Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Glossary
- Notation
- PART I BASIC TECHNIQUES
- 1 Brief Introduction to Phylogenetic Estimation
- 2 Trees
- 3 Constructing Trees from True Subtrees
- 4 Constructing Trees from Qualitative Characters
- 5 Distance-based Tree Estimation Methods
- 6 Consensus and Agreement Trees
- 7 Supertrees
- PART II MOLECULAR PHYLOGENETICS
- Appendix A Primer on Biological Data and Evolution
- Appendix B Algorithm Design and Analysis
- Appendix C Guidelines forWriting Papers About Computational Methods
- Appendix D Projects
- References
- Index
7 - Supertrees
from PART I - BASIC TECHNIQUES
Published online by Cambridge University Press: 26 October 2017
- Frontmatter
- Dedication
- Contents
- Preface
- Glossary
- Notation
- PART I BASIC TECHNIQUES
- 1 Brief Introduction to Phylogenetic Estimation
- 2 Trees
- 3 Constructing Trees from True Subtrees
- 4 Constructing Trees from Qualitative Characters
- 5 Distance-based Tree Estimation Methods
- 6 Consensus and Agreement Trees
- 7 Supertrees
- PART II MOLECULAR PHYLOGENETICS
- Appendix A Primer on Biological Data and Evolution
- Appendix B Algorithm Design and Analysis
- Appendix C Guidelines forWriting Papers About Computational Methods
- Appendix D Projects
- References
- Index
Summary
Introduction
The basic objective of most supertree studies is the assembly of a large species tree from a set of species trees that have been estimated on potentially smaller sets of taxa. Indeed, it is generally believed that construction of the Tree of Life, which will encompass millions of species, will require supertree methods, because no software will be able to deal with the computational challenges involved in such a difficult task. More recently, however, new uses of supertree methods have been discovered, especially in the context of divide-andconquer strategies. This chapter examines both applications of supertree methods.
Traditionally, supertree methods were used to combine trees computed by different researchers that had already been estimated for different taxon sets. In this case, the person constructing the supertree has no control over the inputs, neither how the different subset trees were constructed nor how the taxon sets of the different subset trees overlap. Furthermore, the person constructing the supertree may not have easy access to the data (e.g., sequence alignments) on which the subset trees were constructed.
A modern and more interesting use of supertree methods is in the context of a divideand- conquer strategy to construct a very large tree, or to enable a statistically powerful but computationally intensive method to be applied to a larger dataset. In such a strategy, a large set of taxa is divided into overlapping subsets, trees are estimated (using the desired method) on the subsets, and the estimated subset trees are combined into a tree on the full set of taxa using a supertree method.
Divide-and-conquer techniques for constructing large trees have many desirable features: (1) the subsets can be made small enough that expensive methods can be used to construct trees on them; (2) different methods can be used on each subset, thus making it possible to better address heterogeneity within the full dataset; and (3) the subsets can be created so as to have desirable overlap patterns. The first two of these features tend to increase the accuracy of the estimated subset trees, while the third feature can make it easier to construct an accurate supertree from the subset trees (Wilkinson and Cotton, 2006). We will return to the topic of divide-and-conquer strategies and how to use them to construct large trees under a variety of scenarios in Chapter 11.
- Type
- Chapter
- Information
- Computational PhylogeneticsAn Introduction to Designing Methods for Phylogeny Estimation, pp. 121 - 142Publisher: Cambridge University PressPrint publication year: 2017