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Analysis on the reconstruction accuracy of the Fitch method for inferring ancestral states by Jialiang Yang; Jun Li; Liuhuan Dong; Stefan Grünewald is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Analysis on the reconstruction accuracy of the Fitch method for inferring ancestral states about?

## Background As one of the most widely used parsimony methods for ancestral reconstruction, the Fitch method minimizes the total number of hypothetical substitutions along all branches of a tree to explain the evolution of a character. Due to the extensive usage of this method, it has become a scientific endeavor in recent years to study the reconstruction accuracies of the Fitch method. However, most studies are restricted to 2-state evolutionary models and a study for higher-state models is needed since DNA sequences take the format of 4-state series and protein sequences even have 20 states. ## Results In this paper, the ambiguous and unambiguous reconstruction accuracy of the Fitch method are studied for N-state evolutionary models. Given an arbitrary phylogenetic tree, a recurrence system is first presented to calculate iteratively the two accuracies. As complete binary tree and comb-shaped tree are the two extremal evolutionary tree topologies according to balance, we focus on the reconstruction accuracies on these two topologies and analyze their asymptotic properties. Then, 1000 Yule trees with 1024 leaves are generated and analyzed to simulate real evolutionary scenarios.

Who reads Analysis on the reconstruction accuracy of the Fitch method for inferring ancestral states?

It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.

Author
Jialiang Yang; Jun Li; Liuhuan Dong; Stefan Grünewald
Publisher
BioMed Central; Springer (Biomed Central Ltd.); [London]: BioMed Central, [2000]-; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1471-2105)
Published
2011
Language
EN
Field
Biochemistry, Genetics and Molecular Biology (Life Sciences)

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