Cite as: Cold Spring Harb. Protoc.; 2008; doi:10.1101/pdb.top34

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topic_introductionTopic Introduction

The Maximum Likelihood Approach for Phylogenetic Prediction

David W. Mount

Adapted from "Phylogenetic Prediction," Chapter 7, in Bioinformatics: Sequence and Genome Analysis, 2nd edition, by David W. Mount. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, USA, 2004.


INTRODUCTION

Maximum likelihood (ML) methods are especially useful for phylogenetic prediction when there is considerable variation among the sequences in the multiple sequence alignment (msa) to be analyzed. ML methods start with a simple model, in this case a model of rates of evolutionary change in nucleic acid or protein sequences and tree models that represent a pattern of evolutionary change, and then adjust the model until there is a best fit to the observed data. For phylogenetic analysis, the observed data are the observed sequence variations found within the columns of an msa. The ML method is similar to the maximum parsimony method in that the analysis is performed on each column of an msa.


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D. W. Mount
Using Progressive Methods for Global Multiple Sequence Alignment
CSH Protocols, July 1, 2009; 2009(7): pdb.top43 - pdb.top43.
[Abstract] [Full Text]