Advances in Computer Simulation of Genome Evolution: Toward More Realistic Evolutionary Genomics Analysis by Approximate Bayesian Computation

Abstract

NGS technologies present a fast and cheap generation of genomic data. Nevertheless, ancestral genome inference is not so straightforward due to complex evolutionary processes acting on this material such as inversions, translocations, and other genome rearrangements that, in addition to their implicit complexity, can co-occur and confound ancestral inferences. Recently, models of genome evolution that accommodate such complex genomic events are emerging. This letter explores these novel evolutionary models and proposes their incorporation into robust statistical approaches based on computer simulations, such as approximate Bayesian computation, that may produce a more realistic evolutionary analysis of genomic data. Advantages and pitfalls in using these analytical methods are discussed. Potential applications of these ancestral genomic inferences are also pointed out.

Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies

“…Indeed, the Pyvolve engine has already successfully been applied to investigate the relationship between mutation-selection and dN / dS modeling frameworks and to identify estimation biases in certain dN / dS models [ 18 ]. Moreover, we believe that Pyvolve provides a convenient tool for easy incorporation of complex simulations, for instance those used in approximate Bayesian computation (ABC) or MCMC methods [ 40 ], into Python pipelines.…”

Trends in substitution models of molecular evolution

“…In this concern, in addition to the development of new empirical models, codon models may follow two interesting trends. First, the consideration of heterogeneity along the sequence and over time, where different sites/regions and time periods could evolve under different models (Arenas, 2015a ; Zoller et al, 2015 ). Note that these partition schemes can be very realistic, for example by considering different models for coding and non-coding regions.…”

“…Therefore, although current research on amino acid substitution models is providing more sophisticated models, these models are usually not applied by evolutionary biologists because they are not implemented in evolutionary frameworks and, consequently, these models are often forgotten. In order to consider complex substitution models in model selection and in evolutionary analysis, an alternative strategy can be the approximate Bayesian computation (ABC) approach (Beaumont, 2010 ; Csilléry et al, 2010 ; Sunnaker et al, 2013 ; Lopes et al, 2014 ; Arenas, 2015a ). Basically, simulated data under different complex models are contrasted with real data through multiple regression adjustments to identify the model that best fits the real data, and to estimate the parameter values of the model corresponding to the studied dataset (see Lopes et al, 2014 for an example of ABC using complex codon models).…”

Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies

“…Indeed, the Pyvolve engine has already 109 successfully been applied to investigate the relationship between mutation-selection and 110 dN/dS modeling frameworks and to identify estimation biases in certain dN/dS 111 models [18]. Moreover, we believe that Pyvolve provides a convenient tool for easy 112 incorporation of complex simulations, for instance those used in approximate Bayesian 113 computation (ABC) or MCMC methods [40], into Python pipelines.…”