 Research
 Open Access
 Published:
ASTRALIII: polynomial time species tree reconstruction from partially resolved gene trees
BMC Bioinformatics volume 19, Article number: 153 (2018)
Abstract
Background
Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions.
Results
We introduce ASTRALIII, which substantially improves the running time of ASTRALII and guarantees polynomial running time as a function of both the number of species (n) and the number of genes (k). ASTRALIII limits the bipartition constraint set (X) to grow at most linearly with n and k. Moreover, it handles polytomies more efficiently than ASTRALII, exploits similarities between gene trees better, and uses several techniques to avoid searching parts of the search space that are mathematically guaranteed not to include the optimal tree. The asymptotic running time of ASTRALIII in the presence of polytomies is \(O\left ((nk)^{1.726} D \right)\) where D=O(nk) is the sum of degrees of all unique nodes in input trees. The running time improvements enable us to test whether contracting low support branches in gene trees improves the accuracy by reducing noise. In extensive simulations, we show that removing branches with very low support (e.g., below 10%) improves accuracy while overly aggressive filtering is harmful. We observe on a biological avian phylogenomic dataset of 14K genes that contracting low support branches greatly improve results.
Conclusions
ASTRALIII is a faster version of the ASTRAL method for phylogenetic reconstruction and can scale up to 10,000 species. With ASTRALIII, low support branches can be removed, resulting in improved accuracy.
Background
The potential for genomewide discordance of evolutionary histories [1, 2] has motivated the development of several approaches for species phylogeny reconstruction. Reconstructing a collection of gene trees, each inferred from a different part of the genome, and then summarizing them to get a species tree is one such approach and is used by many phylogenomic projects (e.g., [3–7]) (while “gene trees” need not be inferred from functional genes, following the conventions of the field, we will refer to them as such). This twostep approach stands in contrast to concatenation [8], where all the data are combined and analyzed in a single analysis. The twostep approach aims to account for discordances between gene trees and the species tree (but its effectiveness is debated [9–12]) and is more computationally efficient than statistical coestimation of gene trees and the species tree [13]. Incomplete lineage sorting (ILS) is a ubiquitous [14] cause of discordance. ILS is typically modeled by the multispecies coalescent model (MSCM) [15, 16], where branches of the species tree represent populations, and lineages are allowed to coalesce inside each branch; lineages that fail to coalesce at the root of each branch are moved to the parent branch.
Many “summary” methods have been developed to infer a species tree from a collection of input trees. Examples include MPEST [17], NJst [18], ASTRID [19], DISTIQUE [20], ASTRAL [21, 22] and STAR [23], which only use gene tree topologies, and GLASS [24] and STEAC [23], which also use branch lengths. These methods are all proved statistically consistent under the MSCM, given errorfree input gene trees; when input trees are inferred from sequence data, statistical consistency is not guaranteed [25]. Most methods take rooted gene trees as input, but some methods (e.g., ASTRAL, NJst/ASTRID and DISTIQUE) use unrooted input trees. ASTRALII [22] is currently one of the commonly used summary methods.
In this paper, we introduce an improved version of ASTRAL called ASTRALIII. As we will show, compared to ASTRALII, the new version has better running time without sacrificing accuracy. The improvements in the running time are both theoretical (reducing the asymptotic running time so that it is guaranteed to grow polynomially with the dataset size) and empirical.
Methods
Notations and definitions
Let the set of n species be called L and let G be the set of k input gene trees on L. Let [d] represent the set {1,2…,d}. We use Q(t) to denote the set of quartet trees induced by a tree t. Any subset of L is called a cluster. We define a partition as a set of clusters that are pairwise mutually exclusive; note that we abuse the term “partition” here because the union of all clusters in a partition need not give the complete set. Each node in an unrooted tree defines a partition. A bipartition (tripartition) is a partition with cardinality two (three); a partition with cardinality at least four corresponds to a multifurcation (also referred to as a polytomy). Let X (the constraint bipartition set) be a set of clusters such that for each A∈X, we also have L−A∈X. We use Y to represent the set of all tripartitions that can be build from X:
We use E to denote the set of all unique partitions and their frequency in G. Thus,
where N(g) is the set of all partitions representing all internal nodes in the tree g. We also define D as the sum of the cardinalities of unique partitions in gene trees:
ASTRAL (old versions)
The problem addressed by ASTRAL is to find the tree that shares the maximum number of induced quartet topologies with the collection of input gene trees: Problem statement: Given a set G of input gene trees, find the species tree t that maximizes \(\sum _{g \in G} Q(g)\cap Q(t)\).
Lafond and Scornavacca recently proved this problem is NPhard [26].
ASTRALI and ASTRALII algorithms
ASTRAL solves a constrained version of the problem where a set of clusters X restricts bipartitions that the output species tree may include (recall ∀A∈X:L−A∈X). Note that setting X to the powerset solves the unconstrained problem. Based on the fact that an unrooted quartet species tree always matches the most likely unrooted quartet gene tree [27], ASTRAL is proved statistically consistent [21].
ASTRAL uses dynamic programming to solve the problem using the recursive relation:
where the function w(T) scores each tripartition T=(ABC) against each node in each input gene tree. Let partition \(M=\left (M_{1}M_{2}...M_{d}\right)\) represent an internal node of degree d in a gene tree. The overall contribution of T to the score of any species tree that includes T is:
where, defining \(a_{i}=A\cap M_{i}\), \(b_{i}=B\cap M_{i}\), and \(c_{i}=C\cap M_{i}\), we have:
As previously proved [21], QI(T,M) computes twice the number of quartet trees that are going to be shared between any two trees if one includes only T and the other includes only M. ASTRALII requires \(\Theta \left (d^{3}\right)\) time for computing QI(.), making its overall running time \(O\left (n^{3}kY\right)\) with polytomies of unbounded degrees or O(nkY) in the absence of polytomies.
Noting trivially that Y<X^{2}, the previously published running time analysis of ASTRALII was \(O\left (nkX^{2}\right)\) for binary gene trees and \(O\left (n^{3}kX^{2}\right)\) for trees with polytomies. A recent result by Kane and Tao [28] (motivated by the analysis of ASTRAL) proved that \(\phantom {\dot {i}\!}Y\leq X^{3/log_{3}(27/4)}\). This result immediately gives us a better upper bound on the running time.
Corollary 1
ASTRALII runs in \(O\left (nkX^{{1.726}}\right)\) and \(O\left (n^{3}kX^{{1.726}}\right)\), respectively, with and without polytomies in gene trees.
In ASTRALI, X is the set of all bipartitions observed in input gene trees. While sufficient for statistical consistency and often for accuracy, under some conditions, this set X is too restrictive. To address this shortcoming, ASTRALII [22] uses several heuristics (see Additional file 1: Appendix A) and further expands the set X. Even though ATRALII tries to limit X, it does not provide any guarantees as to how it grows with n and k. In the worst case, X can grow exponentially, and thus, ASTRALII does not guarantee polynomial running time. The relatively high accuracy of ASTRALII has been shown in several simulations [20, 22, 29, 30] and it has been adopted by the community as one of the main methods used in phylogenomics. ASTRAL has the ability to compute branch lengths in coalescent units [2] and a measure of branch support called local posterior probability [31].
Limitations of ASTRALII
Several shortcomings of ASTRALII in terms of running time are addressed here (ASTRALIII); our improvements, in turn, enable new types of analyses.
While ASTRALII can analyze datasets with a thousand species and gene trees in reasonable time, it does not easily scale to many tens of thousands of input trees. Datasets with more than ten thousand loci are already available (e.g., [5]) and as more genomes are sequenced, more are destined to become available in the near future. Moreover, being able to handle large k (i.e., numbers of input trees) enables using multiple trees per locus (e.g., a Bayesian sample) as input to ASTRAL. The limited scalability of ASTRAL with k has two reasons. First, the set X is not bounded in ASTRALII and can grow to become the power set. Thus, in ASTRALII, Xcan theoretically grow exponentially with n. We fix this in ASTRALIII by modifying heuristics that form the set X so that they all guarantee that X=O(nk). The second cause of the slowdown is that computing each w(T) for a tripartition T requires Θ(nk). This computation does not exploit similarities between gene trees, a shortcoming that we fix in ASTRALIII.
Beyond large k, ASTRALII, which scales as O(n^{3}kX^{1.726}) in the presence of polytomies, can quickly become prohibitively slow for input trees with large polytomies. ASTRALIII uses a mathematical trick to enable scoring of gene tree polytomies in time similar to binary nodes. The ability to handle large polytomies in input gene trees is important for two reasons. Some of the conditions that are conducive to ILS, namely shallow trees, are also likely to produce identical gene sequence data for several species. The gene tree should leave the relationship between identical sequences unresolved (FastTree [32] automatically does it and RAxML, which outputs an arbitrary resolution, warns the user about the input). Moreover, all summary methods, including ASTRAL, are sensitive to gene tree estimation error [22, 33–37]. One way of dealing with gene tree error, previously studied in the context of minimizing deep coalescence [38], is to contract low support branches in gene trees and use these unresolved trees as input to the summary method. While earlier studies found no evidence that this approach helps ASTRALII when the support is judged by SHlike FastTree support [22], no study has tested this approach with bootstrap support values. We will for the first time evaluate the effectiveness of contracting branches with low bootstrap support and show that conservative filtering of very low support branches does, in fact, help the accuracy.
ASTRALIII
ASTRALIII has six new features:

1.
Heuristics for building the set X are modified to ensure X=O(nk). This step alone (without subsequent improvements) guarantees the overall running time is \(O\left ((nk)^{2.726}\right)\) for binary gene trees and \(O\left (n^{4.726}k^{2.726}\right)\) for polytomies.

2.
Heuristics for building the set X are modified to enlarge X for gene trees with polytomies without breaking X=O(nk) guarantees. This can impact the accuracy and empirical running times but not the asymptotic running time.

3.
A new way of computing w(q) is introduced to reduce the running time for scoring a gene tree to O(n), instead of \(O\left (n^{3}\right)\), in the presence of polytomies. This step, combined with the previous steps, reduces the total running time to \(O\left ((nk)^{2.726}\right)\) irrespective of whether gene trees have polytomies.

4.
A polytree is used to represent gene trees, and this enables an algorithm that reduces the overall running time from \(O\left ((nk)^{2.726}\right)\) to O(D.(nk)^{1.726}), which is the final theoretical analysis of ATRALIII running time.

5.
A new algorithm, similar to A* [39], is used to compute an upperbound on the best possible resolution of a clade; we need not expand a clade recursively when its upperbound is below the best available score. The worst case asymptotic running time does not change due to this feature.

6.
A twostage heuristic mechanism is designed to further tighten the upper bounds used in pruning unnecessary parts of the search space. The worst case asymptotic running time is not impacted.
A beta version of ASTRALIII was recently described [40] and that version included features 3–5 but not the others. We next describe each improvement.
New search space: X=O(nk)
ASTRALII uses several heuristic methods to build X (see the original paper [22] for details). The main method involves computing several extended majority consensus trees from gene trees and then resolving polytomies in these consensus trees using three techniques (mentioned below). These steps are repeated for 10 rounds or more until very few (less than a constant threshold) of the bipartitions observed are new to X. Because the number of rounds is not constant or a function of n and k, we cannot bound how X grows with n and k for ASTRALII. In ASTRALIII, we limit the number of rounds by a constant value (default set to 110). This enables us to provide guarantees of a polynomial growth of X with n and k.
To get to X=O(nk), we need further changes. As mentioned, three techniques are used to resolve each polytomy of degree d in extended majority consensus trees. The first technique uses a precomputed distance matrix to build a UPGMA tree starting from sides of the polytomy and adds the new bipartitions from the UPGMA tree to X. This can only add O(d)=O(n) resolutions. The second technique computes a greedy consensus of gene trees subsampled to randomly selected taxa (one from each side of the polytomy) and adds bipartitions from the greedy consensus to X. This also can only add O(d) new bipartitions. The third resolution samples a taxon from each side of the polytomy, computes d caterpillar trees, each constructed based on decreasing similarity to each sampled taxon, and adds the bipartitions from all these caterpillar trees to X. This quadratic resolution step can add \(O\left (d^{2}\right)=O\left (n^{2}\right)\) bipartitions to X. To have X=O(n), we need to change this step. Let d_{1}…d_{ r } be the list of all polytomy degrees in an extended majority consensus tree in the ascending ordered. We find the smallest threshold q such that \(\sum _{i=1}^{q} d_{i}^{2} \leq c n\) for some constant c (default =25). In ASTRALIII, we apply the quadratic resolution technique only for polytomies d_{1}…d_{ q }; this, by definition, ensures no more than O(d)=O(n) bipartitions are added in each round.
New search space: handling gene tree polytomies
We also change the way ASTRAL builds X in the presence of gene tree polytomies. Our goal is to increase X compared to ASTRALII for multifurcating gene trees. However, X is enlarged at most by a constant factor and we retain X=O(nk).
If a gene tree includes polytomies, ASTRALII adds bipartitions implied by resolutions of that polytomy to the set X using a guide tree g. To build g, a greedy consensus of all gene trees is computed and is further refined to become binary by applying UPGMA to each polytomy of the greedy tree using a precomputed similarity matrix (see the original paper [22] for details). To resolve a gene tree polytomy of degree d, ASTRALII first randomly samples d taxa, each from one side of the polytomy. Let S be the sampled taxa. All bipartitions from the tree g restricted to the set S of leaves are added to X. While in ASTRALII this process is done only once, in ASTRALIII, we repeat the process three times with different random samples S. This increases X but at most by a constant factor. The enlarged X can lead to improved accuracy when input trees include many polytomies.
The second change in ASTRALIII is that we now use a UPGMA tree inferred based on the similarity matrix as the guide tree. We observed that the UPGMA tree summarizes the input gene trees more accurately than the greedy tree (see Additional file 1: Table S1). Finally, in ASTRALIII, we improve the definition of the similarity matrix in the presence of gene tree polytomies. Unlike in ASTRALII, we ensure that unresolved quartet trees induced by gene trees do not increase the similarity between pairs of taxa included in those quartets. Note that the similarity matrix, which is based on quartets, should not be confused with the quartet score optimized by ASTRAL.
Efficient handling of Polytomies
Recall that ASTRALII uses Eq. 4 to score a tripartition against a polytomy of size d in Θ(d^{3}) time. Our next Lemma shows that this can be improved.
Lemma 1
Let QI(T,M) be twice the number of quartet tree topologies shared between an unrooted tree that only includes a node corresponding to the tripartition T=(ABC) and another tree that includes only a node corresponding to a partition \(M=\left (M_{1}M_{2}...M_{d}\right)\) of degree d; then, QI(T,M) can be computed in time Θ(d).
Proof
In Θ(d) time, we can compute:
where \(a_{i}=A\cap M_{i}\) and \(b_{i}=B\cap M_{i}\); we can also compute S_{ b }, S_{ c }, S_{ a,c } and S_{ b,c } (similarly defined). Equation 4 computes twice the number of quartet tree topologies shared between an unrooted tree with internal node T and another tree with one internal node M [22]. Equation 4 can be rewritten as:
(the derivation is given in the Additional file 1: Appendix B). Computing Eq. 6 instead of Eq. 4 clearly reduces the running time to Θ(d) instead of \(\Theta \left (d^{3}\right)\). □
ASTRAL needs to score each of the Y tripartitions considered in the dynamic programming against each internal node of each input gene tree. The sum of degrees of k trees on n leaves is O(nk) (since that sum can never exceed the number of bipartitions in gene trees) and thus:
Corollary 2
Scoring a tripartition (i.e., computing w) can be done in O(nk).
Gene trees as a Polytree
ASTRALII scores each dynamic programming tripartition against each individual node of each gene tree. However, nodes that are repeated in several gene trees need not be recomputed. Recalling the definitions of E and D (Eqs. 1 and 2),
Lemma 2
The score of a tripartition T=(ABC) against all gene trees (i.e., the w(T) score) can be computed in Θ(D).
Proof
In ASTRALIII, we keep track of nodes that appear in multiple trees. This enables us to reduce the total calculation by using multiplicities:
We achieve this in two steps. In the first step, for each distinct gene tree cluster W, we compute the cardinality of the intersection of W and sets A, B, and C once using a depthfirst search with memoization. Let children(W) denote the set of children of W in an arbitrarily chosen tree g∈G containing W. Then, we have the following recursive relation:
(ditto for \(W \cap B\) and \(W \cap C\)). All such intersection values can be computed in a postorder traversal of a polytree. In this polytree, all unique clusters in the gene trees are represented as vertices and parentchild relations are represented as edges; note that when a cluster has different resolutions in two different input trees, we arbitrary choose one set of children in building the polytree. The polytree will include no more than D edges; thus, the time complexity of traversing this polytree (to compute Eq. 8) for all nodes is O(D). Once all intersections are computed, in the second step, we simply compute the sum in Eq. 7. Each QI(.) computation requires Θ(d) by Lemma 1. Recalling that \(D=\sum _{(M,c)\in E} M\), it is clear that computing Eq. 7 requires Θ(D). Therefore, both steps can be performed in Θ(D). □
Theorem 1
The running time of ASTRALIII grows as \(O\left (D(nk)^{{1.726}}\right)\) for both binary and multifurcating gene trees.
Proof
By results of Kane and Tao [28], the size of the set Y is \(O\left (X^{{1.726}}\right)\), and for each element in Y, by Lemma 2, we require O(D) to compute the weights, regardless of the presence or absence of polytomies. The running time of ASTRAL is dominated by computing the weights [22]. Thus, the overall running time is \(O(DY)=O\left (DX^{1.726}\right)\). Moreover, ASTRALIII forces X to grow as O(nk), giving the overall running time of \(O\left (D(nk)^{1.726}\right)\) □
Trimming of the dynamic programming
We now introduce an upperbound (proved in Additional file 1: Appendix B):
Let U(A,A^{″})=U(A^{″})+U(A−A^{″})+w(A^{″}A−A^{″}L−A). Since V(A)≤U(A), for any (A^{′}A−A^{′}L−A^{′})∈Y and (A^{″}A−A^{″}L−A^{″})∈Y, we no longer need to recursively compute V(A^{″}) and V(A−A^{″}) when U(A,A^{″})≤V(A,A^{′}). When computing V(A) by maximizing the score over all resolutions of A, imagine that we first encounter A^{′} and then A^{″}. We avoid expanding A^{″} when U(A,A^{″})≤V(A,A^{′}). This approach clearly makes the order of processing of the resolutions important. To heuristically improve the efficiency of this approach, we order all (A^{′}A−A^{′}L−A)∈Y according to U(A,A^{′}). Note that computing U(A) does not require recursive computations down the dynamic programming DAG. Thus, the use of this upperbound results in the trimming of the search space. However, as far as we can tell, this trimming does not improve the theoretical running time.
Twostaged αtrimming
In order to further trim the search space, another upperbound of V(A) is calculated. For a given α≥1 and any ordering of the set \(\left \{A' : (A'AA'LA) \in Y \right \}\) denoted by A_{1}…A_{ r }, we define V_{ α }(A) as follows.
We can compute V_{ α }(A) using an algorithm equivalent to our dynamic programming for computing V(A), except that, as resolutions of a clade A are being tested, a new one is accepted only if it improves upon the previous best resolution by a factor of α (thus, α=1 simply reproduces our existing dynamic programming). When computing V_{ α }(A), for any i<j, if \(\alpha \left (V_{\alpha }(A,A_{i})\right) \geq U(A,A_{j})\), then it is guaranteed that \(\alpha \left (V_{\alpha }(A,A_{i})\right) \geq V_{\alpha }\left (A,A_{j}\right)\), and thus we no longer need to recursively compute V_{ α }(A_{ j }) and V_{ α }(A−A_{ j }). After all V_{ α }(A) values are computed for some choice of α, we turn to computing V(A).
Observe that V_{ α }(A)≤V(A)≤αV_{ α }(A). Let U_{ α }(A,A_{ j }) be defined as
and note that U_{ α }(A,A_{ j })≥V(A,A_{ j })=V(A_{ j })+V(A−A_{ j })+w(A_{ j }A−A_{ j }L−A). Thus, during the dynamic programming, for i<j, if V(A,A_{ i })>U_{ α }(A,A_{ j }), then it is guaranteed that V(A,A_{ i })≥V(A,A_{ j }), and thus we no longer need to recursively compute V(A_{ j }) and V(A−A_{ j }). The hope is that the U_{ α } function will give us tighter upper bounds compared to the U function previously defined. Whether this happens or not depends on the choice of α, the order of visiting clusters, and the particularities of a dataset.
While any choice of α≥1 would guarantee the correct solution to the dynamic programming, we have empirically selected a heuristic to choose α. We set \(\alpha = \frac {U(L)}{g(L)}\), where g(A)=g(A_{ i })+g(A−A_{ i })+w(A_{ i }A−A_{ i }L−A) where i=arg max_{ j }U(A_{ j })+U(A−A_{ j })+w(A_{ j }A−A_{ j }L−A) and g(A)=0 for A=1. Just as before, we order the clusters in the decreasing order of U(A,A_{ i }).
Results
Experimental setup
We study three research questions: RQ1: Can contracting low support branches improve the accuracy of ASTRAL? RQ2: How do the running time and search space compare between ASTRALII and ASTRALIII? RQ3: How accurate is ASTRALIII, which guarantees polynomial size search space, compared to ATRALII?
Datasets
Avian biological dataset:
Neoavian relationships show extremely high levels of gene tree discord, perhaps because their ancestors experienced a rapid radiation [5]. A dataset of 48 genomes representing all avian orders has been used to partially resolve this rapid radiation [5]. A set of 14,446 loci (including exons, introns, and UCEs) was used to produce two reference species trees using concatenation and using a coalescentbased method [5, 33]. We use the set of all unbinned gene trees and compare ASTRALIII with and without contraction against both reference trees.
Simulated avianlike dataset:
This simulated dataset, previously used to emulate the biological avian dataset [33], has three model conditions with respect to the simulated levels of ILS: 1X is the default, whereas 0.5X divides each branch length in half (increasing ILS) and 2X multiplies them by 2 (reducing ILS). Average RF distances between true species tree and true gene trees are 0.35, 0.47, and 0.59, respectively for 2X, 1X, and 0.5X. To further test the impact of gene tree estimation error, sequence lengths were also varied to create four model conditions: 250bp alignments (0.67 RF distance between true gene trees and estimated gene trees), 500bp (0.54 RF), 1000bp (0.39 RF) and 1500bp (0.30 RF), all based on the 1X ILS. We use 1000 gene trees, and 20 replicates per condition. Gene trees are estimated using RAxML [41] with 200 replicates of bootstrapping.
SimPhyhomogeneous (S100):
We simulated a new 101taxon dataset using SimPhy [42] with 50 replicates, each with a different species tree. The species trees are simulated under the birthonly process with birth rate 10^{−7}, fixed haploid N_{ e } of 400K, and the number of generations sampled from a lognormal distribution with mean 2.5M. For each replicate, 1000 true gene trees are simulated under the MSCM (exact commands shown in Additional file 1: Appendix C and parameters given in Additional file 1: Table S2). The average normalized RF distance between true species trees and true gene trees was in most replicates in the \(\left [0.3,0.6\right ]\) range, with an average of 0.46 (Fig. 1). We use Indelible [43] to simulate the nucleotide sequences along the gene trees using the GTR evolutionary model [44] with 4 different fixed sequence lengths: 1600, 800, 400, and 200bp. We then use FastTree2 [32] to estimate both ML and 100 bootstrapped gene trees under the GTR+ Γ (requiring more than two million runs in total). Gene tree estimation error, measured by the FN rate between the true gene trees and the estimated gene trees, depended on the sequence length as shown in Fig. 1 (0.55, 0.42, 0.31, and 0.23 on average for 200bp, 400bp, 800bp, and 1600bp, respectively). We sample 1000, 500, 200, or 50 genes to generate datasets with varying numbers of gene trees.
SimPhyASTRAL2 (S200):
This dataset (201 taxa) is from the ASTRALII paper [22]. We use its most challenging model conditions with max tree height set to 500K generations and two rates of speciation: 10^{−6} and 10^{−7} (respectively, recent and deep speciation events). Compared to S100, this dataset has a much higher level of ILS. This was the only case in the ASTRALII paper where enlarging X substantially impacted accuracy [22]. We use S200 to test if our changes to X have compromised the accuracy. Like S100, gene alignments have varying lengths and mutation rates, leading to a wide range of gene tree error [22]. We analyze the data using 1000, 200, or 50 genes, and each model condition has 50 replicates; following the original paper, three replicates with low signal are removed.
Methods and Evaluation
We compare ASTRALIII (version 5.5.4) to ASTRALII (version 4.11.1) in terms of running time and accuracy. To address RQ1, we draw bootstrap support values on the ML gene trees and then contract branches with bootstrap support up to a threshold (0, 3, 5, 7, 10, 20, 33, 50, and 75%,) using the newick utility package [45]. Together with the original gene trees, we have 10 different versions of ASTRALIII.
To measure the accuracy of estimated species trees, we use False Negative (FN) rate. Note that in all our species tree comparisons, FN rate is equivalent to normalized Robinson–Foulds (RF) [46] metric because the ASTRAL species trees are fully resolved. All running times are measured on a cluster with servers with Intel(R) Xeon(R) CPU E52680 v3 @ 2.50GHz; each run was assigned to a single process, sharing cache and memory with other jobs.
RQ1: Impact of contracting low support branches on accuracy
We investigate RQ1 on the two simulated datasets where bootstrapping was feasible (avian and S100) and on the real avian dataset. On S200, due to its size, bootstrapping was not feasible and thus we cannot test RQ1.
S100
On this dataset, contracting very low support branches in most cases improves the accuracy (Fig. 2 and Additional file 1: Table S3). However, the excessive removal of branches with high, moderate, or occasionally low support degrades the accuracy. Nevertheless, filtering at 10% is always beneficial on average (Additional file 1: Table S3). The threshold where contracting starts to become detrimental depends on the condition, especially the number of gene trees and the alignment length, perhaps representing a signal to noise ratio tradeoff.
As the number of genes increases, the optimal threshold for contracting also tends to increase. Combining all model conditions, the error continues to drop until a 20% contracting threshold with 1000 genes, whereas no substantial improvement is observed after contracting branches with 5% support for 50 genes (Fig. 2). Nevertheless, removing branches with 10% or 20% does not increase the error with 50 genes. Perhaps, with few gene trees, removing branches of low support leaves us with very little information left; thus, regardless of whether we contract or not, we don’t get much signal around the most difficult branches. In contrast, when many gene trees are given, perhaps even after removing many branches, still enough gene trees with a resolution around difficult species tree branches are left.
The alignment length and gene tree error also impact the effect of contraction. For short alignments (200bp) and 1000 genes, contracting branches with up to 10% support reduces the species tree error by 21% (from 8.8% with no contraction to 6.9%). As alignment length grows, benefits of gene tree contraction diminish, so that with 1600bp genes, the reduction in error is merely from 4.1 to 3.7%. This pattern is perhaps expected because, with longer alignments, branch support is expected to increase. Thus, with longer gene alignments and consequently better gene trees with higher support, there is less room for improvement by reducing the noise. Consistent with this explanation, grouping replicates based on average gene tree error gives similar results as grouping by alignment length (see Additional file 1: Figure S1).
avianlike simulations
On the avian simulated dataset, contracting low support branches helps accuracy marginally, but the extent of impact depends on the model condition (Fig. 3). With moderate ILS (2X), we see no improvements as a result of contracting low support branches, perhaps because the average error is below 5% even with no contraction, leaving little room for improvements. Increasing ILS, we start to see improvements using contracted gene trees. Removing branches of up to 5% support reduces the error from 13 to 11% with 0.5X, and from 8 to 7% for the 1X condition.
When ILS is fixed to 1X and sequence length is varied (Fig. 3), contracting is helpful mostly with short sequences (e.g., 250 bp). With longer sequences, where gene tree estimation error is low, little or no improvement in accuracy is obtained. The best accuracy is typically observed by contracting at 0–5%. The gains in accuracy comparing no contraction to contraction at 0, 3, 5% thresholds are statistically significant (p=0.017, 0.028, and 0.013) according to onetailed paired ttests.
Avian biological dataset
The original analyses on this dataset [5, 33] report that MPEST [17] run on 14,446 gene trees produces a tree that conflicts with strong evidence from the literature and other analyses on the same dataset. The statistical binning method was developed to address this shortcoming by combining loci together to reduce gene tree error [33, 34]. MPEST run on binned gene trees (i.e., binned MPEST) produced results [5, 33] that were largely congruent with the concatenation using ExaML [47] and differed in only five branches with low support (Fig. 4a, b); both trees were used as the reference [5]. Here, we test if simply contracting low support gene tree branches and using ASTRALIII produces trees congruent with the reference trees.
Similar to MPEST, when ATRALIII is run on 14,446 gene trees with no contraction, the results differ in nine and 11 branches, respectively, with respect to the reference binned MPEST and concatenation trees (Fig. 4c). Moreover, this tree contradicts some strong results from the avian analyses (e.g., not recovering the Columbea group [5]). ASTRALIII with no contraction finishes in 32 hours, but with contraction, depending on the threshold, it takes 3 to 84 h (>50 h for 0 – 20% thresholds and <26 hours for 33 – 75%). Contracting 0% branches has minimal impact on the discordance (eight discordant branches with binned MPEST instead of nine). However, contracting low support branches with 3–33% thresholds dramatically reduces the discordance with the reference tree (2, 2, 4, 2, 3, and 3 discordant branches, respectively, for 3, 5, 7, 10, 20, and 33%). Three thresholds (3, 5, and 10%) produce an identical tree (Fig. 4d). The remaining differences are among the branches that are deemed unresolved by Jarvis et al. and change among the reference trees as well [5]. Contracting at 50 and 75% thresholds, however, increases discordance to five and six branches, respectively.
Thus, consistent with simulations, contracting very low support branches seems to produce the best results, when judged by similarity with the reference trees. To summarize, ASTRALIII obtained on unbinned but collapsed gene trees agreed with all major relations in Jarvis et al., including the novel Columbea group, whereas the unresolved tree missed important clades (Fig. 4).
RQ2: Running time improvements
Varying the number of genes (k)
We compare ASTRALIII to ASTRALII on the avian simulated dataset, changing the number of genes from 2^{8} to 2^{14} and forcing X to be the same for both versions to enable comparing impacts of improved weight calculation (Fig. 5). We allow each replicate run to take up to two days. ASTRALIII improves the running time over ASTRALII and the extent of the improvement depends on k (see Additional file 1: Figure S2). With 1000 genes or more, there is at least a 2.1X improvement. With 2^{13} genes, the largest value where both versions could run, ASTRALIII finishes on average 3.2 times faster than ASTRALII (234 versus 758 minutes). ASTRALII is not able to finish on the dataset with k=2^{14}, while ASTRALIII finishes on all conditions. Moreover, fitting a line to the average running time in the loglog scale graph reveals that on this dataset, the running time of ASTRALIII on average grows as O(k^{2.08}), which is better than that of ASTRALII at O(k^{2.28}), and both are better than the theoretical worst case, which is O(k^{2.726}). These results are consistent with the fact that ASTRALIII considers similarities between gene tree nodes.
Running time for large polytomies
ASTRALIII has a clear advantage compared to ASTRALII with respect to the running time when gene trees include polytomies (Fig. 6a and Additional file 1: Figure S3). Since ASTRALII and ASTRALIII can have a different set X, we show the running time per each weight calculation (i.e., Eq. 3). As we contract low support branches and hence increase the prevalence of polytomies, the weight calculation time quickly grows for ASTRALII, whereas, in ASTRALIII, the weight calculation time remains flat, or even decreases. These results are consistent with a change of asymptotic running time to score a polytomy of size d from O(d^{3}) in ASTRALII to O(d) in ASTRALIII.
The search space
Comparing the size of the search space (X) between ATRALII and ASTRALIII shows that as intended, the search space is decreased in size for cases with no polytomy but can increase in the presence of polytomies (Fig. 6b). With no contraction, on average, X is always smaller for ASTRALIII than ASTRALII. With few errorprone gene trees (50 gene trees from 200bp alignments), the search space has reduced dramatically but with many genes or highquality gene trees, the reductions are minimal. Moreover, the search space for gene trees estimated from short alignments (e.g., 200 bp) is several times larger than those based on longer alignments (e.g., 1600 bp) for both methods. These are results of the first feature of ASTRALIII that forces the search space to grow at O(nk).
Contracting low support branches initially increases the search space. This is because ASTRALIII unlike ASTRALII adds multiple resolutions per polytomy to X. Further contraction results in reductions in X, presumably because many polytomies exist and they are resolved similarly inside ASTRALIII.
RQ3: ASTRALII versus ASTRALIII accuracy
Despite limiting X to grow at most linearly with n and k, the accuracy of ASTRALIII remains unchanged compared to ASTRALII (Table 1 and Additional file 1: Figures S4–S7). Importantly, even for the very challenging S200 dataset, the accuracy is not reduced substantially even though X is reduced by up to 47%. Over all datasets, differences in error are less than 0.002, except for three datasets where the error of ASTRALIII was less than ASTRALII by 0.003, 0.005, and 0.006 and two cases where the error increased by 0.004. Over all datasets, the differences between ASTRALII and ASTRALIII were not statistically significant according to a paired ttest (pvalue = 0.496). Since ASTRALIII has a reduced search space, its quartet scores are typically slightly lower than ASTRALII, but these reductions are never more than 0.06%. As expected, the largest drops in the quartet score happen for the challenging S200 dataset with only 50 gene trees. The search space reduces in almost all cases and the reductions can be as much as 72%. Thus, the improved running time of ASTRALIII does not come at the price of reduced accuracy.
Discussion
Below we further comment on ASTRALIII in terms of accuracy and running time. We finish by comparing ASTRALIII and ASTRALIIIbeta.
Accuracy
Although tree accuracy can improve with contracted gene trees, the gap between performance on true gene trees and estimated gene trees remains wide (Additional file 1: Table S3). On the S100 dataset, respectively for 50, 200, 500, and 1000 genes, the best average error with 1600bp gene trees among all contraction levels were 9.8%, 5.9%, 4.3%, and 3.7% compared to 7.0%, 3.7%, 2.4%, and 1.5% with true gene trees. Thus, while contracting low support branches helps in addressing gene tree error, improved methods of gene tree estimation remain crucial. Our results also indicate that in the presence of noisy gene trees, increased numbers of genes are needed to achieve high accuracy. For example, on the S100 dataset, with 1000 gene trees of only 200bp and contracting with a 10% threshold, the species tree error was 6.9%, which slightly outperformed the accuracy with only 50 true gene trees. This observation encourages the use of a large number of gene trees; incidentally, a main feature of ASTRALIII is improved running time with many genes.
The best choice of the threshold of contraction was somewhat sensitive to the dataset. Testing up to 1000 gene trees, we observed that more gene trees clearly increased the optimal threshold, but did not test beyond 1000 genes. One can predict that perhaps the trend may continue but also that the optimal threshold will not indefinitely increase. Similarly, we saw that the amount of gene tree error due to lack of signal impacts the optimal threshold. One may expect that other sources of error, including incorrect orthology, incorrect alignment, and model misspecifications may also impact the optimal threshold. Regardless of the choice of the optimal threshold, it seems that the largest benefits are associated with removing the least supported branches. Overall, a threshold of 10% seemed to provide a good default value.
In most datasets, a substantial accuracy improvement was observed when 0% BS branches were removed. Branches of 0% support are presumably resolved arbitrarily. The use of conserved genes or closely related taxa can increase instances where two or more taxa have identical sequences in some genes. Some tree estimation methods generate binary trees even under such conditions. Removing branches that are arbitrarily resolved make sense and, as our results indicate, improves accuracy.
The main competitor of ASTRAL is NJst [18] and its fast implementation, ASTRID [19], but these tools are not able to handle polytomies in input gene trees. ASTRALIII makes it efficient to use unresolved gene trees. Moreover, beyond contracting low support branches, other strategies could be used to reduce impacts of gene tree uncertainty. Previous studies indicate that simply using the set of all bootstrap gene tree replicates as input to ASTRAL increases error [21], perhaps due to the increased noise [31, 35]. However, using a sample from the Bayesian distribution for each gene tree may improve the accuracy of ASTRAL.
Finally, theoretical implications of removing low support branches are less clear than its empirical impact. In principle, branches that have low support are not necessarily expected to be randomly selected among gene trees. Thus, while our empirical results support the use of (conservative) filtering, the resulting procedure may lose statistical guarantees of consistency. Future work should study conditions where ASTRAL remains statistically consistent with contracted gene trees.
Running time
Large n
To assess limits of ASTRALIII in terms of scalability, we tested it on 20 replicates of a dataset with 5,000 species and 1000 true gene trees (simulation procedure described in Additional file 1: Appendix C and parameters given in Additional file 1: Table S4). ASTRALIII took between 2 and 62 h to run on this dataset (9.4 hours on average). We also attempted to test ASTRALIII on four replicates of a dataset with 10,000 species and 1000 true gene trees, allowing a week of running time. Of the four replicates, two were able to finish within the allotted time. Thus, depending on the nature of the data, ASTRALIII may be able to scale to datasets with up to 10,000 species given sufficient running time.
Average running time, X, and Y
The ASTRALIII running time analysis is based on several worstcase assumptions, and real data may grow less rapidly with both n and k. Overall, although the exact value depends on the dataset and especially the amount of discordance, the running time of ASTRAL seems to grow roughly quadratically with both n and k (i.e., proportionally to n^{2}k^{2}); see Additional file 1: Figures S2 and S8.
ASTRALIII bounds X to grow at most linearly with n and k. Empirically, we observe that X grows sublinearly with k\(\left (\mathrm {close to} O\left (k^{\frac {3}{4}}\right)\right)\) on the avian simulated dataset (Fig. 7a). Note that the avian dataset has one of the highest levels of ILS; the dependence on k is expected to be lower for datasets with lower gene tree discordance. Testing the growth with n is more difficult because as n changes, other factors such as the amount of discordance also change. Nevertheless, across all the datasets that we had available, we tested the change in running time for fixed k as n changes and observed a linear growth (Fig. 7b), matching the worstcase scenario.
Finally, establishing empirical running time growth requires establishing the rate of the growth of Y with respect to X. The Y≤X^{1.726} upperbound is for specialized formations of the set X [28]. Empirically, as X increases, the size of Y in ASTRALIII does not increase as fast as the worstcase scenario implies. Across all of our ASTRALIII runs in this paper, Y ranged in 90% of our runs between X^{1.07} and X^{1.20}, and the overall average was X^{1.11} (Fig. 7c).
Comparisons to ASTRALIIIbeta
The beta version of ASTRALIII [40] included features 3–5 but not changes to X (features 1 and 2) or the twostaged αtrimming technique (feature 6). For completeness, we compared ASTRALIIIbeta and ASTRALIII in terms of accuracy, quartet score, and the running time (Table 2). Accuracy and quartet scores are very similar, perhaps with a small improvement since the beta version. The search space is reduced since the beta version (due to features 1 and 2), and the running times are substantially decreased (at least by half in most cases). The reductions in the running time are due to αtrimming, reduced X, in addition to further improvements in details of our implementation of the polytree datastructure.
To further demonstrate the impact of the αtrimming feature, we randomly chose 18 species from the avian dataset with 1500bp and 1X ILS. On this limited dataset, we ran ASTRALIII in its exact mode (i.e., setting X to the power set) with 100 gene trees. Without any trimming of the dynamic programming (i.e., without features 5 and 6), the running time was 40 minutes. Emulating ASTRALIIIbeta, we disabled αtrimming but kept the trimming (feature 5) and the running time reduced to 33 min. Adding the αtrimming feature dramatically reduced the running time to 13 min. Thus, when X includes many bipartitions that have very little promise in improving the quartet score (as in the exact mode of ASTRAL), the αtrimming approach is very effective in reducing the running time.
Conclusions
We introduced ASTRALIII, which compared to ASTRALII, improves scalability, especially for datasets with large k and many polytomies. These improvements enabled us to test the accuracy of ASTRAL after contracting low support branches. Overall, we observed improvements in accuracy when very low support branches were contracted, but also evidence that aggressive filtering reduces the accuracy. ASTRALIII bounds the theoretical running time to \(O\left ((nk)^{{1.726}}.D\right)\) where D=O(nk) is the sum of degrees of all unique gene tree nodes. In practice, the running time tends to grow no worse than quadratically with both n and k.
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Acknowledgements
Authors thank anonymous reviewers.
Funding
This work was supported by the National Science Foundation grant IIS1565862 to SM, MHR, and ES. Computations were performed on the San Diego Supercomputer Center (SDSC) through XSEDE allocations, which is supported by the NSF grant ACI1053575. The publication cost of this article was funded by the NSF grant IIS1565862.
Availability of data and materials
ASTRALIII is available at https://github.com/smirarab/ASTRAL. The data presented in this paper can be found at https://gitlab.com/esayyari/ASTRALIII.
About this supplement
This article has been published as part of BMC Bioinformatics Volume 19 Supplement 6, 2018: Proceedings of the 15th Annual Research in Computational Molecular Biology (RECOMB) Comparative Genomics Satellite Workshop: bioinformatics. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume19supplement6.
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CZ, MHR, and SM designed the algorithms and CZ and MHR implemented them. SM, ES, and CZ designed the experiments and all authors contributed to running experiments and analyzing the data. All authors contributed to the writing. All authors read and approved the final manuscript.
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Correspondence to Siavash Mirarab.
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From RECOMBCG  2017: The Fifteenth RECOMB Comparative Genomics Satellite Conference Barcelona, Spain. 0406 October 2017
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Supplementary material and appendices. Appendices A, B, and C in addition to Figures S1–S8, and Tables S1–S4 are all provided as one Additional file 1. (PDF 476 kb)
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Keywords
 Phylogenomics
 Incomplete lineage sorting
 ASTRAL