Review Article | Published:

Molecular phylogenetics: principles and practice

Nature Reviews Genetics volume 13, pages 303314 (2012) | Download Citation

Abstract

Phylogenies are important for addressing various biological questions such as relationships among species or genes, the origin and spread of viral infection and the demographic changes and migration patterns of species. The advancement of sequencing technologies has taken phylogenetic analysis to a new height. Phylogenies have permeated nearly every branch of biology, and the plethora of phylogenetic methods and software packages that are now available may seem daunting to an experimental biologist. Here, we review the major methods of phylogenetic analysis, including parsimony, distance, likelihood and Bayesian methods. We discuss their strengths and weaknesses and provide guidance for their use.

Key points

  • The rapid accumulation of genome sequence data has made phylogenetics an indispensable tool to various branches of biology. However, it has also posed considerable statistical and computational challenges to data analysis.

  • Distance, parsimony, likelihood and Bayesian methods of phylogenetic analysis have different strengths and weaknesses. Although distance methods are good for large data sets of highly similar sequences, likelihood and Bayesian methods often have more power and are more robust, especially for inferring deep phylogenies.

  • Assessing phylogenetic uncertainty remains a difficult statistical problem.

  • Data partitioning may have an important influence on the phylogenetic analysis of genome-scale data sets.

  • Systematic biases, such as long-branch attraction, may be more important than random sampling errors in the analysis of genomic-scale data sets.

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Acknowledgements

We thank the three referees for their constructive comments and M. Hasegawa and B. Zhong for providing the seed-plant phylogenies of Fig. 3. Z.Y. is supported by a UK Biotechnology and Biological Sciences Research Council grant and a Royal Society Wolfson Research Merit Award. B.R. is supported by a US National Institutes of Health grant.

Author information

Affiliations

  1. Center for Computational and Evolutionary Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.

    • Ziheng Yang
    •  & Bruce Rannala
  2. Department of Biology, University College London, Darwin Building, Gower Street, London WC1E 6BT, UK.

    • Ziheng Yang
  3. Genome Center and Department of Evolution and Ecology, University of California, Davis, California 95616, USA.

    • Bruce Rannala

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Ziheng Yang.

Glossary

Systematics

The inference of phylogenetic relationships among species and the use of such information to classify species.

Taxonomy

The description, classification and naming of species.

Coalescent

The process of joining ancestral lineages when the genealogical relationships of a random sample of sequences from a modern population are traced back.

Gene trees

The phylogenetic or genealogical tree of sequences at a gene locus or genomic region.

Statistical phylogeography

The statistical analysis of population data from closely related species to infer population parameters and processes such as population sizes, demography, migration patterns and rates.

Species tree

A phylogenetic tree for a set of species that underlies the gene trees at individual loci.

Systematic errors

Errors that are due to an incorrect model assumption. They are exacerbated when the data size increases.

Random sampling errors

Errors or uncertainties in parameter estimates owing to limited data.

Cluster algorithm

An algorithm of assigning a set of individuals to groups (or clusters) so that objects of the same cluster are more similar to each other than those from different clusters. Hierarchical cluster analysis can be agglomerative (starting with single elements and successively joining them into clusters) or divisive (starting with all objects and successively dividing them into partitions).

Markov chain

A stochastic sequence (or chain) of states with the property that, given the current state, the probabilities for the next state do not depend on the past states.

Transitions

Substitutions between the two pyrimidines (T↔C) or between the two purines (A↔G).

Transversions

Substitutions between a pyrimidine and a purine (T or C↔A or G).

Unrooted trees

Phylogenetic trees for which the location of the root is unspecified.

Long-branch attraction

The phenomenon of inferring an incorrect tree with long branches grouped together by parsimony or by model-based methods under simplistic models.

Likelihood ratio test

A general hypothesis-testing method that uses the likelihood to compare two nested hypotheses, often using the χ2 distribution to assess significance.

Molecular clock

The hypothesis or observation that the evolutionary rate is constant over time or across lineages.

Prior distribution

The distribution assigned to parameters before the analysis of the data.

Posterior distribution

The distribution of the parameters (or models) conditional on the data. It combines the information in the prior and in the data (likelihood).

Markov chain Monte Carlo algorithms

(MCMC algorithms). A Monte Carlo simulation is a computer simulation of a biological process using random numbers. An MCMC algorithm is a Monte Carlo simulation algorithm that generates a sample from a target distribution (often a Bayesian posterior distribution).

Clades

Groups of species that have descended from a common ancestor.

Graphical processing units

(GPU). Specialized units that are traditionally used to manipulate output on a video display and have recently been explored for use in parallel computation.

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