Phylogenetic Inference Problem Research Articles

An evolutionary algorithm based on parsimony for the multiobjective phylogenetic network inference problem

Phylogenetic networks can represent evolutionary phenomena that phylogenetic trees cannot describe, such as parallelism, convergence, reversion, hybridisation, recombination, and horizontal transference. The phylogenetic inference problem can be seen as an optimisation problem, searching for the most qualified network among the possible topologies, based on an inference criterion. However, different criteria may result in several topologies of networks, which could conflict with each other. Multi-objective optimisation can handle conflicting objectives, reducing the bias associated with the dependency on a specific criterion. In this work, we define the multi-objective phylogenetic inference problem based on networks to consider reticular phenomena and propose an ad-hoc evolutionary algorithm to treat it: MO-PhyNet. This algorithm is based on the Non-dominated Sorting Genetic Algorithm II designed to infer rooted phylogenetic networks by minimising three criteria: (1) parsimony hardwired, (2) parsimony softwired, and (3) the number of reticulations. The formalisation of the phylogenetic inference based on networks as a multi-objective optimisation problem allows us to obtain solutions considering conflicting inference criteria, resulting in different reticulated topologies representing distinct evolutionary hypotheses. The MO-PhyNet results identify Pareto set of solutions that show a relationship between the hardwired parsimony and the minimum reticulations criteria. Additionally, MO-PhyNet obtains better solutions than other strategies in terms of the optimised criteria by allowing to visualise incongruences and horizontal phenomena. This work is the first attempt to address the inference of phylogenetic networks considering multi-objective optimisation concerning the current literature to the best of our knowledge.

On the role of metaheuristic optimization in bioinformatics

AbstractMetaheuristic algorithms are employed to solve complex and large‐scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics.

Taxa hold little information about organisms: Some inferential problems in biological systematics

The taxa that appear in biological classifications are commonly seen as representing information about the traits of their member organisms. This paper examines in what way taxa feature in the storage and retrieval of such information. I will argue that taxa do not actually store much information about the traits of their member organisms. Rather, I want to suggest, taxa should be understood as functioning to localize organisms in the genealogical network of life on Earth. Taxa store information about where organisms are localized in the network, which is important background information when it comes to establishing knowledge about organismal traits, but it is not itself information about these traits. The view of species and higher taxa that is proposed here follows from examining three problems that occur in contemporary biological systematics and are discussed here: the problem of generalization over taxa, the problem of phylogenetic inference, and the problematic nature of the Tree of Life.

A Memetic Algorithm Based on an NSGA-II Scheme for Phylogenetic Tree Inference

Phylogenetic inference allows building a hypothesis about the evolutionary relationships between a group of species, which is usually represented as a tree. The phylogenetic inference problem can be seen as an optimization problem, searching for the most qualified tree among all the possible topologies according to a selected criterion. These criteria can be based on different principles. Due to the combinatorial number of possible topologies, diverse heuristics and meta-heuristics have been proposed to find approximated solutions according to one criterion. However, these methods may result in several phylogeny trees which could be in conflict with one another. In order to deal with this problem, models based on multiobjective optimization with different configurations have been used. In this paper, we propose an ad-hoc multiobjective memetic algorithm (MO-MA) to infer phylogeny using two objectives: 1) maximum parsimony and 2) likelihood. Several population operators and local search strategies are proposed and evaluated in order to measure their contribution to the algorithm. Additionally, we perform a comparison among different configurations and tree rearrangement strategies. The results show that the proposed MO-MA is able to identify a Pareto set of solutions that include new trees which were nondominated by solutions from the current state of the art single-objective optimization tools. Furthermore, the MO-MA improves the results presented in the literature for multiobjective approaches in all of the studied data sets. These results make our proposal a good alternative for phylogenetic inference.

Polynomial-Time Algorithms for Phylogenetic Inference Problems involving duplication and reticulation.

A common problem in phylogenetics is to try to infer a species phylogeny from gene trees. We consider different variants of this problem. The first variant, called Unrestricted Minimal Episodes Inference, aims at inferring a species tree based on a model with speciation and duplication where duplications are clustered in duplication episodes. The goal is to minimize the number of such episodes. The second variant, Parental Hybridization, aims at inferring a species network based on a model with speciation and reticulation. The goal is to minimize the number of reticulation events. It is a variant of the well-studied Hybridization Number problem with a more generous view on which gene trees are consistent with a given species network. We show that these seemingly different problems are in fact closely related and can, surprisingly, both be solved in polynomial time, using a structure we call "beaded trees". However, we also show that methods based on these problems have to be used with care because the optimal species phylogenies always have a restricted form. To mitigate this problem, we introduce a new variant of Unrestricted Minimal Episodes Inference that minimizes the duplication episode depth. We prove that this new variant of the problem can also be solved in polynomial time.

A novel approach based on multiobjective variable mesh optimization to Phylogenetics

One of the most relevant problems in Bioinformaticsand Computational Biology is the search and reconstruction ofthe most accurate phylogenetic tree that explains, as exactly aspossible, the evolutionary relationships among species from agiven dataset. Different criteria have been employed to evaluatethe accuracy of evolutionary hypothesis in order to guide a searchalgorithm towards the best tree. However, these criteria may leadto distinct phylogenies, which are often conflicting among them.Therefore, a multi-objective approach can be useful. In this work,we present a phylogenetic adaptation of a multiobjective variablemesh optimization algorithm for inferring phylogenies, to tacklethe phylogenetic inference problem according to two optimalitycriteria: maximum parsimony and maximum likelihood. Theaim of this approach is to propose a complementary view ofphylogenetics in order to generate a set of trade-off phylogenetictopologies that represent a consensus between both criteria.Experiments on four real nucleotide datasets show that ourproposal can achieve promising results, under both multiobjectiveand biological approaches, with regard to other classical andrecent multiobjective metaheuristics from the state-of-the-art.

Anchoring quartet-based phylogenetic distances and applications to species tree reconstruction.

BackgroundInferring species trees from gene trees using the coalescent-based summary methods has been the subject of much attention, yet new scalable and accurate methods are needed.ResultsWe introduce DISTIQUE, a new statistically consistent summary method for inferring species trees from gene trees under the coalescent model. We generalize our results to arbitrary phylogenetic inference problems; we show that two arbitrarily chosen leaves, called anchors, can be used to estimate relative distances between all other pairs of leaves by inferring relevant quartet trees. This results in a family of distance-based tree inference methods, with running times ranging between quadratic to quartic in the number of leaves.ConclusionsWe show in simulated studies that DISTIQUE has comparable accuracy to leading coalescent-based summary methods and reduced running times.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3098-z) contains supplementary material, which is available to authorized users.

Application of multigene phylogenetics and site-stripping to resolve intraordinal relationships in the Rhodymeniales (Rhodophyta).

Previous molecular assessments of the red algalorder Rhodymeniales have confirmed its monophyly and distinguished the six currently recognized families (viz. Champiaceae, Faucheaceae, Fryeellaceae, Hymenocladiaceae, Lomentariaceae, and Rhodymeniaceae); however, relationships among most of these families have remained unresolved possibly as a result of substitution saturation at deeper phylogenetic nodes. The objective of the current study was to improve rhodymenialean systematics by increasing taxonomic representation and using a more robust multigene dataset of mitochondrial (COB, COI/COI-5P), nuclear (LSU, EF2) and plastid markers (psbA, rbcL). Additionally, we aimed to prevent phylogenetic inference problems associated with substitution saturation (particularly at the interfamilial nodes) by removing fast-evolving sites and analyzing a series of progressively more conservative alignments. The Rhodymeniales was resolved as two major lineages: (i) the Fryeellaceae as sister to the Faucheaceae and Lomentariaceae; and (ii) the Rhodymeniaceae allied to the Champiaceae and Hymenocladiaceae. Support at the interfamilial nodes was highest when 20% ofvariable sites were removed. Inclusion of Binghamiopsis, Chamaebotrys, and Minium, which wereabsent in previous phylogenetic investigations, established their phylogenetic affinities while assessment of two genera consistently polyphyletic in phylogenetic analyses, Erythrymenia and Lomentaria, resulted in the proposition of the novel genera Perbella and Fushitsunagia. The taxonomic position of Drouetia was reinvestigated with re-examination ofholotype material of D.coalescens to clarify tetrasporangial development in this genus. In addition, we added three novel Australian species to Drouetia as a result of ongoing DNA barcoding assessments-D.aggregata sp. nov., D.scutellata sp. nov., and D.viridescens sp. nov.

Bayesian Phylogenetic Inference Using a Combinatorial Sequential Monte Carlo Method

The application of Bayesian methods to large-scale phylogenetics problems is increasingly limited by computational issues, motivating the development of methods that can complement existing Markov chain Monte Carlo (MCMC) schemes. Sequential Monte Carlo (SMC) methods are approximate inference algorithms that have become very popular for time series models. Such methods have been recently developed to address phylogenetic inference problems but currently available techniques are only applicable to a restricted class of phylogenetic tree models compared to MCMC. In this article, we propose an original combinatorial SMC (CSMC) method to approximate posterior phylogenetic tree distributions, which is applicable to a general class of models and can be easily combined with MCMC to infer evolutionary parameters. Our method only relies on the existence of a flexible partially ordered set structure and is more generally applicable to sampling problems on combinatorial spaces. We demonstrate that the proposed CSMC algorithm provides consistent estimates under weak assumptions, is computationally fast, and is additionally easily parallelizable. Supplementary materials for this article are available online.

On the design of shared memory approaches to parallelize a multiobjective bee-inspired proposal for phylogenetic reconstruction

Current efforts in solving computationally demanding optimization problems in bioinformatics rely on the combination of bioinspired computing and parallelism. The multiobjective nature shown by a wide variety of these problems represents an additional challenge, as the optimization of multiple objective functions involves growing computational requirements. In this work, a multiobjective metaheuristic inspired by honey bees is applied to tackle the phylogenetic inference problem. For this purpose, two parallel implementations for shared memory architectures are proposed: a synchronous generational model and a novel non-generational design inspired by the asynchronous behaviour of bees in nature. Experiments on six real biological data sets and comparisons with other parallel biological methods point out the relevance of applying nature-inspired parallelization strategies, addressing the performance pitfalls shown by traditional parallelization schemes.

Parallel Multiobjective Metaheuristics for Inferring Phylogenies on Multicore Clusters

The development of efficient parallel algorithms based on mixed mode programming represents one of the most popular lines of research in current bioinformatics. By exploiting hardware resources at inter-node/intra-node level, we can address grand computational challenges which involve the optimization of multiple objective functions simultaneously. In this sense, the inference of evolutionary trees represents one of the most difficult NP-hard problems in the field. Tackling such a problem requires efficient parallel designs to take advantage of the characteristics of modern multicore clusters. In this paper, we aim to solve the phylogenetic inference problem by applying MPI/OpenMP schemes to two multiobjective metaheuristics: fast non-dominated sorting genetic algorithm and multiobjective firefly algorithm. In order to assess the performance achieved by these proposals under different system and problem sizes, we have conducted experiments on six real nucleotide data sets according to a statistical methodology. Our parallel and multiobjective metrics point out the relevance of combining hybrid programming and novel bioinspired designs with regard to other parallel and biological approaches from the literature.

A New Effective Method for Estimating Missing Values in the Sequence Data Prior to Phylogenetic Analysis

In this article we address the problem of phylogenetic inference from nucleic acid data containing missing bases. We introduce a new effective approach, called “Probabilistic estimation of missing values” (PEMV), allowing one to estimate unknown nucleotides prior to computing the evolutionary distances between them. We show that the new method improves the accuracy of phylogenetic inference compared to the existing methods “Ignoring Missing Sites” (IMS), “Proportional Distribution of Missing and Ambiguous Bases” (PDMAB) included in the PAUP software [ 26 ]. The proposed strategy for estimating missing nucleotides is based on probabilistic formulae developed in the framework of the Jukes-Cantor [ 10 ] and Kimura 2-parameter [ 11 ] models. The relative performances of the new method were assessed through simulations carried out with the SeqGen program [ 20 ], for data generation, and the BioNJ method [ 7 ], for inferring phylogenies. We also compared the new method to the DNAML program [ 5 ] and “Matrix Representation using Parsimony” (MRP) [ 13 , 19 ] considering an example of 66 eutherian mammals originally analyzed in [ 17 ].

Comparing bootstrap and posterior probability values in the four-taxon case.

Assessment of the reliability of a given phylogenetic hypothesis is an important step in phylogenetic analysis. Historically, the nonparametric bootstrap procedure has been the most frequently used method for assessing the support for specific phylogenetic relationships. The recent employment of Bayesian methods for phylogenetic inference problems has resulted in clade support being expressed in terms of posterior probabilities. We used simulated data and the four-taxon case to explore the relationship between nonparametric bootstrap values (as inferred by maximum likelihood) and posterior probabilities (as inferred by Bayesian analysis). The results suggest a complex association between the two measures. Three general regions of tree space can be identified: (1) the neutral zone, where differences between mean bootstrap and mean posterior probability values are not significant, (2) near the two-branch corner, and (3) deep in the two-branch corner. In the last two regions, significant differences occur between mean bootstrap and mean posterior probability values. Whether bootstrap or posterior probability values are higher depends on the data in support of alternative topologies. Examination of star topologies revealed that both bootstrap and posterior probability values differ significantly from theoretical expectations; in particular, there are more posterior probability values in the range 0.85-1 than expected by theory. Therefore, our results corroborate the findings of others that posterior probability values are excessively high. Our results also suggest that extrapolations from single topology branch-length studies are unlikely to provide any general conclusions regarding the relationship between bootstrap and posterior probability values.

Bootstrapping phylogenies: Large deviations and dispersion effects

SUMMARY A large deviation result is established for the bootstrap empirical distribution in a finite sample space, thereby validating both nonparametric and parametric bootstrapping in certain phylogenetic inference problems. The bias previously observed in the bootstrap distribution of the estimated tree topology is shown to stem from dispersion effects in the joint distribution of sample and bootstrap empirical distributions. Both results are examined for maximum likelihood estimation in a three-taxon model having particularly simple geometry. They are also applicable to discrete parameter problems outside of phylogenetic inference.

Reconstructing the Past: Parsimony, Evolution, and Inference.

Reconstructing the Past seeks to clarify and help resolve the vexing methodological issues that arise when biologists try to answer such questions as whether human beings are more closely related to chimps than they are to gorillas. It explores the case for considering the philosophical idea of simplicity/parsimony as a useful principle for evaluating taxonomic theories of evolutionary relationships.For the past two decades, evolutionists have been vigorously debating the appropriate methods that should be used in systematics, the field that aims at reconstructing phylogenetic relationships among species. This debate over phylogenetic inference, Elliott Sober observes, raises broader questions of hypothesis testing and theory evaluation that run head on into long standing issues concerning simplicity/parsimony in the philosophy of science.Sober treats the problem of phylogenetic inference as a detailed case study in which the philosophical idea of simplicity/parsimony can be tested as a principle of theory evaluation. Bringing together philosophy and biology, as well as statistics, Sober builds a general framework for understanding the circumstances in which parsimony makes sense as a tool of phylogenetic inference. Along the way he provides a detailed critique of parsimony in the biological literature, exploring the strengths and limitations of both statistical and nonstatistical cladistic arguments.