Molecular phylogenetics: principles and practice (original) (raw)

A biologist’s guide to Bayesian phylogenetic analysis

Nature Ecology & Evolution, 2017

Bayesian methods have become very popular in molecular phylogenetics due to the availability of user-friendly software implementing sophisticated models of evolution. However, Bayesian phylogenetic models are complex, and analyses are often carried out using default settings, which may not be appropriate. Here, we summarize the major features of Bayesian phylogenetic inference and discuss Bayesian computation using Markov chain Monte Carlo (MCMC), the diagnosis of an MCMC run, and ways of summarising the MCMC sample. We discuss the specification of the prior, the choice of the substitution model, and partitioning of the data. Finally, we provide a list of common Bayesian phylogenetic software and provide recommendations as to their use.

Phylogenetic inference using molecular data

2009

We review phylogenetic inference methods with a special emphasis on inference from molecular data. We begin with a general comment on phylogenetic inference using DNA sequences, followed by a clear statement of the relevance of a good alignment of sequences. Then we provide a general description of models of sequence evolution, including evolutionary models that account for rate heterogeneity along the DNA sequences or complex secondary structure (i.e., ribosomal genes). We then present an overall description of the most relevant inference methods, focusing on key concepts of general interest. We point out the most relevant traits of methods such as maximum parsimony (MP), distance methods, maximum likelihood (ML) and Bayesian inference (BI). Finally, we discuss different measures of support for the estimated phylogeny and discuss how this relates to confidence in particular nodes of a phylogeny reconstruction.

Phylogenetic analyses: A toolbox expanding towards Bayesian methods

International Journal of Plant Genomics, 2008

The reconstruction of phylogenies is becoming an increasingly simple activity. This is mainly due to two reasons: the democratization of computing power and the increased availability of sophisticated yet user-friendly software. This review describes some of the latest additions to the phylogenetic toolbox, along with some of their theoretical and practical limitations. It is shown that Bayesian methods are under heavy development, as they offer the possibility to solve a number of long-standing issues and to integrate several steps of the phylogenetic analyses into a single framework. Specific topics include not only phylogenetic reconstruction, but also the comparison of phylogenies, the detection of adaptive evolution, and the estimation of divergence times between species.

A Review on Phylogenetic Analysis: A Journey through Modern Era

Phylogenetic analysis may be considered to be a highly reliable and important bioinformatics tool. The importance of phylogenetic analysis lies in its simple manifestation and easy handling of data. The simple tree representation of the evolution makes the phylogenetic analysis easier to comprehend and represent as well. The varied applications of phylogenetics in different fields of biology make this analysis an absolute necessity. The different aspects of phylogenetic analysis have been described in a comprehensive manner. This review may be useful to those who would like to have a firsthand knowledge of phylogenetics.

Molecular Phylogenetics: Mathematical Framework and Unsolved Problems

Structural Approaches to Sequence Evolution, 2007

Phylogenetic relationship is essential in dating evolutionary events, reconstructing ancestral genes, predicting sites that are important to natural selection and, ultimately, understanding genomic evolution Three categories of phylogenetic methods are currently used: the distance-based, the maximum parsimony, and the maximum likelihood method. Here I present the mathematical framework of these methods and their rationales, provide computational details for each of them, illustrate analytically and numerically the potential biases inherent in these methods, and outline computational challenges and unresolved problems. This is followed by a brief discussion of the Bayesian approach that has recently been used in molecular phylogenetics.

ReviewArticle Phylogenetic Analyses: A Toolbox Expanding towards Bayesian Methods

5 Quantitative Approaches to Phylogenetics

Handbook of Paleoanthropology, 2007

We review Hennigian, maximum likelihood, and different Bayesian approaches to quantitative phylogenetic analysis and discuss their strengths and weaknesses. We also discuss various protocols for assessing the relative robustness of one's results. Hennigian approaches are justified by the Darwinian concepts of phylogenetic conservatism and the cohesion of homologies, embodied in Hennig's Auxiliary Principle, and applied using outgroup comparisons. They use parsimony as an epistemological tool. Maximum likelihood and Bayesian likelihood approaches are based on an ontological use of parsimony, choosing the simplest model possible to explain the data. All methods identify the same core of unambiguous data in any given data set, producing highly similar results. Disagreements most often stem from insufficient numbers of unambiguous characters in one or more of the data types. Appeals to Popperian philosophy cannot justify any kind of phylogenetic analysis, because they argue from effect to cause rather than cause to effect. Nor can any approach be justified by statistical consistency, because all may be consistent or inconsistent depending on the data being analyzed. If analyses based on different types of data or using different methods of phylogeny reconstruction, or some combination of both, do not produce the same results, more data are needed.

Quantitative phylogenetic analysis in the 21st century

2007

We review Hennigian phylogenetics and compare it with Maximum parsimony, Maximum likelihood, and Bayesian likelihood approaches. All methods use the principle of parsimony in some form. Hennigian-based approaches are justifi ed ontologically by the Darwinian concepts of phylogenetic conservatism and cohesion of homologies, embodied in Hennig's Auxiliary Principle, and applied by outgroup comparisons. Parsimony is used as an epistemological tool, applied a posteriori to choose the most robust hypothesis when there are confl icting data. Quantitative methods use parsimony as an ontological criterion: Maximum parsimony analysis uses unweighted parsimony, Maximum likelihood weight all characters equally that explain the data, and Bayesian likelihood relying on weighting each character partition that explains the data. Different results most often stem from insuffi cient data, in which case each quantitative method treats ambiguities differently. All quantitative methods produce networks. The networks can be converted into trees by rooting them. If the rooting is done in accordance with Hennig's Auxiliary Principle, using outgroup comparisons, the resulting tree can then be interpreted as a phylogenetic hypothesis. As the size of the data set increases, likelihood methods select models that allow an increasingly greater number of a priori possibilities, converging on the Hennigian perspective that nothing is prohibited a priori. Thus, all methods produce similar results, regardless of data type, especially when their networks are rooted using outgroups. Appeals to Popperian philosophy cannot justify any kind of phylogenetic analysis, because they argue from effect to cause rather than from cause to effect. Nor can particular methods be justifi ed on the basis of statistical consistency, because all may be consistent or inconsistent depending on the data. If analyses using different types of data and/or different methods of phylogeny reconstruction do not produce the same results, more data are needed.

Current Advances in Molecular Phylogenetics

BioMed Research International, 2014

Since its inception some 50 years ago, phylogenetics has permeated nearly every branch of biology. Initially developed to classify objects based on a set of cladistic rules, it has now become the central paradigm of evolutionary biology and a key framework for making sense of a wide range of disciplines [1], such as genomics [2], community ecology [3], epidemiology [4], conservation biology , and population dynamics , to name just a few. It is a testament to the power of phylogenetic methods that its application has expanded far beyond its original inception, now including the study of human culture, such as language and cultural memes .

Application and accuracy of molecular phylogenies

Science, 1994

Molecular investigations of evolutionary history are being used to study subjects as diverse as the epidemiology of acquired immune deficiency syndrome and the origin of life. These studies depend on accurate estimates of phylogeny. The performance of methods of phylogenetic analysis can be assessed by numerical simulation studies and by the experimental evolution of organisms in controlled laboratory situations. Both kinds of assessment indicate that existing methods are effective at estimating phylogenies over awide range of evolutionary conditions, especially if information about substitution bias is used to provide differential weightings for character transformations.

Molecular phylogenetics: principles and practice (original) (raw)

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