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      Elucidation of the Biosynthetic Pathway of Vitamin B Groups and Potential Secondary Metabolite Gene Clusters Via Genome Analysis of a Marine Bacterium Pseudoruegeria sp. M32A2M

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          Abstract

          The symbiotic nature of the relationship between algae and marine bacteria is well-studied among the complex microbial interactions. The mutual profit between algae and bacteria occurs via nutrient and vitamin exchange. It is necessary to analyze the genome sequence of a bacterium to predict its symbiotic relationships. In this study, the genome of a marine bacterium, Pseudoruegeria sp. M32A2M , isolated from the south-eastern isles (GeoJe-Do) of South Korea, was sequenced and analyzed. A draft genome (91 scaffolds) of 5.5 Mb with a DNA G+C content of 62.4% was obtained. In total, 5,101 features were identified from gene annotation, and 4,927 genes were assigned to functional proteins. We also identified transcription core proteins, RNA polymerase subunits, and sigma factors. In addition, full flagella-related gene clusters involving the flagellar body, motor, regulator, and other accessory compartments were detected even though the genus Pseudoruegeria is known to comprise non-motile bacteria. Examination of annotated KEGG pathways revealed that Pseudoruegeria sp. M32A2M has the metabolic pathways for all seven vitamin Bs, including thiamin (vitamin B1), biotin (vitamin B7), and cobalamin (vitamin B12), which are necessary for symbiosis with vitamin B auxotroph algae. We also identified gene clusters for seven secondary metabolites including ectoine, homoserine lactone, beta-lactone, terpene, lasso peptide, bacteriocin, and non-ribosomal proteins.

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          Most cited references62

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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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            NCBI prokaryotic genome annotation pipeline

            Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/.
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              KEGG as a reference resource for gene and protein annotation

              KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an integrated database resource for biological interpretation of genome sequences and other high-throughput data. Molecular functions of genes and proteins are associated with ortholog groups and stored in the KEGG Orthology (KO) database. The KEGG pathway maps, BRITE hierarchies and KEGG modules are developed as networks of KO nodes, representing high-level functions of the cell and the organism. Currently, more than 4000 complete genomes are annotated with KOs in the KEGG GENES database, which can be used as a reference data set for KO assignment and subsequent reconstruction of KEGG pathways and other molecular networks. As an annotation resource, the following improvements have been made. First, each KO record is re-examined and associated with protein sequence data used in experiments of functional characterization. Second, the GENES database now includes viruses, plasmids, and the addendum category for functionally characterized proteins that are not represented in complete genomes. Third, new automatic annotation servers, BlastKOALA and GhostKOALA, are made available utilizing the non-redundant pangenome data set generated from the GENES database. As a resource for translational bioinformatics, various data sets are created for antimicrobial resistance and drug interaction networks.
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                Author and article information

                Journal
                J Microbiol Biotechnol
                J Microbiol Biotechnol
                Journal of Microbiology and Biotechnology
                The Korean Society for Microbiology and Biotechnology
                1017-7825
                1738-8872
                28 April 2020
                17 January 2020
                : 30
                : 4
                : 505-514
                Affiliations
                [1 ]Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
                [2 ]KI for the BioCentury, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
                [3 ]Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
                Author notes
                [* ] Corresponding authors S.C. Phone: +82-42-350-2660 Fax: +82-42-350-5620 E-mail: shcho95@ 123456kaist.ac.kr B.-K.C. E-mail: bcho@ 123456kaist.ac.kr
                Article
                JMB-30-4-505
                10.4014/jmb.1911.11006
                9728324
                31986560
                48c7b8d1-d8c5-471e-a463-ac05077357a0
                Copyright© 2020 by The Korean Society for Microbiology and Biotechnology

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

                History
                : 5 November 2019
                : 12 January 2020
                Categories
                Research article
                Molecular and Cellular Microbiology (MCM)
                Molecular Genetics, Omics, and Systems Biology

                pseudoruegeria sp. m32a2m,whole-genome sequencing,vitamin b,secondary metabolite

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