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A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Algorithms, data structures, and numerics for likelihood-based phylogenetic inference of huge trees.

Fernando Izquierdo-Carrasco¹, Stephen A Smith, Alexandros Stamatakis

¹The Exelixis Lab, Scientific Computing Group, Heidelberg Institute for Theoretical Studies, Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Germany. Fernando.Izquierdo@h-its.org

BMC Bioinformatics

|December 15, 2011

Summary

New methods improve large-scale phylogenetic tree reconstruction by addressing computational challenges in maximum likelihood analysis. These techniques enhance speed and reduce memory needs for analyzing vast molecular sequence data.

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Area of Science:

Computational Biology
Phylogenetics
Bioinformatics

Background:

Rapid growth in molecular sequence data strains phylogenetic analysis capabilities.
Large-scale maximum likelihood (ML) phylogenetic analyses face computational hurdles: numerical stability, search algorithm scalability, and memory demands.

Purpose of the Study:

To develop and implement methods for overcoming computational challenges in large-scale ML phylogenetic reconstruction.
To enhance the efficiency and scalability of tree inference programs.

Main Methods:

Introduced a novel search strategy to accelerate tree inference.
Adapted the Subtree Equality Vector technique for phylogenomic datasets with missing data.
Discussed numerical stability issues with the Γ model of rate heterogeneity.

Main Results:

A new search strategy reduced tree inference time by over 50% with comparable tree accuracy.
Subtree Equality Vector adaptation decreased execution times and memory by up to 50% for datasets with missing data.
Proposed alternative rate heterogeneity models to address numerical stability on large trees.

Conclusions:

Addressed key computational issues in large-scale ML tree reconstruction.
Provided open-source implementations of proposed solutions.
Solutions are applicable to various likelihood-based tree inference programs.