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Reconstruction of real and simulated phylogenies based on quartet plurality inference.

ABSTRACT: BACKGROUND:Deciphering the history of life on Earth has long been regarded as one of the most central tasks in biology. In past years, widespread discordance between the evolutionary histories of different groups of orthologous genes of prokaryotes have been revealed, primarily due to horizontal gene transfers (HGTs). Nonetheless, evidence that support a strong tree-like signal of evolution have been uncovered, despite the presence of HGT events. Therefore, a challenging task is to distill this tree-like signal from the noise induced by all sources of non-tree-like events. RESULTS:In this work we tackle this question, using real and simulated data. We first tighten a recent related theoretical result in this field. In a simulation study, we infer individual quartet topologies, and then use the inferred quartets to reconstruct simulated species trees. We demonstrate that accurate tree reconstruction is feasible despite surprisingly high rates of HGT. In a real data study, we construct phylogenies of two sets of prokaryotes, and show that our tree reconstruction scheme is comparable with (and complementary better than) other commonly used methods. CONCLUSIONS:Using a blend of theoretical and empirical investigations, our study proves the feasibility of accurate quartet-based phylogenetic reconstruction, the vast impact of HGT events notwithstanding.

SUBMITTER: Avni E

PROVIDER: S-EPMC6101080 | biostudies-other | 2018 Aug

REPOSITORIES: biostudies-other

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Reconstruction of real and simulated phylogenies based on quartet plurality inference.

Avni Eliran E Snir Sagi S

BMC genomics 20180813 Suppl 6

<h4>Background</h4>Deciphering the history of life on Earth has long been regarded as one of the most central tasks in biology. In past years, widespread discordance between the evolutionary histories of different groups of orthologous genes of prokaryotes have been revealed, primarily due to horizontal gene transfers (HGTs). Nonetheless, evidence that support a strong tree-like signal of evolution have been uncovered, despite the presence of HGT events. Therefore, a challenging task is to disti ...[more]

PMID: 30367577

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Project description:Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2-13% vs. 31-75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population.

Project description:BackgroundOverlapping genes (OGs) in bacterial genomes are pairs of adjacent genes of which the coding sequences overlap partly or entirely. With the rapid accumulation of sequence data, many OGs in bacterial genomes have now been identified. Indeed, these might prove a consistent feature across all microbial genomes. Our previous work suggests that OGs can be considered as robust markers at the whole genome level for the construction of phylogenies. An online, interactive web server for inferring phylogenies is needed for biologists to analyze phylogenetic relationships among a set of bacterial genomes of interest.DescriptionBPhyOG is an online interactive server for reconstructing the phylogenies of completely sequenced bacterial genomes on the basis of their shared overlapping genes. It provides two tree-reconstruction methods: Neighbor Joining (NJ) and Unweighted Pair-Group Method using Arithmetic averages (UPGMA). Users can apply the desired method to generate phylogenetic trees, which are based on an evolutionary distance matrix for the selected genomes. The distance between two genomes is defined by the normalized number of their shared OG pairs. BPhyOG also allows users to browse the OGs that were used to infer the phylogenetic relationships. It provides detailed annotation for each OG pair and the features of the component genes through hyperlinks. Users can also retrieve each of the homologous OG pairs that have been determined among 177 genomes. It is a useful tool for analyzing the tree of life and overlapping genes from a genomic standpoint.ConclusionBPhyOG is a useful interactive web server for genome-wide inference of any potential evolutionary relationship among the genomes selected by users. It currently includes 177 completely sequenced bacterial genomes containing 79,855 OG pairs, the annotation and homologous OG pairs of which are integrated comprehensively. The reliability of phylogenies complemented by annotations make BPhyOG a powerful web server for genomic and genetic studies. It is freely available at http://cmb.bnu.edu.cn/BPhyOG.

Dataset Information

Reconstruction of real and simulated phylogenies based on quartet plurality inference.

Publications

Reconstruction of real and simulated phylogenies based on quartet plurality inference.

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