MBE Advance Access originally published online on October 12, 2006
Molecular Biology and Evolution 2007 24(1):159-170; doi:10.1093/molbev/msl144
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Research Articles |
Evolutionary Model Selection with a Genetic Algorithm: A Case Study Using Stem RNA
* Department of Pathology, University of California, San Diego
Bioinformatics Research Center, North Carolina State University
School of Medicine, University of Wales, Swansea, United Kingdom
E-mail: spond{at}ucsd.edu.
Accepted for publication October 2, 2006.
The choice of a probabilistic model to describe sequence evolution can and should be justified. Underfitting the data through the use of overly simplistic models may miss out on interesting phenomena and lead to incorrect inferences. Overfitting the data with models that are too complex may ascribe biological meaning to statistical artifacts and result in falsely significant findings.
We describe a likelihood-based approach for evolutionary model selection. The procedure employs a genetic algorithm (GA) to quickly explore a combinatorially large set of all possible time-reversible Markov models with a fixed number of substitution rates. When applied to stem RNA data subject to well-understood evolutionary forces, the models found by the GA 1) capture the expected overall rate patterns a priori; 2) fit the data better than the best available models based on a priori assumptions, suggesting subtle substitution patterns not previously recognized; 3) cannot be rejected in favor of the general reversible model, implying that the evolution of stem RNA sequences can be explained well with only a few substitution rate parameters; and 4) perform well on simulated data, both in terms of goodness of fit and the ability to estimate evolutionary rates. We also investigate the utility of several distance measures for comparing and contrasting inferred evolutionary models.
Using widely available small computer clusters, our approach allows, for the first time, to evaluate the performance of existing RNA evolutionary models by comparing them with a large pool of candidate models and to validate common modeling assumptions. In addition, the new method provides the foundation for rigorous selection and comparison of substitution models for other types of sequence data.
Key Words: RNA sequence evolution • secondary structure • model selection • genetic algorithms • multimodel inference
Robin Bush, Associate Editor
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