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      A practical approach to phylogenomics: the phylogeny of ray-finned fish (Actinopterygii) as a case study

      , 1 , 1 , 2 , , 3

      BMC Evolutionary Biology

      BioMed Central

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          Abstract

          Background

          Molecular systematics occupies one of the central stages in biology in the genomic era, ushered in by unprecedented progress in DNA technology. The inference of organismal phylogeny is now based on many independent genetic loci, a widely accepted approach to assemble the tree of life. Surprisingly, this approach is hindered by lack of appropriate nuclear gene markers for many taxonomic groups especially at high taxonomic level, partially due to the lack of tools for efficiently developing new phylogenetic makers. We report here a genome-comparison strategy to identifying nuclear gene markers for phylogenetic inference and apply it to the ray-finned fishes – the largest vertebrate clade in need of phylogenetic resolution.

          Results

          A total of 154 candidate molecular markers – relatively well conserved, putatively single-copy gene fragments with long, uninterrupted exons – were obtained by comparing whole genome sequences of two model organisms, Danio rerio and Takifugu rubripes. Experimental tests of 15 of these (randomly picked) markers on 36 taxa (representing two-thirds of the ray-finned fish orders) demonstrate the feasibility of amplifying by PCR and directly sequencing most of these candidates from whole genomic DNA in a vast diversity of fish species. Preliminary phylogenetic analyses of sequence data obtained for 14 taxa and 10 markers (total of 7,872 bp for each species) are encouraging, suggesting that the markers obtained will make significant contributions to future fish phylogenetic studies.

          Conclusion

          We present a practical approach that systematically compares whole genome sequences to identify single-copy nuclear gene markers for inferring phylogeny. Our method is an improvement over traditional approaches (e.g., manually picking genes for testing) because it uses genomic information and automates the process to identify large numbers of candidate makers. This approach is shown here to be successful for fishes, but also could be applied to other groups of organisms for which two or more complete genome sequences exist, which has important implications for assembling the tree of life.

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          Most cited references 51

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          ProtTest: selection of best-fit models of protein evolution.

          Using an appropriate model of amino acid replacement is very important for the study of protein evolution and phylogenetic inference. We have built a tool for the selection of the best-fit model of evolution, among a set of candidate models, for a given protein sequence alignment. ProtTest is available under the GNU license from http://darwin.uvigo.es
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            Fishes of the World

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              Automatic clustering of orthologs and in-paralogs from pairwise species comparisons.

              Orthologs are genes in different species that originate from a single gene in the last common ancestor of these species. Such genes have often retained identical biological roles in the present-day organisms. It is hence important to identify orthologs for transferring functional information between genes in different organisms with a high degree of reliability. For example, orthologs of human proteins are often functionally characterized in model organisms. Unfortunately, orthology analysis between human and e.g. invertebrates is often complex because of large numbers of paralogs within protein families. Paralogs that predate the species split, which we call out-paralogs, can easily be confused with true orthologs. Paralogs that arose after the species split, which we call in-paralogs, however, are bona fide orthologs by definition. Orthologs and in-paralogs are typically detected with phylogenetic methods, but these are slow and difficult to automate. Automatic clustering methods based on two-way best genome-wide matches on the other hand, have so far not separated in-paralogs from out-paralogs effectively. We present a fully automatic method for finding orthologs and in-paralogs from two species. Ortholog clusters are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for both orthologs and in-paralogs. The program, called INPARANOID, was tested on all completely sequenced eukaryotic genomes. To assess the quality of INPARANOID results, ortholog clusters were generated from a dataset of worm and mammalian transmembrane proteins, and were compared to clusters derived by manual tree-based ortholog detection methods. This study led to the identification with a high degree of confidence of over a dozen novel worm-mammalian ortholog assignments that were previously undetected because of shortcomings of phylogenetic methods.A WWW server that allows searching for orthologs between human and several fully sequenced genomes is installed at http://www.cgb.ki.se/inparanoid/. This is the first comprehensive resource with orthologs of all fully sequenced eukaryotic genomes. Programs and tables of orthology assignments are available from the same location. Copyright 2001 Academic Press.
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                Author and article information

                Journal
                BMC Evol Biol
                BMC Evolutionary Biology
                BioMed Central (London )
                1471-2148
                2007
                20 March 2007
                : 7
                : 44
                Affiliations
                [1 ]School of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA
                [2 ]Department of Mathematics, University of Nebraska, Omaha, NE 68182, USA
                [3 ]Department of Biology, University of Nebraska, Omaha, NE 68182, USA
                Article
                1471-2148-7-44
                10.1186/1471-2148-7-44
                1838417
                17374158
                Copyright © 2007 Li et al; licensee BioMed Central Ltd.

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

                Categories
                Methodology Article

                Evolutionary Biology

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