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      The nuclear receptor gene family in the Pacific oyster, Crassostrea gigas, contains a novel subfamily group

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          Abstract

          Background

          Nuclear receptors are a superfamily of transcription factors important in key biological, developmental and reproductive processes. Several of these receptors are ligand- activated and through their ability to bind endogenous and exogenous ligands, are potentially vulnerable to xenobiotics. Molluscs are key ecological species in defining aquatic and terrestrial habitats and are sensitive to xenobiotic compounds in the environment. However, the understanding of nuclear receptor presence, function and xenobiotic disruption in the phylum Mollusca is limited.

          Results

          Here, forty-three nuclear receptor sequences were mined from the genome of the Pacific oyster, Crassostrea gigas. They include members of NR0-NR5 subfamilies, notably lacking any NR6 members. Phylogenetic analyses of the oyster nuclear receptors have been conducted showing the presence of a large novel subfamily group not previously reported, which is named NR1P. Homologues to all previous identified nuclear receptors in other mollusc species have also been determined including the putative heterodimer partner retinoid X receptor, estrogen receptor and estrogen related receptor.

          Conclusion

          C. gigas contains a highly diverse set of nuclear receptors including a novel NR1 group, which provides important information on presence and evolution of this transcription factor superfamily in invertebrates. The Pacific oyster possesses two members of NR3, the sex steroid hormone receptor analogues, of which there are 9 in humans. This provides increasing evidence that steroid ligand specific expansion of this family is deuterostome specific. This new knowledge on divergence and emergence of nuclear receptors in C. gigas provides essential information for studying regulation of molluscan gene expression and the potential effects of xenobiotics.

          Electronic supplementary material

          The online version of this article (doi:10.1186/1471-2164-15-369) contains supplementary material, which is available to authorized users.

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          Most cited references 169

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

           Robert Edgar (2004)
          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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            New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

            PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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              MRBAYES: Bayesian inference of phylogenetic trees.

              The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo. MRBAYES, including the source code, documentation, sample data files, and an executable, is available at http://brahms.biology.rochester.edu/software.html.
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                Author and article information

                Contributors
                sv265@exeter.ac.uk
                t.s.galloway@exeter.ac.uk
                brett.lyons@cefas.co.uk
                tim.bean@cefas.co.uk
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                15 May 2014
                15 May 2014
                2014
                : 15
                : 1
                Affiliations
                [ ]School of Biosciences, College of Life and Environmental Sciences, University of Exeter, Stocker Road, Exeter, EX4 4QD UK
                [ ]Centre for Environment, Fisheries and Aquaculture Science, Cefas Weymouth Laboratory, Barrack Road, Weymouth, DT4 8UB UK
                Article
                6129
                10.1186/1471-2164-15-369
                4070562
                24885009
                © Vogeler et al.; licensee BioMed Central Ltd. 2014

                This article is published under license to 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 credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Categories
                Research Article
                Custom metadata
                © The Author(s) 2014

                Genetics

                transcription factor, xenobiotics, bivalve, hormone receptor, mollusc

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