ALBERT

Description: Metagenomics has changed the face of virus discovery by enabling the accurate identification of viral genome sequences without requiring isolation of the viruses. As a result, metagenomic virus discovery leaves the first and most fundamental question about any novel virus unanswered: What host does the virus infect? The diversity of the global virosphere and the volumes of data obtained in metagenomic sequencing projects demand computational tools for virus–host prediction. We focus on bacteriophages (phages, viruses that infect bacteria), the most abundant and diverse group of viruses found in environmental metagenomes. By analyzing 820 phages with annotated hosts, we review and assess the predictive power of in silico phage–host signals. Sequence homology approaches are the most effective at identifying known phage–host pairs. Compositional and abundance-based methods contain significant signal for phage–host classification, providing opportunities for analyzing the unknowns in viral metagenomes. Together, these computational approaches further our knowledge of the interactions between phages and their hosts. Importantly, we find that all reviewed signals significantly link phages to their hosts, illustrating how current knowledge and insights about the interaction mechanisms and ecology of coevolving phages and bacteria can be exploited to predict phage–host relationships, with potential relevance for medical and industrial applications.

Description: Species interaction networks are shaped by abiotic and biotic factors. Here, as part of the Tara Oceans project, we studied the photic zone interactome using environmental factors and organismal abundance profiles and found that environmental factors are incomplete predictors of community structure. We found associations across plankton functional types and phylogenetic groups to be nonrandomly distributed on the network and driven by both local and global patterns. We identified interactions among grazers, primary producers, viruses, and (mainly parasitic) symbionts and validated network-generated hypotheses using microscopy to confirm symbiotic relationships. We have thus provided a resource to support further research on ocean food webs and integrating biological components into ocean models.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Lima-Mendez, Gipsi -- Faust, Karoline -- Henry, Nicolas -- Decelle, Johan -- Colin, Sebastien -- Carcillo, Fabrizio -- Chaffron, Samuel -- Ignacio-Espinosa, J Cesar -- Roux, Simon -- Vincent, Flora -- Bittner, Lucie -- Darzi, Youssef -- Wang, Jun -- Audic, Stephane -- Berline, Leo -- Bontempi, Gianluca -- Cabello, Ana M -- Coppola, Laurent -- Cornejo-Castillo, Francisco M -- d'Ovidio, Francesco -- De Meester, Luc -- Ferrera, Isabel -- Garet-Delmas, Marie-Jose -- Guidi, Lionel -- Lara, Elena -- Pesant, Stephane -- Royo-Llonch, Marta -- Salazar, Guillem -- Sanchez, Pablo -- Sebastian, Marta -- Souffreau, Caroline -- Dimier, Celine -- Picheral, Marc -- Searson, Sarah -- Kandels-Lewis, Stefanie -- Tara Oceans coordinators -- Gorsky, Gabriel -- Not, Fabrice -- Ogata, Hiroyuki -- Speich, Sabrina -- Stemmann, Lars -- Weissenbach, Jean -- Wincker, Patrick -- Acinas, Silvia G -- Sunagawa, Shinichi -- Bork, Peer -- Sullivan, Matthew B -- Karsenti, Eric -- Bowler, Chris -- de Vargas, Colomban -- Raes, Jeroen -- New York, N.Y. -- Science. 2015 May 22;348(6237):1262073. doi: 10.1126/science.1262073.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Microbiology and Immunology, Rega Institute KU Leuven, Herestraat 49, 3000 Leuven, Belgium. VIB Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. Department of Applied Biological Sciences (DBIT) Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. ; Station Biologique de Roscoff, CNRS, UMR 7144, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, Universite Pierre et Marie Curie (UPMC) Universite Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. ; Station Biologique de Roscoff, CNRS, UMR 7144, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, Universite Pierre et Marie Curie (UPMC) Universite Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), Inserm U1024, CNRS UMR 8197, Paris, F-75005 France. ; Department of Microbiology and Immunology, Rega Institute KU Leuven, Herestraat 49, 3000 Leuven, Belgium. VIB Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. Department of Applied Biological Sciences (DBIT) Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. Interuniversity Institute of Bioinformatics in Brussels (IB), ULB Machine Learning Group, Computer Science Department, Universite Libre de Bruxelles (ULB), Brussels, Belgium. ; Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, 85721, USA. ; VIB Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), Inserm U1024, CNRS UMR 8197, Paris, F-75005 France. ; Station Biologique de Roscoff, CNRS, UMR 7144, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, Universite Pierre et Marie Curie (UPMC) Universite Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), Inserm U1024, CNRS UMR 8197, Paris, F-75005 France. Institut de Biologie Paris-Seine, CNRS FR3631, F-75005, Paris, France. ; VIB Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. Department of Applied Biological Sciences (DBIT) Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. ; Department of Microbiology and Immunology, Rega Institute KU Leuven, Herestraat 49, 3000 Leuven, Belgium. VIB Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. ; CNRS, UMR 7093, Laboratoire d'Oceanographie de Villefranche (LOV), Observatoire Oceanologique, F-06230 Villefranche-sur-mer, France. Sorbonne Universites, UPMC Paris 06, UMR 7093, Laboratoire d'Oceanographie de Villefranche (LOV), Observatoire Oceanologique, F-06230 Villefranche-sur-mer, France. ; Interuniversity Institute of Bioinformatics in Brussels (IB), ULB Machine Learning Group, Computer Science Department, Universite Libre de Bruxelles (ULB), Brussels, Belgium. ; Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM)-Consejo Superior de Investigaciones Cientificas (CSIC), Pg. Maritim de la Barceloneta, 37-49, Barcelona E08003, Spain. ; Sorbonne Universites, UPMC, Universite Paris 06, CNRS-Institut pour la Recherche et le Developpement-Museum National d'Histoire Naturelle, Laboratoire d'Oceanographie et du Climat: Experimentations et Approches Numeriques (LOCEAN) Laboratory, 4 Place Jussieu, 75005, Paris, France. ; KU Leuven, Laboratory of Aquatic Ecology, Evolution and Conservation, Charles Deberiotstraat 32, 3000 Leuven. ; PANGAEA, Data Publisher for Earth and Environmental Science, University of Bremen, Hochschulring 18, 28359 Bremen, Germany. MARUM, Center for Marine Environmental Sciences, University of Bremen, Hochschulring 18, 28359 Bremen, Germany. ; Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. Directors' Research, European Molecular Biology Laboratory, Heidelberg, Germany. ; Institute for Chemical Research, Kyoto University, Gokasho, Uji, 611-0011 Kyoto, Japan. ; Department of Geosciences, Laboratoire de Meteorologie Dynamique (LMD), Ecole Normale Superieure, 24 rue Lhomond, 75231 Paris Cedex 05, France. Laboratoire de Physique des Ocean, Universite de Bretagne Occidentale (UBO)-Institut Universaire Europeen de la Mer (IUEM), Palce Copernic, 29820 Polouzane, France. ; Commissariat a l'Energie Atomique (CEA), Genoscope, 2 rue Gaston Cremieux, 91000 Evry, France. CNRS, UMR 8030, 2 rue Gaston Cremieux, 91000 Evry, France. Universite d'Evry, UMR 8030, CP5706 Evry, France. ; Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. ; Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. Max-Delbruck-Centre for Molecular Medicine, 13092 Berlin, Germany. ; Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), Inserm U1024, CNRS UMR 8197, Paris, F-75005 France. Directors' Research, European Molecular Biology Laboratory, Heidelberg, Germany. jeroen.raes@vib-kuleuven.be vargas@sb-roscoff.fr cbowler@biologie.ens.fr karsenti@embl.de. ; Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), Inserm U1024, CNRS UMR 8197, Paris, F-75005 France. jeroen.raes@vib-kuleuven.be vargas@sb-roscoff.fr cbowler@biologie.ens.fr karsenti@embl.de. ; Station Biologique de Roscoff, CNRS, UMR 7144, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, Universite Pierre et Marie Curie (UPMC) Universite Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. jeroen.raes@vib-kuleuven.be vargas@sb-roscoff.fr cbowler@biologie.ens.fr karsenti@embl.de. ; Department of Microbiology and Immunology, Rega Institute KU Leuven, Herestraat 49, 3000 Leuven, Belgium. VIB Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. Department of Applied Biological Sciences (DBIT) Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. jeroen.raes@vib-kuleuven.be vargas@sb-roscoff.fr cbowler@biologie.ens.fr karsenti@embl.de.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25999517" target="_blank"〉PubMed〈/a〉

Description: Fecal microbiome variation in the average, healthy population has remained under-investigated. Here, we analyzed two independent, extensively phenotyped cohorts: the Belgian Flemish Gut Flora Project (FGFP; discovery cohort; N = 1106) and the Dutch LifeLines-DEEP study (LLDeep; replication; N = 1135). Integration with global data sets (N combined = 3948) revealed a 14-genera core microbiota, but the 664 identified genera still underexplore total gut diversity. Sixty-nine clinical and questionnaire-based covariates were found associated to microbiota compositional variation with a 92% replication rate. Stool consistency showed the largest effect size, whereas medication explained largest total variance and interacted with other covariate-microbiota associations. Early-life events such as birth mode were not reflected in adult microbiota composition. Finally, we found that proposed disease marker genera associated to host covariates, urging inclusion of the latter in study design.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Falony, Gwen -- Joossens, Marie -- Vieira-Silva, Sara -- Wang, Jun -- Darzi, Youssef -- Faust, Karoline -- Kurilshikov, Alexander -- Bonder, Marc Jan -- Valles-Colomer, Mireia -- Vandeputte, Doris -- Tito, Raul Y -- Chaffron, Samuel -- Rymenans, Leen -- Verspecht, Chloe -- De Sutter, Lise -- Lima-Mendez, Gipsi -- D'hoe, Kevin -- Jonckheere, Karl -- Homola, Daniel -- Garcia, Roberto -- Tigchelaar, Ettje F -- Eeckhaudt, Linda -- Fu, Jingyuan -- Henckaerts, Liesbet -- Zhernakova, Alexandra -- Wijmenga, Cisca -- Raes, Jeroen -- New York, N.Y. -- Science. 2016 Apr 29;352(6285):560-4. doi: 10.1126/science.aad3503. Epub 2016 Apr 28.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉KU Leuven-University of Leuven, Department of Microbiology and Immunology, Leuven, Belgium. VIB, Center for the Biology of Disease, Leuven, Belgium. ; KU Leuven-University of Leuven, Department of Microbiology and Immunology, Leuven, Belgium. VIB, Center for the Biology of Disease, Leuven, Belgium. Vrije Universiteit Brussel, Faculty of Sciences and Bioengineering Sciences, Microbiology Unit, Brussels, Belgium. ; Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia. Novosibirsk State University, Novosibirsk, Russia. ; University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, Netherlands. ; VIB, Center for the Biology of Disease, Leuven, Belgium. Vrije Universiteit Brussel, Faculty of Sciences and Bioengineering Sciences, Microbiology Unit, Brussels, Belgium. ; University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, Netherlands. Top Institute Food and Nutrition, Wageningen, Netherlands. ; University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, Netherlands. University of Groningen, University Medical Center Groningen, Department of Pediatrics, 9700 RB Groningen, Netherlands. ; KU Leuven-University of Leuven, Department of Microbiology and Immunology, Leuven, Belgium. KU Leuven-University Hospitals Leuven, Department of General Internal Medicine, Leuven, Belgium. ; KU Leuven-University of Leuven, Department of Microbiology and Immunology, Leuven, Belgium. VIB, Center for the Biology of Disease, Leuven, Belgium. Vrije Universiteit Brussel, Faculty of Sciences and Bioengineering Sciences, Microbiology Unit, Brussels, Belgium. jeroen.raes@kuleuven.be.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27126039" target="_blank"〉PubMed〈/a〉

Description: : When designing a case-control study to investigate differences in microbial composition, it is fundamental to assess the sample sizes needed to detect an hypothesized difference with sufficient statistical power. Our application includes power calculation for (i) a recoded version of the two-sample generalized Wald test of the ‘HMP’ R -package for comparing community composition, and (ii) the Wilcoxon-Mann-Whitney test for comparing operational taxonomic unit-specific abundances between two samples (optional). The simulation-based power calculations make use of the Dirichlet-Multinomial model to describe and generate abundances. The web interface allows for easy specification of sample and effect sizes. As an illustration of our application, we compared the statistical power of the two tests, with and without stratification of samples. We observed that statistical power increases considerably when stratification is employed, meaning that less samples are needed to detect the same effect size with the same power. Availability and implementation : The web interface is written in R code using Shiny ( RStudio Inc., 2016 ) and it is available at https://fedematt.shinyapps.io/shinyMB . The R code for the recoded generalized Wald test can be found at https://github.com/mafed/msWaldHMP . Contact: Federico.Mattiello@UGent.be

Description: The radiated-power-density diagnostic on the equatorial midplane for the NSTX-U tokamak will be upgraded to measure the radial structure of the photon emissivity profile with an improved radial resolution. This diagnostic will enhance the characterization and studies of power balance, impurity transport, and MHD. The layout and response expected of the new system is shown for different plasma conditions and impurity concentrations. The effect of toroidal rotation driving poloidal asymmetries in the core radiation from high-Z impurities is also addressed.

Description: With high-throughput sequencing (HTS), we are able to explore the hidden world of microscopic organisms to an unpre-cedented level. The fast development of molecular technology and statistical methods means that microbial ecologists must keep their toolkits updated. Here, we review and evaluate some of the more widely adopted and emerging techniques for analysis of diversity and community composition, and the inference of species interactions from co-occurrence data generated by HTS of marker genes. We emphasize the importance of observational biases and statistical properties of the data and methods. The aim of the review is to critically discuss the advantages and disadvantages of established and emerging statistical methods, and to contribute to the integration of HTS-based marker gene data into community ecology.