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Covarion
Additional recommended knowledgeDeveloping a computational algorithm suitable for identifying sites with high evolutionary rates from a static dataset is a challenge due to the constraints of autocorrelation. The original statement of the method used a rough stochastic model of the evolutionary process designed to identify transiently high-variability codon sites. Abandoning the requirement that rates be autocorrelated on a given DNA or RNA molecule allows extension of substitution matrix methods to the covarion model. The matrix at right represents a covarion-based modification to the three-state Kimura substitution model, where the vertical axis represents the original state and the horizontal axis the destination state. The two rates, 0 and 1, define a pair of mutation states; transitions can occur between state 0 and state 1 at any time, but nucleotides can only mutate in state 1. That is, the rate of mutation in state 0 is 0. Here α and β are the standard Kimura parameters for transition and transversion mutations, κδ is the rate of transition between a site being invariant (state 0) and variable (state 1), and δ is the rate of transition between a site being variable (state 1) and invariant (state 0). Because nucleotide sequences do not themselves reflect the difference between a 0 or 1 state, an observation of a given nucleotide is treated as ambiguous; that is, if a given site contains a C nucleotide, it is ambiguous between C0 and C1 states. References
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This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Covarion". A list of authors is available in Wikipedia. |