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      The Ctenophore Genome and the Evolutionary Origins of Neural Systems

      1 , 2 , 3 , 4 , 1 , 1 , 1 , 3 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 1 , 8 , 1 , 1 , 1 , 2 , 12 , 12 , 1 , 1 , 3 , 1 , 3 , 1 , 7 , 8 , 1 , 1 , 3 , 13 , 3 , 11 , 5 , 6 , 14 , 5 , 6 , 15 , 3 , 8 , 7 , 8 , 16 , 17 , 4 , 1


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          The origins of neural systems remain unresolved. In contrast to other basal metazoans, ctenophores, or comb jellies, have both complex nervous and mesoderm-derived muscular systems. These holoplanktonic predators also have sophisticated ciliated locomotion, behaviour and distinct development. Here, we present the draft genome of Pleurobrachia bachei, Pacific sea gooseberry, together with ten other ctenophore transcriptomes and show that they are remarkably distinct from other animal genomes in their content of neurogenic, immune and developmental genes. Our integrative analyses place Ctenophora as the earliest lineage within Metazoa. This hypothesis is supported by comparative analysis of multiple gene families, including the apparent absence of HOX genes, canonical microRNA machinery, and reduced immune complement in ctenophores. Although two distinct nervous systems are well-recognized in ctenophores, many bilaterian neuron-specific genes and genes of “classical” neurotransmitter pathways either are absent or, if present, are not expressed in neurons. Our metabolomic and physiological data are consistent with the hypothesis that ctenophore neural systems, and possibly muscle specification, evolved independently from those in other animals.

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          • Record: found
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          Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

           S Altschul (1997)
          The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
            • Record: found
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            MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

            Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from
              • Record: found
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              Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.


                Author and article information

                21 January 2015
                21 May 2014
                5 June 2014
                23 February 2015
                : 510
                : 7503
                : 109-114
                [1 ]The Whitney Laboratory for Marine Bioscience, University of Florida, 9505 Ocean Shore Blvd., St. Augustine, Florida 32080, USA
                [2 ]Department of Neuroscience & McKnight Brain Institute, University of Florida, Gainesville, FL 32611, USA
                [3 ]Friday Harbor Laboratories, University of Washington, Friday Harbor, WA 98250, USA
                [4 ]Department of Biological Sciences, Auburn University, 101 Rouse Life Sciences, Auburn, Alabama 36849, USA
                [5 ]Centre for Genomic Regulation (CRG), Dr. Aiguader 88, 08003 Barcelona, Spain
                [6 ]Universitat Pompeu Fabra (UPF), Barcelona, Spain
                [7 ]Department of Psychiatry, University of Massachusetts Medical School, l303 Belmont Street, Worcester MA 01605, USA
                [8 ]Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), Gubkina 3, Moscow 119991, RF
                [9 ]Department of Chemistry and the Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
                [10 ]European Research Institute for the Biology of Ageing, University of Groningen Medical Center, Antonius Deusinglaan 1, Building 3226, Room 03.34, 9713 AV Groningen; The Netherlands
                [11 ]Department of Medical Biophysics and Department of Immunology, University of Toronto, Sunnybrook Research Institute 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada
                [12 ]Genetic Information Research Institute, 1925 Landings Dr., Mountain View, CA 94043, USA
                [13 ]Program in Molecular Medicine, University of Massachusetts Medical School, 222 Maple Avenue, Shrewsbury, Massachusetts 01545, USA
                [14 ]Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK
                [15 ]Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluis Companys 23, 08010 Barcelona, Spain
                [16 ]Center for Brain Neurobiology and Neurogenetics and Institute of Cytology and Genetics, RAS, Lavrentyev Ave., 10, Novosibirsk 630090, RF
                [17 ]Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory, 119991, Moscow, RF
                Author notes
                Corresponding to: Leonid L Moroz ( moroz@ ), Principal Investigator; Kenneth Halanych ( ken@ ), phylogenomiucs; Evgeny I. Rogaev ( Evgeny.Rogaev@ ), gDNA-seq; Andrea B. Kohn ( abkohn@ ), RNA-seq



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