Rooted triple consensus and anomalous gene trees.
Ontology highlight
ABSTRACT: Background
Anomalous gene trees (AGTs) are gene trees with a topology different from a species tree that are more probable to observe than congruent gene trees. In this paper we propose a rooted triple approach to finding the correct species tree in the presence of AGTs.Results
Based on simulated data we show that our method outperforms the extended majority rule consensus strategy, while still resolving the species tree. Applying both methods to a metazoan data set of 216 genes, we tested whether AGTs substantially interfere with the reconstruction of the metazoan phylogeny.Conclusion
Evidence of AGTs was not found in this data set, suggesting that erroneously reconstructed gene trees are the most significant challenge in the reconstruction of phylogenetic relationships among species with current data. The new method does however rule out the erroneous reconstruction of deep or poorly resolved splits in the presence of lineage sorting.
SUBMITTER: Ewing GB
PROVIDER: S-EPMC2409437 | biostudies-literature | 2008 Apr
REPOSITORIES: biostudies-literature
Publications
Rooted triple consensus and anomalous gene trees.
Ewing Gregory B GB Ebersberger Ingo I Schmidt Heiko A HA von Haeseler Arndt A
BMC evolutionary biology 20080425
<h4>Background</h4>Anomalous gene trees (AGTs) are gene trees with a topology different from a species tree that are more probable to observe than congruent gene trees. In this paper we propose a rooted triple approach to finding the correct species tree in the presence of AGTs.<h4>Results</h4>Based on simulated data we show that our method outperforms the extended majority rule consensus strategy, while still resolving the species tree. Applying both methods to a metazoan data set of 216 genes, ...[more]
Similar Datasets
Project description:Methods for inferring species trees from gene trees motivated by incomplete lineage sorting typically use either rooted gene trees to infer a rooted species tree, or use unrooted gene trees to infer an unrooted species tree, which is then typically rooted using one or more outgroups. Theoretically, however, it has been known since 2011 that it is possible to consistently infer the root of the species tree directly from unrooted gene trees without assuming an outgroup. Here, we use approximate Bayesian computation to infer the root of the species tree from unrooted gene trees assuming the multispecies coalescent model. It is hoped that this approach will be useful in cases where an appropriate outgroup is difficult to find and gene trees do not follow a molecular clock. We use approximate Bayesian computation to infer the root of the species tree from unrooted gene trees. This approach could also be useful when there is prior information that makes a small number of root locations plausible in an unrooted species tree.
| S-EPMC5680165 | biostudies-literature
Project description:BackgroundPhylogenetic trees based on sequences from a set of taxa can be incongruent due to horizontal gene transfer (HGT). By identifying the HGT events, we can reconcile the gene trees and derive a taxon tree that adequately represents the species' evolutionary history. One HGT can be represented by a rooted Subtree Prune and Regraft (RSPR) operation and the number of RSPRs separating two trees corresponds to the minimum number of HGT events. Identifying the minimum number of RSPRs separating two trees is NP-hard, but the problem can be reduced to fixed parameter tractable. A number of heuristic and two exact approaches to identifying the minimum number of RSPRs have been proposed. This is the first implementation delivering an exact solution as well as the intermediate trees connecting the input trees.ResultsWe present the SPR Identification Tool (SPRIT), a novel algorithm that solves the fixed parameter tractable minimum RSPR problem and its GPL licensed Java implementation. The algorithm can be used in two ways, exhaustive search that guarantees the minimum RSPR distance and a heuristic approach that guarantees finding a solution, but not necessarily the minimum one. We benchmarked SPRIT against other software in two different settings, small to medium sized trees i.e. five to one hundred taxa and large trees i.e. thousands of taxa. In the small to medium tree size setting with random artificial incongruence, SPRIT's heuristic mode outperforms the other software by always delivering a solution with a low overestimation of the RSPR distance. In the large tree setting SPRIT compares well to the alternatives when benchmarked on finding a minimum solution within a reasonable time. SPRIT presents both the minimum RSPR distance and the intermediate trees.ConclusionsWhen used in exhaustive search mode, SPRIT identifies the minimum number of RSPRs needed to reconcile two incongruent rooted trees. SPRIT also performs quick approximations of the minimum RSPR distance, which are comparable to, and often better than, purely heuristic solutions. Put together, SPRIT is an excellent tool for identification of HGT events and pinpointing which taxa have been involved in HGT.
| S-EPMC2829038 | biostudies-literature
Project description:Concatenated sequence alignments are often used to infer species-level relationships. Previous studies have shown that analysis of concatenated data using maximum likelihood (ML) can produce misleading results when loci have differing gene tree topologies due to incomplete lineage sorting. Here, we develop a polynomial time method that utilizes the modified mincut supertree algorithm to construct an estimated species tree from inferred rooted triples of concatenated alignments. We term this method SuperMatrix Rooted Triple (SMRT) and use the notation SMRT-ML when rooted triples are inferred by ML. We use simulations to investigate the performance of SMRT-ML under Jukes-Cantor and general time-reversible substitution models for four- and five-taxon species trees and also apply the method to an empirical data set of yeast genes. We find that SMRT-ML converges to the correct species tree in many cases in which ML on the full concatenated data set fails to do so. SMRT-ML can be conservative in that its output tree is often partially unresolved for problematic clades. We show analytically that when the species tree is clocklike and mutations occur under the Cavender-Farris-Neyman substitution model, as the number of genes increases, SMRT-ML is increasingly likely to infer the correct species tree even when the most likely gene tree does not match the species tree. SMRT-ML is therefore a computationally efficient and statistically consistent estimator of the species tree when gene trees are distributed according to the multispecies coalescent model.
| S-EPMC2877557 | biostudies-literature
Project description:Phylogenetic analysis is used to analyze the evolution of species according to the characteristics of biological sequences. The analytical results are generally represented by phylogenetic trees. NJ (neighbor joining) is a frequently used algorithm for constructing phylogenetic trees because of its few assumptions, fast operation, and high accuracy, and is based on the distance between taxa. It is known that NJ usually constructs different phylogenetic trees for the same dataset with differences in input order, which are known as “tied trees.” This article proposes an improved method of NJ, called ENJ (extended neighbor joining). The ENJ can join several (currently limited to three) nodes with the same minimum distance into a new node, rather than joining two nodes in one iteration, so it can construct triple phylogenetic trees. We have inferred the formulas for updating the distance values and calculating the branch lengths for the ENJ algorithm. We have tested the ENJ with simulated and real data. The experimental results show that, compared with other methods, the trees constructed by the ENJ have greater similarity to the initial trees, and the ENJ is much faster than the NJ algorithm. Moreover, we have constructed a phylogenetic tree for the novel coronavirus (COVID-19) and related coronaviruses by ENJ, which shows that COVID-19 and SARS-CoV are closer than other coronaviruses. Because it differs from the existing phylogenetic trees for those coronaviruses, we constructed a phylogenetic network for them. The network shows those species have had a reticulate evolution. Graphical Abstract Accurately inferring evolutionary relationships between species can help humans to understand the evolutionary history and the mechanism. ENJ is an efficient and effective algorithm for constructing phylogenetic trees, which can construct triple phylogenetic trees, if necessary. The trees constructed by ENJ can better describe the original information and reflect the real evolutionary relationship of species.
| S-EPMC7779534 | biostudies-literature
Project description:Mollusks are the most morphologically disparate living animal phylum, they have diversified into all habitats, and have a deep fossil record. Monophyly and identity of their eight living classes is undisputed, but relationships between these groups and patterns of their early radiation have remained elusive. Arguments about traditional morphological phylogeny focus on a small number of topological concepts but often without regard to proximity of the individual classes. In contrast, molecular studies have proposed a number of radically different, inherently contradictory, and controversial sister relationships. Here, we assembled a data set of 42 unique published trees describing molluscan interrelationships. We used these data to ask several questions about the state of resolution of molluscan phylogeny compared with a null model of the variation possible in random trees constructed from a monophyletic assemblage of eight terminals. Although 27 different unique trees have been proposed from morphological inference, the majority of these are not statistically different from each other. Within the available molecular topologies, only four studies to date have included the deep sea class Monoplacophora; but 36.4% of all trees are not significantly different. We also present supertrees derived from two data partitions and three methods, including all available molecular molluscan phylogenies, which will form the basis for future hypothesis testing. The supertrees presented here were not constructed to provide yet another hypothesis of molluscan relationships, but rather to algorithmically evaluate the relationships present in the disparate published topologies. Based on the totality of available evidence, certain patterns of relatedness among constituent taxa become clear. The internodal distance is consistently short between a few taxon pairs, particularly supporting the relatedness of Monoplacophora and the chitons, Polyplacophora. Other taxon pairs are rarely or never found in close proximity, such as the vermiform Caudofoveata and Bivalvia. Our results have specific utility for guiding constructive research planning to better test relationships in Mollusca as well as other problematic groups. Taxa with consistently proximate relationships should be the focus of a combined approach in a concerted assessment of potential genetic and anatomical homology, whereas unequivocally distant taxa will make the most constructive choices for exemplar selection in higher level phylogenomic analyses.
| S-EPMC4395843 | biostudies-literature
Project description:MOTIVATION:Cancer phylogenies are key to studying tumorigenesis and have clinical implications. Due to the heterogeneous nature of cancer and limitations in current sequencing technology, current cancer phylogeny inference methods identify a large solution space of plausible phylogenies. To facilitate further downstream analyses, methods that accurately summarize such a set T of cancer phylogenies are imperative. However, current summary methods are limited to a single consensus tree or graph and may miss important topological features that are present in different subsets of candidate trees. RESULTS:We introduce the Multiple Consensus Tree (MCT) problem to simultaneously cluster T and infer a consensus tree for each cluster. We show that MCT is NP-hard, and present an exact algorithm based on mixed integer linear programming (MILP). In addition, we introduce a heuristic algorithm that efficiently identifies high-quality consensus trees, recovering all optimal solutions identified by the MILP in simulated data at a fraction of the time. We demonstrate the applicability of our methods on both simulated and real data, showing that our approach selects the number of clusters depending on the complexity of the solution space T. AVAILABILITY AND IMPLEMENTATION:https://github.com/elkebir-group/MCT. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6612807 | biostudies-other
Project description:Here we present a DNA sequence study that incorporates intraspecific variation from all five genera of hominoids (apes and humans). Recently it has been claimed that using single individuals to analyze species' relationships might be misleading if within-species variation is great. Our results indicate that despite high intraspecific variation in mitochondrial cytochrome oxidase subunit II gene sequences of some hominoids, humans and chimpanzees are nonetheless significantly most closely related. We also report the observation that variation within the gorilla species exceeds that between common and pygmy chimpanzee species, a finding with implications for conservation. In contrast, humans are less mitochondrially diverse than lowland gorillas inhabiting western Africa.
| S-EPMC44714 | biostudies-other
Project description:A wide variety of problems in computational biology, most notably the assessment of orthology, are solved with the help of reciprocal best matches. Using an evolutionary definition of best matches that captures the intuition behind the concept we clarify rigorously the relationships between reciprocal best matches, orthology, and evolutionary events under the assumption of duplication/loss scenarios. We show that the orthology graph is a subgraph of the reciprocal best match graph (RBMG). We furthermore give conditions under which an RBMG that is a cograph identifies the correct orthlogy relation. Using computer simulations we find that most false positive orthology assignments can be identified as so-called good quartets-and thus corrected-in the absence of horizontal transfer. Horizontal transfer, however, may introduce also false-negative orthology assignments.
| S-EPMC7052050 | biostudies-literature
Project description:MotivationPhylogenomics faces a dilemma: on the one hand, most accurate species and gene tree estimation methods are those that co-estimate them; on the other hand, these co-estimation methods do not scale to moderately large numbers of species. The summary-based methods, which first infer gene trees independently and then combine them, are much more scalable but are prone to gene tree estimation error, which is inevitable when inferring trees from limited-length data. Gene tree estimation error is not just random noise and can create biases such as long-branch attraction.ResultsWe introduce a scalable likelihood-based approach to co-estimation under the multi-species coalescent model. The method, called quartet co-estimation (QuCo), takes as input independently inferred distributions over gene trees and computes the most likely species tree topology and internal branch length for each quartet, marginalizing over gene tree topologies and ignoring branch lengths by making several simplifying assumptions. It then updates the gene tree posterior probabilities based on the species tree. The focus on gene tree topologies and the heuristic division to quartets enables fast likelihood calculations. We benchmark our method with extensive simulations for quartet trees in zones known to produce biased species trees and further with larger trees. We also run QuCo on a biological dataset of bees. Our results show better accuracy than the summary-based approach ASTRAL run on estimated gene trees.Availability and implementationQuCo is available on https://github.com/maryamrabiee/quco.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9235488 | biostudies-literature
Project description:Background:Horizontal gene transfer (HGT), a process of acquisition and fixation of foreign genetic material, is an important biological phenomenon. Several approaches to HGT inference have been proposed. However, most of them either rely on approximate, non-phylogenetic methods or on the tree reconciliation, which is computationally intensive and sensitive to parameter values. Results:We investigate the locus tree inference problem as a possible alternative that combines the advantages of both approaches. We present several algorithms to solve the problem in the parsimony framework. We introduce a novel tree mapping, which allows us to obtain a heuristic solution to the problems of locus tree inference and duplication classification. Conclusions:Our approach allows for faster comparisons of gene and species trees and improves known algorithms for duplication inference in the presence of polytomies in the species trees. We have implemented our algorithms in a software tool available at https://github.com/mciach/LocusTreeInference.
| S-EPMC5985597 | biostudies-literature