Algorithms of Evolution

Evolutionary mechanisms have inspired mathematical modeling, and computational methods underpin investigations into bioinformatics and biological evolution.

maximum likelihood method

Continuously varying traits such as body size or gene expression level evolve during the history of species or gene lineages. To test hypotheses about the evolution of such traits, the maximum likelihood (ML) method is often used. [r]

ML analyses employ statistical methods that incorporate differential rates of substitution among lineages. ML methods reduce errors attributable to long branch attraction (LBA), which can be a problem with phylogenetic analyses, particularly those analyses that employ the non-parametric statistical method termed maximum parsimony. In the case of rapid DNA evolution in which the same nucleotide convergently evolves at the same locus in different lineages, parsimony erroneously interprets this similarity as a synapomorphy, that is, as having evolved only once in the common ancestor of the two lineages. (Problems with LBA can also be reduced by breaking up long branches by adding taxa that are related to those with the long branches.)

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