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The exon shuffling theory posits that intronic recombination creates new domain combinations, facilitating the evolution of novel protein function. This theory predicts that introns will be preferentially situated near domain boundaries. Many studies have sought evidence for exon shuffling by testing the correspondence between introns and domain boundaries against chance intron positioning. Here, we present an empirical investigation of how the choice of null model influences significance. Although genome-wide studies have used a uniform null model, exclusively, more realistic null models have been proposed for single gene studies. We extended these models for genome-wide analyses and applied them to 21 metazoan and fungal genomes. Our results show that compared with the other two models, the uniform model does not recapitulate genuine exon lengths, dramatically underestimates the probability of chance agreement, and overestimates the significance of intron-domain correspondence by as much as 100 orders of magnitude. Model choice had much greater impact on the assessment of exon shuffling in fungal genomes than in metazoa, leading to different evolutionary conclusions in seven of the 16 fungal genomes tested. Genome-wide studies that use this overly permissive null model may exaggerate the importance of exon shuffling as a general mechanism of multidomain evolution. 

Abstract MotivationSimulation is an essential technique for generating biomolecular data with a ‘known’ history for use in validating phylogenetic inference and other evolutionary methods. On longer time scales, simulation supports investigations of equilibrium behavior and provides a formal framework for testing competing evolutionary hypotheses. Twenty years of molecular evolution research have produced a rich repertoire of simulation methods. However, current models do not capture the stringent constraints acting on the domain insertions, duplications, and deletions by which multidomain architectures evolve. Although these processes have the potential to generate any combination of domains, only a tiny fraction of possible domain combinations are observed in nature. Modeling these stringent constraints on domain order and co-occurrence is a fundamental challenge in domain architecture simulation that does not arise with sequence and gene family simulation. ResultsHere, we introduce a stochastic model of domain architecture evolution to simulate evolutionary trajectories that reflect the constraints on domain order and co-occurrence observed in nature. This framework is implemented in a novel domain architecture simulator, DomArchov, using the Metropolis–Hastings algorithm with data-driven transition probabilities. The use of a data-driven event module enables quick and easy redeployment of the simulator for use in different taxonomic and protein function contexts. Using empirical evaluation with metazoan datasets, we demonstrate that domain architectures simulated by DomArchov recapitulate properties of genuine domain architectures that reflect the constraints on domain order and adjacency seen in nature. This work expands the realm of evolutionary processes that are amenable to simulation. Availability and implementationDomArchov is written in Python 3 and is available at http://www.cs.cmu.edu/~durand/DomArchov. The data underlying this article are available via the same link. Supplementary informationSupplementary data are available at Bioinformatics online.