
About this supplement
This article has been published as part of BMC Genomics Volume 18 Supplement 2, 2017. Selected articles from the 15th Asia Pacific Bioinformatics Conference (APBC 2017): genomics. The full contents of the supplement are available online http://bmcgenomics.biomedcentral.com/articles/supplements/volume18supplement2.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
References
 Gogarten JP, Townsend JP. Horizontal gene transfer, genome innovation and evolution. Nat Rev Microbiol. 2005; 3(9):679–87.View ArticlePubMedGoogle Scholar
 Soucy SM, Huang J, Gogarten JP. Horizontal gene transfer: building the web of life. Nat Rev Genet. 2015; 16(8):472–82.View ArticlePubMedGoogle Scholar
 Doolittle WF, Brunet TDP. What is the tree of life?PLoS Genet. 2016; 12(4):1005912.View ArticleGoogle Scholar
 Mallet J. Hybrid speciation. Nature. 2007; 446(7133):279–83.View ArticlePubMedGoogle Scholar
 Fontaine MC, Pease JB, Steele A, Waterhouse RM, Neafsey DE, Sharakhov IV, Jiang X, Hall AB, Catteruccia F, Kakani E, et al. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science. 2015; 347(6217):27–8.View ArticleGoogle Scholar
 Martin DP, Lemey P, Posada D. Analysing recombination in nucleotide sequences. Mol Ecol Resour. 2011; 11(6):943–55.View ArticlePubMedGoogle Scholar
 Doolittle WF, Bapteste E. Pattern pluralism and the tree of life hypothesis. Proc Nat Acad Sci USA. 2007; 104(7):2043–9.View ArticlePubMedPubMed CentralGoogle Scholar
 Bapteste E, van Iersel L, Janke A, Kelchner S, Kelk S, McInerney JO, Morrison DA, Nakhleh L, Steel M, Stougie L, et al. Networks: expanding evolutionary thinking. Trends Genet. 2013; 29(8):439–41.View ArticlePubMedGoogle Scholar
 Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol. 2006; 23(2):254–67.View ArticlePubMedGoogle Scholar
 Huson DH, Rupp R, Scornavacca C. Phylogenetic Networks: Concepts, Algorithms and Applications. Cambridge: Cambridge University Press; 2010.View ArticleGoogle Scholar
 Morrison DA. Introduction to Phylogenetic Networks. Uppsala: RJR Productions; 2011.Google Scholar
 Gusfield D. ReCombinatorics: The Algorithmics of Ancestral Recombination Graphs and Explicit Phylogenetic Networks. Cambridge: MIT Press; 2014.Google Scholar
 Huber KT, van Iersel L, Moulton V, Wu T. How much information is needed to infer reticulate evolutionary histories?Syst Biol. 2015; 64(1):102–11.View ArticlePubMedGoogle Scholar
 Kanj IA, Nakhleh L, Than C, Xia G. Seeing the trees and their branches in the network is hard. Theor Comput Sci. 2008; 401(1–3):153–64.View ArticleGoogle Scholar
 van Iersel L, Semple C, Steel M. Locating a tree in a phylogenetic network. Inform Process Lett. 2010; 110(23):1037–43.View ArticleGoogle Scholar
 Gunawan ADM, DasGupta B, Zhang L. A decomposition theorem and two algorithms for reticulationvisible networks. Inform. Comput. (to be accepted). In: Proc. of RECOMB 2016: 2016. p. 266. The journal version is available online: arXiv:1603.08655.Google Scholar
 Zhang L. On treebased phylogenetic networks. J Comp Biol. 2016; 23(7):553–65.View ArticleGoogle Scholar
 Cardona G, Llabrés M, Rosselló F, Valiente G. Metrics for phylogenetic networks i: Generalizations of the robinsonfoulds metric. IEEEACM Trans Comput Biol Bioinform. 2009; 6(1):46–61.View ArticleGoogle Scholar
 Huson DH, Scornavacca C. Dendroscope 3: an interactive tool for rooted phylogenetic trees and networks. Syst Biol. 2012; 61(6):1061–7.View ArticlePubMedGoogle Scholar
 Gunawan ADM, Lu B, Zhang L. A program for verification of phylogenetic network models. Bioinformatics. 2016; 32(17):i503–i510.View ArticlePubMedGoogle Scholar
 Charlton ND, Carbone I, Tavantzis SM, Cubeta MA. Phylogenetic relatedness of the M2 doublestranded RNA in Rhizoctonia fungi. Mycologia. 2008; 100(4):555–64.View ArticlePubMedGoogle Scholar
 Grass Phylogeny Working Group. Phylogeny and subfamilial classification of the grasses (Poaceae). Ann Mo Bot Gard. 2001; 88(3):373–457.View ArticleGoogle Scholar
 Schmidt HA. Phylogenetic trees from large datasets. Düsseldorf: PhD thesis, University of Düsseldorf; 2003.Google Scholar
 van Iersel L, Kelk S, Rupp R, Huson D. Phylogenetic networks do not need to be complex: using fewer reticulations to represent conflicting clusters. Bioinformatics. 2010; 26(12):124–31.View ArticleGoogle Scholar
 Albrecht B. Computing all hybridization networks for multiple binary phylogenetic input trees. BMC Bioinforma. 2015; 16(1):236.View ArticleGoogle Scholar
 Huson DH, Rupp R. Summarizing multiple gene trees using cluster networks. In: Algorithms in Bioinformatics (WABI), Vol. 5251 of Lecture Notes in Bioinformatics. Berlin: Springer: 2008. p. 296–305.Google Scholar
 Huson DH, Rupp R, Berry V, Gambette P, Paul C. Computing galled networks from real data. Bioinformatics. 2009; 25(12):85–93.View ArticleGoogle Scholar
 Wu Y. Close lower and upper bounds for the minimum reticulate network of multiple phylogenetic trees. Bioinformatics. 2010; 26(12):140–8.View ArticleGoogle Scholar
 Wen D, Yu Y, Hahn MW, Nakhleh L. Reticulate evolutionary history and extensive introgression in mosquito species revealed by phylogenetic network analysis. Mol Ecol. 2016; 25(11):2361–72.View ArticlePubMedGoogle Scholar