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Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies.

Spielman SJ, Wilke CO - PLoS ONE (2015)

Bottom Line: All model parameters are fully customizable.Users can additionally specify custom evolutionary models, with custom rate matrices and/or states to evolve.This flexibility makes Pyvolve a convenient framework not only for simulating sequences under a wide variety of conditions, but also for developing and testing new evolutionary models.

View Article: PubMed Central - PubMed

Affiliation: Department of Integrative Biology, Center for Computational Biology and Bioinformatics, and Institute of Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712, United States of America.

ABSTRACT
We introduce Pyvolve, a flexible Python module for simulating genetic data along a phylogeny using continuous-time Markov models of sequence evolution. Easily incorporated into Python bioinformatics pipelines, Pyvolve can simulate sequences according to most standard models of nucleotide, amino-acid, and codon sequence evolution. All model parameters are fully customizable. Users can additionally specify custom evolutionary models, with custom rate matrices and/or states to evolve. This flexibility makes Pyvolve a convenient framework not only for simulating sequences under a wide variety of conditions, but also for developing and testing new evolutionary models. Pyvolve is an open-source project under a FreeBSD license, and it is available for download, along with a detailed user-manual and example scripts, from http://github.com/sjspielman/pyvolve.

No MeSH data available.


Pyvolve accurately evolves sequences under homogenous, site-wise rate heterogeneity, and branch-specific rate heterogeneity.A) Nucleotide alignments simulated under the JC69 [36] model along two-taxon trees with varying branch lengths, which represent the substitution rate. Points represent the mean observed substitution rate for the 50 alignment replicates simulated under the given value, and error bars represent standard deviations. The red line indicates the x = y line. B) Codon alignments simulated under an MG94-style [32] model with varying values for the dN/dS parameter. Points represent the mean dN/dS inferred from the 50 alignment replicates simulated under the given dN/dS value, and error bars represent standard deviations. The red line indicates the x = y line. C) Site-wise heterogeneity simulated with an MG94-style [32] model with varying dN/dS values across sites. Horizontal lines indicate the simulated dN/dS value for each dN/dS category. D) Branch-wise heterogeneity simulated with an MG94-style [32] model with each branch evolving according to a distinct dN/dS value. Horizontal lines indicate the simulated dN/dS value for each branch, as shown in the inset phylogeny. The lowest dN/dS category (dN/dS = 0.1) was applied to the internal branch (shown in gray). All code and data used to validate Pyvolve’s performance and generate this figure are available in S1 File.
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pone.0139047.g002: Pyvolve accurately evolves sequences under homogenous, site-wise rate heterogeneity, and branch-specific rate heterogeneity.A) Nucleotide alignments simulated under the JC69 [36] model along two-taxon trees with varying branch lengths, which represent the substitution rate. Points represent the mean observed substitution rate for the 50 alignment replicates simulated under the given value, and error bars represent standard deviations. The red line indicates the x = y line. B) Codon alignments simulated under an MG94-style [32] model with varying values for the dN/dS parameter. Points represent the mean dN/dS inferred from the 50 alignment replicates simulated under the given dN/dS value, and error bars represent standard deviations. The red line indicates the x = y line. C) Site-wise heterogeneity simulated with an MG94-style [32] model with varying dN/dS values across sites. Horizontal lines indicate the simulated dN/dS value for each dN/dS category. D) Branch-wise heterogeneity simulated with an MG94-style [32] model with each branch evolving according to a distinct dN/dS value. Horizontal lines indicate the simulated dN/dS value for each branch, as shown in the inset phylogeny. The lowest dN/dS category (dN/dS = 0.1) was applied to the internal branch (shown in gray). All code and data used to validate Pyvolve’s performance and generate this figure are available in S1 File.

Mentions: To evaluate Pyvolve under the most basic of conditions, site-homogeneity, we simulated both nucleotide and codon data sets. We evolved nucleotide sequences under the JC69 model [36] across several phylogenies with varying branch lengths (representing the substitution rate), and we evolved codon sequences under a MG94-style model [32] with varying values of dN/dS. All alignments were simulated along a two-taxon tree and contained 100,000 positions. We simulated 50 replicates for each branch length and/or dN/dS parameterization. As shown in Fig 2A and 2B, the observed number of changes agreed precisely with the specified parameters.


Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies.

Spielman SJ, Wilke CO - PLoS ONE (2015)

Pyvolve accurately evolves sequences under homogenous, site-wise rate heterogeneity, and branch-specific rate heterogeneity.A) Nucleotide alignments simulated under the JC69 [36] model along two-taxon trees with varying branch lengths, which represent the substitution rate. Points represent the mean observed substitution rate for the 50 alignment replicates simulated under the given value, and error bars represent standard deviations. The red line indicates the x = y line. B) Codon alignments simulated under an MG94-style [32] model with varying values for the dN/dS parameter. Points represent the mean dN/dS inferred from the 50 alignment replicates simulated under the given dN/dS value, and error bars represent standard deviations. The red line indicates the x = y line. C) Site-wise heterogeneity simulated with an MG94-style [32] model with varying dN/dS values across sites. Horizontal lines indicate the simulated dN/dS value for each dN/dS category. D) Branch-wise heterogeneity simulated with an MG94-style [32] model with each branch evolving according to a distinct dN/dS value. Horizontal lines indicate the simulated dN/dS value for each branch, as shown in the inset phylogeny. The lowest dN/dS category (dN/dS = 0.1) was applied to the internal branch (shown in gray). All code and data used to validate Pyvolve’s performance and generate this figure are available in S1 File.
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Related In: Results  -  Collection

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pone.0139047.g002: Pyvolve accurately evolves sequences under homogenous, site-wise rate heterogeneity, and branch-specific rate heterogeneity.A) Nucleotide alignments simulated under the JC69 [36] model along two-taxon trees with varying branch lengths, which represent the substitution rate. Points represent the mean observed substitution rate for the 50 alignment replicates simulated under the given value, and error bars represent standard deviations. The red line indicates the x = y line. B) Codon alignments simulated under an MG94-style [32] model with varying values for the dN/dS parameter. Points represent the mean dN/dS inferred from the 50 alignment replicates simulated under the given dN/dS value, and error bars represent standard deviations. The red line indicates the x = y line. C) Site-wise heterogeneity simulated with an MG94-style [32] model with varying dN/dS values across sites. Horizontal lines indicate the simulated dN/dS value for each dN/dS category. D) Branch-wise heterogeneity simulated with an MG94-style [32] model with each branch evolving according to a distinct dN/dS value. Horizontal lines indicate the simulated dN/dS value for each branch, as shown in the inset phylogeny. The lowest dN/dS category (dN/dS = 0.1) was applied to the internal branch (shown in gray). All code and data used to validate Pyvolve’s performance and generate this figure are available in S1 File.
Mentions: To evaluate Pyvolve under the most basic of conditions, site-homogeneity, we simulated both nucleotide and codon data sets. We evolved nucleotide sequences under the JC69 model [36] across several phylogenies with varying branch lengths (representing the substitution rate), and we evolved codon sequences under a MG94-style model [32] with varying values of dN/dS. All alignments were simulated along a two-taxon tree and contained 100,000 positions. We simulated 50 replicates for each branch length and/or dN/dS parameterization. As shown in Fig 2A and 2B, the observed number of changes agreed precisely with the specified parameters.

Bottom Line: All model parameters are fully customizable.Users can additionally specify custom evolutionary models, with custom rate matrices and/or states to evolve.This flexibility makes Pyvolve a convenient framework not only for simulating sequences under a wide variety of conditions, but also for developing and testing new evolutionary models.

View Article: PubMed Central - PubMed

Affiliation: Department of Integrative Biology, Center for Computational Biology and Bioinformatics, and Institute of Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712, United States of America.

ABSTRACT
We introduce Pyvolve, a flexible Python module for simulating genetic data along a phylogeny using continuous-time Markov models of sequence evolution. Easily incorporated into Python bioinformatics pipelines, Pyvolve can simulate sequences according to most standard models of nucleotide, amino-acid, and codon sequence evolution. All model parameters are fully customizable. Users can additionally specify custom evolutionary models, with custom rate matrices and/or states to evolve. This flexibility makes Pyvolve a convenient framework not only for simulating sequences under a wide variety of conditions, but also for developing and testing new evolutionary models. Pyvolve is an open-source project under a FreeBSD license, and it is available for download, along with a detailed user-manual and example scripts, from http://github.com/sjspielman/pyvolve.

No MeSH data available.