ILS-Aware Analysis of Low-Homoplasy Retroelement Insertions: Inference of Species Trees and Introgression Using Quartets

Abstract DNA sequence alignments have provided the majority of data for inferring phylogenetic relationships with both concatenation and coalescent methods. However, DNA sequences are susceptible to extensive homoplasy, especially for deep divergences in the Tree of Life. Retroelement insertions have emerged as a powerful alternative to sequences for deciphering evolutionary relationships because these data are nearly homoplasy-free. In addition, retroelement insertions satisfy the “no intralocus-recombination” assumption of summary coalescent methods because they are singular events and better approximate neutrality relative to DNA loci commonly sampled in phylogenomic studies. Retroelements have traditionally been analyzed with parsimony, distance, and network methods. Here, we analyze retroelement data sets for vertebrate clades (Placentalia, Laurasiatheria, Balaenopteroidea, Palaeognathae) with 2 ILS-aware methods that operate by extracting, weighting, and then assembling unrooted quartets into a species tree. The first approach constructs a species tree from retroelement bipartitions with ASTRAL, and the second method is based on split-decomposition with parsimony. We also develop a Quartet-Asymmetry test to detect hybridization using retroelements. Both ILS-aware methods recovered the same species-tree topology for each data set. The ASTRAL species trees for Laurasiatheria have consecutive short branch lengths in the anomaly zone whereas Palaeognathae is outside of this zone. For the Balaenopteroidea data set, which includes rorquals (Balaenopteridae) and gray whale (Eschrichtiidae), both ILS-aware methods resolved balaeonopterids as paraphyletic. Application of the Quartet-Asymmetry test to this data set detected 19 different quartets of species for which historical introgression may be inferred. Evidence for introgression was not detected in the other data sets.

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An ABBA-BABA Test for Introgression Using Retroposon Insertion Data

10.1101/709477 ◽

2019 ◽

Cited By ~ 2

Author(s):

Mark S. Springer ◽

John Gatesy

Keyword(s):

Dna Sequences ◽

Incomplete Lineage Sorting ◽

Species Tree ◽

Data Sets ◽

Lineage Sorting ◽

Sequence Alignments ◽

Data Set ◽

Short Branch ◽

Phylogenetic Methods ◽

Branch Lengths

AbstractDNA sequence alignments provide the majority of data for inferring phylogenetic relationships with both concatenation and coalescence methods. However, DNA sequences are susceptible to extensive homoplasy, especially for deep divergences in the Tree of Life. Retroposon insertions have emerged as a powerful alternative to sequences for deciphering evolutionary relationships because these data are nearly homoplasy-free. In addition, retroposon insertions satisfy the ‘no intralocus recombination’ assumption of summary coalescence methods because they are singular events and better approximate neutrality relative to DNA sequences commonly applied in phylogenomic work. Retroposons have traditionally been analyzed with phylogenetic methods that ignore incomplete lineage sorting (ILS). Here, we analyze three retroposon data sets for mammals (Placentalia, Laurasiatheria, Balaenopteroidea) with two different ILS-aware methods. The first approach constructs a species tree from retroposon bipartitions with ASTRAL, and the second is a modification of SVD-Quartets. We also develop a χ2 Quartet-Asymmetry Test to detect hybridization using retroposon data. Both coalescence methods recovered the same topology for each of the three data sets. The ASTRAL species tree for Laurasiatheria has consecutive short branch lengths that are consistent with an anomaly zone situation. For the Balaenopteroidea data set, which includes rorquals (Balaenopteridae) and gray whale (Eschrichtiidae), both coalescence methods recovered a topology that supports the paraphyly of Balaenopteridae. Application of the χ2 Quartet-Asymmetry Test to this data set detected 16 different quartets of species for which historical hybridization may be inferred, but significant asymmetry was not detected in the placental root and Laurasiatheria analyses.

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ILS-Aware Analyses of Retroelement Insertions in the Anomaly Zone

10.1101/2020.09.29.319038 ◽

2020 ◽

Author(s):

Erin K. Molloy ◽

John Gatesy ◽

Mark S. Springer

Keyword(s):

Dna Sequences ◽

Incomplete Lineage Sorting ◽

Species Tree ◽

Species Trees ◽

Lineage Sorting ◽

Short Branch ◽

Multispecies Coalescent ◽

Branch Lengths ◽

Tree Estimation ◽

Infinite Sites Model

AbstractA major shortcoming of concatenation methods for species tree estimation is their failure to account for incomplete lineage sorting (ILS). Coalescence methods explicitly address this problem, but make various assumptions that, if violated, can result in worse performance than concatenation. Given the challenges of analyzing DNA sequences with both concatenation and coalescence methods, retroelement insertions have emerged as powerful phylogenomic markers for species tree estimation. We show that two recently proposed methods, SDPquartets and ASTRAL_BP, are statistically consistent estimators of the species tree under the multispecies coalescent model, with retroelement insertions following a neutral infinite sites model of mutation. The accuracy of these and other methods for inferring species trees with retroelements has not been assessed in simulation studies. We simulate retroelements for four different species trees, including three with short branch lengths in the anomaly zone, and assess the performance of eight different methods for recovering the correct species tree. We also examine whether ASTRAL_BP recovers accurate internal branch lengths for internodes of various lengths (in coalescent units). Our results indicate that two recently proposed ILS-aware methods, ASTRAL_BP and SDPquartets, as well as the newly proposed ASTRID_BP, always recover the correct species tree on data sets with large numbers of retroelements even when there are extremely short species-tree branches in the anomaly zone. Dollo parsimony performed almost as well as these ILS-aware methods. By contrast, unordered parsimony, polymorphism parsimony, and MDC recovered the correct species tree in the case of a pectinate tree with four ingroup taxa in the anomaly zone, but failed to recover the correct tree in more complex anomaly-zone situations with additional lineages impacted by extensive incomplete lineage sorting. Camin-Sokal parsimony always reconstructed an incorrect tree in the anomaly zone. ASTRAL_BP accurately estimated branch lengths when internal branches were very short as in anomaly zone situations, but branch lengths were upwardly biased by more than 35% when species tree branches were longer. We derive a mathematical correction for these distortions, assuming the expected number of new retroelement insertions per generation is constant across the species tree. We also show that short branches do not need to be corrected even when this assumption does not hold; therefore, the branch lengths estimates produced by ASTRAL_BP may provide insight into whether an estimated species tree is in the anomaly zone.

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Disentangling Sources of Gene Tree Discordance in Phylogenomic Data Sets: Testing Ancient Hybridizations in Amaranthaceae s.l

Systematic Biology ◽

10.1093/sysbio/syaa066 ◽

2020 ◽

Cited By ~ 1

Author(s):

Diego F Morales-Briones ◽

Gudrun Kadereit ◽

Delphine T Tefarikis ◽

Michael J Moore ◽

Stephen A Smith ◽

...

Keyword(s):

Network Inference ◽

Incomplete Lineage Sorting ◽

Gene Tree ◽

Phylogenetic Network ◽

Species Tree ◽

Data Sets ◽

Species Trees ◽

Lineage Sorting ◽

Data Set ◽

Gene Tree Discordance

Abstract Gene tree discordance in large genomic data sets can be caused by evolutionary processes such as incomplete lineage sorting and hybridization, as well as model violation, and errors in data processing, orthology inference, and gene tree estimation. Species tree methods that identify and accommodate all sources of conflict are not available, but a combination of multiple approaches can help tease apart alternative sources of conflict. Here, using a phylotranscriptomic analysis in combination with reference genomes, we test a hypothesis of ancient hybridization events within the plant family Amaranthaceae s.l. that was previously supported by morphological, ecological, and Sanger-based molecular data. The data set included seven genomes and 88 transcriptomes, 17 generated for this study. We examined gene-tree discordance using coalescent-based species trees and network inference, gene tree discordance analyses, site pattern tests of introgression, topology tests, synteny analyses, and simulations. We found that a combination of processes might have generated the high levels of gene tree discordance in the backbone of Amaranthaceae s.l. Furthermore, we found evidence that three consecutive short internal branches produce anomalous trees contributing to the discordance. Overall, our results suggest that Amaranthaceae s.l. might be a product of an ancient and rapid lineage diversification, and remains, and probably will remain, unresolved. This work highlights the potential problems of identifiability associated with the sources of gene tree discordance including, in particular, phylogenetic network methods. Our results also demonstrate the importance of thoroughly testing for multiple sources of conflict in phylogenomic analyses, especially in the context of ancient, rapid radiations. We provide several recommendations for exploring conflicting signals in such situations. [Amaranthaceae; gene tree discordance; hybridization; incomplete lineage sorting; phylogenomics; species network; species tree; transcriptomics.]

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StarBEAST2 brings faster species tree inference and accurate estimates of substitution rates

10.1101/070169 ◽

2016 ◽

Cited By ~ 2

Author(s):

Huw A. Ogilvie ◽

Remco R. Bouckaert ◽

Alexei J. Drummond

Keyword(s):

Gene Tree ◽

Species Tree ◽

Divergence Times ◽

Robust Estimator ◽

Rate Variation ◽

Data Sets ◽

Species Trees ◽

Sequence Alignments ◽

Substitution Rates ◽

Computational Performance

AbstractFully Bayesian multispecies coalescent (MSC) methods like *BEAST estimate species trees from multiple sequence alignments. Today thousands of genes can be sequenced for a given study, but using that many genes with *BEAST is intractably slow. An alternative is to use heuristic methods which compromise accuracy or completeness in return for speed. A common heuristic is concatenation, which assumes that the evolutionary history of each gene tree is identical to the species tree. This is an inconsistent estimator of species tree topology, a worse estimator of divergence times, and induces spurious substitution rate variation when incomplete lineage sorting is present. Another class of heuristics directly motivated by the MSC avoids many of the pitfalls of concatenation but cannot be used to estimate divergence times. To enable fuller use of available data and more accurate inference of species tree topologies, divergence times, and substitution rates, we have developed a new version of *BEAST called StarBEAST2. To improve convergence rates we add analytical integration of population sizes, novel MCMC operators and other optimisations. Computational performance improved by 13.5× to 13.8× when analysing empirical data sets, and an average of 33.1 × across 30 simulated data sets. To enable accurate estimates of per-species substitution rates we introduce species tree relaxed clocks, and show that StarBEAST2 is a more powerful and robust estimator of rate variation than concatenation. StarBEAST2 is available through the BEAUTi package manager in BEAST 2.4 and above.

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Bayesian Inference of Species Trees using Diffusion Models

Systematic Biology ◽

10.1093/sysbio/syaa051 ◽

2020 ◽

Vol 70 (1) ◽

pp. 145-161 ◽

Cited By ~ 1

Author(s):

Marnus Stoltz ◽

Boris Baeumer ◽

Remco Bouckaert ◽

Colin Fox ◽

Gordon Hiscott ◽

...

Keyword(s):

Bayesian Inference ◽

Numerical Algorithms ◽

Diffusion Models ◽

Model Parameters ◽

Data Sets ◽

Species Trees ◽

Computationally Efficient ◽

Data Set ◽

Snp Data ◽

Binary Markers

Abstract We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.]

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Bagging Approach for Medical Plants Recognition Based on Their DNA Sequences

International Journal of Social Ecology and Sustainable Development ◽

10.4018/ijsesd.2018100103 ◽

2018 ◽

Vol 9 (4) ◽

pp. 45-60

Author(s):

Mohamed Elhadi Rahmani ◽

Abdelmalek Amine ◽

Reda Mohamed Hamou

Keyword(s):

Dna Sequences ◽

Majority Vote ◽

Data Sets ◽

Data Set ◽

Drug Production ◽

Medical Plants

Many drugs in modern medicines originate from plants and the first step in drug production, is the recognition of plants needed for this purpose. This article presents a bagging approach for medical plants recognition based on their DNA sequences. In this work, the authors have developed a system that recognize DNA sequences of 14 medical plants, first they divided the 14-class data set into bi class sub-data sets, then instead of using an algorithm to classify the 14-class data set, they used the same algorithm to classify the sub-data sets. By doing so, they have simplified the problem of classification of 14 plants into sub-problems of bi class classification. To construct the subsets, the authors extracted all possible pairs of the 14 classes, so they gave each class more chances to be well predicted. This approach allows the study of the similarity between DNA sequences of a plant with each other plants. In terms of results, the authors have obtained very good results in which the accuracy has been doubled (from 45% to almost 80%). Classification of a new sequence was completed according to majority vote.

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Terraces in Gene Tree Reconciliation-Based Species Tree Inference

10.1101/2020.04.17.047092 ◽

2020 ◽

Author(s):

Michael J. Sanderson ◽

Michelle M. McMahon ◽

Mike Steel

Keyword(s):

Incomplete Lineage Sorting ◽

Gene Tree ◽

Solution Space ◽

Species Tree ◽

Gene Trees ◽

Species Trees ◽

Lineage Sorting ◽

Data Set ◽

Tree Reconciliation ◽

The Impact

AbstractTerraces in phylogenetic tree space are sets of trees with identical optimality scores for a given data set, arising from missing data. These were first described for multilocus phylogenetic data sets in the context of maximum parsimony inference and maximum likelihood inference under certain model assumptions. Here we show how the mathematical properties that lead to terraces extend to gene tree - species tree problems in which the gene trees are incomplete. Inference of species trees from either sets of gene family trees subject to duplication and loss, or allele trees subject to incomplete lineage sorting, can exhibit terraces in their solution space. First, we show conditions that lead to a new kind of terrace, which stems from subtree operations that appear in reconciliation problems for incomplete trees. Then we characterize when terraces of both types can occur when the optimality criterion for tree search is based on duplication, loss or deep coalescence scores. Finally, we examine the impact of assumptions about the causes of losses: whether they are due to imperfect sampling or true evolutionary deletion.

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Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation

Energies ◽

10.3390/en14196156 ◽

2021 ◽

Vol 14 (19) ◽

pp. 6156

Author(s):

Stefan Hensel ◽

Marin B. Marinov ◽

Michael Koch ◽

Dimitar Arnaudov

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Data Sets ◽

Cloud Detection ◽

Camera System ◽

Data Set ◽

Network Methods ◽

Segmentation Approach ◽

Coverage Prediction ◽

Short Time

This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.

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Phylogenetic analysis of the druid flies (Diptera: Schizophora: Clusiidae) based on morphological and molecular data

Insect Systematics & Evolution ◽

10.1163/187631210x500628 ◽

2010 ◽

Vol 41 (3) ◽

pp. 231-274 ◽

Cited By ~ 4

Author(s):

Jinzhong Fu ◽

Owen Lonsdale ◽

Brian Wiegmann ◽

Stephen Marshall

Keyword(s):

Cytochrome Oxidase ◽

Dna Sequences ◽

Phylogenetic Reconstruction ◽

Molecular Data ◽

Morphological Data ◽

Data Sets ◽

Cytochrome Oxidase Subunit ◽

Data Set ◽

Oxidase Subunit ◽

Morphological And Molecular Data

AbstractIn this paper, the Clusiidae (Diptera: Schizophora) is analyzed phylogenetically using morphological and molecular data sets, and then redefined on the basis of derived morphological characters. The biology and distribution of the Clusiidae are also reviewed, a key is provided to the World genera, the status of the genus Craspedochaeta Czerny is reevaluated and the type of Heterochroa pictipennis Wulp is discussed. Molecular data sets include genomic DNA sequences from the mitochondrial genes COI (cytochrome oxidase subunit I) and COII (cytochrome oxidase subunit II), the large ribosomal nuclear subunit 28S, and the nuclear protein-coding carbomoylphosphate synthase (CPS) domain of CAD (or “rudimentary”). Genes were analyzed separately, in combination with each other, and in combination with a morphological data set. Although individual molecular data sets often provided conflicting phylogenetic signals, the topologies of the cladograms produced from each data set alone or in combination were largely similar. Most genus-level relationships and several basal divergences were unresolved, but Apiochaeta was very strongly and consistently supported as Sobarocephalinae, not Clusiinae. The Clusiinae and Sobarocephalinae are subsequently redefined using an adjusted morphological tree — retaining Apiochaeta in the Sobarocephalinae — that is only slightly longer (8.4%, or seven steps) than the most parsimonious tree. Our results illustrate the benefits of multiple independent data sets for phylogenetic reconstruction in order to verify and refine existing classifications.

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Linking Branch Lengths across Sets of Loci Provides the Highest Statistical Support for Phylogenetic Inference

Molecular Biology and Evolution ◽

10.1093/molbev/msz291 ◽

2019 ◽

Vol 37 (4) ◽

pp. 1202-1210 ◽

Cited By ~ 6

Author(s):

David A Duchêne ◽

K Jun Tong ◽

Charles S P Foster ◽

Sebastián Duchêne ◽

Robert Lanfear ◽

...

Keyword(s):

Empirical Data ◽

Sequence Data ◽

Branch Length ◽

Data Sets ◽

Gene Trees ◽

Data Set ◽

Branch Lengths ◽

Statistical Support ◽

Distinct Branch ◽

Consistent Support

Abstract Evolution leaves heterogeneous patterns of nucleotide variation across the genome, with different loci subject to varying degrees of mutation, selection, and drift. In phylogenetics, the potential impacts of partitioning sequence data for the assignment of substitution models are well appreciated. In contrast, the treatment of branch lengths has received far less attention. In this study, we examined the effects of linking and unlinking branch-length parameters across loci or subsets of loci. By analyzing a range of empirical data sets, we find consistent support for a model in which branch lengths are proportionate between subsets of loci: gene trees share the same pattern of branch lengths, but form subsets that vary in their overall tree lengths. These models had substantially better statistical support than models that assume identical branch lengths across gene trees, or those in which genes form subsets with distinct branch-length patterns. We show using simulations and empirical data that the complexity of the branch-length model with the highest support depends on the length of the sequence alignment and on the numbers of taxa and loci in the data set. Our findings suggest that models in which branch lengths are proportionate between subsets have the highest statistical support under the conditions that are most commonly seen in practice. The results of our study have implications for model selection, computational efficiency, and experimental design in phylogenomics.

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