The neighbor-joining method: a new method for reconstructing phylogenetic trees.
Naruya Saitou,Masatoshi Nei +1 more
TLDR
The neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods for reconstructing phylogenetic trees from evolutionary distance data.Abstract:
A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.read more
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Mathematics of Evolution and Phylogeny
TL;DR: This book considers evolution at different scales: sequences, genes, gene families, organelles, genomes and species, and explains the basis and covers the recent results in this highly topical area.
References
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Book
Molecular Evolutionary Genetics
TL;DR: Recent developments of statistical methods in molecular phylogenetics are reviewed and it is shown that the mathematical foundations of these methods are not well established, but computer simulations and empirical data indicate that currently used methods produce reasonably good phylogenetic trees when a sufficiently large number of nucleotides or amino acids are used.
Book
Principles of numerical taxonomy
Robert R. Sokal,P.H.A. Sneath +1 more
TL;DR: The authors continued the story of psychology with added research and enhanced content from the most dynamic areas of the field, such as cognition, gender and diversity studies, neuroscience and more, while at the same time using the most effective teaching approaches and learning tools.