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Inference of Phylogenetic Trees

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Tutorials in Mathematical Biosciences IV

Part of the book series: Lecture Notes in Mathematics ((LNMBIOS,volume 1922))

Study of the evolutionary relationships among organisms has been of interest to scientists for over 100 years. The earliest attempts at inferring evolutionary relatedness relied solely on observable species characteristics. Modern molecular techniques, however, have made available an abundance of DNA sequence data, which can be used to study these relationships. Today, it is common to consider the information contained in both types of data in order to obtain robust estimates of evolutionary histories.

Estimation of the phylogenetic relationships among a collection of organisms given genetic data for these organisms can be divided into two distinct problems. The first is to define the particular criterion by which we compare the fit of a particular phylogenetic hypothesis to the observed data. The second is to search the space of possible phylogenies for the particular tree or trees that provide the best fit to the data. In this chapter, we give an overview of these two problems, with particular emphasis on the maximum parsimony and maximum likelihood criteria for comparing trees. Techniques for searching the space of trees for optimal phylogenies under these criteria are also discussed. Throughout the chapter, we use two data sets to illustrate the main ideas. We begin by defining some of the commonly used terminology, and by providing a careful description of the data used in phylogenetic analysis.

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  1. Departments of Statistics and Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, 43210, USA

    L. S. Kubatko

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  1. Mathematical Biosciences Institute, Ohio State University, 231 West 18th Avenue, Columbus, OH, 43210-1292, USA

    Avner Friedman

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Kubatko, L.S. (2008). Inference of Phylogenetic Trees. In: Friedman, A. (eds) Tutorials in Mathematical Biosciences IV. Lecture Notes in Mathematics, vol 1922. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74331-6_1

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