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      Functional Annotation and Comparative Analysis of a Zygopteran Transcriptome

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          Abstract

          In this paper we present a de novo assembly of the transcriptome of the damselfly ( Enallagma hageni) through the use of 454 pyrosequencing. E. hageni is a member of the suborder Zygoptera, in the order Odonata, and Odonata organisms form the basal lineage of the winged insects (Pterygota). To date, sequence data used in phylogenetic analysis of Enallagma species have been derived from either mitochondrial DNA or ribosomal nuclear DNA. This Enallagma transcriptome contained 31,661 contigs that were assembled and translated into 14,813 individual open reading frames. Using these data, we constructed an extensive dataset of 634 orthologous nuclear protein-encoding genes across 11 species of Arthropoda and used Bayesian techniques to elucidate the position of Enallagma in the arthropod phylogenetic tree. Additionally, we demonstrated that the Enallagma transcriptome contains 169 genes that are evolving at rates that differ relative to those of the rest of the transcriptome (29 accelerated and 140 decreased), and, through multiple Gene Ontology searches and clustering methods, we present the first functional annotation of any palaeopteran’s transcriptome in the literature.

          Most cited references25

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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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            Amino acid substitution matrices from protein blocks.

            Methods for alignment of protein sequences typically measure similarity by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups.
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              The impact of next-generation sequencing technology on genetics.

              If one accepts that the fundamental pursuit of genetics is to determine the genotypes that explain phenotypes, the meteoric increase of DNA sequence information applied toward that pursuit has nowhere to go but up. The recent introduction of instruments capable of producing millions of DNA sequence reads in a single run is rapidly changing the landscape of genetics, providing the ability to answer questions with heretofore unimaginable speed. These technologies will provide an inexpensive, genome-wide sequence readout as an endpoint to applications ranging from chromatin immunoprecipitation, mutation mapping and polymorphism discovery to noncoding RNA discovery. Here I survey next-generation sequencing technologies and consider how they can provide a more complete picture of how the genome shapes the organism.
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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                1 April 2013
                April 2013
                : 3
                : 4
                : 763-770
                Affiliations
                [* ]Rutgers, The State University of New Jersey, Department of Genetics, Piscataway, New Jersey 08854-8082
                []Department of Biological Science, Dartmouth College, New Hampshire 03755
                Author notes

                Supporting information is available online at http://www.g3journal.org/lookup/suppl/doi:10.1534/g3.113.005637/-/DC1

                Sequence and transcriptome data from this article have been deposited in the National Center for Biotechnology Information (NCBI) database under Enallagma hageni BioProject no. PRJNA185185 ID:185185, which contains links and access to insect sampling data under BioSample SAMN01881995 and raw sequencing under Sequence Read Archive (SRA) SRR649536 and transcriptome SUB156504 data.

                [1 ]Corresponding author: Rutgers, The State University of New Jersey, Department of Genetics, Nelson Bio Labs-B416, 604 Allison Road, Piscataway, NJ 08854-8082. E-mail: alexander.shanku@ 123456rutgers.edu
                Article
                GGG_005637
                10.1534/g3.113.005637
                3618363
                23550132
                b1797839-dd11-4b7a-8358-65afd66f1b07
                Copyright © 2013 Shanku et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 January 2013
                : 25 February 2013
                Page count
                Pages: 8
                Categories
                Investigations
                Custom metadata
                v1

                Genetics
                Genetics

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