scholarly journals Capturing heterotachy through multi-gamma site models

2015 ◽  
Author(s):  
Remco Bouckaert ◽  
Peter Lockhart
Keyword(s):  
Sequence Data ◽  
Computational Cost ◽  
Realistic Model ◽  
Rate Variation ◽  
Specific Rate ◽  
Data Sets ◽  
Sequence Evolution ◽  
Rate Heterogeneity ◽  
Real World Data ◽  
Sequence Alignments

Most methods for performing a phylogenetic analysis based on sequence alignments of gene data assume that the mechanism of evolution is constant through time. It is recognised that some sites do evolve somewhat faster than others, and this can be captured using a (gamma) rate heterogeneity model. Further, some species have shorter replication times than others, and this results in faster rates of substitution in some lineages. This feature of lineage specific rate variation can be captured to some extent, by using relaxed clock models. However, it is also clear that there are additional poorly characterised features of sequence data that can sometimes lead to extreme differences in lineage specific rates. This variation is poorly captured by constant time reversible substitution models. The significance of extreme lineage specific rate differences is that they lead both to errors in reconstructing evolutionary relationships as well as biased estimates for the age of ancestral nodes. We propose a new model that allows gamma rate heterogeneity to change on branches, thus offering a more realistic model of sequence evolution. It adds negligible computational cost to likelihood calculations. We illustrate its effectiveness with an example of green algae and land-plants. For many real world data sets, we find a much better fit with multi-gamma sites models as well as substantial differences in ancestral node date estimates.

scholarly journals A Bayesian Approach for Detecting Mass-Extinction Events When Rates of Lineage Diversification Vary

2015 ◽  
Author(s):  
Michael R. May ◽  
Sebastian Höhna ◽  
Brian R. Moore
Keyword(s):  
Mass Extinction ◽  
Process Model ◽  
Sequence Data ◽  
Diversification Rate ◽  
Rate Variation ◽  
Specific Rate ◽  
Extinction Rate ◽  
Molecular Sequence Data ◽  
Extinction Events ◽  
Mass Extinction Events

The paleontological record chronicles numerous episodes of mass extinction that severely culled the Tree of Life. Biologists have long sought to assess the extent to which these events may have impacted particular groups. We present a novel method for detecting mass-extinction events from phylogenies estimated from molecular sequence data. We develop our approach in a Bayesian statistical framework, which enables us to harness prior information on the frequency and magnitude of mass-extinction events. The approach is based on an episodic stochastic-branching process model in which rates of speciation and extinction are constant between rate-shift events. We model three types of events: (1) instantaneous tree-wide shifts in speciation rate; (2) instantaneous tree-wide shifts in extinction rate, and; (3) instantaneous tree-wide mass-extinction events. Each of the events is described by a separate compound Poisson process (CPP) model, where the waiting times between each event are exponentially distributed with event-specific rate parameters. The magnitude of each event is drawn from an event-type specific prior distribution. Parameters of the model are then estimated using a reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm. We demonstrate via simulation that this method has substantial power to detect the number of mass-extinction events, provides unbiased estimates of the timing of mass-extinction events, while exhibiting an appropriate (i.e., below 5%) false discovery rate even in the case of background diversification rate variation. Finally, we provide an empirical application of this approach to conifers, which reveals that this group has experienced two major episodes of mass extinction. This new approach?the CPP on Mass Extinction Times (CoMET) model?provides an effective tool for identifying mass-extinction events from molecular phylogenies, even when the history of those groups includes more prosaic temporal variation in diversification rate.

scholarly journals A Three-Level Optimization Model for Nonlinearly Separable Clustering

2020 ◽  
Vol 34 (04) ◽  
pp. 3211-3218
Author(s):  
Liang Bai ◽  
Jiye Liang
Keyword(s):  
Optimization Model ◽  
Clustering Algorithms ◽  
Complex Structure ◽  
Computational Cost ◽  
Real Data ◽  
Data Sets ◽  
Real World Data ◽  
Clustering Problem ◽  
Efficiency And Effectiveness ◽  
Clustering Problems

Due to the complex structure of the real-world data, nonlinearly separable clustering is one of popular and widely studied clustering problems. Currently, various types of algorithms, such as kernel k-means, spectral clustering and density clustering, have been developed to solve this problem. However, it is difficult for them to balance the efficiency and effectiveness of clustering, which limits their real applications. To get rid of the deficiency, we propose a three-level optimization model for nonlinearly separable clustering which divides the clustering problem into three sub-problems: a linearly separable clustering on the object set, a nonlinearly separable clustering on the cluster set and an ensemble clustering on the partition set. An iterative algorithm is proposed to solve the optimization problem. The proposed algorithm can use low computational cost to effectively recognize nonlinearly separable clusters. The performance of this algorithm has been studied on synthetical and real data sets. Comparisons with other nonlinearly separable clustering algorithms illustrate the efficiency and effectiveness of the proposed algorithm.

scholarly journals Phylogenetic Tree-based Pipeline for Uncovering Mutational Patterns during Influenza Virus Evolution

2019 ◽  
Author(s):  
Fransiskus Xaverius Ivan ◽  
Akhila Deshpande ◽  
Chun Wei Lim ◽  
Xinrui Zhou ◽  
Jie Zheng ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
Influenza A ◽  
Sequence Data ◽  
Antigenic Drift ◽  
Sequence Evolution ◽  
Sequence Alignments ◽  
Robust Estimates ◽  
Viral Genes ◽  
Competitive Model ◽  
Strict Molecular Clock

AbstractVarious computational and statistical approaches have been proposed to uncover the mutational patterns of rapidly evolving influenza viral genes. Nonetheless, the approaches mainly rely on sequence alignments which could potentially lead to spurious mutations obtained by comparing sequences from different clades that coexist during particular periods of time. To address this issue, we propose a phylogenetic tree-based pipeline that takes into account the evolutionary structure in the sequence data. Assuming that the sequences evolve progressively under a strict molecular clock, considering a competitive model that is based on a certain Markov model, and using a resampling approach to obtain robust estimates, we could capture statistically significant single-mutations and co-mutations during the sequence evolution. Moreover, by considering the results obtained from analyses that consider all paths and the longest path in the resampled trees, we can categorize the mutational sites and suggest their relevance. Here we applied the pipeline to investigate the 50 years of evolution of the HA sequences of influenza A/H3N2 viruses. In addition to confirming previous knowledge on the A/H3N2 HA evolution, we also demonstrate the use of the pipeline to classify mutational sites according to whether they are able to enhance antigenic drift, compensate other mutations that enhance antigenic drift, or both.

scholarly journals Genome-scale rates of evolutionary change in bacteria

2016 ◽  
Author(s):  
Sebastian Duchêne ◽  
Kathryn E. Holt ◽  
François-Xavier Weill ◽  
Simon Le Hello ◽  
Jane Hawkey ◽  
...  
Keyword(s):  
Evolutionary Dynamics ◽  
Sequence Data ◽  
Temporal Structure ◽  
Evolutionary Rates ◽  
Whole Genome Sequence ◽  
Rate Variation ◽  
Data Sets ◽  
Inverse Association ◽  
Nucleotide Substitutions ◽  
Ecological Processes

ABSTRACTEstimating the rates at which bacterial genomes evolve is critical to understanding major evolutionary and ecological processes such as disease emergence, long-term host-pathogen associations, and short-term transmission patterns. The surge in bacterial genomic data sets provides a new opportunity to estimate these rates and reveal the factors that shape bacterial evolutionary dynamics. For many organisms estimates of evolutionary rate display an inverse association with the time-scale over which the data are sampled. However, this relationship remains unexplored in bacteria due to the difficulty in estimating genome-wide evolutionary rates, which are impacted by the extent of temporal structure in the data and the prevalence of recombination. We collected 36 whole genome sequence data sets from 16 species of bacterial pathogens to systematically estimate and compare their evolutionary rates and assess the extent of temporal structure in the absence of recombination. The majority (28/36) of data sets possessed sufficient clock-like structure to robustly estimate evolutionary rates. However, in some species reliable estimates were not possible even with “ancient DNA” data sampled over many centuries, suggesting that they evolve very slowly or that they display extensive rate variation among lineages. The robustly estimated evolutionary rates spanned several orders of magnitude, from 10−6 to 10−8 nucleotide substitutions site-1 year-1. This variation was largely attributable to sampling time, which was strongly negatively associated with estimated evolutionary rates, with this relationship best described by an exponential decay curve. To avoid potential estimation biases such time-dependency should be considered when inferring evolutionary time-scales in bacteria.

scholarly journals An Overview of Multiple Sequence Alignments and Cloud Computing in Bioinformatics

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Jurate Daugelaite ◽  
Aisling O' Driscoll ◽  
Roy D. Sleator
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Sequence Data ◽  
Cloud Base ◽  
Data Sets ◽  
Next Generation ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Computing Technologies

Multiple sequence alignment (MSA) of DNA, RNA, and protein sequences is one of the most essential techniques in the fields of molecular biology, computational biology, and bioinformatics. Next-generation sequencing technologies are changing the biology landscape, flooding the databases with massive amounts of raw sequence data. MSA of ever-increasing sequence data sets is becoming a significant bottleneck. In order to realise the promise of MSA for large-scale sequence data sets, it is necessary for existing MSA algorithms to be run in a parallelised fashion with the sequence data distributed over a computing cluster or server farm. Combining MSA algorithms with cloud computing technologies is therefore likely to improve the speed, quality, and capability for MSA to handle large numbers of sequences. In this review, multiple sequence alignments are discussed, with a specific focus on the ClustalW and Clustal Omega algorithms. Cloud computing technologies and concepts are outlined, and the next generation of cloud base MSA algorithms is introduced.

PC 2D-COS: A Principal Component Base Approach to Two-Dimensional Correlation Spectroscopy

2020 ◽  
Vol 74 (4) ◽  
pp. 460-472 ◽  
Author(s):  
Julian Hniopek ◽  
Michael Schmitt ◽  
Jürgen Popp ◽  
Thomas Bocklitz
Keyword(s):  
Computational Cost ◽  
Principal Component ◽  
Correlation Spectroscopy ◽  
High Spectral Resolution ◽  
Data Sets ◽  
Two Dimensional ◽  
Real World Data ◽  
Data Set ◽  
Multiple Data Sets ◽  
2D Correlation Spectroscopy

This paper introduces the newly developed principal component powered two-dimensional (2D) correlation spectroscopy (PC 2D-COS) as an alternative approach to 2D correlation spectroscopy taking advantage of a dimensionality reduction by principal component analysis. It is shown that PC 2D-COS is equivalent to traditional 2D correlation analysis while providing a significant advantage in terms of computational complexity and memory consumption. These features allow for an easy calculation of 2D correlation spectra even for data sets with very high spectral resolution or a parallel analysis of multiple data sets of 2D correlation spectra. Along with this reduction in complexity, PC 2D-COS offers a significant noise rejection property by limiting the set of principal components used for the 2D correlation calculation. As an example for the application of truncated PC 2D-COS a temperature-dependent Raman spectroscopic data set of a fullerene-anthracene adduct is examined. It is demonstrated that a large reduction in computational cost is possible without loss of relevant information, even for complex real world data sets.

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

2016 ◽  
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.

scholarly journals Correction: A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis (Preprint)

2020 ◽  
Author(s):  
Carla Mavian ◽  
Simone Marini ◽  
Mattia Prosperi ◽  
Marco Salemi
Keyword(s):  
Sequence Data ◽  
Sampling Bias ◽  
Systematic Investigation ◽  
Sufficient Information ◽  
Data Sets ◽  
Sequence Alignments ◽  
Phylogenetic Information ◽  
Genome Data ◽  
Country Specific

UNSTRUCTURED The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been growing exponentially, affecting over 4 million people and causing enormous distress to economies and societies worldwide. A plethora of analyses based on viral sequences has already been published both in scientific journals and through non–peer-reviewed channels to investigate the genetic heterogeneity and spatiotemporal dissemination of SARS-CoV-2. However, a systematic investigation of phylogenetic information and sampling bias in the available data is lacking. Although the number of available genome sequences of SARS-CoV-2 is growing daily and the sequences show increasing phylogenetic information, country-specific data still present severe limitations and should be interpreted with caution. The objective of this study was to determine the quality of the currently available SARS-CoV-2 full genome data in terms of sampling bias as well as phylogenetic and temporal signals to inform and guide the scientific community. We used maximum likelihood–based methods to assess the presence of sufficient information for robust phylogenetic and phylogeographic studies in several SARS-CoV-2 sequence alignments assembled from GISAID (Global Initiative on Sharing All Influenza Data) data released between March and April 2020. Although the number of high-quality full genomes is growing daily, and sequence data released in April 2020 contain sufficient phylogenetic information to allow reliable inference of phylogenetic relationships, country-specific SARS-CoV-2 data sets still present severe limitations. At the present time, studies assessing within-country spread or transmission clusters should be considered preliminary or hypothesis-generating at best. Hence, current reports should be interpreted with caution, and concerted efforts should continue to increase the number and quality of sequences required for robust tracing of the epidemic.

scholarly journals A Snapshot of SARS-CoV-2 Genome Availability up to April 2020 and its Implications: Data Analysis (Preprint)

2020 ◽  
Author(s):  
Carla Mavian ◽  
Simone Marini ◽  
Mattia Prosperi ◽  
Marco Salemi
Keyword(s):  
Sequence Data ◽  
Sampling Bias ◽  
Systematic Investigation ◽  
Sufficient Information ◽  
Data Sets ◽  
Sequence Alignments ◽  
Phylogenetic Information ◽  
Genome Data ◽  
Country Specific

BACKGROUND The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been growing exponentially, affecting over 4 million people and causing enormous distress to economies and societies worldwide. A plethora of analyses based on viral sequences has already been published both in scientific journals and through non–peer-reviewed channels to investigate the genetic heterogeneity and spatiotemporal dissemination of SARS-CoV-2. However, a systematic investigation of phylogenetic information and sampling bias in the available data is lacking. Although the number of available genome sequences of SARS-CoV-2 is growing daily and the sequences show increasing phylogenetic information, country-specific data still present severe limitations and should be interpreted with caution. OBJECTIVE The objective of this study was to determine the quality of the currently available SARS-CoV-2 full genome data in terms of sampling bias as well as phylogenetic and temporal signals to inform and guide the scientific community. METHODS We used maximum likelihood–based methods to assess the presence of sufficient information for robust phylogenetic and phylogeographic studies in several SARS-CoV-2 sequence alignments assembled from GISAID (Global Initiative on Sharing All Influenza Data) data released between March and April 2020. RESULTS Although the number of high-quality full genomes is growing daily, and sequence data released in April 2020 contain sufficient phylogenetic information to allow reliable inference of phylogenetic relationships, country-specific SARS-CoV-2 data sets still present severe limitations. CONCLUSIONS At the present time, studies assessing within-country spread or transmission clusters should be considered preliminary or hypothesis-generating at best. Hence, current reports should be interpreted with caution, and concerted efforts should continue to increase the number and quality of sequences required for robust tracing of the epidemic.

scholarly journals Bayesian Evaluation of Temporal Signal in Measurably Evolving Populations

2020 ◽  
Vol 37 (11) ◽  
pp. 3363-3379 ◽  
Author(s):  
Sebastian Duchene ◽  
Philippe Lemey ◽  
Tanja Stadler ◽  
Simon Y W Ho ◽  
David A Duchene ◽  
...  
Keyword(s):  
Molecular Clock ◽  
Ancient Dna ◽  
Time Window ◽  
Sequence Data ◽  
H1n1 Influenza ◽  
Rate Variation ◽  
Data Sets ◽  
Molecular Sequence Data ◽  
Wide Range ◽  
Sampling Times

Abstract Phylogenetic methods can use the sampling times of molecular sequence data to calibrate the molecular clock, enabling the estimation of evolutionary rates and timescales for rapidly evolving pathogens and data sets containing ancient DNA samples. A key aspect of such calibrations is whether a sufficient amount of molecular evolution has occurred over the sampling time window, that is, whether the data can be treated as having come from a measurably evolving population. Here, we investigate the performance of a fully Bayesian evaluation of temporal signal (BETS) in sequence data. The method involves comparing the fit to the data of two models: a model in which the data are accompanied by the actual (heterochronous) sampling times, and a model in which the samples are constrained to be contemporaneous (isochronous). We conducted simulations under a wide range of conditions to demonstrate that BETS accurately classifies data sets according to whether they contain temporal signal or not, even when there is substantial among-lineage rate variation. We explore the behavior of this classification in analyses of five empirical data sets: modern samples of A/H1N1 influenza virus, the bacterium Bordetella pertussis, coronaviruses from mammalian hosts, ancient DNA from Hepatitis B virus, and mitochondrial genomes of dog species. Our results indicate that BETS is an effective alternative to other tests of temporal signal. In particular, this method has the key advantage of allowing a coherent assessment of the entire model, including the molecular clock and tree prior which are essential aspects of Bayesian phylodynamic analyses.

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