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      Development of a time-series shotgun metagenomics database for monitoring microbial communities at the Pacific coast of Japan

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          Abstract

          Although numerous metagenome, amplicon sequencing-based studies have been conducted to date to characterize marine microbial communities, relatively few have employed full metagenome shotgun sequencing to obtain a broader picture of the functional features of these marine microbial communities. Moreover, most of these studies only performed sporadic sampling, which is insufficient to understand an ecosystem comprehensively. In this study, we regularly conducted seawater sampling along the northeastern Pacific coast of Japan between March 2012 and May 2016. We collected 213 seawater samples and prepared size-based fractions to generate 454 subsets of samples for shotgun metagenome sequencing and analysis. We also determined the sequences of 16S rRNA (n = 111) and 18S rRNA (n = 47) gene amplicons from smaller sample subsets. We thereafter developed the Ocean Monitoring Database for time-series metagenomic data ( http://marine-meta.healthscience.sci.waseda.ac.jp/omd/), which provides a three-dimensional bird’s-eye view of the data. This database includes results of digital DNA chip analysis, a novel method for estimating ocean characteristics such as water temperature from metagenomic data. Furthermore, we developed a novel classification method that includes more information about viruses than that acquired using BLAST. We further report the discovery of a large number of previously overlooked (TAG)n repeat sequences in the genomes of marine microbes. We predict that the availability of this time-series database will lead to major discoveries in marine microbiome research.

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          Most cited references31

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          The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

          SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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            BLAST+: architecture and applications

            Background Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. Results We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. Conclusion The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
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              MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph.

              MEGAHIT is a NGS de novo assembler for assembling large and complex metagenomics data in a time- and cost-efficient manner. It finished assembling a soil metagenomics dataset with 252 Gbps in 44.1 and 99.6 h on a single computing node with and without a graphics processing unit, respectively. MEGAHIT assembles the data as a whole, i.e. no pre-processing like partitioning and normalization was needed. When compared with previous methods on assembling the soil data, MEGAHIT generated a three-time larger assembly, with longer contig N50 and average contig length; furthermore, 55.8% of the reads were aligned to the assembly, giving a fourfold improvement. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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                Author and article information

                Contributors
                takashi.gojobori@kaust.edu.sa
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                9 June 2021
                9 June 2021
                2021
                : 11
                : 12222
                Affiliations
                [1 ]GRID grid.26999.3d, ISNI 0000 0001 2151 536X, Laboratory of Aquatic Molecular Biology and Biotechnology, Graduate School of Agricultural and Life Sciences, , The University of Tokyo, Yayoi, ; Bunkyo, Tokyo Japan
                [2 ]Japan Software Management Co., Ltd., Kinko-cho, Yokohama, Kanagawa Japan
                [3 ]GRID grid.410851.9, ISNI 0000 0004 1764 1824, Japan Fisheries Research and Education Agency, ; Shinurashima, Kanagawa, Yokohama, Kanagawa Japan
                [4 ]GRID grid.140139.e, ISNI 0000 0001 0746 5933, Biodiversity Division, , National Institute for Environmental Studies, ; 16-2 Onogawa, Tsukuba, Ibaraki Japan
                [5 ]GRID grid.411756.0, Faculty of Marine Science and Technology, , Fukui Prefectural University, ; 1-1 Gakuen-cho, Obama, Fukui Japan
                [6 ]GRID grid.275033.0, ISNI 0000 0004 1763 208X, Department of Genetics, , SOKENDAI, ; Mishima, Japan
                [7 ]GRID grid.288127.6, ISNI 0000 0004 0466 9350, National Institute of Genetics, ; Mishima, Japan
                [8 ]GRID grid.505613.4, Preeminent Medical Photonics Education & Research Center, , Hamamatsu University School of Medicine, ; 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka Japan
                [9 ]GRID grid.410786.c, ISNI 0000 0000 9206 2938, School of Marine Biosciences, , Kitasato University, ; Sagamihara, Kanagawa Japan
                [10 ]GRID grid.26999.3d, ISNI 0000 0001 2151 536X, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, , The University of Tokyo, ; 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561 Japan
                [11 ]GRID grid.177174.3, ISNI 0000 0001 2242 4849, Department of Bioscience and Biotechnology, Graduate School of Bioresource and Bioenvironmental Sciences, , Kyushu University, ; Fukuoka, Japan
                [12 ]GRID grid.45672.32, ISNI 0000 0001 1926 5090, Computational Bioscience Research Center, , King Abdullah University of Science and Technology, ; Thuwal, Saudi Arabia
                Article
                91615
                10.1038/s41598-021-91615-3
                8190148
                4108314f-3869-46cd-9b1d-265e4b4cc4af
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 August 2020
                : 27 May 2021
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                © The Author(s) 2021

                Uncategorized
                genetic databases,microbial ecology
                Uncategorized
                genetic databases, microbial ecology

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