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      TOPALi v2: a rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops

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          Abstract

          Summary: TOPALi v2 simplifies and automates the use of several methods for the evolutionary analysis of multiple sequence alignments. Jobs are submitted from a Java graphical user interface as TOPALi web services to either run remotely on high-performance computing clusters or locally (with multiple cores supported). Methods available include model selection and phylogenetic tree estimation using the Bayesian inference and maximum likelihood (ML) approaches, in addition to recombination detection methods. The optimal substitution model can be selected for protein or nucleic acid (standard, or protein-coding using a codon position model) data using accurate statistical criteria derived from ML co-estimation of the tree and the substitution model. Phylogenetic software available includes PhyML, RAxML and MrBayes.

          Availability: Freely downloadable from http://www.topali.org for Windows, Mac OS X, Linux and Solaris.

          Contact: iain.milne@ 123456scri.ac.uk

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          Most cited references 4

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          Choosing appropriate substitution models for the phylogenetic analysis of protein-coding sequences.

          Although phylogenetic inference of protein-coding sequences continues to dominate the literature, few analyses incorporate evolutionary models that consider the genetic code. This problem is exacerbated by the exclusion of codon-based models from commonly employed model selection techniques, presumably due to the computational cost associated with codon models. We investigated an efficient alternative to standard nucleotide substitution models, in which codon position (CP) is incorporated into the model. We determined the most appropriate model for alignments of 177 RNA virus genes and 106 yeast genes, using 11 substitution models including one codon model and four CP models. The majority of analyzed gene alignments are best described by CP substitution models, rather than by standard nucleotide models, and without the computational cost of full codon models. These results have significant implications for phylogenetic inference of coding sequences as they make it clear that substitution models incorporating CPs not only are a computationally realistic alternative to standard models but may also frequently be statistically superior.
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            Molecular phylogenetics: state-of-the-art methods for looking into the past.

            As the amount of molecular sequence data in the public domain grows, so does the range of biological topics that it influences through evolutionary considerations. In recent years, a number of developments have enabled molecular phylogenetic methodology to keep pace. Likelihood-based inferential techniques, although controversial in the past, lie at the heart of these new methods and are producing the promised advances in the understanding of sequence evolution. They allow both a wide variety of phylogenetic inferences from sequence data and robust statistical assessment of all results. It cannot remain acceptable to use outdated data analysis techniques when superior alternatives exist. Here, we discuss the most important and exciting methods currently available to the molecular phylogeneticist.
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              Variation in evolutionary processes at different codon positions.

              Evolutionary studies commonly model single nucleotide substitutions and assume that they occur as independent draws from a unique probability distribution across the sequence studied. This assumption is violated for protein-coding sequences, and we consider modeling approaches where codon positions (CPs) are treated as separate categories of sites because within each category the assumption is more reasonable. Such "codon-position" models have been shown to explain the evolution of codon data better than homogenous models in previous studies. This paper examines the ways in which codon-position models outperform homogeneous models and characterizes the differences in estimates of model parameters across CPs. Using the PANDIT database of multiple species DNA sequence alignments, we quantify the differences in the evolutionary processes at the 3 CPs in a systematic and comprehensive manner, characterizing previously undescribed features of protein evolution. We relate our findings to the functional constraints imposed by the genetic code, protein function, and the types of mutation that cause synonymous and nonsynonymous codon changes. The results increase our understanding of selective constraints and could be incorporated into phylogenetic analyses or gene-finding techniques in the future. The methods used are extended to an overlapping reading frame data set, and we discover that overlapping reading frames do not necessarily cause more stringent evolutionary constraints.
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                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1460-2059
                1 January 2009
                4 November 2008
                4 November 2008
                : 25
                : 1
                : 126-127
                Affiliations
                1Scottish Crop Research Institute, 2Biomathematics and Statistics Scotland (BioSS), SCRI, Invergowrie, Dundee DD2 5DA and 3Biomathematics and Statistics Scotland (BioSS), JCMB, The King's Buildings, Edinburgh EH9 3JZ, UK
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Martin Bishop

                Article
                btn575
                10.1093/bioinformatics/btn575
                2638937
                18984599
                © 2008 The Author(s)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Applications Note
                Phylogenetics

                Bioinformatics & Computational biology

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