ALBERT

Description: Microbes are dominant drivers of biogeochemical processes, yet drawing a global picture of functional diversity, microbial community structure, and their ecological determinants remains a grand challenge. We analyzed 7.2 terabases of metagenomic data from 243 Tara Oceans samples from 68 locations in epipelagic and mesopelagic waters across the globe to generate an ocean microbial reference gene catalog with 〉40 million nonredundant, mostly novel sequences from viruses, prokaryotes, and picoeukaryotes. Using 139 prokaryote-enriched samples, containing 〉35,000 species, we show vertical stratification with epipelagic community composition mostly driven by temperature rather than other environmental factors or geography. We identify ocean microbial core functionality and reveal that 〉73% of its abundance is shared with the human gut microbiome despite the physicochemical differences between these two ecosystems.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Sunagawa, Shinichi -- Coelho, Luis Pedro -- Chaffron, Samuel -- Kultima, Jens Roat -- Labadie, Karine -- Salazar, Guillem -- Djahanschiri, Bardya -- Zeller, Georg -- Mende, Daniel R -- Alberti, Adriana -- Cornejo-Castillo, Francisco M -- Costea, Paul I -- Cruaud, Corinne -- d'Ovidio, Francesco -- Engelen, Stefan -- Ferrera, Isabel -- Gasol, Josep M -- Guidi, Lionel -- Hildebrand, Falk -- Kokoszka, Florian -- Lepoivre, Cyrille -- Lima-Mendez, Gipsi -- Poulain, Julie -- Poulos, Bonnie T -- Royo-Llonch, Marta -- Sarmento, Hugo -- Vieira-Silva, Sara -- Dimier, Celine -- Picheral, Marc -- Searson, Sarah -- Kandels-Lewis, Stefanie -- Tara Oceans coordinators -- Bowler, Chris -- de Vargas, Colomban -- Gorsky, Gabriel -- Grimsley, Nigel -- Hingamp, Pascal -- Iudicone, Daniele -- Jaillon, Olivier -- Not, Fabrice -- Ogata, Hiroyuki -- Pesant, Stephane -- Speich, Sabrina -- Stemmann, Lars -- Sullivan, Matthew B -- Weissenbach, Jean -- Wincker, Patrick -- Karsenti, Eric -- Raes, Jeroen -- Acinas, Silvia G -- Bork, Peer -- New York, N.Y. -- Science. 2015 May 22;348(6237):1261359. doi: 10.1126/science.1261359.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. sunagawa@embl.de karsenti@embl.de jeroen.raes@vib-kuleuven.be sacinas@icm.csic.es bork@embl.de. ; Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. ; Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium. Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. Department of Applied Biological Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. ; CEA-Institut de Genomique, GENOSCOPE, 2 rue Gaston Cremieux, 91057 Evry, France. ; Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM)-CSIC, Pg. Maritim de la Barceloneta, 37-49, Barcelona E08003, Spain. ; Sorbonne Universites, UPMC, Universite Paris 06, CNRS-IRD-MNHN, LOCEAN Laboratory, 4 Place Jussieu, 75005 Paris France. ; CNRS, UMR 7093, Laboratoire d'Oceanographie de Villefranche-sur-Mer, Observatoire Oceanologique, F-06230 Villefranche-sur-mer, France. Sorbonne Universites, UPMC Universite Paris 06, UMR 7093, LOV, Observatoire Oceanologique, F-06230 Villefranche-sur-mer, France. ; Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, F-75005 Paris, France. Laboratoire de Physique des Oceans UBO-IUEM, Place Copernic 29820 Plouzane, France. ; Aix Marseille Universite CNRS IGS UMR 7256, 13288 Marseille, France. ; Department of Ecology and Evolutionary Biology, University of Arizona, 1007 East Lowell Street, Tucson, AZ 85721, USA. ; Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM)-CSIC, Pg. Maritim de la Barceloneta, 37-49, Barcelona E08003, Spain. Department of Hydrobiology, Federal University of Sao Carlos (UFSCar), Rodovia Washington Luiz, 13565-905 Sao Carlos, Sao Paulo, Brazil. ; Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, F-75005 Paris, France. CNRS, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, UPMC Universite Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. ; Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. Directors' Research, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. ; Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, F-75005 Paris, France. ; CNRS, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. Sorbonne Universites, UPMC Universite Paris 06, UMR 7144, Station Biologique de Roscoff, Place Georges Teissier, 29680 Roscoff, France. ; CNRS UMR 7232, BIOM, Avenue du Fontaule, 66650 Banyuls-sur-Mer, France. Sorbonne Universites Paris 06, OOB UPMC, Avenue du Fontaule, 66650 Banyuls-sur-Mer, France. ; Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Naples, Italy. ; CEA-Institut de Genomique, GENOSCOPE, 2 rue Gaston Cremieux, 91057 Evry, France. CNRS, UMR 8030, CP5706, Evry, France. Universite d'Evry, UMR 8030, CP5706, Evry, France. ; Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-001, Japan. ; PANGAEA, Data Publisher for Earth and Environmental Science, University of Bremen, Bremen, Germany. MARUM, Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany. ; Department of Geosciences, Laboratoire de Meteorologie Dynamique (LMD), Ecole Normale Superieure, 24 rue Lhomond, 75231 Paris Cedex 05, France. Laboratoire de Physique des Oceans UBO-IUEM, Place Copernic, 29820 Plouzane, France. ; Ecole Normale Superieure, Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, F-75005 Paris, France. Directors' Research, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. sunagawa@embl.de karsenti@embl.de jeroen.raes@vib-kuleuven.be sacinas@icm.csic.es bork@embl.de. ; Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium. Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. Department of Applied Biological Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. sunagawa@embl.de karsenti@embl.de jeroen.raes@vib-kuleuven.be sacinas@icm.csic.es bork@embl.de. ; Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM)-CSIC, Pg. Maritim de la Barceloneta, 37-49, Barcelona E08003, Spain. sunagawa@embl.de karsenti@embl.de jeroen.raes@vib-kuleuven.be sacinas@icm.csic.es bork@embl.de. ; Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany. Max-Delbruck-Centre for Molecular Medicine, 13092 Berlin, Germany. sunagawa@embl.de karsenti@embl.de jeroen.raes@vib-kuleuven.be sacinas@icm.csic.es bork@embl.de.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25999513" target="_blank"〉PubMed〈/a〉

Description: The biological carbon pump is the process by which CO2 is transformed to organic carbon via photosynthesis, exported through sinking particles, and finally sequestered in the deep ocean. While the intensity of the pump correlates with plankton community composition, the underlying ecosystem structure driving the process remains largely uncharacterized. Here we use environmental and metagenomic data gathered during the Tara Oceans expedition to improve our understanding of carbon export in the oligotrophic ocean. We show that specific plankton communities, from the surface and deep chlorophyll maximum, correlate with carbon export at 150 m and highlight unexpected taxa such as Radiolaria and alveolate parasites, as well as Synechococcus and their phages, as lineages most strongly associated with carbon export in the subtropical, nutrient-depleted, oligotrophic ocean. Additionally, we show that the relative abundance of a few bacterial and viral genes can predict a significant fraction of the variability in carbon export in these regions.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851848/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4851848/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Guidi, Lionel -- Chaffron, Samuel -- Bittner, Lucie -- Eveillard, Damien -- Larhlimi, Abdelhalim -- Roux, Simon -- Darzi, Youssef -- Audic, Stephane -- Berline, Leo -- Brum, Jennifer R -- Coelho, Luis Pedro -- Espinoza, Julio Cesar Ignacio -- Malviya, Shruti -- Sunagawa, Shinichi -- Dimier, Celine -- Kandels-Lewis, Stefanie -- Picheral, Marc -- Poulain, Julie -- Searson, Sarah -- Tara Oceans Consortium Coordinators -- Stemmann, Lars -- Not, Fabrice -- Hingamp, Pascal -- Speich, Sabrina -- Follows, Mick -- Karp-Boss, Lee -- Boss, Emmanuel -- Ogata, Hiroyuki -- Pesant, Stephane -- Weissenbach, Jean -- Wincker, Patrick -- Acinas, Silvia G -- Bork, Peer -- de Vargas, Colomban -- Iudicone, Daniele -- Sullivan, Matthew B -- Raes, Jeroen -- Karsenti, Eric -- Bowler, Chris -- Gorsky, Gabriel -- England -- Nature. 2016 Apr 28;532(7600):465-70. doi: 10.1038/nature16942. Epub 2016 Feb 10.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Sorbonne Universites, UPMC Universite Paris 06, CNRS, Laboratoire d'oceanographie de Villefranche (LOV), Observatoire Oceanologique, 06230 Villefranche-sur-Mer, France. ; Department of Oceanography, University of Hawaii, Honolulu, Hawaii 96822, USA. ; Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium. ; Center for the Biology of Disease, VIB, Herestraat 49, 3000 Leuven, Belgium. ; Department of Applied Biological Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium. ; Sorbonne Universites, UPMC Univ Paris 06, CNRS, Institut de Biologie Paris-Seine (IBPS), Evolution Paris Seine, F-75005, Paris, France. ; Ecole Normale Superieure, PSL Research University, Institut de Biologie de l'Ecole Normale Superieure (IBENS), CNRS UMR 8197, INSERM U1024, 46 rue d'Ulm, F-75005 Paris, France. ; Sorbonne Universites, UPMC Universite Paris 06, CNRS, Laboratoire Adaptation et Diversite en Milieu Marin, Station Biologique de Roscoff, 29680 Roscoff, France. ; LINA UMR 6241, Universite de Nantes, EMN, CNRS, 44322 Nantes, France. ; Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721, USA. ; Structural and Computational Biology, European Molecular Biology Laboratory, Meyerhofstr. 1, 69117 Heidelberg, Germany. ; Directors' Research European Molecular Biology Laboratory Meyerhofstr. 1, 69117 Heidelberg, Germany. ; CEA - Institut de Genomique, GENOSCOPE, 2 rue Gaston Cremieux, 91057 Evry, France. ; Aix Marseille Universite, CNRS, IGS, UMR 7256, 13288 Marseille, France. ; Department of Geosciences, Laboratoire de Meteorologie Dynamique (LMD), Ecole Normale Superieure, 24 rue Lhomond, 75231 Paris CEDEX 05, France. ; Dept of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. ; School of Marine Sciences, University of Maine, Orono, Maine 04469, USA. ; Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan. ; PANGAEA, Data Publisher for Earth and Environmental Science, University of Bremen, 28359 Bremen, Germany. ; MARUM, Center for Marine Environmental Sciences, University of Bremen, 28359 Bremen, Germany. ; CNRS, UMR 8030, CP 5706 Evry, France. ; Universite d'Evry, UMR 8030, CP 5706 Evry, France. ; Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM)-CSIC, Pg. Maritim de la Barceloneta 37-49, Barcelona E0800, Spain. ; Max-Delbruck-Centre for Molecular Medicine, 13092 Berlin, Germany. ; Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Naples, Italy.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26863193" target="_blank"〉PubMed〈/a〉

Description: Atomic line opacities play a crucial role in stellar astrophysics. They strongly modify the radiative transfer in stars, therefore, impacting their physical structure. Ultimately, most of our knowledge of stellar population systems (stars, clusters, galaxies, etc.) relies on the accuracy with which we understand and reproduce the stellar spectra. With such a wide impact on astronomy, it would be ideal to have access to a complete, accurate and precise list of atomic transitions. This, unfortunately, is not the case. Few atomic transitions had their parameters actually measured in the laboratory, and for most of the lines the parameters were calculated with low-precision atomic energy levels. Only a small fraction of the lines were calibrated empirically. For the purpose of computing a stellar spectral grid with a complete coverage of spectral types and luminosity classes, this situation is rather limiting. We have implemented an innovative method to perform a robust calibration of atomic line lists used by spectral synthesis codes called alicce : Atomic Lines Calibration using the Cross-Entropy algorithm. Here, we describe the implementation and validation of the method, using synthetic spectra which simulates the signal-to-noise, spectral resolution and rotational velocities typical of high-quality observed spectra. We conclude that the method is efficient for calibrating atomic line lists.

Description: : MOCAT2 is a software pipeline for metagenomic sequence assembly and gene prediction with novel features for taxonomic and functional abundance profiling. The automated generation and efficient annotation of non-redundant reference catalogs by propagating pre-computed assignments from 18 databases covering various functional categories allows for fast and comprehensive functional characterization of metagenomes. Availability and Implementation: MOCAT2 is implemented in Perl 5 and Python 2.7, designed for 64-bit UNIX systems and offers support for high-performance computer usage via LSF, PBS or SGE queuing systems; source code is freely available under the GPL3 license at http://mocat.embl.de . Contact : bork@embl.de Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Neutrophil extracellular traps (NETs) are believed to be essential in controlling several bacterial pathogens. Quantification of NETs in vitro is an important tool in studies aiming to clarify the biological and chemical factors contributing to NET production, stabilization and degradation. This estimation can be performed on the basis of fluorescent microscopy images using appropriate labelings. In this context, it is desirable to automate the analysis to eliminate both the tedious process of manual annotation and possible operator-specific biases. Results: We propose a framework for the automated determination of NET content, based on visually annotated images which are used to train a supervised machine-learning method. We derive several methods in this framework. The best results are obtained by combining these into a single prediction. The overall Q 2 of the combined method is 93%. By having two experts label part of the image set, we were able to compare the performance of the algorithms to the human interoperator variability. We find that the two operators exhibited a very high correlation on their overall assessment of the NET coverage area in the images ( R 2 is 97%), although there were consistent differences in labeling at pixel level ( Q 2 , which unlike R 2 does not correct for additive and multiplicative biases, was only 89%). Availability and implementation: Open source software (under the MIT license) is available at https://github.com/luispedro/Coelho2015_NetsDetermination for both reproducibility and application to new data. Contact: luis@luispedro.org Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Author(s): D. L. Rocco, A. de Campos, A. Magnus G. Carvalho, A. O. dos Santos, L. M. da Silva, S. Gama, M. S. da Luz, P. von Ranke, N. A. de Oliveira, A. A. Coelho, L. P. Cardoso, and J. A. Souza This paper presents the results of an investigation of the magnetic and structural properties of Mn 1 − x Fe x As compounds under hydrostatic pressure and chemical doping. The chemical doping was performed by using low Fe doping levels ( x = 0 , 0.003, 0.006, 0.010, 0.015, and 0.018), which emulates the negat… [Phys. Rev. B 93, 054431] Published Mon Feb 29, 2016

Description: Motivation: Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images of the same representative protein. Here, we frame a more challenging and useful problem where previously unseen proteins are to be classified. Results: Using CD-tagging, we generated two new image datasets for evaluation of this problem, which contain several different proteins for each location class. Evaluation of previous methods on these new datasets showed that it is much harder to train a classifier that generalizes across different proteins than one that simply recognizes a protein it was trained on. We therefore developed and evaluated additional approaches, incorporating novel modifications of local features techniques. These extended the notion of local features to exploit both the protein image and any reference markers that were imaged in parallel. With these, we obtained a large accuracy improvement in our new datasets over existing methods. Additionally, these features help achieve classification improvements for other previously studied datasets. Availability: The datasets are available for download at http://murphylab.web.cmu.edu/data/ . The software was written in Python and C++ and is available under an open-source license at http://murphylab.web.cmu.edu/software/ . The code is split into a library, which can be easily reused for other data and a small driver script for reproducing all results presented here. A step-by-step tutorial on applying the methods to new datasets is also available at that address. Contact: murphy@cmu.edu Supplementary information: Supplementary data are available at Bioinformatics online.