Title: On the Need for New Measures of Phylogenomic Support
Abstract The scale of data sets used to infer phylogenies has grown dramatically in the last decades, providing researchers with an enormous amount of information with which to draw inferences about evolutionary history. However, standard approaches to assessing confidence in those inferences (e.g., nonparametric bootstrap proportions [BP] and Bayesian posterior probabilities [PPs]) are still deeply influenced by statistical procedures and frameworks that were developed when information was much more limited. These approaches largely quantify uncertainty caused by limited amounts of data, which is often vanishingly small with modern, genome-scale sequence data sets. As a consequence, today’s phylogenomic studies routinely report near-complete confidence in their inferences, even when different studies reach strongly conflicting conclusions and the sites and loci in a single data set contain much more heterogeneity than our methods assume or can accommodate. Therefore, we argue that BPs and marginal PPs of bipartitions have outlived their utility as the primary means of measuring phylogenetic support for modern phylogenomic data sets with large numbers of sites relative to the number of taxa. Continuing to rely on these measures will hinder progress towards understanding remaining sources of uncertainty in the most challenging portions of the Tree of Life. Instead, we encourage researchers to examine the ideas and methods presented in this special issue of Systematic Biology and to explore the area further in their own work. The papers in this special issue outline strategies for assessing confidence and uncertainty in phylogenomic data sets that move beyond stochastic error due to limited data and offer promise for more productive dialogue about the challenges that we face in reaching our shared goal of understanding the history of life on Earth.[Big data; gene tree variation; genomic era; statistical bias.] more »« less
Singhal, Sonal; Colston, Timothy J; Grundler, Maggie R; Smith, Stephen A; Costa, Gabriel C; Colli, Guarino R; Moritz, Craig; Pyron, R Alexander; Rabosky, Daniel L(
, Systematic Biology)
Ruane, Sara
(Ed.)
Abstract Genome-scale data have the potential to clarify phylogenetic relationships across the tree of life but have also revealed extensive gene tree conflict. This seeming paradox, whereby larger data sets both increase statistical confidence and uncover significant discordance, suggests that understanding sources of conflict is important for accurate reconstruction of evolutionary history. We explore this paradox in squamate reptiles, the vertebrate clade comprising lizards, snakes, and amphisbaenians. We collected an average of 5103 loci for 91 species of squamates that span higher-level diversity within the clade, which we augmented with publicly available sequences for an additional 17 taxa. Using a locus-by-locus approach, we evaluated support for alternative topologies at 17 contentious nodes in the phylogeny. We identified shared properties of conflicting loci, finding that rate and compositional heterogeneity drives discordance between gene trees and species tree and that conflicting loci rarely overlap across contentious nodes. Finally, by comparing our tests of nodal conflict to previous phylogenomic studies, we confidently resolve 9 of the 17 problematic nodes. We suggest this locus-by-locus and node-by-node approach can build consensus on which topological resolutions remain uncertain in phylogenomic studies of other contentious groups. [Anchored hybrid enrichment (AHE); gene tree conflict; molecular evolution; phylogenomic concordance; target capture; ultraconserved elements (UCE).]
McTavish, Emily Jane(
, Biodiversity Information Science and Standards)
All life on earth is linked by a shared evolutionary history. Even before Darwin developed the theory of evolution, Linnaeus categorized types of organisms based on their shared traits. We now know these traits derived from these species’ shared ancestry. This evolutionary history provides a natural framework to harness the enormous quantities of biological data being generated today.
The Open Tree of Life project is a collaboration developing tools to curate and share evolutionary estimates (phylogenies) covering the entire tree of life (Hinchliff et al. 2015, McTavish et al. 2017). The tree is viewable at https://tree.opentreeoflife.org, and the data is all freely available online. The taxon identifiers used in the Open Tree unified taxonomy (Rees and Cranston 2017) are mapped to identifiers across biological informatics databases, including the Global Biodiversity Information Facility (GBIF), NCBI, and others. Linking these identifiers allows researchers to easily unify data from across these different resources (Fig. 1). Leveraging a unified evolutionary framework across the diversity of life provides new avenues for integrative wide scale research. Downstream tools, such as R packages developed by the R OpenSci foundation (rotl, rgbif) (Michonneau et al. 2016, Chamberlain 2017) and others tools (Revell 2012), make accessing and combining this information straightforward for students as well as researchers (e.g. https://mctavishlab.github.io/BIO144/labs/rotl-rgbif.html).
Figure 1.
Example linking phylogenetic relationships accessed from the Open Tree of Life with specimen location data from Global Biodiversity Information Facility.
For example, a recent publication by Santorelli et al. 2018 linked evolutionary information from Open Tree with species locality data gathered from a local field study as well as GBIF species location records to test a river-barrier hypothesis in the Amazon. By combining these data, the authors were able test a widely held biogeographic hypothesis across 1952 species in 14 taxonomic groups, and found that a river that had been postulated to drive endemism, was in fact not a barrier to gene flow. However, data provenance and taxonomic name reconciliation remain key hurdles to applying data from these large digital biodiversity and evolution community resources to answering biological questions. In the Amazonian river analysis, while they leveraged use of GBIF records as a secondary check on their species records, they relied on their an intensive local field study for their major conclusions, and preferred taxon specific phylogenetic resources over Open Tree where they were available (Santorelli et al. 2018). When Li et al. 2018 assessed large scale phylogenetic approaches, including Open Tree, for measuring community diversity, they found that synthesis phylogenies were less resolved than purpose-built phylogenies, but also found that these synthetic phylogenies were sufficient for community level phylogenetic diversity analyses. Nonetheless, data quality concerns have limited adoption of analyses data from centralized resources (McTavish et al. 2017). Taxonomic name recognition and reconciliation across databases also remains a hurdle for large scale analyses, despite several ongoing efforts to improve taxonomic interoperability and unify taxonomies, such at Catalogue of Life + (Bánki et al. 2018).
In order to support innovative science, large scale digital data resources need to facilitate data linkage between resources, and address researchers' data quality and provenance concerns. I will present the model that the Open Tree of Life is using to provide evolutionary data at the scale of the entire tree of life, while maintaining traceable provenance to the publications and taxonomies these evolutionary relationships are inferred from. I will discuss the hurdles to adoption of these large scale resources by researchers, as well as the opportunities for new research avenues provided by the connections between evolutionary inferences and biodiversity digital databases.
Adams, Richard; DeGiorgio, Michael(
, Molecular Biology and Evolution)
Satta, Yoko
(Ed.)
Abstract Likelihood-based tests of phylogenetic trees are a foundation of modern systematics. Over the past decade, an enormous wealth and diversity of model-based approaches have been developed for phylogenetic inference of both gene trees and species trees. However, while many techniques exist for conducting formal likelihood-based tests of gene trees, such frameworks are comparatively underdeveloped and underutilized for testing species tree hypotheses. To date, widely used tests of tree topology are designed to assess the fit of classical models of molecular sequence data and individual gene trees and thus are not readily applicable to the problem of species tree inference. To address this issue, we derive several analogous likelihood-based approaches for testing topologies using modern species tree models and heuristic algorithms that use gene tree topologies as input for maximum likelihood estimation under the multispecies coalescent. For the purpose of comparing support for species trees, these tests leverage the statistical procedures of their original gene tree-based counterparts that have an extended history for testing phylogenetic hypotheses at a single locus. We discuss and demonstrate a number of applications, limitations, and important considerations of these tests using simulated and empirical phylogenomic data sets that include both bifurcating topologies and reticulate network models of species relationships. Finally, we introduce the open-source R package SpeciesTopoTestR (SpeciesTopology Tests in R) that includes a suite of functions for conducting formal likelihood-based tests of species topologies given a set of input gene tree topologies.
A long‐standing question in biology is how organisms change through time and space in response to their environment. This knowledge is of particular relevance to predicting how organisms might respond to future environmental changes caused by human‐induced global change. Usually researchers make inferences about past events based on an understanding of current static genetic patterns, but these are limited in their capacity to inform on underlying past processes. Natural history collections (NHCs) represent a unique and critical source of information to provide temporally deep and spatially broad time‐series of samples. By using NHC samples, researchers can directly observe genetic changes over time and space and link those changes with specific ecological/evolutionary events. Until recently, such genetic studies were hindered by the intrinsic challenges of NHC samples (i.e. low yield of highly fragmented DNA). However, recent methodological and technological developments have revolutionized the possibilities in the novel field of NHC genomics. In this Special Feature, we compile a range of studies spanning from methodological aspects to particular case studies which demonstrate the enormous potential of NHC samples for accessing large genomic data sets from the past to advance our knowledge on how populations and species respond to global change at multiple spatial–temporal scales. We also highlight possible limitations, recommendations and a few opportunities for future researchers aiming to study NHC genomics.
Gable, Simone M.; Byars, Michael I.; Literman, Robert; Tollis, Marc; Hahn, ed., Matthew(
, Systematic Biology)
Abstract
To examine phylogenetic heterogeneity in turtle evolution, we collected thousands of high-confidence single-copy orthologs from 19 genome assemblies representative of extant turtle diversity and estimated a phylogeny with multispecies coalescent and concatenated partitioned methods. We also collected next-generation sequences from 26 turtle species and assembled millions of biallelic markers to reconstruct phylogenies based on annotated regions from the western painted turtle (Chrysemys picta bellii) genome (coding regions, introns, untranslated regions, intergenic, and others). We then measured gene tree-species tree discordance, as well as gene and site heterogeneity at each node in the inferred trees, and tested for temporal patterns in phylogenomic conflict across turtle evolution. We found strong and consistent support for all bifurcations in the inferred turtle species phylogenies. However, a number of genes, sites, and genomic features supported alternate relationships between turtle taxa. Our results suggest that gene tree-species tree discordance in these data sets is likely driven by population-level processes such as incomplete lineage sorting. We found very little effect of substitutional saturation on species tree topologies, and no clear phylogenetic patterns in codon usage bias and compositional heterogeneity. There was no correlation between gene and site concordance, node age, and DNA substitution rate across most annotated genomic regions. Our study demonstrates that heterogeneity is to be expected even in well-resolved clades such as turtles, and that future phylogenomic studies should aim to sample as much of the genome as possible in order to obtain accurate phylogenies for assessing conservation priorities in turtles. [Discordance; genomes; phylogeny; turtles.]
Thomson, Robert C, and Brown, Jeremy M. On the Need for New Measures of Phylogenomic Support. Retrieved from https://par.nsf.gov/biblio/10333742. Systematic Biology 71.4 Web. doi:10.1093/sysbio/syac002.
Thomson, Robert C, & Brown, Jeremy M. On the Need for New Measures of Phylogenomic Support. Systematic Biology, 71 (4). Retrieved from https://par.nsf.gov/biblio/10333742. https://doi.org/10.1093/sysbio/syac002
@article{osti_10333742,
place = {Country unknown/Code not available},
title = {On the Need for New Measures of Phylogenomic Support},
url = {https://par.nsf.gov/biblio/10333742},
DOI = {10.1093/sysbio/syac002},
abstractNote = {Abstract The scale of data sets used to infer phylogenies has grown dramatically in the last decades, providing researchers with an enormous amount of information with which to draw inferences about evolutionary history. However, standard approaches to assessing confidence in those inferences (e.g., nonparametric bootstrap proportions [BP] and Bayesian posterior probabilities [PPs]) are still deeply influenced by statistical procedures and frameworks that were developed when information was much more limited. These approaches largely quantify uncertainty caused by limited amounts of data, which is often vanishingly small with modern, genome-scale sequence data sets. As a consequence, today’s phylogenomic studies routinely report near-complete confidence in their inferences, even when different studies reach strongly conflicting conclusions and the sites and loci in a single data set contain much more heterogeneity than our methods assume or can accommodate. Therefore, we argue that BPs and marginal PPs of bipartitions have outlived their utility as the primary means of measuring phylogenetic support for modern phylogenomic data sets with large numbers of sites relative to the number of taxa. Continuing to rely on these measures will hinder progress towards understanding remaining sources of uncertainty in the most challenging portions of the Tree of Life. Instead, we encourage researchers to examine the ideas and methods presented in this special issue of Systematic Biology and to explore the area further in their own work. The papers in this special issue outline strategies for assessing confidence and uncertainty in phylogenomic data sets that move beyond stochastic error due to limited data and offer promise for more productive dialogue about the challenges that we face in reaching our shared goal of understanding the history of life on Earth.[Big data; gene tree variation; genomic era; statistical bias.]},
journal = {Systematic Biology},
volume = {71},
number = {4},
author = {Thomson, Robert C and Brown, Jeremy M},
editor = {Carstens, Bryan}
}
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