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Bioinformatics

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Attention for Chapter 14: Inferring Trees.
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Chapter title
Inferring Trees.
Chapter number 14
Book title
Bioinformatics
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6622-6_14
Pubmed ID
Book ISBNs
978-1-4939-6620-2, 978-1-4939-6622-6
Authors

Simon Whelan, David A. Morrison

Editors

Jonathan M. Keith

Abstract

Molecular evolution can reveal the relationship between sets of homologous sequences and the patterns of change that occur during their evolution. An important aspect of these studies is the inference of a phylogenetic tree, which explicitly describes evolutionary relationships between homologous sequences. This chapter provides an introduction to evolutionary trees and how to infer them from sequence data using some commonly used inferential methodology. It focuses on statistical methods for inferring trees and how to assess the confidence one should have in any resulting tree, with a particular emphasis on the underlying assumptions of the methods and how they might affect the tree estimate. There is also some discussion of the underlying algorithms used to perform tree search and recommendations regarding the performance of different algorithms. Finally, there are a few practical guidelines, including how to combine multiple software packages to improve inference, and a comparison between Bayesian and Maximum likelihood phylogenetics.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 11 24%
Researcher 8 18%
Student > Ph. D. Student 7 16%
Student > Master 6 13%
Student > Doctoral Student 3 7%
Other 6 13%
Unknown 4 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 38%
Biochemistry, Genetics and Molecular Biology 16 36%
Immunology and Microbiology 2 4%
Medicine and Dentistry 2 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 1 2%
Unknown 6 13%