Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies

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...

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Vydané v:PloS one Ročník 10; číslo 9; s. e0139047
Hlavní autori: Spielman, Stephanie J., Wilke, Claus O.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 23.09.2015
Public Library of Science (PLoS)
Predmet:
Amino Acid Sequence - genetics
Amino acids
Animals
Base Sequence - genetics
Bioinformatics
Biological evolution
Computer Simulation
Deoxyribonucleic acid
DNA
Downloading
Evolution
Genomes
Genomics - methods
Humans
Hypotheses
Hypothesis testing
Markov Chains
Models, Genetic
Molecular biology
Mutation
Phylogenetics
Phylogeny
Proteins
Pythons
United States
United States > US
ISSN:1932-6203, 1932-6203
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Shrnutí: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.
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Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: SJS COW. Performed the experiments: SJS. Analyzed the data: SJS. Contributed reagents/materials/analysis tools: SJS. Wrote the paper: SJS COW.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0139047