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  • 1
    Publication Date: 2016-08-27
    Description: Recently, a new Chlamydia -related organism, Protochlamydia naegleriophila KNic, was discovered within a Naegleria amoeba. To decipher the mechanisms at play in the modeling of genomes from the Protochlamydia genus, we sequenced the full genome of Pr. naegleriophila , which includes a 2,885,090 bp chromosome and a 145,285 bp megaplasmid. For the first time within the Chlamydiales order, we describe the presence of a clustered regularly interspaced short palindromic repeats (CRISPR) system, the immune system of bacteria, located on the chromosome. It is composed of a small CRISPR locus comprising eight repeats and associated cas - cse genes of the subtype I-E. A CRISPR locus is also present within Chlamydia sp. Diamant, another Pr. naegleriophila strain, suggesting that the CRISPR system was acquired by a common ancestor of Pr. naegleriophila , after its divergence from Pr. amoebophila. Both nucleotide bias and comparative genomics approaches identified probable horizontal gene acquisitions within two and four genomic islands in Pr. naegleriophila KNic and Diamant genomes, respectively. The plasmid encodes an F -type conjugative system highly similar to 1) that found in the Pam100G genomic island of Pr. amoebophila UWE25 chromosome, as well as on the plasmid of Rubidus massiliensis and 2) to the three genes remaining in the chromosome of Parachlamydia acanthamoebae strains. Therefore, this conjugative system was likely acquired on an ancestral plasmid before the divergence of Parachlamydiaceae . Overall, this new complete Pr. naegleriophila genome sequence enables further investigation of the dynamic processes shaping the genomes of the family Parachlamydiaceae and the genus Protochlamydia.
    Electronic ISSN: 1759-6653
    Topics: Biology
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  • 2
    Publication Date: 2004-01-31
    Description: Predatory bacteria remain molecularly enigmatic, despite their presence in many microbial communities. Here we report the complete genome of Bdellovibrio bacteriovorus HD100, a predatory Gram-negative bacterium that invades and consumes other Gram-negative bacteria. Its surprisingly large genome shows no evidence of recent gene transfer from its prey. A plethora of paralogous gene families coding for enzymes, such as hydrolases and transporters, are used throughout the life cycle of B. bacteriovorus for prey entry, prey killing, and the uptake of complex molecules.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Rendulic, Snjezana -- Jagtap, Pratik -- Rosinus, Andrea -- Eppinger, Mark -- Baar, Claudia -- Lanz, Christa -- Keller, Heike -- Lambert, Carey -- Evans, Katy J -- Goesmann, Alexander -- Meyer, Folker -- Sockett, R Elizabeth -- Schuster, Stephan C -- New York, N.Y. -- Science. 2004 Jan 30;303(5658):689-92.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Max-Planck-Institute for Developmental Biology, 72076 Tubingen, Germany.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/14752164" target="_blank"〉PubMed〈/a〉
    Keywords: Adenosine Triphosphate/metabolism ; Amino Acids/metabolism ; Bacterial Adhesion/genetics ; Bacterial Proteins/biosynthesis/genetics/metabolism ; Bdellovibrio/cytology/*genetics/*growth & development/physiology ; Biological Transport ; Cell Membrane/metabolism ; Computational Biology ; Cytosol/metabolism ; Fimbriae, Bacterial/genetics/physiology ; Flagella/genetics/physiology ; Gene Transfer, Horizontal ; Genes, Bacterial ; *Genome, Bacterial ; Genomics ; Gram-Negative Bacteria ; Hydrolases/genetics/metabolism ; Membrane Transport Proteins/genetics/metabolism ; Open Reading Frames ; Peptidoglycan/metabolism ; Sequence Analysis, DNA
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 3
    Publication Date: 2013-12-20
    Description: Sugar beet (Beta vulgaris ssp. vulgaris) is an important crop of temperate climates which provides nearly 30% of the world's annual sugar production and is a source for bioethanol and animal feed. The species belongs to the order of Caryophylalles, is diploid with 2n = 18 chromosomes, has an estimated genome size of 714-758 megabases and shares an ancient genome triplication with other eudicot plants. Leafy beets have been cultivated since Roman times, but sugar beet is one of the most recently domesticated crops. It arose in the late eighteenth century when lines accumulating sugar in the storage root were selected from crosses made with chard and fodder beet. Here we present a reference genome sequence for sugar beet as the first non-rosid, non-asterid eudicot genome, advancing comparative genomics and phylogenetic reconstructions. The genome sequence comprises 567 megabases, of which 85% could be assigned to chromosomes. The assembly covers a large proportion of the repetitive sequence content that was estimated to be 63%. We predicted 27,421 protein-coding genes supported by transcript data and annotated them on the basis of sequence homology. Phylogenetic analyses provided evidence for the separation of Caryophyllales before the split of asterids and rosids, and revealed lineage-specific gene family expansions and losses. We sequenced spinach (Spinacia oleracea), another Caryophyllales species, and validated features that separate this clade from rosids and asterids. Intraspecific genomic variation was analysed based on the genome sequences of sea beet (Beta vulgaris ssp. maritima; progenitor of all beet crops) and four additional sugar beet accessions. We identified seven million variant positions in the reference genome, and also large regions of low variability, indicating artificial selection. The sugar beet genome sequence enables the identification of genes affecting agronomically relevant traits, supports molecular breeding and maximizes the plant's potential in energy biotechnology.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Dohm, Juliane C -- Minoche, Andre E -- Holtgrawe, Daniela -- Capella-Gutierrez, Salvador -- Zakrzewski, Falk -- Tafer, Hakim -- Rupp, Oliver -- Sorensen, Thomas Rosleff -- Stracke, Ralf -- Reinhardt, Richard -- Goesmann, Alexander -- Kraft, Thomas -- Schulz, Britta -- Stadler, Peter F -- Schmidt, Thomas -- Gabaldon, Toni -- Lehrach, Hans -- Weisshaar, Bernd -- Himmelbauer, Heinz -- England -- Nature. 2014 Jan 23;505(7484):546-9. doi: 10.1038/nature12817. Epub 2013 Dec 18.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany [2] Centre for Genomic Regulation (CRG), C. Dr. Aiguader 88, 08003 Barcelona, Spain [3] Universitat Pompeu Fabra (UPF), C. Dr. Aiguader 88, 08003 Barcelona, Spain [4]. ; Bielefeld University, CeBiTec and Department of Biology, Universitatsstrasse 25, 33615 Bielefeld, Germany. ; 1] Centre for Genomic Regulation (CRG), C. Dr. Aiguader 88, 08003 Barcelona, Spain [2] Universitat Pompeu Fabra (UPF), C. Dr. Aiguader 88, 08003 Barcelona, Spain. ; TU Dresden, Department of Biology, Zellescher Weg 20b, 01217 Dresden, Germany. ; University of Leipzig, Department of Computer Science, Hartelstrasse 16-18, 04107 Leipzig, Germany. ; Max Planck Genome Centre Cologne, Carl-von-Linne-Weg 10, 50829 Koln, Germany. ; Syngenta, Box 302, 26123 Landskrona, Sweden. ; KWS SAAT AG, Grimsehlstrasse 31, 37574 Einbeck, Germany. ; 1] Centre for Genomic Regulation (CRG), C. Dr. Aiguader 88, 08003 Barcelona, Spain [2] Universitat Pompeu Fabra (UPF), C. Dr. Aiguader 88, 08003 Barcelona, Spain [3] Institucio Catalana de Recerca i Estudis Avancats (ICREA), Pg. Lluis Companys 23, 08010 Barcelona, Spain. ; Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany. ; 1] Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany [2] Centre for Genomic Regulation (CRG), C. Dr. Aiguader 88, 08003 Barcelona, Spain [3] Universitat Pompeu Fabra (UPF), C. Dr. Aiguader 88, 08003 Barcelona, Spain.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24352233" target="_blank"〉PubMed〈/a〉
    Keywords: Beta vulgaris/*genetics ; Biofuels/supply & distribution ; Carbohydrate Metabolism ; Chromosomes, Plant/genetics ; Crops, Agricultural/*genetics ; Ethanol/metabolism ; Genome, Plant/*genetics ; Genomics ; In Situ Hybridization, Fluorescence ; Molecular Sequence Data ; Phylogeny ; Sequence Analysis, DNA ; Spinacia oleracea/genetics
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
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  • 4
  • 5
    Publication Date: 2013-09-20
    Description: Motivation: The research area metabolomics achieved tremendous popularity and development in the last couple of years. Owing to its unique interdisciplinarity, it requires to combine knowledge from various scientific disciplines. Advances in the high-throughput technology and the consequently growing quality and quantity of data put new demands on applied analytical and computational methods. Exploration of finally generated and analyzed datasets furthermore relies on powerful tools for data mining and visualization. Results: To cover and keep up with these requirements, we have created MeltDB 2.0, a next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions. Availability: The system is publicly available at https://meltdb.cebitec.uni-bielefeld.de . A login is required but freely available. Contact: nkessler@cebitec.uni-bielefeld.de
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 6
    Publication Date: 2016-07-06
    Description: The rapidly increasing availability of microbial genome sequences has led to a growing demand for bioinformatics software tools that support the functional analysis based on the comparison of closely related genomes. By utilizing comparative approaches on gene level it is possible to gain insights into the core genes which represent the set of shared features for a set of organisms under study. Vice versa singleton genes can be identified to elucidate the specific properties of an individual genome. Since initial publication, the EDGAR platform has become one of the most established software tools in the field of comparative genomics. Over the last years, the software has been continuously improved and a large number of new analysis features have been added. For the new version, EDGAR 2.0, the gene orthology estimation approach was newly designed and completely re-implemented. Among other new features, EDGAR 2.0 provides extended phylogenetic analysis features like AAI (Average Amino Acid Identity) and ANI (Average Nucleotide Identity) matrices, genome set size statistics and modernized visualizations like interactive synteny plots or Venn diagrams. Thereby, the software supports a quick and user-friendly survey of evolutionary relationships between microbial genomes and simplifies the process of obtaining new biological insights into their differential gene content. All features are offered to the scientific community via a web-based and therefore platform-independent user interface, which allows easy browsing of precomputed datasets. The web server is accessible at http://edgar.computational.bio .
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 7
    Publication Date: 2014-08-06
    Description: Motivation: Fast algorithms and well-arranged visualizations are required for the comprehensive analysis of the ever-growing size of genomic and transcriptomic next-generation sequencing data. Results: ReadXplorer is a software offering straightforward visualization and extensive analysis functions for genomic and transcriptomic DNA sequences mapped on a reference. A unique specialty of ReadXplorer is the quality classification of the read mappings. It is incorporated in all analysis functions and displayed in ReadXplorer's various synchronized data viewers for (i) the reference sequence, its base coverage as (ii) normalizable plot and (iii) histogram, (iv) read alignments and (v) read pairs. ReadXplorer's analysis capability covers RNA secondary structure prediction, single nucleotide polymorphism and deletion–insertion polymorphism detection, genomic feature and general coverage analysis. Especially for RNA-Seq data, it offers differential gene expression analysis, transcription start site and operon detection as well as RPKM value and read count calculations. Furthermore, ReadXplorer can combine or superimpose coverage of different datasets. Availability and implementation: ReadXplorer is available as open-source software at http://www.readxplorer.org along with a detailed manual. Contact: rhilker@mikrobio.med.uni-giessen.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 8
    Publication Date: 2016-12-20
    Description: Motivation: The vast amount of already available and currently generated read mapping data requires comprehensive visualization, and should benefit from bioinformatics tools offering a wide spectrum of analysis functionality from just one source. Appropriate handling of multiple mapped reads during mapping analyses remains an issue that demands improvement. Results: The capabilities of the read mapping analysis and visualization tool ReadXplorer were vastly enhanced. Here, we present an even finer granulated read mapping classification, improving the level of detail for analyses and visualizations. The spectrum of automatic analysis functions has been broadened to include genome rearrangement detection as well as correlation analysis between two mapping data sets. Existing functions were refined and enhanced, namely the computation of differentially expressed genes, the read count and normalization analysis and the transcription start site detection. Additionally, ReadXplorer 2 features a highly improved support for large eukaryotic data sets and a command line version, enabling its integration into workflows. Finally, the new version is now able to display any kind of tabular results from other bioinformatics tools. Availability and Implementation: http://www.readxplorer.org Contact: readxplorer@computational.bio.uni-giessen.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 9
  • 10
    Publication Date: 2005-07-01
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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