The use of parsimony in testing phylogenetic hypotheses

With the advance of cladistic theory differences in principle between it and other systematic techniques are few but of fundamental importance. In the mechanics of classification they are confined to ranking and the rejection of paraphyletic taxa. In cladistic analysis, leading to cladograms, trees and phylogeny reconstruction, inconsistencies in apparent synapomorphies are said to be resolved using Popper's hypothetico-deductive method together with the principle of parsi However, not only do cladists not use Popper's methodology, which is inconsistent with parsimony, but their use of parsimony is invalid as a test of phylo The only accepted extrinsic test of a classification is that enunciated by John Stuart Mill. It has been claimed that cladistic classifications yield the best results when judged by Mill's criteria, but this is only possibly the case with analytic classifications produced by numerical techniques. No satisfactory test exists in normal (synthetic) cladism for distinguishing synapomorphy from homoplasy. The effects of this are particularly dire in cladograms and classifications involving fossils in which a Stufenreihe arrangement is adopted.     

Dual coding hypotheses for neural information representation

Information is represented and processed in neural systems in various ways. The rate coding, population coding, and temporal coding are typical examples of representation. It is a hot issue in neuroscience what kinds of coding is used in real neural systems. Different regions of the brain may resort to different coding strategies. Moreover, recent studies suggest the possibility of dual or multiple codes, in which different modes of information are embedded in one neural system. The present paper reviews various possibilities of neural codes focusing on dual codes.     

Sample size for a phylogenetic inference.

The objective of this work is to describe sample-size calculations for the inference of a nonzero central branch length in an unrooted four-species phylogeny. Attention is restricted to independent binary characters, such as might be obtained from an alignment of the purine-pyrimidine sequences of a nucleic acid molecule. A statistical test based on a multinomial model for character-state configurations is described. The importance of including invariable sites in models for sequence change is demonstrated, and their effect on sample size is quantified. The methods are applied to a four-species alignment of small-subunit rRNA sequences derived from two archaebacteria, a eubacteria and a eukaryote. We conclude that the information in these sequences is not sufficient to resolve the branching order of this tree. Estimates of the number of aligned nucleotide positions required to provide a reasonably powerful test are given.     

The evolution of base composition and phylogenetic inference

Base composition varies at all levels of the phylogenetic hierarchy and throughout the genome, and can be caused by active selection or passive mutation pressure. This variation can make reconstructing trees difficult. However, recent tree-based analyses have shed light on the forces responsible for the evolution of base composition, forces that might be very general. More explicit tree-based work is encouraged.     

Towards an inclusive philosophy for phylogenetic inference

We defend and expand on our earlier proposal for an inclusive philosophical framework for phylogenetics, based on an interpretation of Popperian corroboration that is decoupled from the popular falsificationist interpretation of Popperian philosophy. Any phylogenetic inference method can provide Popperian "evidence" or "test statements" based on the method's goodness-of-fit values for different tree hypotheses. Corroboration, or the severity of that test, requires that the evidence is improbable without the hypothesis, given only background knowledge that includes elements of chance. This framework contrasts with attempted Popperian justifications for cladistic parsimony--in which evidence is the data, background knowledge is restricted to descent with modification, and "corroboration," as a by-product of nonfalsification, is to be measured by cladistic parsimony. Recognition that cladistic "corroboration" reflects only goodness-of-fit, not corroboration/severity, makes it clear that standard cladistic prohibitions, such as restrictions on the evolutionary models to be included in "background knowledge," have no philosophical status. The capacity to assess Popperian corroboration neither justifies nor excludes any phylogenetic method, but it does provide a framework in phylogenetics for learning from errors--cases where apparent good evidence is probable even without the hypothesis. We explore these issues in the context of corroboration assessments applied to likelihood methods and to a new form of parsimony. These different forms of evidence and corroboration assessment point also to a new way to combine evidence--not at the level of overall fit, but at the level of overall corroboration/severity. We conclude that progress in an inclusive phylogenetics will be well served by the rejection of cladistic philosophy.     

The illogical basis of phylogenetic nomenclature

The current advocacy for the so-called PhyloCode has a history rooted in twentieth-century arguments among biologists and philosophers regarding a putative distinction between classes and individuals. From this seemingly simple and innocuous discussion have come supposed distinctions between definitions and diagnosis, classification and systematization, and now Linnaean and “phylogenetic” nomenclature. Nevertheless, the metaphysical dichotomy of class versus individual, insofar as its standard applications to the issue of biological taxonomy are concerned, is an outdated remnant of early logical positivist thinking. Current views on natural kinds and their definitions under a scientific realist perspective provide grounds for rejecting the class versus individual dichotomy altogether insofar as biological entities are concerned. We review the role of natural kinds in scientific practice and the nature of definitions and scientific classifications. Although inherent instabilities of the PhyloCode are clearly sufficient to argue against the general application of this nominally phylogenetic system, our goal here is to address serious and fundamental flaws in its very foundation by exposing the unsubstantiated philosophical assumptions preceding and subtending it.

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Resumen  Las propuestas actuales en favor del llamado Código de Nomenclatura Filogenética (Phylo-Code) tienen una historia basada en argumentos desarrollados, durante el siglo veinte, por biólogos y filósofos sobre una distinción putativa entre clases e individuos. De esta simple y aparentemente inocua discusión han surgido supuestas distinciones entre definición y diagnosis, clasificación y sistematización, y ahora entre nomenclatura Lineana y “filogenética.” Sin embargo, la dicotomía metafísica clase contra individuo, al menos en lo concerniente a su aplicación estándar al tema de taxonomía biológica, es un remanente obsoleto del pensamiento positivista lógico. Opiniones actuales sobre categorías naturales y sus definiciones bajo la perspectiva del realismo científico proveen bases para rechazar por completo dicha dicotomía, al menos en lo que concierne a las entidades biológicas. En este artículo se revisa el papel de las categorías naturales en la práctica científica, y la naturaleza de las definiciones y la clasificación científica. Aún cuando la inestabilidad inherente en el Código de Nomenclatura Filogenética es claramente suficiente para argumentar contra la aplicación general de este sistema nominal filogenético, el objetivo de este artículo es mostrar las serias y fundamentales deficiencias en sus propias bases al exponer las suposiciones filosóficas sin fundamento que le preceden y sustentan.

     

Bayesian inference of character evolution

Much recent progress in evolutionary biology is based on the inference of ancestral states and past transformations in important traits on phylogenetic trees. These exercises often assume that the tree is known without error and that ancestral states and character change can be mapped onto it exactly. In reality, there is often considerable uncertainty about both the tree and the character mapping. Recently introduced Bayesian statistical methods enable the study of character evolution while simultaneously accounting for both phylogenetic and mapping uncertainty, adding much needed credibility to the reconstruction of evolutionary history.     

The logical basis of phylogenetic taxonomy

Phylogenetic taxonomy, like modern Linnean taxonomy, was modeled on a phylogenetic tree rather than a cladogram and, like its predecessor, perpetuates the use of morphology as a means of recognizing clades. Both practices have generated confusion in graphical representation, operational terminology, and definitional rationale in phylogenetic taxonomy, the history of which is traced. The following points are made: (1) cladograms, rather than trees or hybrid cladogram-trees, provide the framework for the simplest graphical depiction of phylogenetic definitions; (2) a complete notational scheme for phylogenetic definitions is presented that distinguishes symbolic notation from shorthand and longhand versions; (3) phylogenetic definitions are composed of three components (paradigm, specifier, qualifier) arranged in two fundamental patterns-node and stem; (4) apomorphies do not constitute a fundamental definitional pattern but rather serve to qualify a stem-based definition (as do time and geographic range); (5) formulation of phylogenetic definitions involves three heuristic criteria (stability, simplicity, prior use); (6) reasoned definitional revision is encouraged and better defined (textual substitution, first-and second-order revision); and (7) a database, TaxonSearch, allows rapid recall of taxonomic and definitional information.     

Accounting for gene rate heterogeneity in phylogenetic inference

Traditionally, phylogenetic analyses over many genes combine data into a contiguous block. Under this concatenated model, all genes are assumed to evolve at the same rate. However, it is clear that genes evolve at very different rates and that accounting for this rate heterogeneity is important if we are to accurately infer phylogenies from heterogeneous multigene data sets. There remain open questions regarding how best to incorporate gene rate parameters into phylogenetic models and which properties of real data correlate with improved fit over the concatenated model. In this study, two methods of accounting for gene rate heterogeneity are compared: the n-parameter method, which allows for each of the n gene partitions to have a gene rate parameter, and the alpha-parameter method, which fits a distribution to the gene rates. Results demonstrate that the n-parameter method is both computationally faster and in general provides a better fit over the concatenated model than the alpha-parameter method. Furthermore, improved model fit over the concatenated model is highly correlated with the presence of a gene with a slow relative rate of evolution.     

A structural EM algorithm for phylogenetic inference.

A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum likelihood (ML) analysis. Unfortunately, searching for the maximum likelihood phylogenetic tree is computationally prohibitive for large data sets. In this paper, we describe a new algorithm that uses Structural Expectation Maximization (EM) for learning maximum likelihood phylogenetic trees. This algorithm is similar to the standard EM method for edge-length estimation, except that during iterations of the Structural EM algorithm the topology is improved as well as the edge length. Our algorithm performs iterations of two steps. In the E-step, we use the current tree topology and edge lengths to compute expected sufficient statistics, which summarize the data. In the M-Step, we search for a topology that maximizes the likelihood with respect to these expected sufficient statistics. We show that searching for better topologies inside the M-step can be done efficiently, as opposed to standard methods for topology search. We prove that each iteration of this procedure increases the likelihood of the topology, and thus the procedure must converge. This convergence point, however, can be a suboptimal one. To escape from such "local optima," we further enhance our basic EM procedure by incorporating moves in the flavor of simulated annealing. We evaluate these new algorithms on both synthetic and real sequence data and show that for protein sequences even our basic algorithm finds more plausible trees than existing methods for searching maximum likelihood phylogenies. Furthermore, our algorithms are dramatically faster than such methods, enabling, for the first time, phylogenetic analysis of large protein data sets in the maximum likelihood framework.     

On reduced amino acid alphabets for phylogenetic inference

We investigate the use of Markov models of evolution for reduced amino acid alphabets or bins of amino acids. The use of reduced amino acid alphabets can ameliorate effects of model misspecification and saturation. We present algorithms for 2 different ways of automating the construction of bins: minimizing criteria based on properties of rate matrices and minimizing criteria based on properties of alignments. By simulation, we show that in the absence of model misspecification, the loss of information due to binning is found to be insubstantial, and the use of Markov models at the binned level is found to be almost as effective as the more appropriate missing data approach. By applying these approaches to real data sets where compositional heterogeneity and/or saturation appear to be causing biased tree estimation, we find that binning can improve topological estimation in practice.     

Guided tree topology proposals for Bayesian phylogenetic inference

Increasingly, large data sets pose a challenge for computationally intensive phylogenetic methods such as Bayesian Markov chain Monte Carlo (MCMC). Here, we investigate the performance of common MCMC proposal distributions in terms of median and variance of run time to convergence on 11 data sets. We introduce two new Metropolized Gibbs Samplers for moving through "tree space." MCMC simulation using these new proposals shows faster average run time and dramatically improved predictability in performance, with a 20-fold reduction in the variance of the time to estimate the posterior distribution to a given accuracy. We also introduce conditional clade probabilities and demonstrate that they provide a superior means of approximating tree topology posterior probabilities from samples recorded during MCMC.     

The role of character loss in phylogenetic reconstruction as exemplified for the Annelida

Annelid relationships are controversial, and molecular and morphological analyses provide incongruent estimates. Character loss is identified as a major confounding factor for phylogenetic analyses based on morphological data. A direct approach and an indirect approach for the identification of character loss are discussed. Character loss can frequently be found within annelids and examples of the loss of typical annelid characters, like chaetae, nuchal organs, coelomic cavities and other features, are given. A loss of segmentation is suggested for Sipuncula and Echiura; both are supported as annelid ingroups in molecular phylogenetic analyses. Moreover, character loss can be caused by some modes of heterochronic evolution (paedomorphosis) and, as shown for orbiniid and arenicolid polychaetes, paedomorphic taxa might be misplaced in phylogenies derived from morphology. Different approaches for dealing with character loss in cladistic analyses are discussed. Application of asymmetrical character state transformation costs or usage of a dynamic homology framework represents promising approaches. Identifying character loss prior to a phylogenetic analysis will help to refine morphological data matrices and improve phylogenetic analyses of annelid relationships.     

Assessment of phylogenomic and orthology approaches for phylogenetic inference

MOTIVATION: Phylogenomics integrates the vast amount of phylogenetic information contained in complete genome sequences, and is rapidly becoming the standard for reliably inferring species phylogenies. There are, however, fundamental differences between the ways in which phylogenomic approaches like gene content, superalignment, superdistance and supertree integrate the phylogenetic information from separate orthologous groups. Furthermore, they all depend on the method by which the orthologous groups are initially determined. Here, we systematically compare these four phylogenomic approaches, in parallel with three approaches for large-scale orthology determination: pairwise orthology, cluster orthology and tree-based orthology. RESULTS: Including various phylogenetic methods, we apply a total of 54 fully automated phylogenomic procedures to the fungi, the eukaryotic clade with the largest number of sequenced genomes, for which we retrieved a golden standard phylogeny from the literature. Phylogenomic trees based on gene content show, relative to the other methods, a bias in the tree topology that parallels convergence in lifestyle among the species compared, indicating convergence in gene content. CONCLUSIONS: Complete genomes are no guarantee for good or even consistent phylogenies. However, the large amounts of data in genomes enable us to carefully select the data most suitable for phylogenomic inference. In terms of performance, the superalignment approach, combined with restrictive orthology, is the most successful in recovering a fungal phylogeny that agrees with current taxonomic views, and allows us to obtain a high-resolution phylogeny. We provide solid support for what has grown to be a common practice in phylogenomics during its advance in recent years. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Empirical evaluation of a prior for Bayesian phylogenetic inference

The Bayesian method of phylogenetic inference often produces high posterior probabilities (PPs) for trees or clades, even when the trees are clearly incorrect. The problem appears to be mainly due to large sizes of molecular datasets and to the large-sample properties of Bayesian model selection and its sensitivity to the prior when several of the models under comparison are nearly equally correct (or nearly equally wrong) and are of the same dimension. A previous suggestion to alleviate the problem is to let the internal branch lengths in the tree become increasingly small in the prior with the increase in the data size so that the bifurcating trees are increasingly star-like. In particular, if the internal branch lengths are assigned the exponential prior, the prior mean mu0 should approach zero faster than 1/square root n but more slowly than 1/n, where n is the sequence length. This paper examines the usefulness of this data size-dependent prior using a dataset of the mitochondrial protein-coding genes from the baleen whales, with the prior mean fixed at mu0=0.1n(-2/3). In this dataset, phylogeny reconstruction is sensitive to the assumed evolutionary model, species sampling and the type of data (DNA or protein sequences), but Bayesian inference using the default prior attaches high PPs for conflicting phylogenetic relationships. The data size-dependent prior alleviates the problem to some extent, giving weaker support for unstable relationships. This prior may be useful in reducing apparent conflicts in the results of Bayesian analysis or in making the method less sensitive to model violations.