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A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Using RAxML to Infer Phylogenies.

Alexandros Stamatakis^1,2

¹Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.

Current Protocols in Bioinformatics

|September 4, 2015

Summary

This summary is machine-generated.

This guide explains how to perform phylogenetic tree inference using Randomized Accelerated Maximum Likelihood (RAxML) and assess tree reliability with bootstrap support values and other statistical measures for biological data analysis.

Keywords:

bootstrap support consensus tree methods maximum likelihood phylogenetics

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Area of Science:

Evolutionary biology
Bioinformatics
Computational biology

Background:

Phylogenetic tree inference is crucial for understanding evolutionary relationships.
Maximum Likelihood (ML) is a standard criterion for constructing these trees.
Accurate phylogenetic analysis requires robust methods for tree construction and support assessment.

Purpose of the Study:

To provide a comprehensive guide for performing phylogenetic inferences using RAxML.
To detail methods for assessing the statistical support of inferred phylogenetic trees.
To outline post-analysis techniques for evaluating and interpreting tree reliability.

Main Methods:

Utilizing Randomized Accelerated Maximum Likelihood (RAxML) for ML phylogenetic inference.
Employing bootstrap procedures and other statistical measures to infer support values.
Conducting post-analyses including significance tests and consensus tree methods.

Main Results:

Demonstration of RAxML for efficient and accurate phylogenetic tree construction.
Methods for quantifying the reliability of inferred evolutionary relationships.
Strategies for interpreting and validating phylogenetic trees, especially those with low support.

Conclusions:

RAxML is a powerful tool for ML-based phylogenetic analysis.
Robust statistical support assessment is essential for reliable evolutionary inferences.
Comprehensive post-analysis is vital for interpreting complex phylogenetic datasets.