scholarly journals Molecular dating for phylogenies containing a mix of populations and species

2019 ◽  
Author(s):  
Beatriz Mello ◽  
Qiqing Tao ◽  
Sudhir Kumar
Keyword(s):  
Bayesian Approach ◽  
Phylogenetic Trees ◽  
Molecular Dating ◽  
Rate Variation ◽  
Species Variation ◽  
Sequence Alignments ◽  
Clock Model ◽  
Divergence Time Estimates ◽  
The Impact ◽  
The Bayesian Approach

AbstractConcurrent molecular dating of population and species divergences is essential in many biological investigations, including phylogeography, phylodynamics, and species delimitation studies. Multiple sequence alignments used in these investigations frequently consist of both intra- and inter-species samples (mixed samples). As a result, the phylogenetic trees contain inter-species, inter-population, and within population divergences. To date these sequence divergences, Bayesian relaxed clock methods are often employed, but they assume the same tree prior for both inter- and intra-species branching processes and require specification of a clock model for branch rates (independent vs. autocorrelated rates models). We evaluated the impact of using the same tree prior on the Bayesian divergence time estimates by analyzing computer-simulated datasets. We also examined the effect of the assumption of independence of evolutionary rate variation among branches when the branch rates are autocorrelated. Bayesian approach with Skyline-coalescent tree priors generally produced excellent molecular dates, with some tree priors (e.g., Yule) performing the best when evolutionary rates were autocorrelated, and lineage sorting was incomplete. We compared the performance of the Bayesian approach with a non-Bayesian, the RelTime method, which does not require specification of a tree prior or selection of a clock model. We found that RelTime performed as well as the Bayesian approach, and when the clock model was mis-specified, RelTime performed slightly better. These results suggest that the computationally efficient RelTime approach is also suitable to analyze datasets containing both populations and species variation.

scholarly journals Using a GTR+Γ substitution model for dating sequence divergence when stationarity and time-reversibility assumptions are violated

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i884-i894
Author(s):  
Jose Barba-Montoya ◽  
Qiqing Tao ◽  
Sudhir Kumar
Keyword(s):  
Divergence Time ◽  
Sequence Divergence ◽  
Molecular Dating ◽  
Divergence Times ◽  
Time Reversibility ◽  
Sequence Alignments ◽  
Divergence Time Estimates ◽  
Time Estimates ◽  
Substitution Process ◽  
The Impact

Abstract Motivation As the number and diversity of species and genes grow in contemporary datasets, two common assumptions made in all molecular dating methods, namely the time-reversibility and stationarity of the substitution process, become untenable. No software tools for molecular dating allow researchers to relax these two assumptions in their data analyses. Frequently the same General Time Reversible (GTR) model across lineages along with a gamma (+Γ) distributed rates across sites is used in relaxed clock analyses, which assumes time-reversibility and stationarity of the substitution process. Many reports have quantified the impact of violations of these underlying assumptions on molecular phylogeny, but none have systematically analyzed their impact on divergence time estimates. Results We quantified the bias on time estimates that resulted from using the GTR + Γ model for the analysis of computer-simulated nucleotide sequence alignments that were evolved with non-stationary (NS) and non-reversible (NR) substitution models. We tested Bayesian and RelTime approaches that do not require a molecular clock for estimating divergence times. Divergence times obtained using a GTR + Γ model differed only slightly (∼3% on average) from the expected times for NR datasets, but the difference was larger for NS datasets (∼10% on average). The use of only a few calibrations reduced these biases considerably (∼5%). Confidence and credibility intervals from GTR + Γ analysis usually contained correct times. Therefore, the bias introduced by the use of the GTR + Γ model to analyze datasets, in which the time-reversibility and stationarity assumptions are violated, is likely not large and can be reduced by applying multiple calibrations. Availability and implementation All datasets are deposited in Figshare: https://doi.org/10.6084/m9.figshare.12594638.

scholarly journals Using a GTR+Γ substitution model for dating sequence divergence when stationarity and time-reversibility assumptions are violated

2020 ◽  
Author(s):  
Jose Barba-Montoya ◽  
Qiqing Tao ◽  
Sudhir Kumar
Keyword(s):  
Divergence Time ◽  
Sequence Divergence ◽  
Molecular Dating ◽  
Divergence Times ◽  
Time Reversibility ◽  
Sequence Alignments ◽  
Divergence Time Estimates ◽  
Time Estimates ◽  
Substitution Process ◽  
The Impact

AbstractMotivationAs the number and diversity of species and genes grow in contemporary datasets, two common assumptions made in all molecular dating methods, namely the time-reversibility and stationarity of the substitution process, become untenable. No software tools for molecular dating allow researchers to relax these two assumptions in their data analyses. Frequently the same General Time Reversible (GTR) model across lineages along with a gamma (+Γ) distributed rates across sites is used in relaxed clock analyses, which assumes time-reversibility and stationarity of the substitution process. Many reports have quantified the impact of violations of these underlying assumptions on molecular phylogeny, but none have systematically analyzed their impact on divergence time estimates.ResultsWe quantified the bias on time estimates that resulted from using the GTR+Γ model for the analysis of computer-simulated nucleotide sequence alignments that were evolved with non-stationary (NS) and non-reversible (NR) substitution models. We tested Bayesian and RelTime approaches that do not require a molecular clock for estimating divergence times. Divergence times obtained using a GTR+Γ model differed only slightly (∼3% on average) from the expected times for NR datasets, but the difference was larger for NS datasets (∼10% on average). The use of only a few calibrations reduced these biases considerably (∼5%). Confidence and credibility intervals from GTR+Γ analysis usually contained correct times. Therefore, the bias introduced by the use of the GTR+Γ model to analyze datasets, in which the time-reversibility and stationarity assumptions are violated, is likely not large and can be reduced by applying multiple calibrations.AvailabilityAll datasets are deposited in Figshare: https://doi.org/10.6084/[email protected]

scholarly journals Reliable confidence intervals for RelTime estimates of evolutionary divergence times

2019 ◽  
Author(s):  
Qiqing Tao ◽  
Koichiro Tamura ◽  
Beatriz Mello ◽  
Sudhir Kumar
Keyword(s):  
Confidence Intervals ◽  
Divergence Time ◽  
Simulated Data ◽  
Molecular Dating ◽  
Divergence Times ◽  
Rate Variation ◽  
Evolutionary Divergence ◽  
Posterior Density ◽  
Divergence Time Estimates ◽  
Highest Posterior Density

AbstractConfidence intervals (CIs) depict the statistical uncertainty surrounding evolutionary divergence time estimates. They capture variance contributed by the finite number of sequences and sites used in the alignment, deviations of evolutionary rates from a strict molecular clock in a phylogeny, and uncertainty associated with clock calibrations. Reliable tests of biological hypotheses demand reliable CIs. However, current non-Bayesian methods may produce unreliable CIs because they do not incorporate rate variation among lineages and interactions among clock calibrations properly. Here, we present a new analytical method to calculate CIs of divergence times estimated using the RelTime method, along with an approach to utilize multiple calibration uncertainty densities in these analyses. Empirical data analyses showed that the new methods produce CIs that overlap with Bayesian highest posterior density (HPD) intervals. In the analysis of computer-simulated data, we found that RelTime CIs show excellent average coverage probabilities, i.e., the true time is contained within the CIs with a 95% probability. These developments will encourage broader use of computationally-efficient RelTime approach in molecular dating analyses and biological hypothesis testing.

scholarly journals A Bayesian Approach for Inferring the Impact of a Discrete Character on Rates of Continuous-Character Evolution in the Presence of Background-Rate Variation

2019 ◽  
Vol 69 (3) ◽  
pp. 530-544 ◽  
Author(s):  
Michael R May ◽  
Brian R Moore
Keyword(s):  
Bayesian Approach ◽  
Null Model ◽  
Character Evolution ◽  
Rate Variation ◽  
Background Rate ◽  
Dependent Rate ◽  
State Dependent ◽  
Discrete Character ◽  
The Impact ◽  
Continuous Character

Abstract Understanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; for example, does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus nonreef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a “straw-man” effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation (“background-rate variation”). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae). [Bayesian phylogenetic comparative methods; continuous-character evolution; data augmentation; discrete-character evolution.]

scholarly journals Plastid phylogenomics of the Gynoxoid group (Senecioneae, Asteraceae) highlights the importance of motif-based sequence alignment amid low genetic distances

2021 ◽  
Author(s):  
Belen Escobari ◽  
Thomas Borsch ◽  
Taylor S. Quedensley ◽  
Michael Gruenstaeudl
Keyword(s):  
Dna Sequence ◽  
Phylogenetic Trees ◽  
Plastid Genome ◽  
Intergenic Spacer ◽  
Genetic Distances ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Plastid Genomes ◽  
Tree Inference ◽  
The Impact

ABSTRACTPREMISEThe genus Gynoxys and relatives form a species-rich lineage of Andean shrubs and trees with low genetic distances within the sunflower subtribe Tussilaginineae. Previous molecular phylogenetic investigations of the Tussilaginineae have included few, if any, representatives of this Gynoxoid group or reconstructed ambiguous patterns of relationships for it.METHODSWe sequenced complete plastid genomes of 21 species of the Gynoxoid group and related Tussilaginineae and conducted detailed comparisons of the phylogenetic relationships supported by the gene, intron, and intergenic spacer partitions of these genomes. We also evaluated the impact of manual, motif-based adjustments of automatic DNA sequence alignments on phylogenetic tree inference.RESULTSOur results indicate that the inclusion of all plastid genome partitions is needed to infer fully resolved phylogenetic trees of the Gynoxoid group. Whole plastome-based tree inference suggests that the genera Gynoxys and Nordenstamia are polyphyletic and form the core clade of the Gynoxoid group. This clade is sister to a clade of Aequatorium and Paragynoxys and also includes some but not all representatives of Paracalia.CONCLUSIONSThe concatenation and combined analysis of all plastid genome partitions and the construction of manually curated, motif-based DNA sequence alignments are found to be instrumental in the recovery of strongly supported relationships of the Gynoxoid group. We demonstrate that the correct assessment of homology in genome-level plastid sequence datasets is crucial for subsequent phylogeny reconstruction and that the manual post-processing of multiple sequence alignments improves the reliability of such reconstructions amid low genetic distances between taxa.

scholarly journals The Implications of Lineage-Specific Rates for Divergence Time Estimation

2019 ◽  
Vol 69 (4) ◽  
pp. 660-670 ◽  
Author(s):  
Tom Carruthers ◽  
Michael J Sanderson ◽  
Robert W Scotland
Keyword(s):  
Estimation Error ◽  
Divergence Time ◽  
Time Estimation ◽  
Rate Variation ◽  
Data Set ◽  
Divergence Time Estimation ◽  
Divergence Time Estimates ◽  
Time Estimates ◽  
The Impact ◽  
Highest Posterior Density

Abstract Rate variation adds considerable complexity to divergence time estimation in molecular phylogenies. Here, we evaluate the impact of lineage-specific rates—which we define as among-branch-rate-variation that acts consistently across the entire genome. We compare its impact to residual rates—defined as among-branch-rate-variation that shows a different pattern of rate variation at each sampled locus, and gene-specific rates—defined as variation in the average rate across all branches at each sampled locus. We show that lineage-specific rates lead to erroneous divergence time estimates, regardless of how many loci are sampled. Further, we show that stronger lineage-specific rates lead to increasing error. This contrasts to residual rates and gene-specific rates, where sampling more loci significantly reduces error. If divergence times are inferred in a Bayesian framework, we highlight that error caused by lineage-specific rates significantly reduces the probability that the 95% highest posterior density includes the correct value, and leads to sensitivity to the prior. Use of a more complex rate prior—which has recently been proposed to model rate variation more accurately—does not affect these conclusions. Finally, we show that the scale of lineage-specific rates used in our simulation experiments is comparable to that of an empirical data set for the angiosperm genus Ipomoea. Taken together, our findings demonstrate that lineage-specific rates cause error in divergence time estimates, and that this error is not overcome by analyzing genomic scale multilocus data sets. [Divergence time estimation; error; rate variation.]

scholarly journals Reliable Confidence Intervals for RelTime Estimates of Evolutionary Divergence Times

2019 ◽  
Vol 37 (1) ◽  
pp. 280-290 ◽  
Author(s):  
Qiqing Tao ◽  
Koichiro Tamura ◽  
Beatriz Mello ◽  
Sudhir Kumar
Keyword(s):  
Confidence Intervals ◽  
Divergence Time ◽  
Simulated Data ◽  
Molecular Dating ◽  
Divergence Times ◽  
Rate Variation ◽  
Evolutionary Divergence ◽  
Posterior Density ◽  
Divergence Time Estimates ◽  
Highest Posterior Density

Abstract Confidence intervals (CIs) depict the statistical uncertainty surrounding evolutionary divergence time estimates. They capture variance contributed by the finite number of sequences and sites used in the alignment, deviations of evolutionary rates from a strict molecular clock in a phylogeny, and uncertainty associated with clock calibrations. Reliable tests of biological hypotheses demand reliable CIs. However, current non-Bayesian methods may produce unreliable CIs because they do not incorporate rate variation among lineages and interactions among clock calibrations properly. Here, we present a new analytical method to calculate CIs of divergence times estimated using the RelTime method, along with an approach to utilize multiple calibration uncertainty densities in dating analyses. Empirical data analyses showed that the new methods produce CIs that overlap with Bayesian highest posterior density intervals. In the analysis of computer-simulated data, we found that RelTime CIs show excellent average coverage probabilities, that is, the actual time is contained within the CIs with a 94% probability. These developments will encourage broader use of computationally efficient RelTime approaches in molecular dating analyses and biological hypothesis testing.

scholarly journals A Bayesian Approach for Inferring the Impact of a Discrete Character on Rates of Continuous-Character Evolution in the Presence of Background-Rate Variation

2019 ◽  
Author(s):  
Michael R. May ◽  
Brian R. Moore
Keyword(s):  
Bayesian Approach ◽  
Null Model ◽  
Character Evolution ◽  
Rate Variation ◽  
Background Rate ◽  
Dependent Rate ◽  
State Dependent ◽  
Discrete Character ◽  
The Impact ◽  
Continuous Character

AbstractUnderstanding how and why rates of character evolution vary across the Tree of Life is central to many evolutionary questions; e.g., does the trophic apparatus (a set of continuous characters) evolve at a higher rate in fish lineages that dwell in reef versus non-reef habitats (a discrete character)? Existing approaches for inferring the relationship between a discrete character and rates of continuous-character evolution rely on comparing a null model (in which rates of continuous-character evolution are constant across lineages) to an alternative model (in which rates of continuous-character evolution depend on the state of the discrete character under consideration). However, these approaches are susceptible to a “straw-man” effect: the influence of the discrete character is inflated because the null model is extremely unrealistic. Here, we describe MuSSCRat, a Bayesian approach for inferring the impact of a discrete trait on rates of continuous-character evolution in the presence of alternative sources of rate variation (“background-rate variation”). We demonstrate by simulation that our method is able to reliably infer the degree of state-dependent rate variation, and show that ignoring background-rate variation leads to biased inferences regarding the degree of state-dependent rate variation in grunts (the fish group Haemulidae). [continuous-character evolution; discrete-character evolution; Bayesian phylogenetic comparative methods; data augmentation]

scholarly journals Are all global alignment algorithms and implementations correct?

2015 ◽  
Author(s):  
Tomáš Flouri ◽  
Kassian Kobert ◽  
Torbjørn Rognes ◽  
Alexandros Stamatakis
Keyword(s):  
Dynamic Programming ◽  
Phylogenetic Analyses ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Global Alignment ◽  
Sequence Alignments ◽  
Optimal Sequence ◽  
Divergence Time Estimates ◽  
Alignment Algorithms ◽  
The Impact

While implementing the algorithm, we discovered two mathematical mistakes in Gotoh's paper that induce sub-optimal sequence alignments. First, there are minor indexing mistakes in the dynamic programming algorithm which become apparent immediately when implementing the procedure. Hence, we report on these for the sake of completeness. Second, there is a more profound problem with the dynamic programming matrix initialization. This initialization issue can easily be missed and find its way into actual implementations. This error is also present in standard text books. Namely, the widely used books by Gusfield and Waterman. To obtain an initial estimate of the extent to which this error has been propagated, we scrutinized freely available undergraduate lecture slides. We found that 8 out of 31 lecture slides contained the mistake, while 16 out of 31 simply omit parts of the initialization, thus giving an incomplete description of the algorithm. Finally, by inspecting ten source codes and running respective tests, we found that five implementations were incorrect. Note that, not all bugs we identified are due to the mistake in Gotoh's paper. Three implementations rely on additional constraints that limit generality. Thus, only two out of ten yield correct results. We show that the error introduced by Gotoh is straightforward to resolve and provide a correct open-source reference implementation. We do believe though, that raising the awareness about these errors is critical, since the impact of incorrect pairwise sequence alignments that typically represent one of the very first stages in any bioinformatics data analysis pipeline can have a detrimental impact on downstream analyses such as multiple sequence alignment, orthology assignment, phylogenetic analyses, divergence time estimates, etc.

scholarly journals How many data clusters are in the Galaxy data set?

2021 ◽  
Author(s):  
Bettina Grün ◽  
Gertraud Malsiner-Walli ◽  
Sylvia Frühwirth-Schnatter
Keyword(s):  
Mixture Model ◽  
Bayesian Approach ◽  
Clear Understanding ◽  
Data Set ◽  
Model Based Clustering ◽  
The Galaxy ◽  
Data Clusters ◽  
The Impact ◽  
Full Factorial ◽  
The Bayesian Approach

AbstractIn model-based clustering, the Galaxy data set is often used as a benchmark data set to study the performance of different modeling approaches. Aitkin (Stat Model 1:287–304) compares maximum likelihood and Bayesian analyses of the Galaxy data set and expresses reservations about the Bayesian approach due to the fact that the prior assumptions imposed remain rather obscure while playing a major role in the results obtained and conclusions drawn. The aim of the paper is to address Aitkin’s concerns about the Bayesian approach by shedding light on how the specified priors influence the number of estimated clusters. We perform a sensitivity analysis of different prior specifications for the mixtures of finite mixture model, i.e., the mixture model where a prior on the number of components is included. We use an extensive set of different prior specifications in a full factorial design and assess their impact on the estimated number of clusters for the Galaxy data set. Results highlight the interaction effects of the prior specifications and provide insights into which prior specifications are recommended to obtain a sparse clustering solution. A simulation study with artificial data provides further empirical evidence to support the recommendations. A clear understanding of the impact of the prior specifications removes restraints preventing the use of Bayesian methods due to the complexity of selecting suitable priors. Also, the regularizing properties of the priors may be intentionally exploited to obtain a suitable clustering solution meeting prior expectations and needs of the application.

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