Widespread paleopolyploidy, gene tree conflict, and recalcitrant relationships among the carnivorous Caryophyllales

ABSTRACTThe carnivorous members of the large, hyperdiverse Caryophyllales (e.g. Venus flytrap, sundews and Nepenthes pitcher plants) represent perhaps the oldest and most diverse lineage of carnivorous plants. However, despite numerous studies seeking to elucidate their evolutionary relationships, the early-diverging relationships remain unresolved.To explore the utility of phylogenomic data sets for resolving relationships among the carnivorous Caryophyllales, we sequenced ten transcriptomes, including all the carnivorous genera except those in the rare West African liana family (Dioncophyllaceae). We used a variety of methods to infer the species tree, examine gene tree conflict and infer paleopolyploidy events.Phylogenomic analyses support the monophyly of the carnivorous Caryophyllales, with an origin of 68-83 mya. In contrast to previous analyses recover the remaining non-core Caryophyllales as non-monophyletic, although there are multiple reasons this result may be spurious and node supporting this relationship contains a significant amount gene tree discordance. We present evidence that the clade contains at least seven independent paleopolyploidy events, previously debated nodes from the literature have high levels of gene tree conflict, and taxon sampling influences topology even in a phylogenomic data set.Our data demonstrate the importance of carefully considering gene tree conflict and taxon sampling in phylogenomic analyses. Moreover, they provide a remarkable example of the propensity for paleopolyploidy in angiosperms, with at least seven such events in a clade of less than 2500 species.

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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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Detecting destabilizing species in the phylogenetic backbone of Potentilla (Rosaceae) using low-copy nuclear markers

AoB Plants ◽

10.1093/aobpla/plaa017 ◽

2020 ◽

Vol 12 (3) ◽

Cited By ~ 1

Author(s):

Nannie L Persson ◽

Ingrid Toresen ◽

Heidi Lie Andersen ◽

Jenny E E Smedmark ◽

Torsten Eriksson

Keyword(s):

Incomplete Lineage Sorting ◽

Gene Tree ◽

Data Sets ◽

Gene Trees ◽

Lineage Sorting ◽

Nuclear Markers ◽

Data Set ◽

Multispecies Coalescent ◽

Single Marker ◽

Named Groups

Abstract The genus Potentilla (Rosaceae) has been subjected to several phylogenetic studies, but resolving its evolutionary history has proven challenging. Previous analyses recovered six, informally named, groups: the Argentea, Ivesioid, Fragarioides, Reptans, Alba and Anserina clades, but the relationships among some of these clades differ between data sets. The Reptans clade, which includes the type species of Potentilla, has been noticed to shift position between plastid and nuclear ribosomal data sets. We studied this incongruence by analysing four low-copy nuclear markers, in addition to chloroplast and nuclear ribosomal data, with a set of Bayesian phylogenetic and Multispecies Coalescent (MSC) analyses. A selective taxon removal strategy demonstrated that the included representatives from the Fragarioides clade, P. dickinsii and P. fragarioides, were the main sources of the instability seen in the trees. The Fragarioides species showed different relationships in each gene tree, and were only supported as a monophyletic group in a single marker when the Reptans clade was excluded from the analysis. The incongruences could not be explained by allopolyploidy, but rather by homoploid hybridization, incomplete lineage sorting or taxon sampling effects. When P. dickinsii and P. fragarioides were removed from the data set, a fully resolved, supported backbone phylogeny of Potentilla was obtained in the MSC analysis. Additionally, indications of autopolyploid origins of the Reptans and Ivesioid clades were discovered in the low-copy gene trees.

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Phylogenomic analysis of the Chilean clade ofLiolaemuslizards (Squamata: Liolaemidae) based on sequence capture data

PeerJ ◽

10.7717/peerj.3941 ◽

2017 ◽

Vol 5 ◽

pp. e3941 ◽

Cited By ~ 7

Author(s):

Alejandra Panzera ◽

Adam D. Leaché ◽

Guillermo D’Elía ◽

Pedro F. Victoriano

Keyword(s):

Incomplete Lineage Sorting ◽

Phylogenetic Analyses ◽

Strong Support ◽

Taxon Sampling ◽

Phylogenomic Analysis ◽

Molecular Systematic ◽

Data Set ◽

Protein Coding ◽

Protein Coding Genes ◽

Phylogenomic Analyses

The genusLiolaemusis one of the most ecologically diverse and species-rich genera of lizards worldwide. It currently includes more than 250 recognized species, which have been subject to many ecological and evolutionary studies. Nevertheless,Liolaemuslizards have a complex taxonomic history, mainly due to the incongruence between morphological and genetic data, incomplete taxon sampling, incomplete lineage sorting and hybridization. In addition, as many species have restricted and remote distributions, this has hampered their examination and inclusion in molecular systematic studies. The aims of this study are to infer a robust phylogeny for a subsample of lizards representing the Chilean clade (subgenusLiolaemus sensu stricto), and to test the monophyly of several of the major species groups. We use a phylogenomic approach, targeting 541 ultra-conserved elements (UCEs) and 44 protein-coding genes for 16 taxa. We conduct a comparison of phylogenetic analyses using maximum-likelihood and several species tree inference methods. The UCEs provide stronger support for phylogenetic relationships compared to the protein-coding genes; however, the UCEs outnumber the protein-coding genes by 10-fold. On average, the protein-coding genes contain over twice the number of informative sites. Based on our phylogenomic analyses, all the groups sampled are polyphyletic.Liolaemus tenuis tenuisis difficult to place in the phylogeny, because only a few loci (nine) were recovered for this species. Topologies or support values did not change dramatically upon exclusion ofL. t. tenuisfrom analyses, suggesting that missing data did not had a significant impact on phylogenetic inference in this data set. The phylogenomic analyses provide strong support for sister group relationships betweenL. fuscus,L. monticola,L. nigroviridisandL. nitidus, andL. plateiandL. velosoi. Despite our limited taxon sampling, we have provided a reliable starting hypothesis for the relationships among many major groups of the Chilean clade ofLiolaemusthat will help future work aimed at resolving theLiolaemusphylogeny.

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Apple Disease Recognition Based on Small-scale Data Sets

Applied Engineering in Agriculture ◽

10.13031/aea.14187 ◽

2021 ◽

Vol 37 (3) ◽

pp. 481-490

Author(s):

Chenyong Song ◽

Dongwei Wang ◽

Haoran Bai ◽

Weihao Sun

Keyword(s):

Identification Accuracy ◽

Model Complexity ◽

Geometric Transformation ◽

Small Scale ◽

Model Parameters ◽

Data Sets ◽

List Type ◽

Data Set ◽

Disease Identification ◽

Scale Data

HighlightsThe proposed data enhancement method can be used for small-scale data sets with rich sample image features.The accuracy of the new model reaches 98.5%, which is better than the traditional CNN method.Abstract: GoogLeNet offers far better performance in identifying apple disease compared to traditional methods. However, the complexity of GoogLeNet is relatively high. For small volumes of data, GoogLeNet does not achieve the same performance as it does with large-scale data. We propose a new apple disease identification model using GoogLeNet’s inception module. The model adopts a variety of methods to optimize its generalization ability. First, geometric transformation and image modification of data enhancement methods (including rotation, scaling, noise interference, random elimination, color space enhancement) and random probability and appropriate combination of strategies are used to amplify the data set. Second, we employ a deep convolution generative adversarial network (DCGAN) to enhance the richness of generated images by increasing the diversity of the noise distribution of the generator. Finally, we optimize the GoogLeNet model structure to reduce model complexity and model parameters, making it more suitable for identifying apple tree diseases. The experimental results show that our approach quickly detects and classifies apple diseases including rust, spotted leaf disease, and anthrax. It outperforms the original GoogLeNet in recognition accuracy and model size, with identification accuracy reaching 98.5%, making it a feasible method for apple disease classification. Keywords: Apple disease identification, Data enhancement, DCGAN, GoogLeNet.

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The Relative Importance of Modeling Site Pattern Heterogeneity Versus Partition-Wise Heterotachy in Phylogenomic Inference

Systematic Biology ◽

10.1093/sysbio/syz021 ◽

2019 ◽

Vol 68 (6) ◽

pp. 1003-1019 ◽

Cited By ~ 7

Author(s):

Huai-Chun Wang ◽

Edward Susko ◽

Andrew J Roger

Keyword(s):

Small Sample ◽

Data Sets ◽

Large Set ◽

Relative Importance ◽

Data Set ◽

Heterogeneous Models ◽

Homogeneous Models ◽

Phylogenomic Analyses ◽

Complete Protein ◽

Protein Alignments

Abstract Large taxa-rich genome-scale data sets are often necessary for resolving ancient phylogenetic relationships. But accurate phylogenetic inference requires that they are analyzed with realistic models that account for the heterogeneity in substitution patterns amongst the sites, genes and lineages. Two kinds of adjustments are frequently used: models that account for heterogeneity in amino acid frequencies at sites in proteins, and partitioned models that accommodate the heterogeneity in rates (branch lengths) among different proteins in different lineages (protein-wise heterotachy). Although partitioned and site-heterogeneous models are both widely used in isolation, their relative importance to the inference of correct phylogenies has not been carefully evaluated. We conducted several empirical analyses and a large set of simulations to compare the relative performances of partitioned models, site-heterogeneous models, and combined partitioned site heterogeneous models. In general, site-homogeneous models (partitioned or not) performed worse than site heterogeneous, except in simulations with extreme protein-wise heterotachy. Furthermore, simulations using empirically-derived realistic parameter settings showed a marked long-branch attraction (LBA) problem for analyses employing protein-wise partitioning even when the generating model included partitioning. This LBA problem results from a small sample bias compounded over many single protein alignments. In some cases, this problem was ameliorated by clustering similarly-evolving proteins together into larger partitions using the PartitionFinder method. Similar results were obtained under simulations with larger numbers of taxa or heterogeneity in simulating topologies over genes. For an empirical Microsporidia test data set, all but one tested site-heterogeneous models (with or without partitioning) obtain the correct Microsporidia+Fungi grouping, whereas site-homogenous models (with or without partitioning) did not. The single exception was the fully partitioned site-heterogeneous analysis that succumbed to the compounded small sample LBA bias. In general unless protein-wise heterotachy effects are extreme, it is more important to model site-heterogeneity than protein-wise heterotachy in phylogenomic analyses. Complete protein-wise partitioning should be avoided as it can lead to a serious LBA bias. In cases of extreme protein-wise heterotachy, approaches that cluster similarly-evolving proteins together and coupled with site-heterogeneous models work well for phylogenetic estimation.

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

10.1101/794370 ◽

2019 ◽

Cited By ~ 2

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 ◽

Molecular Data ◽

Species Trees ◽

Lineage Sorting ◽

Multiple Sources ◽

Gene Tree Discordance ◽

Phylogenomic Analyses

AbstractGene tree discordance in large genomic datasets 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 dataset 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.

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Synthesising artificial patient-level data for Open Science - an evaluation of five methods

10.1101/2020.10.09.20210138 ◽

2020 ◽

Author(s):

Michael Allen ◽

Andrew Salmon

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Synthetic Data ◽

Original Data ◽

Classification Model ◽

Data Sets ◽

List Type ◽

Breast Cancer Data ◽

Data Set ◽

Cancer Data

ABSTRACTBackgroundOpen science is a movement seeking to make scientific research accessible to all, including publication of code and data. Publishing patient-level data may, however, compromise the confidentiality of that data if there is any significant risk that data may later be associated with individuals. Use of synthetic data offers the potential to be able to release data that may be used to evaluate methods or perform preliminary research without risk to patient confidentiality.MethodsWe have tested five synthetic data methods:A technique based on Principal Component Analysis (PCA) which samples data from distributions derived from the transformed data.Synthetic Minority Oversampling Technique, SMOTE which is based on interpolation between near neighbours.Generative Adversarial Network, GAN, an artificial neural network approach with competing networks - a discriminator network trained to distinguish between synthetic and real data., and a generator network trained to produce data that can fool the discriminator network.CT-GAN, a refinement of GANs specifically for the production of structured tabular synthetic data.Variational Auto Encoders, VAE, a method of encoding data in a reduced number of dimensions, and sampling from distributions based on the encoded dimensions.Two data sets are used to evaluate the methods:The Wisconsin Breast Cancer data set, a histology data set where all features are continuous variables.A stroke thrombolysis pathway data set, a data set describing characteristics for patients where a decision is made whether to treat with clot-busting medication. Features are mostly categorical, binary, or integers.Methods are evaluated in three ways:The ability of synthetic data to train a logistic regression classification model.A comparison of means and standard deviations between original and synthetic data.A comparison of covariance between features in the original and synthetic data.ResultsUsing the Wisconsin Breast Cancer data set, the original data gave 98% accuracy in a logistic regression classification model. Synthetic data sets gave between 93% and 99% accuracy. Performance (best to worst) was SMOTE > PCA > GAN > CT-GAN = VAE. All methods produced a high accuracy in reproducing original data means and stabdard deviations (all R-square > 0.96 for all methods and data classes). CT-GAN and VAE suffered a significant loss of covariance between features in the synthetic data sets.Using the Stroke Pathway data set, the original data gave 82% accuracy in a logistic regression classification model. Synthetic data sets gave between 66% and 82% accuracy. Performance (best to worst) was SMOTE > PCA > CT-GAN > GAN > VAE. CT-GAN and VAE suffered loss of covariance between features in the synthetic data sets, though less pronounced than with the Wisconsin Breast Cancer data set.ConclusionsThe pilot work described here shows, as proof of concept, that synthetic data may be produced, which is of sufficient quality to publish with open methodology, to allow people to better understand and test methodology. The quality of the synthetic data also gives promise of data sets that may be used for screening of ideas, or for research project (perhaps especially in an education setting).More work is required to further refine and test methods across a broader range of patient-level data sets.

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Multispecies coalescent analysis unravels the non-monophyly and controversial relationships of Hexapoda

10.1101/187997 ◽

2017 ◽

Author(s):

Lucas A. Freitas ◽

Beatriz Mello ◽

Carlos G. Schrago

Keyword(s):

Phylogenetic Relationships ◽

Gene Tree ◽

Genomic Data ◽

Population Level ◽

Phylogenetic Inference ◽

Evolutionary Relationships ◽

Effective Population ◽

Multispecies Coalescent ◽

Population Sizes ◽

Phylogenomic Analyses

AbstractWith the increase in the availability of genomic data, sequences from different loci are usually concatenated in a supermatrix for phylogenetic inference. However, as an alternative to the supermatrix approach, several implementations of the multispecies coalescent (MSC) have been increasingly used in phylogenomic analyses due to their advantages in accommodating gene tree topological heterogeneity by taking account population-level processes. Moreover, the development of faster algorithms under the MSC is enabling the analysis of thousands of loci/taxa. Here, we explored the MSC approach for a phylogenomic dataset of Insecta. Even with the challenges posed by insects, due to large effective population sizes coupled with short deep internal branches, our MSC analysis could recover several orders and evolutionary relationships in agreement with current insect systematics. However, some phylogenetic relationships were not recovered by MSC methods. Most noticeable, a remiped crustacean was positioned within the Insecta. Additionally, the interordinal relationships within Polyneoptera and Neuropteroidea contradicted recent works, by suggesting the non-monophyly of Neuroptera. We notice, however, that these phylogenetic arrangements were also poorly supported by previous analyses and that they were sensitive to gene sampling.

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Partitioned Gene-Tree Analyses and Gene-Based Topology Testing Help Resolve Incongruence in a Phylogenomic Study of Host-Specialist Bees (Apidae: Eucerinae)

Molecular Biology and Evolution ◽

10.1093/molbev/msaa277 ◽

2020 ◽

Author(s):

Felipe V Freitas ◽

Michael G Branstetter ◽

Terry Griswold ◽

Eduardo A B Almeida

Keyword(s):

Incomplete Lineage Sorting ◽

Gene Tree ◽

Species Tree ◽

Data Sets ◽

Gene Trees ◽

Lineage Sorting ◽

Data Set ◽

Analytical Strategy ◽

Analytical Approaches ◽

Phylogenomic Study

Abstract Incongruence among phylogenetic results has become a common occurrence in analyses of genome-scale data sets. Incongruence originates from uncertainty in underlying evolutionary processes (e.g., incomplete lineage sorting) and from difficulties in determining the best analytical approaches for each situation. To overcome these difficulties, more studies are needed that identify incongruences and demonstrate practical ways to confidently resolve them. Here, we present results of a phylogenomic study based on the analysis 197 taxa and 2,526 ultraconserved element (UCE) loci. We investigate evolutionary relationships of Eucerinae, a diverse subfamily of apid bees (relatives of honey bees and bumble bees) with >1,200 species. We sampled representatives of all tribes within the group and >80% of genera, including two mysterious South American genera, Chilimalopsis and Teratognatha. Initial analysis of the UCE data revealed two conflicting hypotheses for relationships among tribes. To resolve the incongruence, we tested concatenation and species tree approaches and used a variety of additional strategies including locus filtering, partitioned gene-trees searches, and gene-based topological tests. We show that within-locus partitioning improves gene tree and subsequent species-tree estimation, and that this approach, confidently resolves the incongruence observed in our data set. After exploring our proposed analytical strategy on eucerine bees, we validated its efficacy to resolve hard phylogenetic problems by implementing it on a published UCE data set of Adephaga (Insecta: Coleoptera). Our results provide a robust phylogenetic hypothesis for Eucerinae and demonstrate a practical strategy for resolving incongruence in other phylogenomic data sets.

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DGEN: A Test Statistic for Detection of General Introgression Scenarios

10.1101/348649 ◽

2018 ◽

Cited By ~ 3

Author(s):

Ryan A. Leo Elworth ◽

Chabrielle Allen ◽

Travis Benedict ◽

Peter Dulworth ◽

Luay Nakhleh

Keyword(s):

Evolutionary Biology ◽

Incomplete Lineage Sorting ◽

Gene Tree ◽

Genetic Material ◽

Phylogenetic Network ◽

Data Sets ◽

Lineage Sorting ◽

Test Statistic ◽

Tree Model ◽

Data Set

AbstractWhen two species hybridize, one outcome is the integration of genetic material from one species into the genome of the other, a process known as introgression. Detecting introgression in genomic data is a very important question in evolutionary biology. However, given that hybridization occurs between closely related species, a compli-cating factor for introgression detection is the presence of incomplete lineage sorting, or ILS. The D-statistic, famously referred to as the “ABBA-BABA” test, was pro-posed for introgression detection in the presence of ILS in data sets that consist of four genomes. More recently, DFOIL—a set of statistics—was introduced to extend the D-statistic to data sets of five genomes.The major contribution of this paper is demonstrating that the invariants underly-ing both the D-statistic and DFOIL can be derived automatically from the probability mass functions of gene tree topologies under the null species tree model and alterna-tive phylogenetic network model. Computational requirements aside, this automatic derivation provides a way to generalize these statistics to data sets of any size and with any scenarios of introgression. We demonstrate the accuracy of the general statistic, which we call DGEN, on simulated data sets with varying rates of introgression, and apply it to an empirical data set of mosquito genomes.We have implemented DGEN and made it available, both as a graphical user interface tool and as a command-line tool, as part of the freely available, open-source software package ALPHA (https://github.com/chilleo/ALPHA).

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