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      Opportunities and Challenges for Genomic Data Analyses in Biobanks: A Call for Papers

      The GSA Journals are calling for submissions of papers on biobank-scale genomic data analyses. The closing date for submissions is May 31 2024.

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      CeMbio - The Caenorhabditis elegans Microbiome Resource

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          Abstract

          The study of microbiomes by sequencing has revealed a plethora of correlations between microbial community composition and various life-history characteristics of the corresponding host species. However, inferring causation from correlation is often hampered by the sheer compositional complexity of microbiomes, even in simple organisms. Synthetic communities offer an effective approach to infer cause-effect relationships in host-microbiome systems. Yet the available communities suffer from several drawbacks, such as artificial (thus non-natural) choice of microbes, microbe-host mismatch ( e.g., human microbes in gnotobiotic mice), or hosts lacking genetic tractability. Here we introduce CeMbio, a simplified natural Caenorhabditis elegans microbiota derived from our previous meta-analysis of the natural microbiome of this nematode. The CeMbio resource is amenable to all strengths of the C. elegans model system, strains included are readily culturable, they all colonize the worm gut individually, and comprise a robust community that distinctly affects nematode life-history. Several tools have additionally been developed for the CeMbio strains, including diagnostic PCR primers, completely sequenced genomes, and metabolic network models. With CeMbio, we provide a versatile resource and toolbox for the in-depth dissection of naturally relevant host-microbiome interactions in C. elegans.

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          Most cited references34

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          A protocol for generating a high-quality genome-scale metabolic reconstruction.

          Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.
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            Using DECIPHER v2.0 to Analyze Big Biological Sequence Data in R

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              BUSCO: Assessing Genome Assembly and Annotation Completeness

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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                15 July 2020
                September 2020
                : 10
                : 9
                : 3025-3039
                Affiliations
                [* ]Department of Evolutionary Ecology and Genetics, Christian-Albrechts University, Kiel, Germany
                []Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, Houston TX
                []Medical Systems Biology, University Medical Center Schleswig-Holstein, Kiel, Germany
                [§ ]Institute of Biology of the Ecole Normale Supérieure, Paris, France
                [** ]Department of Integrative Biology, University of California, Berkeley CA
                Author notes
                [2 ]Corresponding authors: Alkek Center for Metagenomics and Microbiome Research, Baylor College of Medicine, 1 Baylor Plaza, MS:385, Houston, Texas 77030. E-mail: Buck.Samuel@ 123456bcm.edu . Evolutionary Ecology Genetics, Zoological Institute, Christian-Albrecht Universitaet zu Kiel, Am Botanischen Garten 1-9, 24118 Kiel, Germany. E-mail: hschulenburg@ 123456zoologie.uni-kiel.de
                [1]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-0336-8711
                http://orcid.org/0000-0003-3568-0766
                http://orcid.org/0000-0002-5041-1954
                http://orcid.org/0000-0003-2849-1713
                http://orcid.org/0000-0003-4194-043X
                http://orcid.org/0000-0002-2635-3141
                http://orcid.org/0000-0003-1332-2220
                http://orcid.org/0000-0001-8004-9514
                http://orcid.org/0000-0002-1413-913X
                http://orcid.org/0000-0002-4347-3997
                Article
                GGG_401309
                10.1534/g3.120.401309
                7466993
                32669368
                958d18d8-5156-423b-bf26-8f06c5b7c7e4
                Copyright © 2020 Dirksen et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 April 2020
                : 07 July 2020
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 81, Pages: 15
                Funding
                Funded by: NIH, DOI https://doi.org/10.13039/100000052;
                Award ID: DP2DK116645
                Funded by: JGI
                Award ID: CSP-503338
                Funded by: German Science Foundation
                Award ID: CRC 1182
                Categories
                Software and Data Resources

                Genetics
                c. elegans,microbiome resource,host-microbe interactions,synthetic communities,metabolic networks

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