Description: The South Pacific Gyre (SPG) covers 10% of the ocean’s surface and is often regarded as a marine biological desert. To gain an on-site overview of the remote, ultraoligotrophic microbial community of the SPG, we developed a novel onboard analysis pipeline, which combines next-generation sequencing with fluorescence in situ hybridization and automated cell enumeration. We tested the pipeline during the SO-245 “UltraPac” cruise from Chile to New Zealand and found that the overall microbial community of the SPG was highly similar to those of other oceanic gyres. The SPG was dominated by 20 major bacterial clades, including SAR11, SAR116, the AEGEAN-169 marine group, SAR86, Prochlorococcus, SAR324, SAR406, and SAR202. Most of the bacterial clades showed a strong vertical (20 m to 5,000 m), but only a weak longitudinal (80°W to 160°W), distribution pattern. Surprisingly, in the central gyre, Prochlorococcus, the dominant photosynthetic organism, had only low cellular abundances in the upper waters (20 to 80 m) and was more frequent around the 1% irradiance zone (100 to 150 m). Instead, the surface waters of the central gyre were dominated by the SAR11, SAR86, and SAR116 clades known to harbor light-driven proton pumps. The alphaproteobacterial AEGEAN-169 marine group was particularly abundant in the surface waters of the central gyre, indicating a potentially interesting adaptation to ultraoligotrophic waters and high solar irradiance. In the future, the newly developed community analysis pipeline will allow for on-site insights into a microbial community within 35 h of sampling, which will permit more targeted sampling efforts and hypothesis-driven research.

Description: 〈jats:p〉 Microorganisms comprise an immense phylogenetic and metabolic diversity, inhabit every conceivable niche on earth, and play a fundamental role in global biogeochemical processes. Among others, their study is highly relevant to develop biotechnological applications, understand ecosystem processes and monitor environmental systems. Functional traits (FTs) (i.e., measurable properties of an organism that influence its fitness (McGill et al. 2006)) provide complementary information to the taxonomic composition to improve the characterization of microbial communities and study their ecology (Martiny et al. 2012). The application of FT-based approaches can be particularly enhanced when coupled with metagenomics, which as a culture-independent method, allows us to obtain the genetic material of microorganisms from the environment: Metagenomic data can be used to compute functional traits at the genome level from a random sample of individuals in a microbial community, irrespective of their taxonomic affiliation (Fierer et al. 2014). Previous works using FT-based approaches in metagenomics include the study of community assembly processes (Burke et al. 2011) and responses to environmental change (Leff et al. 2015), and ecosystem functioning (Babilonia et al. 2018). 〈/jats:p〉 〈jats:p〉 In this work, we present the Metagenomic Traits pipeline: Mg-Traits. Mg-Traits is dedicated to the computation of 25 (and counting) functional traits in short-read metagenomic data, ranging from GC content and amino acid composition to functional diversity and average genome size (see Fig. 1). As an example application, we used the Mg-Traits pipeline to process the 139 prokaryotic metagenomes of the TARA Oceans data set (Sunagawa et al. 2015). In this analysis, we observed that the computed metagenomic traits track community changes along the water column, which denote microorganisms’ environmental adaptations. 〈/jats:p〉 〈jats:p〉 Mg-Traits allows the systematic computation of a comprehensive set of metagenomic functional traits, which can be used to generate a functional and taxonomic fingerprint and reveal the predominant life-history strategies and ecological processes in a microbial community. Mg-Traits contributes to improving the exploitation of metagenomic data and facilitates comparative and quantitative studies. Considering the high genomic plasticity of microorganisms and their capacity to rapidly adapt to changing environmental conditions, Mg-Traits constitutes a valuable tool to monitor environmental systems. 〈/jats:p〉 〈jats:p〉 〈/jats:p〉 〈jats:p〉The Mg-Traits pipeline is available at https://github.com/pereiramemo/metagenomic_pipelines. It is programmed in AWK, BASH, and R, and it was devised using a modular design to facilitate the integration of new metagenomic traits.〈/jats:p〉

Description: 〈jats:p〉 〈jats:bold〉Background: The NFDI process in Germany〈/jats:bold〉 〈/jats:p〉 〈jats:p〉The digital revolution is fundamentally transforming research data and methods. Mastering this transformation poses major challenges for stakeholders in the domains of science and policy. The process of digitalisation creates immense opportunities, but it must be structured proactively. To this end, the establishment of effective governance mechanisms for research data management (RDM) is of fundamental importance and will be one key driver for successful research and innovation in the future. In 2016 the German Council for Information Infrastructures (RfII) recommended the establishment of a “Nationale Forschungsdateninfrastruktur” (National Research Data Infrastructure, or NFDI), which will serve as the backbone for research data management in Germany. The NFDI should be implemented as a dynamic national collaborative network that grows over time and is composed of various specialised nodes (consortia). The talk will provide a short overview of the status and objectives of the NFDI. It will commence with a description of the goals of the NFDI4BioDiversity consortium which was established for the targeted support of the biodiversity community with data management.〈/jats:p〉 〈jats:p〉 〈jats:bold〉The NFDI4BioDiversity Consortium: Biodiversity, Ecology & Environmental Data〈/jats:bold〉 〈/jats:p〉 〈jats:p〉Biodiversity is more than just the diversity of living species. It includes genetic diversity, functional diversity, interactions and the diversity of whole ecosystems. Mankind continuous to dramatically impact the earth’s ecosystem: species dying-out genetic diversity as well as whole ecosystems are endangered or already lost. Next to the loss of charismatic species and conspicuous change in ecosystems, we are experiencing a quiet loss of common species which together has captured high level policy attention. This has impacts on vital ecosystem services that provide the foundation of human well-being.〈/jats:p〉 〈jats:p〉A general understanding of the status, trends and drivers of the biodiversity on earth is urgently needed to devise conservation responses. Besides the fact that data are often scattered across repositories or not accessible at all, the main challenge for integrative studies is the heterogeneity of measurements and observation types, combined with a substantial lack of documentation. This leads to inconsistencies and incompatibilities in data structures, interfaces and semantics and thus hinders the re-usability of data to answer scientifically and socially relevant questions. Synthesis as well as hypothesis generation will only proceed when data are compliant with the FAIR (Findable, Accessible, Interoperable and Re-usable) data principles.〈/jats:p〉 〈jats:p〉Over the last five years these key challenges have been addressed by the DFG funded German Federation for Biological Data (GFBio) project. GFBio encompasses technical, organizational, financial, and community aspects to raise awareness for research data management in biodiversity research and environmental sciences. To foster sustainability across this federated infrastructure the not-for-profit association “Gesellschaft für biologische Daten e.V. (GFBio e.V.)” has been set up in 2016 as an independent legal entity.〈/jats:p〉 〈jats:p〉NFDI4BioDiversity builds on the experience and established user community of GFBio and takes advantage of GFBio e.V. GFBio already comprises data centers for nucleotide and environmental data as well as the seven well-established data centers of Germany´s largest natural science research facilities, museums and world’s most diverse microbiological resource collection. The network is now extended to include the network of botanical gardens and the largest collections of crop plants and their wild relatives. All collections together host more than 75% of all museum objects (150 millions) in Germany and >80% of all described microbial species. They represent the biggest and internationally-relevant data repositories.〈/jats:p〉 〈jats:p〉NFDI4BioDiversity will extend its community engagement at the science-society-policy interface by including farm animal biology, crop sciences, biodiversity monitoring and citizen science, as well as systems biology encompassing world-leading tools and collections for FAIR data management. Partners of the German Network for Bioinformatics Infrastructure (de.NBI) provide large scale data analysis and storage capacities in the cloud, as well as extensive continuous training and education experiences. Dedicated personnel will be responsible for the mutual exchange of data and experiences with NFDI4Life-Umbrella,NFDI4Earth, NFDI4Chem, NFDI4Health and beyond.〈/jats:p〉 〈jats:p〉As digitalization and liberation of data proceeds, NFDI4BioDiversity will foster community standards, quality management and documentation as well as the harmonization and synthesis of heterogeneous data. It will pro-actively engage the user community to build a coordinated data management platform for all types of biodiversity data as a dedicated added value service for all users of NFDI. 〈/jats:p〉

Description: 〈jats:p〉Nucleic acid and protein sequencing-based analyses are increasingly applied to determine origin, identity and traits of environmental (biological) objects and organisms. In this context, the need for corresponding data structures has become evident. As existing schemas and community standards in the domains of biodiversity and molecular biological research are comparatively limited with regard to the number of generic and specific elements, previous schemas for describing the physical and digital objects need to be replaced or expanded by new elements for covering the requirements from meta-omics techniques and operational details. On the one hand, schemas and standards are hitherto mostly focussed on elements, descriptors, or concepts that are relevant for data exchange and publication, on the other hand, detailed operational aspects regarding origin context and laboratory processing, as well as data management details, like the documentation of physical and digital object identifiers, are rather neglected.〈/jats:p〉 〈jats:p〉The conceptual schema for Meta-omics Data and Collection Objects (MOD-CO; https://www.mod-co.net/) has been set up recently Rambold et al. 2019. It includes design elements (descriptors or concepts), describing structural and operational details along the work- and dataflow from gathering environmental samples to the various transformation, transaction, and measurement steps in the laboratory up to sample and data publication and archiving. The concepts are named according to a multipartite naming structure, describing internal hierarchies and are arranged in concept (sub-)collections. By supporting various kinds of data record relationships, the schema allows for the concatenation of individual records of the operational segments along a workflow (Fig. 1). Thus, it may serve as a logical and structural backbone for laboratory information management systems. The concept structure in version 1.0 comprises 653 descriptors (concepts) and 1,810 predefined descriptor states, organised in 37 concept (sub-)collections. The published version 1.0 is available as various schema representations of identical content (https://www.mod-co.net/wiki/Schema_Representations). A normative XSD (= XML Schema Definition) for the schema version 1.0 is available under http://schema.mod-o.net/MOD-CO_1.0.xsd.〈/jats:p〉 〈jats:p〉The MOD-CO concepts might be integrated as descriptor/element structures in the relational database DiversityDescriptions (DWB-DD) an open-source and freely available software of the Diversity Workbench (DWB; https://diversityworkbench.net/Portal/DiversityDescriptions; https://diversityworkbench.net). Currently, DWB-DD is installed at the Data Center of the Bavarian Natural History Collections (SNSB) to build an instance of its own for storing and maintaining MOD-CO-structured meta-omics research data packages and enrich them with ‘metadata’ elements from the community standards Ecological Markup Language (EML), Minimum Information about any (x) Sequence (MIxS), Darwin Core (DwC) and Access to Biological Collection Data (ABCD). These activities are achieved in the context of ongoing FAIR ('Findable, Accessible, Interoperable and Reuseable') biodiversity research data publishing via the German Federation for Biological Data (GFBio) network (https://www.gfbio.org/). Version 1.1 of the schema with extended collections of structural and operational design concepts is scheduled for 2020.〈/jats:p〉

Description: 〈jats:p〉Ribosomal DNA (rDNA) has become the primary target molecule for phylogenetic reconstruction and the cultivation-independent detection and quantification of microorganisms (barcoding). With the advent of high-throughput sequencing technologies (Next Generation Sequencing (NGS), PCR-based amplicon sequencing of rDNA fragments for diversity screening is now a routine technology, at least in environmental sciences. The resulting exponential increase of publicly available rDNA sequences demands specialized reference databases (Fig. 1).〈/jats:p〉 〈jats:p〉SILVA (from Latin〈jats:italic〉 silva〈/jats:italic〉, meaning forest) is designed to provide a comprehensive web resource for up-to-date, quality-controlled databases of aligned rDNA sequences from the Bacteria, Archaea and Eukaryota〈jats:italic〉 〈/jats:italic〉domains and the corresponding online services (Glöckner et al. 2017, Quast et al. 2012).〈/jats:p〉 〈jats:p〉The current SILVA database (release 132) contains 6,073,181 small subunit and 907,382 large subunit rRNA gene sequences. All sequences are checked for anomalies, carry a rich set of sequence-associated contextual information, multiple taxonomic classifications (EMBL-EBI/ENA, RDP and GTDB) and the latest validly-described nomenclature. SILVA maintains manually curated reference alignments of 75,000 ribosomal RNA genes, both 16S/18S (small subunit, SSU) and 23S/28S (large subunit, LSU). With every full release, a manually curated guide tree is provided that contains the latest taxonomy and nomenclature based on multiple references.〈/jats:p〉 〈jats:p〉SILVA is the only rDNA database project worldwide where special emphasis is given to the consistent naming of clades of uncultivated (environmental) sequences where no validly-described cultivated representative is available (Yilmaz et al. 2014). SILVA incorporates other unique features, including a comprehensive 23S/28S database of aligned rDNA sequences and alignments that contain Eukaryota sequences.〈/jats:p〉 〈jats:p〉SILVA is an active partner of RNACentral. RNAcentral is a public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of Expert Databases. The SILVA team is a member of the Bergey’s Board of Trustees which provides the authoritative taxonomy for 〈jats:italic〉Bacteria〈/jats:italic〉 and 〈jats:italic〉Archaea〈/jats:italic〉 as well as the Protist Reference Taxonomy Project UniEuk funded by the Gordon and Betty Moore Foundation to create a unified taxonomic framework〈/jats:p〉 〈jats:p〉In 2018 SILVA became an ELIXIR Core Data Resource. ELIXIR Core Data Resources are a set of European data resources of fundamental importance to the wider life-science community and the long-term preservation of biological data.〈/jats:p〉 〈jats:p〉To facilitate classification tasks for high-throughput rDNA data the SILVAngs has been implemented and released in 2013. SILVAngs is a data analysis service for rDNA reads from high-throughput sequencing (NGS) approaches based on an automatic software pipeline. It uses the SILVA rDNA databases, taxonomies, and alignments as a reference. It facilitates the classification of rDNA reads and provides a wealth of results (tables, graphs and sequence files) for download. SILVAngs serves several thousands of registered users, which processes thousands of projects per year.〈/jats:p〉 〈jats:p〉The application spectrum of the SILVA databases ranges from environmental sciences, microbiology, agriculture, biochemistry, biotechnology to medicine in academia and industry.〈/jats:p〉

Description: Rapid changes of the biosphere observed in recent years are caused by both small and large scale drivers, like shifts in temperature, transformations in land-use, or changes in the energy budget of systems. While the latter processes are easily quantifiable, documentation of the loss of biodiversity and community structure is more difficult. Changes in organismal abundance and diversity are barely documented. Censuses of species are usually fragmentary and inferred by often spatially, temporally and ecologically unsatisfactory simple species lists for individual study sites. Thus, detrimental global processes and their drivers often remain unrevealed. A major impediment to monitoring species diversity is the lack of human taxonomic expertise that is implicitly required for large-scale and fine-grained assessments. Another is the large amount of personnel and associated costs needed to cover large scales, or the inaccessibility of remote but nonetheless affected areas. To overcome these limitations we propose a network of Automated Multisensor stations for Monitoring of species Diversity (AMMODs) to pave the way for a new generation of biodiversity assessment centers. This network combines cutting-edge technologies with biodiversity informatics and expert systems that conserve expert knowledge. Each AMMOD station combines autonomous samplers for insects, pollen and spores, audio recorders for vocalizing animals, sensors for volatile organic compounds emitted by plants (pVOCs) and camera traps for mammals and small invertebrates. AMMODs are largely self-containing and have the ability to pre-process data (e.g. for noise filtering) prior to transmission to receiver stations for storage, integration and analyses. Installation on sites that are difficult to access require a sophisticated and challenging system design with optimum balance between power requirements, bandwidth for data transmission, required service, and operation under all environmental conditions for years. An important prerequisite for automated species identification are databases of DNA barcodes, animal sounds, for pVOCs, and images used as training data for automated species identification. AMMOD stations thus become a key component to advance the field of biodiversity monitoring for research and policy by delivering biodiversity data at an unprecedented spatial and temporal resolution.