By Jeff J. Doyle, Brandon S. Gaut (auth.), Jeff J. Doyle, Brandon S. Gaut (eds.)
ISBN-10: 9401058334
ISBN-13: 9789401058339
ISBN-10: 9401142211
ISBN-13: 9789401142212
Plant molecular biology has produced an ever-increasing flood of information approximately genes and genomes. Evolutionary biology and systematics offers the context for synthesizing this knowledge. This publication brings jointly contributions from evolutionary biologists, systematists, developmental geneticists, biochemists, and others engaged on diversified elements of plant biology whose paintings touches to various levels on plant molecular evolution. The ebook is prepared in 3 elements, the 1st of which introduces vast subject matters in evolutionary biology and summarizes advances in plant molecular phylogenetics, with emphasis on version plant platforms. the second one phase offers a chain of case reports of gene relatives evolution, whereas the 3rd supplies overviews of the evolution of significant plant techniques akin to ailment resistance, nodulation, hybridization, transposable components and genome evolution, and polyploidy.
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Goldman N, Yang ZH: Codon-based model of nucleotide substitution for protein coding DNA sequences. Mol Bioi Evolll: 725-736 (1994). Goloboff PA: Estimating character weights during tree search. Cladislics 9: 83-91 (1993). Goloboff PA: Tree searches under Sankoff parsimony. Cladistics 14: 229-237 (1998). Gu X, Li W-H: Bias corrected paralinear and LogDet distances and tests of molecular clocks and phylogenies under nonstationary nucleotide frequencies. Mol Bioi Evol 13: 1375-1383 (1996). Guttman DS, Dykhuizcn DE: Dctecting sweeps in naturally occurring Escherichia coli.
Estimates in this algorithmic fashion that makes likelihood methods attractive in complex situations where explicit formulas for estimates can not be obtained. Modem computing technology, in principle, allows maximum likelihood estimation whenever it is possible to evaluate the likelihood function given numerical values for the unknown parameters. Coin tossing example: likelihood ratio tests A second type of question we might address involves evaluating a priori hypotheses about unknown parameters.
Given values of 8t for each branch in the phylogeny, we can then use Equation (2) to compute the probability of the observed nucleotides at site k in our three sequences. The likelihood function for the complete data set is obtained by assuming that all sites in the sequence evolve independently of one another. Under this assumption, the overall likelihood is simply the product of the individual site likelihoods: 1 sites L(8, S) = 2 ~ ~~-~ 2 3 [1h. g. 3 in branch 1, so do all of the other sites), the maximum likelihood estimates of the 8t are found by optimizing Equation (2) over all possible values of the collection of 8 t.
Plant Molecular Evolution by Jeff J. Doyle, Brandon S. Gaut (auth.), Jeff J. Doyle, Brandon S. Gaut (eds.)
by Ronald
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