Combining trees as a way of combining data sets for phylogenetic inference, and the desirability of combining gene trees

ABSTRACTA statistical framework of model comparison and model validation is essential to resolving the debates over concatenation and coalescent models in phylogenomic data analysis. A set of statistical tests are here applied and developed to evaluate and compare the adequacy of substitution, concatenation, and multispecies coalescent (MSC) models across 47 phylogenomic data sets collected across tree of life. Tests for substitution models and the concatenation assumption of topologically concordant gene trees suggest that a poor fit of substitution models (44% of loci rejecting the substitution model) and concatenation models (38% of loci rejecting the hypothesis of topologically congruent gene trees) is widespread. Logistic regression shows that the proportions of GC content and informative sites are both negatively correlated with the fit of substitution models across loci. Moreover, a substantial violation of the concatenation assumption of congruent gene trees is consistently observed across 6 major groups (birds, mammals, fish, insects, reptiles, and others, including other invertebrates). In contrast, among those loci adequately described by a given substitution model, the proportion of loci rejecting the MSC model is 11%, significantly lower than those rejecting the substitution and concatenation models, and Bayesian model comparison strongly favors the MSC over concatenation across all data sets. Species tree inference suggests that loci rejecting the MSC have little effect on species tree estimation. Due to computational constraints, the Bayesian model validation and comparison analyses were conducted on the reduced data sets. A complete analysis of phylogenomic data requires the development of efficient algorithms for phylogenetic inference. Nevertheless, the concatenation assumption of congruent gene trees rarely holds for phylogenomic data with more than 10 loci. Thus, for large phylogenomic data sets, model comparison analyses are expected to consistently and more strongly favor the coalescent model over the concatenation model. Our analysis reveals the value of model validation and comparison in phylogenomic data analysis, as well as the need for further improvements of multilocus models and computational tools for phylogenetic inference.

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Phylogenetic signal is associated with the degree of variation in root-to-tip distances

10.1101/2020.01.28.923805 ◽

2020 ◽

Author(s):

Mezzalina Vankan ◽

Simon Y.W. Ho ◽

Carolina Pardo-Diaz ◽

David A. Duchêne

Keyword(s):

Sequence Data ◽

Phylogenetic Signal ◽

Gene Tree ◽

Phylogenetic Inference ◽

Genomic Region ◽

Published Data ◽

Data Sets ◽

Gene Trees ◽

Species Trees ◽

Data Set

AbstractThe phylogenetic information contained in sequence data is partly determined by the overall rate of nucleotide substitution in the genomic region in question. However, phylogenetic signal is affected by various other factors, such as heterogeneity in substitution rates across lineages. These factors might be able to predict the phylogenetic accuracy of any given gene in a data set. We examined the association between the accuracy of phylogenetic inference across genes and several characteristics of branch lengths in phylogenomic data. In a large number of published data sets, we found that the accuracy of phylogenetic inference from genes was consistently associated with their mean statistical branch support and variation in their gene tree root-to-tip distances, but not with tree length and stemminess. Therefore, a signal of constant evolutionary rates across lineages appears to be beneficial for phylogenetic inference. Identifying the causes of variation in root-to-tip lengths in gene trees also offers a potential way forward to increase congruence in the signal across genes and improve estimates of species trees from phylogenomic data sets.

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Linking Branch Lengths Across Loci Provides the Best Fit for Phylogenetic Inference

10.1101/467449 ◽

2018 ◽

Cited By ~ 2

Author(s):

David A. Duchêne ◽

K. Jun Tong ◽

Charles S. P. Foster ◽

Sebastián Duchêne ◽

Robert Lanfear ◽

...

Keyword(s):

Gene Tree ◽

Branch Length ◽

Phylogenetic Inference ◽

Data Sets ◽

Length Variation ◽

Gene Trees ◽

Data Set ◽

Branch Lengths ◽

Future Work ◽

Best Fit

AbstractEvolution leaves heterogeneous patterns of nucleotide variation across the genome, with different loci subject to varying degrees of mutation, selection, and drift. Appropriately modelling this heterogeneity is important for reliable phylogenetic inference. One modelling approach in statistical phylogenetics is to apply independent models of molecular evolution to different groups of sites, where the groups are usually defined by locus, codon position, or combinations of the two. The potential impacts of partitioning data for the assignment of substitution models are well appreciated. Meanwhile, 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. By analysing a range of empirical data sets, we find that the best-fitting model for phylogenetic inference is consistently one in which branch lengths are proportionally linked: gene trees have the same pattern of branch-length variation, but with varying absolute tree lengths. This model provided a substantially better fit than those that either assumed identical branch lengths across gene trees or that allowed each gene tree to have its own distinct set of branch lengths. Using simulations, we show that the fit of the three different models of branch lengths varies with the length of the sequence alignment and with the number of taxa in the data set. Our findings suggest that a model with proportionally linked branch lengths across loci is likely to provide the best fit under the conditions that are most commonly seen in practice. In future work, improvements in fit might be afforded by models with levels of complexity intermediate to proportional and free branch lengths. The results of our study have implications for model selection, computational efficiency, and experimental design in phylogenomics.

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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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VikNGS: A C ++ Variant Integration Kit for Next Generation Sequencing Association Analysis

Bioinformatics ◽

10.1093/bioinformatics/btz716 ◽

2019 ◽

Cited By ~ 1

Author(s):

Zeynep Baskurt ◽

Scott Mastromatteo ◽

Jiafen Gong ◽

Richard F Wintle ◽

Stephen W Scherer ◽

...

Keyword(s):

Next Generation Sequencing ◽

Genetic Association ◽

Association Analysis ◽

Supplementary Information ◽

Next Generation Sequencing Data ◽

Data Sets ◽

Next Generation ◽

Sequencing Data ◽

Combining Data ◽

Generation Sequencing

Abstract Integration of next generation sequencing data (NGS) across different research studies can improve the power of genetic association testing by increasing sample size and can obviate the need for sequencing controls. If differential genotype uncertainty across studies is not accounted for, combining data sets can produce spurious association results. We developed the Variant Integration Kit for NGS (VikNGS), a fast cross-platform software package, to enable aggregation of several data sets for rare and common variant genetic association analysis of quantitative and binary traits with covariate adjustment. VikNGS also includes a graphical user interface, power simulation functionality and data visualization tools. Availability The VikNGS package can be downloaded at http://www.tcag.ca/tools/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

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A Novel Approach to Investigate the Effect of Tree Reconstruction Artifacts in Single-Gene Analysis Clarifies Opsin Evolution in Nonbilaterian Metazoans

Genome Biology and Evolution ◽

10.1093/gbe/evaa015 ◽

2020 ◽

Vol 12 (2) ◽

pp. 3906-3916 ◽

Cited By ~ 1

Author(s):

James F Fleming ◽

Roberto Feuda ◽

Nicholas W Roberts ◽

Davide Pisani

Keyword(s):

Phylogenetic Signal ◽

Single Gene ◽

G Protein Coupled Receptors ◽

Data Sets ◽

Gene Trees ◽

Tree Reconstruction ◽

Phylogenetic Information ◽

Novel Approach ◽

Gene Data ◽

G Protein Coupled

Abstract Our ability to correctly reconstruct a phylogenetic tree is strongly affected by both systematic errors and the amount of phylogenetic signal in the data. Current approaches to tackle tree reconstruction artifacts, such as the use of parameter-rich models, do not translate readily to single-gene alignments. This, coupled with the limited amount of phylogenetic information contained in single-gene alignments, makes gene trees particularly difficult to reconstruct. Opsin phylogeny illustrates this problem clearly. Opsins are G-protein coupled receptors utilized in photoreceptive processes across Metazoa and their protein sequences are roughly 300 amino acids long. A number of incongruent opsin phylogenies have been published and opsin evolution remains poorly understood. Here, we present a novel approach, the canary sequence approach, to investigate and potentially circumvent errors in single-gene phylogenies. First, we demonstrate our approach using two well-understood cases of long-branch attraction in single-gene data sets, and simulations. After that, we apply our approach to a large collection of well-characterized opsins to clarify the relationships of the three main opsin subfamilies.

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The Multispecies Coalescent Model Outperforms Concatenation Across Diverse Phylogenomic Data Sets

Systematic Biology ◽

10.1093/sysbio/syaa008 ◽

2020 ◽

Vol 69 (4) ◽

pp. 795-812 ◽

Cited By ~ 1

Author(s):

Xiaodong Jiang ◽

Scott V Edwards ◽

Liang Liu

Keyword(s):

Data Analysis ◽

Model Validation ◽

Bayesian Model ◽

Statistical Tests ◽

Gc Content ◽

Data Sets ◽

Gene Trees ◽

Coalescent Model ◽

Multispecies Coalescent ◽

Substitution Models

Abstract A statistical framework of model comparison and model validation is essential to resolving the debates over concatenation and coalescent models in phylogenomic data analysis. A set of statistical tests are here applied and developed to evaluate and compare the adequacy of substitution, concatenation, and multispecies coalescent (MSC) models across 47 phylogenomic data sets collected across tree of life. Tests for substitution models and the concatenation assumption of topologically congruent gene trees suggest that a poor fit of substitution models, rejected by 44% of loci, and concatenation models, rejected by 38% of loci, is widespread. Logistic regression shows that the proportions of GC content and informative sites are both negatively correlated with the fit of substitution models across loci. Moreover, a substantial violation of the concatenation assumption of congruent gene trees is consistently observed across six major groups (birds, mammals, fish, insects, reptiles, and others, including other invertebrates). In contrast, among those loci adequately described by a given substitution model, the proportion of loci rejecting the MSC model is 11%, significantly lower than those rejecting the substitution and concatenation models. Although conducted on reduced data sets due to computational constraints, Bayesian model validation and comparison both strongly favor the MSC over concatenation across all data sets; the concatenation assumption of congruent gene trees rarely holds for phylogenomic data sets with more than 10 loci. Thus, for large phylogenomic data sets, model comparisons are expected to consistently and more strongly favor the coalescent model over the concatenation model. We also found that loci rejecting the MSC have little effect on species tree estimation. Our study reveals the value of model validation and comparison in phylogenomic data analysis, as well as the need for further improvements of multilocus models and computational tools for phylogenetic inference. [Bayes factor; Bayesian model validation; coalescent prior; congruent gene trees; independent prior; Metazoa; posterior predictive simulation.]

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Image Inpainting for Missing Values in Observational Climate Datasets Using Partial Convolutions in a cuDNN

10.5194/egusphere-egu2020-21555 ◽

2020 ◽

Author(s):

Christopher Kadow ◽

David Hall ◽

Uwe Ulbrich

Keyword(s):

Missing Values ◽

Coupled Model ◽

Image Inpainting ◽

Climate Science ◽

Data Sets ◽

Learning Technology ◽

Climate Change Research ◽

The Past ◽

Combining Data ◽

High Degree

<p>Nowadays climate change research relies on climate information of the past. Historic climate records of temperature observations form global gridded datasets like HadCRUT4, which is investigated e.g. in the IPCC reports. However, record combining data-sets are sparse in the past. Even today they contain missing values. Here we show that machine learning technology can be applied to refill these missing climate values in observational datasets. We found that the technology of image inpainting using partial convolutions in a CUDA accelerated deep neural network can be trained by large Earth system model experiments from NOAA reanalysis (20CR) and the Coupled Model Intercomparison Project phase 5 (CMIP5). The derived deep neural networks are capable to independently refill added missing values of these experiments. The analysis shows a very high degree of reconstruction even in the cross-reconstruction of the trained networks on the other dataset. The network reconstruction reaches a better evaluation than other typical methods in climate science. In the end we will show the new reconstructed observational dataset HadCRUT4 and discuss further investigations.</p>

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