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      ANISEED 2017: extending the integrated ascidian database to the exploration and evolutionary comparison of genome-scale datasets

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      Nucleic Acids Research

      Oxford University Press

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          Abstract

          ANISEED ( www.aniseed.cnrs.fr) is the main model organism database for tunicates, the sister-group of vertebrates. This release gives access to annotated genomes, gene expression patterns, and anatomical descriptions for nine ascidian species. It provides increased integration with external molecular and taxonomy databases, better support for epigenomics datasets, in particular RNA-seq, ChIP-seq and SELEX-seq, and features novel interactive interfaces for existing and novel datatypes. In particular, the cross-species navigation and comparison is enhanced through a novel taxonomy section describing each represented species and through the implementation of interactive phylogenetic gene trees for 60% of tunicate genes. The gene expression section displays the results of RNA-seq experiments for the three major model species of solitary ascidians. Gene expression is controlled by the binding of transcription factors to cis-regulatory sequences. A high-resolution description of the DNA-binding specificity for 131 Ciona robusta (formerly C. intestinalis type A) transcription factors by SELEX-seq is provided and used to map candidate binding sites across the Ciona robusta and Phallusia mammillata genomes. Finally, use of a WashU Epigenome browser enhances genome navigation, while a Genomicus server was set up to explore microsynteny relationships within tunicates and with vertebrates, Amphioxus, echinoderms and hemichordates.

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          Most cited references 55

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          RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

          Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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            OrthoMCL: identification of ortholog groups for eukaryotic genomes.

            The identification of orthologous groups is useful for genome annotation, studies on gene/protein evolution, comparative genomics, and the identification of taxonomically restricted sequences. Methods successfully exploited for prokaryotic genome analysis have proved difficult to apply to eukaryotes, however, as larger genomes may contain multiple paralogous genes, and sequence information is often incomplete. OrthoMCL provides a scalable method for constructing orthologous groups across multiple eukaryotic taxa, using a Markov Cluster algorithm to group (putative) orthologs and paralogs. This method performs similarly to the INPARANOID algorithm when applied to two genomes, but can be extended to cluster orthologs from multiple species. OrthoMCL clusters are coherent with groups identified by EGO, but improved recognition of "recent" paralogs permits overlapping EGO groups representing the same gene to be merged. Comparison with previously assigned EC annotations suggests a high degree of reliability, implying utility for automated eukaryotic genome annotation. OrthoMCL has been applied to the proteome data set from seven publicly available genomes (human, fly, worm, yeast, Arabidopsis, the malaria parasite Plasmodium falciparum, and Escherichia coli). A Web interface allows queries based on individual genes or user-defined phylogenetic patterns (http://www.cbil.upenn.edu/gene-family). Analysis of clusters incorporating P. falciparum genes identifies numerous enzymes that were incompletely annotated in first-pass annotation of the parasite genome.
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              Gene Ontology Consortium: going forward

              The Gene Ontology (GO; http://www.geneontology.org) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge. Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community. The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes. We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions. The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data. Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser. We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                04 January 2018
                15 November 2017
                15 November 2017
                : 46
                : Database issue , Database issue
                : D718-D725
                Affiliations
                CRBM, Université de Montpellier, CNRS, Montpellier, France
                Bioself Communication; 28 rue de la Bibliothèque, F-13001 Marseille, France
                Institut de Biologie Computationnelle, Université de Montpellier, Montpellier, France
                Team VORTEX, Institut de Recherche en Informatique de Toulouse, Universities Toulouse I and III, CNRS, INPT, ENSEEIHT; 2 rue Camichel, BP 7122, F-31071 Toulouse Cedex 7, France
                DYOGEN, IBENS, Département de Biologie, Ecole Normale Supérieure, CNRS, Inserm, PSL Research University, F-75005, Paris, France
                Institut de Génomique Fonctionnelle de Lyon, Université de Lyon, Ecole Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, CNRS; 46 allée d’Italie, F-69364 Lyon, France
                IBDM, Aix-Marseille Université, CNRS, Campus de Luminy, Case 907; 163 Avenue de Luminy, F-13288 Marseille Cedex 9, France
                Division of Biology, Kansas State University, Manhattan, Kansas
                ISEM, Université de Montpellier, CNRS, IRD, EPHE, Montpellier, France
                AFMB, Aix-Marseille Université, CNRS, Campus de Luminy, Case 932, 163 Avenue de Luminy, F-13288 Marseille Cedex 9, France
                Université de Montpellier, Montpellier, France
                Laboratoire de Biologie du Développement de Villefranche-sur-mer (LBDV), Sorbonne Universités, Université Pierre-et-Marie-Curie, CNRS; Quai de la Darse, F-06234 Villefranche-sur-Mer Cedex, France
                Santa Cruz Genomics Institute, MS CBSE, University of California, 1156 High Street, Santa Cruz, CA 95064, USA
                Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Napoli, Italy
                BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI48824, USA
                School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
                Human Genome Center, the Institute of Medical Science, the University of Tokyo, 4-6-1 Shirokanedai, Minato, Tokyo 108-8639, Japan
                Population Health and Reproduction, UC Davis, Davis, CA 95616, USA
                New York University, Center for Developmental Genetics, Department of Biology, 1009 Silver Center, 100 Washington Square East, New York City, NY10003, USA
                Department of Biology, Faculty of Science and Engineering, Konan University, Kobe 658-8501, Japan
                Department of Biological Sciences, Graduate School of Science, Osaka University, 1-1 Machikaneyama-cho, Toyonaka, Osaka 560-0043, Japan
                Department of Zoology, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan
                Friday Harbor Laboratories, 620 University Road, Friday Harbor, WA 98250-9299, USA
                Author notes
                To whom correspondence should be addressed. Email: patrick.lemaire@ 123456crbm.cnrs.fr

                These authors contributed equally to this work as first authors.

                Article
                gkx1108
                10.1093/nar/gkx1108
                5753386
                29149270
                © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                Page count
                Pages: 8
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                Database Issue

                Genetics

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