Inferring Phylogenies

Phylogenies, or evolutionary trees, are the basic structures necessary to think about and analyze differences between species. Statistical, computational, and algorithmic work in this field has been ongoing for four decades now, and there have been great advances in understanding. Yet no book has summarized this work. Inferring Phylogenies does just that in a single, compact volume. Phylogenies are inferred with various kinds of data. This book concentrates on some of the central ones: discretely coded characters, molecular sequences, gene frequencies, and quantitative traits. Also covered are restriction sites, RAPDs, and microsatellites.

Seminal papers by A. W. F. Edwards, published together for the first time with commentaries from leading experts to contextualise his contribution.

Generally, biologists and mathematicians who study the shape and form of organisms have largely been working in isolation from those who work on evolutionary relationships through the analysis of common characteristics. Increasingly however, dialogue between the two communities is beginning to develop - but other than a handful of journal papers, t

It has been argued that lexicostatistical methods cannot be fruitfully applied to the Tibeto-Burman (or the larger Sino-Tibetan) language family. This dissertation first develops a statistical method for determining linguistic cognates (or sound correspondences) between arbitrarily many languages. This method is then applied to a subset of the Sino-Tibetan languages to infer the correspondences and further develops a method for lexicostatistical analysis for subgrouping those languages. The output of these methods agrees very well with that determined by the traditional comparative method. Furthermore, this method yields both greater resolution of the subgroupings, and provides statistical inference of the confidence in the various groupings.

Table of contents

Parsimony analysis (cladistics) has long been one of the most widely used methods of phylogenetic inference in the fields of systematic and evolutionary biology. Moreover it has mathematical attributes that lend itself for use with complex, genomic-scale data sets. This book demonstrates the potential that this powerful hierarchical data summarization method also has for both structural and functional comparative genomic research.

A broad, hands on guide with detailed explanations of current methodology, relevant exercises and popular software tools.