Twisted trees and inconsistency of tree estimation when gaps are treated as missing data -- the impact of model mis-specification in distance corrections

Statistically consistent estimation of phylogenetic trees or gene trees is possible if pairwise sequence dissimilarities can be converted to a set of distances that are proportional to the true evolutionary distances. Susko et al. (2004) reported some strikingly broad results about the forms of inconsistency in tree estimation that can arise if corrected distances are not proportional to the true distances. They showed that if the corrected distance is a concave function of the true distance, then inconsistency due to long branch attraction will occur. If these functions are convex, then two "long branch repulsion" trees will be preferred over the true tree -- though these two incorrect trees are expected to be tied as the preferred true. Here we extend their results, and demonstrate the existence of a tree shape (which we refer to as a "twisted Farris-zone" tree) for which a single incorrect tree topology will be guaranteed to be preferred if the corrected distance function is convex. We also report that the standard practice of treating gaps in sequence alignments as missing data is sufficient to produce non-linear corrected distance functions if the substitution process is not independent of the insertion/deletion process. Taken together, these results imply inconsistent tree inference under mild conditions. For example, if some positions in a sequence are constrained to be free of substitutions and insertion/deletion events while the remaining sites evolve with independent substitutions and insertion/deletion events, then the distances obtained by treating gaps as missing data can support an incorrect tree topology even given an unlimited amount of data.Comment: 29 pages, 3 figure

A supertree pipeline for summarizing phylogenetic and taxonomic information for millions of species

We present a new supertree method that enables rapid estimation of a summary tree on the scale of millions of leaves. This supertree method summarizes a collection of input phylogenies and an input taxonomy. We introduce formal goals and criteria for such a supertree to satisfy in order to transparently and justifiably represent the input trees. In addition to producing a supertree, our method computes annotations that describe which grouping in the input trees support and conflict with each group in the supertree. We compare our supertree construction method to a previously published supertree construction method by assessing their performance on input trees used to construct the Open Tree of Life version 4, and find that our method increases the number of displayed input splits from 35,518 to 39,639 and decreases the number of conflicting input splits from 2,760 to 1,357. The new supertree method also improves on the previous supertree construction method in that it produces no unsupported branches and avoids unnecessary polytomies. This pipeline is currently used by the Open Tree of Life project to produce all of the versions of project’s “synthetic tree” starting at version 5. This software pipeline is called “propinquity”. It relies heavily on “otcetera”—a set of C++ tools to perform most of the steps of the pipeline. All of the components are free software and are available on GitHub

An Algorithm for Calculating the Probability of Classes of Data Patterns on a Genealogy

Felsenstein’s pruning algorithm allows one to calculate the probability of any particular data pattern arising on a phylogeny given a model of character evolution. Here we present a similar dynamic programming algorithm. Our algorithm treats the tree and model as known. The algorithm makes it feasible to calculate the probability that a randomly selected character will be a member of a particular class of character patterns. Specifically, we are interested in binning patterns by the number of parsimony steps and the set of states observed at the tips of the tree. This algorithm was developed to expand the range of data set sizes that can be used with Waddell et al.’s marginal testing approach for assessing the adequacy of a model. The algorithms introduced can also be used in likelihood calculations which correct for ascertainment biases. For example, Lewis introduced an Mkv model which corrects for the lack of constant sites. The probability of a constant pattern arising can be calculated using the algorithm that we present, or by enumerating all possible constant patterns and calculating the probability of each one. Because the number of constant data patterns is small, both methods are efficient. However, elaborations of the Mkv model (such as those in Nylander et al) require calculating the probability of parsimony-uninformative patterns arising. For large trees and characters with many possible character states, the number of possible parismony-uninformative patterns is immense. In these cases, the algorithms introduced here will be more efficient. The algorithm has been implemented in open source software written in C++.JMK would like to thank NIH 5 R25GM62232 and the Initiative for Maximizing Student Development for funding. MTH thanks NSF-DEB-1208393 and NSF-DEB-0732920 for financial support

Phylogenetic assessment of filoviruses: how many lineages of Marburg virus?

This is the publisher's version, also available electronically from http://onlinelibrary.wiley.com.Filoviruses have to date been considered as consisting of one diverse genus (Ebola viruses) and one undifferentiated genus (Marburg virus). We reconsider this idea by means of detailed phylogenetic analyses of sequence data available for the Filoviridae: using coalescent simulations, we ascertain that two Marburg isolates (termed the “RAVN” strain) represent a quite-distinct lineage that should be considered in studies of biogeography and host associations, and may merit recognition at the level of species. In contrast, filovirus isolates recently obtained from bat tissues are not distinct from previously known strains, and should be considered as drawn from the same population. Implications for understanding the transmission geography and host associations of these viruses are discussed.Funded in part by a grant from the National Institutes of Health (R01 TW 8859-3)

A Justification for Reporting the Majority-Rule Consensus Tree in Bayesian Phylogenetics

This is an electronic version of an article published in Systematic Biology [Holder, Mark T., Jeet Sukumaran, and Paul O. Lewis. A justification for reporting majority-rule consensus tree in Bayesian phylogenetics. Systematic Biology, 57(5):814{821, 2008.] Systematic Biology is available online at informaworld http://dx.doi.org/10.1080/1063515080242230

What’s in a Likelihood? Simple Models of Protein Evolution and the Contribution of Structurally Viable Reconstructions to the Likelihood

This is an electronic version of an article published in Systematic Biology [Lakner C, Holder MT, Goldman N, Naylor GJP. 2011. What's in a Likelihood? Simple Models of Protein Evolution and the Contribution of Structurally Viable Reconstructions to the Likelihood Systematic Biology. 60(2):161-174. ]. Systematic Biology is available online at informaworld http://dx.doi.org/10.1093/sysbio/syq088.Most phylogenetic models of protein evolution assume that sites are independent and identically distributed. Interactions between sites are ignored, and the likelihood can be conveniently calculated as the product of the individual site likelihoods. The calculation considers all possible transition paths (also called substitution histories or mappings) that are consistent with the observed states at the terminals, and the probability density of any particular reconstruction depends on the substitution model. The likelihood is the integral of the probability density of each substitution history taken over all possible histories that are consistent with the observed data. We investigated the extent to which transition paths that are incompatible with a protein’s three-dimensional structure contribute to the likelihood. Several empirical amino acid models were tested for sequence pairs of different degrees of divergence. When simulating substitutional histories starting from a real sequence, the structural integrity of the simulated sequences quickly disintegrated. This result indicates that simple models are clearly unable to capture the constraints on sequence evolution. However, when we sampled transition paths between real sequences from the posterior probability distribution according to these same models, we found that the sampled histories were largely consistent with the tertiary structure. This suggests that simple empirical substitution models may be adequate for interpolating changes between observed sequences during phylogenetic inference despite the fact that the models cannot predict the effects of structural constraints from first principles. This study is significant because it provides a quantitative assessment of the biological realism of substitution models from the perspective of protein structure, and it provides insight on the prospects for improving models of protein sequence evolution. [Ancestral state reconstruction; empirical amino acid models; maximum likelihood; phylogenetics; protein structure.

Incorporating the speciation process into species delimitation

The “multispecies” coalescent (MSC) model that underlies many genomic species-delimitation approaches is problematic because it does not distinguish between genetic structure associated with species versus that of populations within species. Consequently, as both the genomic and spatial resolution of data increases, a proliferation of artifactual species results as within-species population lineages, detected due to restrictions in gene flow, are identified as distinct species. The toll of this extends beyond systematic studies, getting magnified across the many disciplines that rely upon an accurate framework of identified species. Here we present the first of a new class of approaches that addresses this issue by incorporating an extended speciation process for species delimitation. We model the formation of population lineages and their subsequent development into independent species as separate processes and provide for a way to incorporate current understanding of the species boundaries in the system through specification of species identities of a subset of population lineages. As a result, species boundaries and within-species lineages boundaries can be discriminated across the entire system, and species identities can be assigned to the remaining lineages of unknown affinities with quantified probabilities. In addition to the identification of species units in nature, the primary goal of species delimitation, the incorporation of a speciation model also allows us insights into the links between population and species-level processes. By explicitly accounting for restrictions in gene flow not only between, but also within, species, we also address the limits of genetic data for delimiting species. Specifically, while genetic data alone is not sufficient for accurate delimitation, when considered in conjunction with other information we are able to not only learn about species boundaries, but also about the tempo of the speciation process itself

Model Parameterization, Prior Distributions, and the General Time-Reversible Model in Bayesian Phylogenetics

This is an electronic version of an article published in Systematic Biology [Zwickl, Derrick J. and Mark T. Holder. Model parameterization, prior distributions and the general time-reversible model in Bayesian phylogenetics. Systematic Biology, 53:877{888, 2004.] Systematic Biology is available online at informaworld http://dx.doi.org/10.1080/10635150490522584.Bayesian phylogenetic methods require the selection of prior probability distributions for all parameters of the model of evolution. These distributions allow one to incorporate prior information into a Bayesian analysis, but even in the absence of meaningful prior information, a prior distribution must be chosen. In such situations, researchers typically seek to choose a prior that will have little effect on the posterior estimates produced by an analysis, allowing the data to dominate. Sometimes a prior that is uniform (assigning equal prior probability density to all points within some range) is chosen for this purpose. In reality, the appropriate prior depends on the parameterization chosen for the model of evolution, a choice that is largely arbitrary. There is an extensive Bayesian literature on appropriate prior choice, and it has long been appreciated that there are parameterizations for which uniform priors can have a strong influence on posterior estimates. We here discuss the relationship between model parameterization and prior specification, using the general time-reversible model of nucleotide evolution as an example. We present Bayesian analyses of 10 simulated data sets obtained using a variety of prior distributions and parameterizations of the general time-reversible model. Uniform priors can produce biased parameter estimates under realistic conditions, and a variety of alternative priors avoid this bias