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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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          Tunicates and not cephalochordates are the closest living relatives of vertebrates.

          Tunicates or urochordates (appendicularians, salps and sea squirts), cephalochordates (lancelets) and vertebrates (including lamprey and hagfish) constitute the three extant groups of chordate animals. Traditionally, cephalochordates are considered as the closest living relatives of vertebrates, with tunicates representing the earliest chordate lineage. This view is mainly justified by overall morphological similarities and an apparently increased complexity in cephalochordates and vertebrates relative to tunicates. Despite their critical importance for understanding the origins of vertebrates, phylogenetic studies of chordate relationships have provided equivocal results. Taking advantage of the genome sequencing of the appendicularian Oikopleura dioica, we assembled a phylogenomic data set of 146 nuclear genes (33,800 unambiguously aligned amino acids) from 14 deuterostomes and 24 other slowly evolving species as an outgroup. Here we show that phylogenetic analyses of this data set provide compelling evidence that tunicates, and not cephalochordates, represent the closest living relatives of vertebrates. Chordate monophyly remains uncertain because cephalochordates, albeit with a non-significant statistical support, surprisingly grouped with echinoderms, a hypothesis that needs to be tested with additional data. This new phylogenetic scheme prompts a reappraisal of both morphological and palaeontological data and has important implications for the interpretation of developmental and genomic studies in which tunicates and cephalochordates are used as model animals.
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            Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities.

            The genetic code-the binding specificity of all transfer-RNAs--defines how protein primary structure is determined by DNA sequence. DNA also dictates when and where proteins are expressed, and this information is encoded in a pattern of specific sequence motifs that are recognized by transcription factors. However, the DNA-binding specificity is only known for a small fraction of the approximately 1400 human transcription factors (TFs). We describe here a high-throughput method for analyzing transcription factor binding specificity that is based on systematic evolution of ligands by exponential enrichment (SELEX) and massively parallel sequencing. The method is optimized for analysis of large numbers of TFs in parallel through the use of affinity-tagged proteins, barcoded selection oligonucleotides, and multiplexed sequencing. Data are analyzed by a new bioinformatic platform that uses the hundreds of thousands of sequencing reads obtained to control the quality of the experiments and to generate binding motifs for the TFs. The described technology allows higher throughput and identification of much longer binding profiles than current microarray-based methods. In addition, as our method is based on proteins expressed in mammalian cells, it can also be used to characterize DNA-binding preferences of full-length proteins or proteins requiring post-translational modifications. We validate the method by determining binding specificities of 14 different classes of TFs and by confirming the specificities for NFATC1 and RFX3 using ChIP-seq. Our results reveal unexpected dimeric modes of binding for several factors that were thought to preferentially bind DNA as monomers.
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              Ultra-fast sequence clustering from similarity networks with SiLiX

              Background The number of gene sequences that are available for comparative genomics approaches is increasing extremely quickly. A current challenge is to be able to handle this huge amount of sequences in order to build families of homologous sequences in a reasonable time. Results We present the software package SiLiX that implements a novel method which reconsiders single linkage clustering with a graph theoretical approach. A parallel version of the algorithms is also presented. As a demonstration of the ability of our software, we clustered more than 3 millions sequences from about 2 billion BLAST hits in 7 minutes, with a high clustering quality, both in terms of sensitivity and specificity. Conclusions Comparing state-of-the-art software, SiLiX presents the best up-to-date capabilities to face the problem of clustering large collections of sequences. SiLiX is freely available at http://lbbe.univ-lyon1.fr/SiLiX.
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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.

                Author information
                http://orcid.org/0000-0001-6001-2677
                Article
                gkx1108
                10.1093/nar/gkx1108
                5753386
                29149270
                e0912aca-f167-454c-9b96-9e9de0d9b966
                © 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

                History
                : 09 November 2017
                : 22 October 2017
                : 18 September 2017
                Page count
                Pages: 8
                Categories
                Database Issue

                Genetics
                Genetics

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