arXiv subjects

Tandy Warnow

Publications and source records attributed to Tandy Warnow.

27 records · Page 2Linked to original sources

arXiv2020

NJst and ASTRID are not statistically consistent under a random model of missing data

Species tree estimation from multi-locus datasets is statistically challenging for multiple reasons, including gene tree heterogeneity across the genome due to incomplete lineage sorting (ILS). Species tree estimation methods have been developed that operate by estimating gene trees and then using those gene trees to estimate the species tree. Several of these methods (e.g., ASTRAL, ASTRID, and NJst) are provably statistically consistent under the multi-species coalescent (MSC) model, provided that the gene trees are estimated correctly, and there is no missing data. Recently, Nute et al. (BMC Genomics 2018) addressed the question of whether these methods remain statistically consistent under random models of taxon deletion, and asserted that they do so. Here we provide a counterexample to one of these theorems, and establish that ASTRID and NJst are not statistically consistent under an i.i.d. model of taxon deletion.

q-bio.PE↗

arXiv2019

Viewing Computer Science through Citation Analysis; Salton and Bergmark Redux

Computer science has experienced dramatic growth and diversification over the last twenty years. Towards a current understanding of the structure of this discipline, we analyze a cohort of the computer science literature using the DBLP database. For insight on the features of this cohort and the relationship within its components, we constructed article level clusters based on either direct citations or co-citations, and reconciled them to major and minor subject categories in the Scopus All Science Journal Classification (ASJC). We described complementary insights from clustering by direct citation and co-citation, and both point to the increase in computer science publications and their scope. Our analysis shows cross-category clusters, some that interact with external fields, such as the biological sciences, while others remain inward looking.

cs.DL↗

arXiv2019

Co-citations in context: disciplinary heterogeneity is relevant

Citation analysis of the scientific literature has been used to study and define disciplinary boundaries, to trace the dissemination of knowledge, and to estimate impact. Co-citation, the frequency with which pairs of publications are cited, provides insight into how documents relate to each other and across fields. Co-citation analysis has been used to characterize combinations of prior work as conventional or innovative and to derive features of highly cited publications. Given the organization of science into disciplines, a key question is the sensitivity of such analyses to frame of reference. Our study examines this question using semantically-themed citation networks. We observe that trends reported to be true across the scientific literature do not hold for focused citation networks, and we conclude that inferring novelty using co-citation analysis and random graph models benefits from disciplinary context.

cs.DL↗

arXiv2019

Large-scale Species Tree Estimation

Species tree estimation is a complex problem, due to the fact that different parts of the genome can have different evolutionary histories than the genome itself. One of the causes for this discord is incomplete lineage sorting (also called deep coalescence), which is a population-level process that produces gene trees that differ from the species tree. The last decade has seen a large number of new methods developed to estimate species trees from multi-locus datasets, specifically addressing this cause of gene tree heterogeneity. In this paper, we review these methods, focusing mainly on issues that relate to analyses of datasets containing large numbers of species or loci (or both). We also discuss divide-and-conquer strategies for enabling species tree estimation methods to run on large datasets, including new approaches that are based on algorithms (such as TreeMerge) for the Disjoint Tree Merger problem.

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arXiv2018

Supertree Construction: Opportunities and Challenges

Supertree construction is the process by which a set of phylogenetic trees, each on a subset of the overall set X of species, is combined into a tree on the full set S. The traditional use of supertree methods is the assembly of a large species tree from previously computed smaller species trees; however, supertree methods are also used to address large-scale tree estimation using divide-and-conquer (i.e., a dataset is divided into overlapping subsets, trees are constructed on the subsets, and then combined using the supertree method). Because most supertree methods are heuristics for NP-hard optimization problems, the use of supertree estimation on large datasets is challenging, both in terms of scalability and accuracy. In this paper, we describe the current state of the art in supertree construction and the use of supertree methods in divide-and-conquer strategies. Finally, we identify directions where future research could lead to improved supertree methods.

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arXiv2018

Long-branch attraction in species tree estimation: inconsistency of partitioned likelihood and topology-based summary methods

With advances in sequencing technologies, there are now massive amounts of genomic data from across all life, leading to the possibility that a robust Tree of Life can be constructed. However, "gene tree heterogeneity", which is when different genomic regions can evolve differently, is a common phenomenon in multi-locus datasets, and reduces the accuracy of standard methods for species tree estimation that do not take this heterogeneity into account. New methods have been developed for species tree estimation that specifically address gene tree heterogeneity, and that have been proven to converge to the true species tree when the number of loci and number of sites per locus both increase (i.e., the methods are said to be "statistically consistent"). Yet, little is known about the biologically realistic condition where the number of sites per locus is bounded. We show that when the sequence length of each locus is bounded (by any arbitrarily chosen value), the most common approaches to species tree estimation that take heterogeneity into account (i.e., traditional fully partitioned concatenated maximum likelihood and newer approaches, called summary methods, that estimate the species tree by combining gene trees) are not statistically consistent, even when the heterogeneity is extremely constrained. The main challenge is the presence of conditions such as long branch attraction that create biased tree estimation when the number of sites is restricted. Hence, our study uncovers a fundamental challenge to species tree estimation using both traditional and new methods.

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arXiv2015

Weighted Statistical Binning: enabling statistically consistent genome-scale phylogenetic analyses

Because biological processes can make different loci have different evolutionary histories, species tree estimation requires multiple loci from across the genome. While many processes can result in discord between gene trees and species trees, incomplete lineage sorting (ILS), modeled by the multi-species coalescent, is considered to be a dominant cause for gene tree heterogeneity. Coalescent-based methods have been developed to estimate species trees, many of which operate by combining estimated gene trees, and so are called summary methods. Because summary methods are generally fast, they have become very popular techniques for estimating species trees from multiple loci. However, recent studies have established that summary methods can have reduced accuracy in the presence of gene tree estimation error, and also that many biological datasets have substantial gene tree estimation error, so that summary methods may not be highly accurate on biologically realistic conditions. Mirarab et al. (Science 2014) presented the statistical binning technique to improve gene tree estimation in multi-locus analyses, and showed that it improved the accuracy of MP-EST, one of the most popular coalescent-based summary methods. Statistical binning, which uses a simple statistical test for combinability and then uses the larger sets of genes to re-calculate gene trees, has good empirical performance, but using statistical binning within a phylogenomics pipeline does not have the desirable property of being statistically consistent. We show that weighting the recalculated gene trees by the bin sizes makes statistical binning statistically consistent under the multispecies coalescent, and maintains the good empirical performance. Thus, "weighted statistical binning" enables highly accurate genome-scale species tree estimation, and is also statistical consistent under the multi-species coalescent model.

q-bio.QM↗

arXiv2015

Ultra-large alignments using Phylogeny-aware Profiles

Many biological questions, including the estimation of deep evolutionary histories and the detection of remote homology between protein sequences, rely upon multiple sequence alignments (MSAs) and phylogenetic trees of large datasets. However, accurate large-scale multiple sequence alignment is very difficult, especially when the dataset contains fragmentary sequences. We present UPP, an MSA method that uses a new machine learning technique - the Ensemble of Hidden Markov Models - that we propose here. UPP produces highly accurate alignments for both nucleotide and amino acid sequences, even on ultra-large datasets or datasets containing fragmentary sequences. UPP is available at https://github.com/smirarab/sepp.

q-bio.GN↗

arXiv2004

Unidentifiable divergence times in rates-across-sites models

The rates-across-sites assumption in phylogenetic inference posits that the rate matrix governing the Markovian evolution of a character on an edge of the putative phylogenetic tree is the product of a character-specific scale factor and a rate matrix that is particular to that edge. Thus, evolution follows basically the same process for all characters, except that it occurs faster for some characters than others. To allow estimation of tree topologies and edge lengths for such models, it is commonly assumed that the scale factors are not arbitrary unknown constants, but rather unobserved, independent, identically distributed draws from a member of some parametric family of distributions. A popular choice is the gamma family. We consider an example of a clock-like tree with three taxa, one unknown edge length, and a parametric family of scale factor distributions that contain the gamma family. This model has the property that, for a generic choice of unknown edge length and scale factor distribution, there is another edge length and scale factor distribution which generates data with exactly the same distribution, so that even with infinitely many data it will be typically impossible to make correct inferences about the unknown edge length.

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