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Parsimony, Phylogeny, and Genomics$
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Victor A. Albert

Print publication date: 2006

Print ISBN-13: 9780199297306

Published to Oxford Scholarship Online: September 2007

DOI: 10.1093/acprof:oso/9780199297306.001.0001

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Using phylogeny to understand genomic evolution

Using phylogeny to understand genomic evolution

Chapter:
(p.180) (p.181) Chapter 10 Using phylogeny to understand genomic evolution
Source:
Parsimony, Phylogeny, and Genomics
Author(s):

David A. Liberles

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199297306.003.0010

As genome sequencing projects have propagated, comparative genomics has emerged as a method of choice for understanding protein function. There are simple approaches for comparing sequences, like relative entropy or binary transformations of gene content comparisons. However, phylogenetic methods that explicitly consider evolutionary history are not only more powerful, but enable additional types of analysis drawing on knowledge in parallel fields, such as ecology, anthropology, and geology. This chapter focuses both on methodological issues and on their application to real genomic-scale problems. Parsimony and maximum likelihood are two phylogenetic approaches that are used and often compared side-by-side. While the choice between them has been contentious at times, they frequently give similar results and where they don't, they can complement each other. Maximum likelihood works well when a good model is available. Parsimony works well when a good model does not or can not exist, as for very complex processes, and also along very short branches where multiple events per position (as in a sequence) are extremely infrequent. Both methods can be used to estimate ancestral states in a phylogenetic tree. Parsimony based ancestral character reconstruction is fast and can be performed easily in large scale genomic applications.

Keywords:   genome sequencing, protein function, sequence comparison, ancestral state reconstruction, parsimony, likelihood, evolutionary model, complex processes

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