CASS: Protein sequence simulation with explicit genotype-phenotype mapping

Grahnen, Johan A.; Liberles, David A.

doi:10.4081/eb.2012.e9

Cited by 10 publications

(9 citation statements)

References 29 publications

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“…Indeed, some of these simulators ignore recombination, which can bias evolutionary inferences [e.g., 118-121]. As a consequence, there is a need for more realistic computer simulators that implement complex substitution models of evolution, not only at the nucleotide level, but also at the codon [e.g., 43, 69], protein [e.g., 122-124] and genome-wide levels [88, 90]. Indeed, recombination (as well as other processes of exchange of genetic material) may generate evolutionary networks [67, 125] that should be considered to properly describe the history of human populations [see 18, 126].…”

Section: The Future Of Spatially Explicit Computer Simulations In Hummentioning

confidence: 99%

Spatial and Temporal Simulation of Human Evolution. Methods, Frameworks and Applications

Benguigui

Arenas

2014

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Analyses of human evolution are fundamental to understand the current gradients of human diversity. In this concern, genetic samples collected from current populations together with archaeological data are the most important resources to study human evolution. However, they are often insufficient to properly evaluate a variety of evolutionary scenarios, leading to continuous debates and discussions. A commonly applied strategy consists of the use of computer simulations based on, as realistic as possible, evolutionary models, to evaluate alternative evolutionary scenarios through statistical correlations with the real data. Computer simulations can also be applied to estimate evolutionary parameters or to study the role of each parameter on the evolutionary process. Here we review the mainly used methods and evolutionary frameworks to perform realistic spatially explicit computer simulations of human evolution. Although we focus on human evolution, most of the methods and software we describe can also be used to study other species. We also describe the importance of considering spatially explicit models to better mimic human evolutionary scenarios based on a variety of phenomena such as range expansions, range shifts, range contractions, sex-biased dispersal, long-distance dispersal or admixtures of populations. We finally discuss future implementations to improve current spatially explicit simulations and their derived applications in human evolution.

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Section: The Future Of Spatially Explicit Computer Simulations In Hummentioning

confidence: 99%

Spatial and Temporal Simulation of Human Evolution. Methods, Frameworks and Applications

Benguigui

Arenas

2014

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“…For example, genomic process such as recombination, coalescent-based models, gene duplication, and migration, may be best simulated with softwares such as ALF [ 41 ], CoalEvol and SGWE [ 11 ], or EvolSimulator [ 42 ]. Simulators which consider the influence of structural and/or biophysical constraints in protein sequence evolution include CASS [ 43 ] or ProteinEvolver [ 44 ]. Similarly, the software REvolver [ 45 ] simulates protein sequences with structural domain constraints by recruiting profile hidden Markov models (pHMMs) to model site-specific substitution processes.…”

Section: Discussionmentioning

confidence: 99%

Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies

Spielman

Wilke

2015

PLoS ONE

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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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“…At a biological level, realistic models can include a description of a genotype–phenotype map wrapped in a population genetic context. For example, nonsynonymous SNPs can be described by their effects on protein stability and protein–protein interaction using expectations from physical chemistry coupled to population genetic descriptions of fixation probabilities (e.g., [Grahnen and Liberles, ; Liberles et al., ]). Another example is using systems biochemistry to generate explicit definitions of phenotype that may ultimately relate to both human and infectious disease [Savageau and Fasani, ], moving further from common assumptions of the independence of action of individual SNPs.…”

Section: The Science Of Genetic Simulationmentioning

confidence: 99%

Genetic Simulation Tools for Post‐Genome Wide Association Studies of Complex Diseases

Chen

Hutter

Mechanic

et al. 2014

Genetic Epidemiology

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Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled “Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases” at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to: (i) identify opportunities, challenges and resource needs for the development and application of genetic simulation models; (ii) improve the integration of tools for modeling and analysis of simulated data; and (iii) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation.

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CASS: Protein sequence simulation with explicit genotype-phenotype mapping

Cited by 10 publications

References 29 publications

Spatial and Temporal Simulation of Human Evolution. Methods, Frameworks and Applications

Spatial and Temporal Simulation of Human Evolution. Methods, Frameworks and Applications

Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies

Genetic Simulation Tools for Post‐Genome Wide Association Studies of Complex Diseases

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