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8 results on '"Genivaldo Gueiros Z. Silva"'

1. Diel population and functional synchrony of microbial communities on coral reefs

Author

Linda Wegley Kelly, Craig E. Nelson, Andreas F. Haas, Douglas S. Naliboff, Sandi Calhoun, Craig A. Carlson, Robert A. Edwards, Michael D. Fox, Mark Hatay, Maggie D. Johnson, Emily L. A. Kelly, Yan Wei Lim, Saichetana Macherla, Zachary A. Quinlan, Genivaldo Gueiros Z. Silva, Mark J. A. Vermeij, Brian Zgliczynski, Stuart A. Sandin, Jennifer E. Smith, and Forest Rohwer

Subjects
Science
Abstract

Microbes structure biogeochemical cycles and food webs in the marine environment. Here, the authors sample coral reef-associated microbes across a 24-hour period, showing clear day–night patterns of microbial populations and thus calling for more studies to consider temporal variation in microbiomes at this scale.

Published
2019
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2. Acidobacteria Subgroups and Their Metabolic Potential for Carbon Degradation in Sugarcane Soil Amended With Vinasse and Nitrogen Fertilizers

Author

Miriam Gonçalves de Chaves, Genivaldo Gueiros Z. Silva, Raffaella Rossetto, Robert Alan Edwards, Siu Mui Tsai, and Acacio Aparecido Navarrete

Subjects
soil metagenome, DNA microarray, mineral and organic fertilizers, carbon cycling, microbe-mediated process in soil, Microbiology, QR1-502
Abstract

Acidobacteria is a predominant bacterial phylum in tropical agricultural soils, including sugarcane cultivated soils. The increased need for fertilizers due to the expansion of sugarcane production is a threat to the ability of the soil to maintain its potential for self-regulation in the long term, in witch carbon degradation has essential role. In this study, a culture-independent approach based on high-throughput DNA sequencing and microarray technology was used to perform taxonomic and functional profiling of the Acidobacteria community in a tropical soil under sugarcane (Saccharum spp.) that was supplemented with nitrogen (N) combined with vinasse. These analyses were conducted to identify the subgroup-level responses to chemical changes and the carbon (C) degradation potential of the different Acidobacteria subgroups. Eighteen Acidobacteria subgroups from a total of 26 phylogenetically distinct subgroups were detected based on high-throughput DNA sequencing, and 16 gene families associated with C degradation were quantified using Acidobacteria-derived DNA microarray probes. The subgroups Gp13 and Gp18 presented the most positive correlations with the gene families associated with C degradation, especially those involved in hemicellulose degradation. However, both subgroups presented low abundance in the treatment containing vinasse. In turn, the Gp4 subgroup was the most abundant in the treatment that received vinasse, but did not present positive correlations with the gene families for C degradation analyzed in this study. The metabolic potential for C degradation of the different Acidobacteria subgroups in sugarcane soil amended with N and vinasse can be driven in part through the increase in soil nutrient availability, especially calcium (Ca), magnesium (Mg), potassium (K), aluminum (Al), boron (B) and zinc (Zn). This soil management practice reduces the abundance of Acidobacteria subgroups, including those potentially involved with C degradation in this agricultural soil.

Published
2019
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3. Sequencing at sea: challenges and experiences in Ion Torrent PGM sequencing during the 2013 Southern Line Islands Research Expedition

Author

Yan Wei Lim, Daniel A. Cuevas, Genivaldo Gueiros Z. Silva, Kristen Aguinaldo, Elizabeth A. Dinsdale, Andreas F. Haas, Mark Hatay, Savannah E. Sanchez, Linda Wegley-Kelly, Bas E. Dutilh, Timothy T. Harkins, Clarence C. Lee, Warren Tom, Stuart A. Sandin, Jennifer E. Smith, Brian Zgliczynski, Mark J.A. Vermeij, Forest Rohwer, and Robert A. Edwards

Subjects
Genomics, Sequencing, Expeditions, Metagenomics, Environmental microbiology, Coral reef, Medicine, Biology (General), QH301-705.5
Abstract

Genomics and metagenomics have revolutionized our understanding of marine microbial ecology and the importance of microbes in global geochemical cycles. However, the process of DNA sequencing has always been an abstract extension of the research expedition, completed once the samples were returned to the laboratory. During the 2013 Southern Line Islands Research Expedition, we started the first effort to bring next generation sequencing to some of the most remote locations on our planet. We successfully sequenced twenty six marine microbial genomes, and two marine microbial metagenomes using the Ion Torrent PGM platform on the Merchant Yacht Hanse Explorer. Onboard sequence assembly, annotation, and analysis enabled us to investigate the role of the microbes in the coral reef ecology of these islands and atolls. This analysis identified phosphonate as an important phosphorous source for microbes growing in the Line Islands and reinforced the importance of L-serine in marine microbial ecosystems. Sequencing in the field allowed us to propose hypotheses and conduct experiments and further sampling based on the sequences generated. By eliminating the delay between sampling and sequencing, we enhanced the productivity of the research expedition. By overcoming the hurdles associated with sequencing on a boat in the middle of the Pacific Ocean we proved the flexibility of the sequencing, annotation, and analysis pipelines.

Published
2014
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4. FOCUS: an alignment-free model to identify organisms in metagenomes using non-negative least squares

Author

Genivaldo Gueiros Z. Silva, Daniel A. Cuevas, Bas E. Dutilh, and Robert A. Edwards

Subjects
Metagenomes, Modeling, k-mer, Medicine, Biology (General), QH301-705.5
Abstract

One of the major goals in metagenomics is to identify the organisms present in a microbial community from unannotated shotgun sequencing reads. Taxonomic profiling has valuable applications in biological and medical research, including disease diagnostics. Most currently available approaches do not scale well with increasing data volumes, which is important because both the number and lengths of the reads provided by sequencing platforms keep increasing. Here we introduce FOCUS, an agile composition based approach using non-negative least squares (NNLS) to report the organisms present in metagenomic samples and profile their abundances. FOCUS was tested with simulated and real metagenomes, and the results show that our approach accurately predicts the organisms present in microbial communities. FOCUS was implemented in Python. The source code and web-sever are freely available at http://edwards.sdsu.edu/FOCUS.

Published
2014
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7. Critical Assessment of Metagenome Interpretation – a benchmark of computational metagenomics software

Author

Alexander Sczyrba, Peter Hofmann, Peter Belmann, David Koslicki, Stefan Janssen, Johannes Dröge, Ivan Gregor, Stephan Majda, Jessika Fiedler, Eik Dahms, Andreas Bremges, Adrian Fritz, Ruben Garrido-Oter, Tue Sparholt Jørgensen, Nicole Shapiro, Philip D. Blood, Alexey Gurevich, Yang Bai, Dmitrij Turaev, Matthew Z. DeMaere, Rayan Chikhi, Niranjan Nagarajan, Christopher Quince, Fernando Meyer, Monika Balvoit, Lars Hestbjerg Hansen, Søren J. Sørensen, Burton K. H. Chia, Bertrand Denis, Jeff L. Froula, Zhong Wang, Robert Egan, Dongwan Don Kang, Jeffrey J. Cook, Charles Deltel, Michael Beckstette, Claire Lemaitre, Pierre Peterlongo, Guillaume Rizk, Dominique Lavenier, Yu-Wei Wu, Steven W. Singer, Chirag Jain, Marc Strous, Heiner Klingenberg, Peter Meinicke, Michael Barton, Thomas Lingner, Hsin-Hung Lin, Yu-Chieh Liao, Genivaldo Gueiros Z. Silva, Daniel A. Cuevas, Robert A. Edwards, Surya Saha, Vitor C. Piro, Bernhard Y. Renard, Mihai Pop, Hans-Peter Klenk, Markus Göker, Nikos C. Kyrpides, Tanja Woyke, Julia A. Vorholt, Paul Schulze-Lefert, Edward M. Rubin, Aaron E. Darling, Thomas Rattei, Alice C. McHardy, Center for Biotechnology (CeBiTec), Universität Bielefeld = Bielefeld University, Technische Fakultät, Universität Bielefeld, Algorithmische Bioinformatik [Düsseldorf], Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], Computational Biology of Infection Research [Braunschweig], Helmholtz Centre for Infection Research (HZI), Braunschweig Integrated Centre of Systems Biology [Braunschweig] (BRICS), Technische Universität Braunschweig = Technical University of Braunschweig [Braunschweig]-Helmholtz Centre for Infection Research (HZI), Department of Mathematics [Corvallis, Oregon], Oregon State University (OSU), Department of Computer Science and Engineering [Univ California San Diego] (CSE - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), Department of Pediatrics [Univ California San Diego] (UC San Diego), School of Medicine [Univ California San Diego] (UC San Diego), University of California (UC)-University of California (UC)-University of California [San Diego] (UC San Diego), Max Planck Institute for Informatics [Saarbrücken], Faculty of Biology [Essen], Universität Duisburg-Essen = University of Duisburg-Essen [Essen], German Center for Infection Research - partner site Hannover-Braunschweig (DZIF), Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research (MPIPZ), Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf]-Max Planck Institute for Plant Breeding Research (MPIPZ)-Universität zu Köln = University of Cologne, Department of Environmental Science [Roskilde] (ENVS), Aarhus University [Aarhus], Section of Microbiology [Copenhagen], Department of Biology [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)-Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Department of Science and Environment [Roskilde], Roskilde University, DOE Joint Genome Institute [Walnut Creek], Pittsburgh Supercomputing Center (PSC), Center for Algorithmic Biotechnology [Saint Petersburg], Institute of Translational Biomedicine [Saint-Petersburg], Saint Petersburg University (SPBU)-Saint Petersburg University (SPBU), Centre of Excellence for Plant and Microbial Sciences (CEPAMS), John Innes Centre [Norwich], Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC)-Chinese Academy of Agricultural Sciences (CAAS), Department of Microbiology and Ecosystem Science [Vienna], University of Vienna [Vienna], iThree Institute, University of Technology Sydney (UTS), Bioinformatics and Sequence Analysis (BONSAI), Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Department of Computational and Systems Biology [Singapore], Genome Institute of Singapore (GIS), Department of Microbiology and Infection [Coventry], Warwick Medical School, University of Warwick [Coventry]-University of Warwick [Coventry], Intel Corporation [Hillsboro], Intel Corporation [USA], Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Department of Molecular Infection Biology [Braunschweig], Joint BioEnergy Institute [Emeryville], Graduate Institute of Biomedical Informatics [Taipei], Taipei Medical University, Biological Systems and Engineering [LBNL Berkeley], Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Max planck Institute for Biology of Ageing [Cologne], Energy Engineering and Geomicrobiology [Calgary], University of Calgary, Institute of Microbiology and Genetics [Göttingen], Georg-August-University = Georg-August-Universität Göttingen, University Medical Center Göttingen (UMG), Institute of Population Health Sciences [Taiwan], National Health Research Institutes [Taiwan] (NHRI), San Diego State University (SDSU), Boyce Thompson Institute [Ithaca], Robert Koch Institute [Berlin] (RKI), Ministry of Education [Brazil], Center for Bioinformatics and Computational Biology [Maryland] (CBCB), University of Maryland [College Park], University of Maryland System-University of Maryland System, School of Biology [Newcastle upon Tyne], Newcastle University [Newcastle], Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH / Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures (DSMZ), biological sciences department [Jeddah], King Abdulaziz University, Institute of Microbiology [Zurich], Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Department of Computer Science and Engineering [San Diego] (CSE-UCSD), University of California-University of California, Department of Pediatrics [san Diego], UC San Diego School of Medicine, Universität Duisburg-Essen [Essen], Universität zu Köln-Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf]-Max Planck Institute for Plant Breeding Research (MPIPZ), University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)-Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), John Innes Centre [Norwich]-Chinese Academy of Agricultural Sciences (CAAS), Centre National de la Recherche Scientifique (CNRS)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Georg-August-University [Göttingen], Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique)

Subjects
0303 health sciences, Biological data, business.industry, Benchmarking, Biology, Data science, Genome, 03 medical and health sciences, 0302 clinical medicine, Software, Metagenomics, Profiling (information science), Critical assessment, Taxonomic rank, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

Published
2017
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8. An Agile Functional Analysis of Metagenomic Data Using SUPER-FOCUS

Author

Genivaldo Gueiros Z, Silva, Fabyano A C, Lopes, and Robert A, Edwards

Subjects
Databases, Genetic, Computational Biology, Metagenome, Metagenomics
Abstract

One of the main goals in metagenomics is to identify the functional profile of a microbial community from unannotated shotgun sequencing reads. Functional annotation is important in biological research because it enables researchers to identify the abundance of functional genes of the organisms present in the sample, answering the question, "What can the organisms in the sample do?" Most currently available approaches do not scale with increasing data volumes, which is important because both the number and lengths of the reads provided by sequencing platforms keep increasing. Here, we present SUPER-FOCUS, SUbsystems Profile by databasE Reduction using FOCUS, an agile homology-based approach using a reduced reference database to report the subsystems present in metagenomic datasets and profile their abundances. SUPER-FOCUS was tested with real metagenomes, and the results show that it accurately predicts the subsystems present in the profiled microbial communities, is computationally efficient, and up to 1000 times faster than other tools. SUPER-FOCUS is freely available at http://edwards.sdsu.edu/SUPERFOCUS .

Published
2017

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