Comparison of methods for estimating the nucleotide substitution matrix View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-12-01

AUTHORS

Maribeth Oscamou, Daniel McDonald, Von Bing Yap, Gavin A Huttley, Manuel E Lladser, Rob Knight

ABSTRACT

BACKGROUND: The nucleotide substitution rate matrix is a key parameter of molecular evolution. Several methods for inferring this parameter have been proposed, with different mathematical bases. These methods include counting sequence differences and taking the log of the resulting probability matrices, methods based on Markov triples, and maximum likelihood methods that infer the substitution probabilities that lead to the most likely model of evolution. However, the speed and accuracy of these methods has not been compared. RESULTS: Different methods differ in performance by orders of magnitude (ranging from 1 ms to 10 s per matrix), but differences in accuracy of rate matrix reconstruction appear to be relatively small. Encouragingly, relatively simple and fast methods can provide results at least as accurate as far more complex and computationally intensive methods, especially when the sequences to be compared are relatively short. CONCLUSION: Based on the conditions tested, we recommend the use of method of Gojobori et al. (1982) for long sequences (> 600 nucleotides), and the method of Goldman et al. (1996) for shorter sequences (< 600 nucleotides). The method of Barry and Hartigan (1987) can provide somewhat more accuracy, measured as the Euclidean distance between the true and inferred matrices, on long sequences (> 2000 nucleotides) at the expense of substantially longer computation time. The availability of methods that are both fast and accurate will allow us to gain a global picture of change in the nucleotide substitution rate matrix on a genomewide scale across the tree of life. More... »

PAGES

511-511

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-9-511

DOI

http://dx.doi.org/10.1186/1471-2105-9-511

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1032019048

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/19046431


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