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《Acta Zoologica Sinica》 2003-03
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Application of Bayesian method for inferring phylogenetic relationships of ticks within the genus Ixodes

Guang XU FANG Qing-Quan ** James. E. KEIRANS Lance. A. DURDEN (Department of Biology and the U.S. National Tick Collection, Georgia Southern University, Statesboro, GA, 30460-8042)  
Three major methods, Neighbor-Joining (NJ), Maximum Parsimony (MP), and Maximum Likelihood (ML), are widely used for phylogenetic reconstruction. ML method has many advantages over NJ and MP but less practical. It is almost impossible to apply ML method to a sequence dataset that contains more than 25 operational taxonomic units (OUT) due to the enormous computational time required and the shortage of computer memories. The Bayesian method is a recently developed method that is similar to ML method in principle, but the computation time is reduced dramatically. Bayesian method inferences tree based on the notion of posterior probabilities: probabilities that are estimated, based on some model (prior expectations) using Markov chain Monte Carlo method. Instead of seeking the tree that maximizes the likelihood of observing the data, it seeks those trees with the greatest likelihood of given the data. Bayesian method produces a set of trees of roughly equal likelihood. To introduce and promote Bayesian method in Chin, we applied this method to the phylogenetic analysis of 19 tick species in the genus Ixodes. The inferred phylogeny is generally congruent with that based on morphology. However, in contrast to the existing hypothesis, 16S rDNA data indicated that I. ricinus species complex is not a monophyletic group. The results from Bayesian method were also similar to that of NJ and MP methods but with a few exceptions. There is no need to conduct bootstrap analysis to evaluate branch support because divergences on the tree are represented by the posterior probabilities.
【Fund】: 美国National Institutes of Health (NIH)基金 (AI40 72 9)的资助~~
【CateGory Index】: Q111
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