{"doi":"10.4056/sigs.3457","title":"The 15th Genomic Standards Consortium meeting","abstract":"GSC15 AbstractsLita ProctorGSC15p01 The trans-NIH Microbiome Working Group (TMWG)Lita ProctorNational Institutes of Health, Bethesda, MD, USACorrespondence: lita.proctor@nih.govKeywords: microbiome, trans-NIH, working groupProgram session: Development of Resources, Tools or Databases Related to the GSC MissionAbstractIn recent years, interest in the microbiome has greatly increased. A newly established group of NIH extramural program staff representing 14 Institutes and Centers (ICs) with a common interest in the microbiome formed in response to this growing interest NIH is a large agency and even internally, it's not always possible to know what areas each IC is funding or planning to fund. The trans-NIH Microbiome Working Group (TMWG) formed to 1) create an internal forum for exchanging information about current microbiome-related funding opportunities, plan joint microbiome-related funding opportunities and explore possible joint microbiome-related activities with other agencies but also 2) establish a clearinghouse for communicating NIH microbiome-related news to the broader scientific community. This poster will highlight some of the current and upcoming activities of the TMWG. M.H. MedemaGSC15p02 MIBiG: Minimal Information about a Biosynthetic Gene ClusterM.H. Medema1,2, P. Yilmaz3, R. Kottmann3, F.O. Glöckner3, R. Breitling2,4, E. Takano1,41Department of Microbial Physiology2Groningen Bioinformatics Centre, University of Groningen, Groningen, The Netherlands3Microbial Genomics and Bioinformatics Group, Max Planck Institute for Marine Microbiology, Bremen, Germany4Faculty of Life Sciences, Manchester Institute of Biotechnology, University of Manchester, United KingdomCorrespondence: m.h.medema@rug.nKeywords: genomic standards, biosynthetic gene cluster, secondary metabolism, natural productsProgram session: Implementing Standards in Genomic and Metagenomic Research ProjectsAbstractBacteria, fungi and plants produce an enormous variety of secondary metabolites with manifold biological activities, e.g., as antibiotics, immunosuppressants, and signaling molecules. The biosynthesis of such molecules is encoded by compact genomic units: biosynthetic gene clusters. Over the past decades, hundreds of biosynthetic gene clusters encoding the biosynthesis of secondary metabolites have been characterized. Although dozens of biosynthetic gene clusters are published and thousands are sequenced annually (with or without their surrounding genome sequence), very little effort has been put into structuring this information. Hence, it is currently very difficult to prioritize gene clusters for experimental characterization, to identify the fundamental architectural principles of biosynthetic gene clusters, to understand which ecological parameters drive their evolution, and to obtain an informative 'parts registry' of building blocks for the synthetic biology of secondary metabolite biosynthesis.Therefore, developing a genomic standard for experimentally characterized biosynthetic gene clusters would be of great value. The standard will build on the MIxS standards for ecological and environmental contextualization [1]. Additionally, biochemical, genomic and pharmaceutical metadata will be added in parameters such as enzyme substrate specificities, operon structure, chemical moieties of the product, metabolic precursors and compound bioactivity. Using the already developed computational pipeline for gene cluster analysis antiSMASH [2], information on characterized biosynthetic gene clusters will be linked to the untapped wealth of thousands of unknown gene clusters that have recently been unearthed by massive genome sequencing efforts. Taken together, this has the potential to guide the characterization of new metabolites by allowing to optimize the sampling of diversity at different levels and to identify the biochemical, genomic and ecological parameters that are key predictors of pharmaceutically relevant biological activities. Moreover, it can transform the unordered pile of literature on secondary metabolites into a structured and annotated catalogue of parts that can be used as building blocks to design new biochemical pathways with synthetic biology [3,4]. Yemin LanGSC15p03 Identifying functional signatures in microorganism genomes related to polymer decompositionYemin Lan1, Nivedita Clark2, Christopher Blackwood2 and Gail Rosen1.1Drexel University2Kent State UniversityCorrespondence: yeminlan@gmail.comKeywords: polymer decomposition, functional signatures, feature selectionProgram session: Development of Resources, Tools or Databases Related to the GSC MissionAbstractPlant cell wall polymers, such as cellulose, xylan, pectin (PGA) and lignin, must be degraded into monomers before being taken up and metabolized by microorganisms. The degradation of polymers, however, is likely to be different among microorganisms, and with growth on different polymers. This variation has been poorly characterized, but could allow for a variety of investigations that help us interpret the ecological strategy microorganisms or microbial communities decompose plant material.By monitoring the respiration rate of 15 microorganisms cultured on four different polymer substrates, and analyzing the gene content of their complete genomes, our project goal is to identify functional signatures of various categories that characterize polymer degradation ability of microorganisms, cultured on different substrates.The complete genomes of 15 soil microorganisms are available from NCBI, whose genes can be mapped to various functional categories, including Metacyc Pathways, Pfams (protein family), Gene Ontology terms and eggNOG orthologous groups. Feature selection methods (TF-iDF and mRMR) and Pearson correlation are used to identify a selected set of functional genomic signatures that best distinguish microorganisms that degrade substrate slowly from those that degrade substrate quickly. We then use support vector machine to classify the genomes based on abundances of selected pathways/Pfams/GO terms/eggNOGs within each genome, and principal component analysis for ordination. We show that better classification or better ordination was achieved when using selected signatures for some cases with PGA and xylan as substrates (with area under the receiver operating characteristic curve 10–15% higher than chance), but not for the others. The method shows the potential of revealing the underlying functional mechanisms important in determining the ability of microorganisms to decompose different polymers. Anna KlindworthGSC15p04 In silico evaluation of primer and primer pairs for 16S ribosomal RNA biodiversityAnna Klindworth1, Elmar Pruesse2, Timmy Schweer1, Joerg Pelpies3, Christian Quast1 and Frank Oliver Glöckner11Max Planck Institute for Marine Microbiology, Bremen, Germany2Jacobs University, Bremen, Germany3Ribocon, Bremen, GermanyCorrespondence: fog@mpi-bremen.deKeywords: 16S rDNA, primer, next generation sequencing, diversity analysis, SILVA TestPrimeProgram session: Development of Resources, Tools or Databases Related to the GSC MissionAbstract16S ribosomal RNA gene (rDNA) amplicon analysis remains the standard approach for the cultivation-independent investigation of microbial diversity. However, the accuracy of these analyses depends strongly on the choice of primers. This issue has been addressed by an in silico evaluation of primer with respect to the SILVA non-redundant reference database (SILVA SSURef NR). A total of 175 primers and 512 primer pairs were analyzed with respect to overall coverage and phylum spectrum for Archaea and Bacteria. Based on this evaluation a selection of 'best available' primer pairs addressing different sequencing platforms is provided in order to serve as a guideline for finding the most suitable primer pair for 16S rDNA analysis in any habitat and for individual research questions. Moreover, the SILVA team developed a new SILVA TestPrime tool (http://www.arb-silva.de/search/testprime) allowing the scientific community to perform an online in silico PCR with their primer pair of interest. Re-evaluation using an up-to-date database can be assured and evaluation of primer pairs prior to amplification both remain attractive in the future [5,6]. Frank Oliver GlöcknerGSC15p05 Micro B3: Marine Microbial Biodiversity, Bioinformatics, BiotechnologyFrank Oliver GlöcknerJacobs University Bremen, Bremen, GermanyCorrespondence: fog@mpi-bremen.deKeywords: Biodiversity, Bioinformatics, Biotechnology, Standards, Ocean Sampling DayProgram session: Development of Resources, Tools or Databases Related to the GSC Mission, Implementing Standards in Genomic and Metagenomic Research ProjectsAbstractThe 32 partner Ocean of Tomorrow Project Micro B3 (Biodiversity, Bioinformatics, Biotechnology, www.microb3.eu) forms teams of experts in bioinformatics, computer science, biology, ecology, oceanography, bioprospecting, biotechnology, ethics and law. The consortium's main aims are to bring together the existing bodies of expertise in ecosystems biology, the processing and interpretation of data, modelling and prediction and the development of intellectual property agreements for the exploitation of high potential commercial applications. At its core Micro B3 aims to develop an innovative, transparent and user friendly open-access system, which will allow for seamless processing, integration, visualisation and accessibility of the huge amount of data collected in ongoing sample campaigns and long-term observations. This will in turn offer new perspectives for the modelling and exploration of marine microbial communities for biotechnological applications.A key boost to the work will be provided by the Ocean Sampling Day (OSD, www.oceansamplingday.org), scheduled to take place on summer solstice — 21 June 2014. OSD will take place worldwide, with pilots conducted in 2012 and 13 to establish standardized sampling techniques. Adhering to the Minimum information checklists (MIxS) standard for describing molecular samples as outlined by the Genomic Standards Consortium will be essential for OSD. The event will generate a massive amount of useful marine microbial data to be included in the project's integrated MB3-Information System, providing the members of the biotechnology team with information to generate hypotheses for more cost- and time-efficient biotechnological testing and applications.In summary Micro B3 is set to revolutionise Europe's capacity for bioinformatics and marine microbial data integration, to the benefit of a variety of disciplines in bioscience, technology, computing, standardisation and law.Micro B3 is financially supported by the 7FP Ocean of Tomorrow Grant #287589 Tonia KorvesGSC15p06 Applying Publicly Available Genomic Metadata to Disease Outbreak InvestigationTonia Korves, Matthew Peterson, Wenling Chang and Lynette HirschmanMITRE, Bedford, MA, USA and McLean, VA, USACorrespondence: tkorves@mitre.orgKeywords: disease outbreaks, data integration, public databases, applying genomic metadataProgram session: Development of Resources, Tools or Databases Related to the GSC MissionAbstractAn important application of genomic metadata is in the investigation of disease outbreaks. The ability to identify the sources of disease outbreaks can prevent repeat events and potentially curtail outbreaks, saving lives, preventing societal disruption, and reducing economic costs. One way to discover the source of a pathogen is to identify other strains with shared biological properties, and then use associated metadata to discover and evaluate potential sources. This process relies on metadata such as collection date, isolation source, and geographic location, and depends on the extent and format in which data is captured. Currently, this data is captured in a variety of formats and information sources, making it challenging to use for pathogen source identification.We will present our work on methods for assembling and integrating metadata from public sources for source identification. We will illustrate this with a proof-of-concept, presenting a mock outbreak investigation of a Salmonella enterica strain. This investigation will start with information about an outbreak strain's DNA sequence and near phylogenetic relatives, and evaluate how public information sources can be used to address where and what type of environment the strain might have come from, whether the strain is an accidental laboratory escapee, and what laboratories have related strains needed for evaluating candidate origins. In the mock investigation, we will utilize a PostgreSQL database that we designed for metadata needed for source investigations, tools we created for automated importation and parsing of metadata from NCBI's BioProject and BioSample, and the LabKey platform to integrate, query, and present data from multiple sources. Data sources will include NCBI databases, MedLine, an MLST database, and StrainInfo. We will discuss the extent to which these tools and current publicly available data can address pathogen origin questions, and insights this might provide for standards and data capture efforts. Antonio GonzalezGSC15p07 Next generation software pipelines and reproducibility for microbial community analysisAntonio Gonzalez1, Yoshiki Vázquez-Baeza2, Will Van Treuren2, Meg Pirrung3, J. Gregory Caporaso4,5 and Rob Knight31BioFrontiers Institute, University of Colorado, Boulder, CO, USA2Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO, USA3University of Colorado Denver, Anschutz Medical Campus, Denver, CO, USA4Department of Computer Science, Northern Arizona University, Flagstaff, AZ, USA5Institute for Genomics and Systems Biology, Argonne National Laboratory, Argonne, IL, USACorrespondence: antgonza@gmail.comKewords: software reproducibility, next generation software tools, microbial analysisProgram session: Development of Resources, Tools or Databases Related to the GSC Mission, Implementing Standards in Genomic and Metagenomic Research ProjectsAbstractNew sequencing technologies both produced an explosion of data and inspired a proliferation of individual scripts written by scientists rather than software developers. These ad hoc scripts were typically not based in software development techniques, and lead to a crossroad comparable to the \"software crisis\" of the 1970s; projects running over budget, overtime, inefficient and hard-to-maintain software, etc. Here we present the use case of the development of QIIME (Quantitative Insights Into Microbial Ecology), which is based on test-driven and agile software development techniques. Test-driven development is the concept of creating positive and negative controls for software, resulting in more robust systems and avoiding common errors. Agile development is a methodology that allows adaptive planning in a collaborative environment, which provides a suitable environment for the creation of bioinformatics' tools. These methodologies not only ensure the reproducibility of results from the software but also facilitate rapid development. We also present Evident, next-generation software for microbial ecology, which runs within a browser and allows researchers to define the sampling effort for new studies by comparing to and relying on results from previously published datasets. Peter DwayndtGSC15p08 Unipept: Exploring biodiversity of complex metaproteome samplesBart Mesuere1, Bart Devreese2, Griet Debyser2, Maarten Aerts3, Peter Vandamme3, Peter Dawyndt11Department of Applied Mathematics and Computer Science, Faculty of Sciences, Ghent University, Ghent, Belgium2Laboratory for Protein Biochemistry and Biomolecular Engineering, Faculty of Sciences, Ghent University, Ghent, Belgium3Laboratory for Microbiology, Faculty of Sciences, Ghent University, Ghent, Belgium;Correspondences: Bart.Mesuere@UGent.beKeywords: metaproteomics, tryptic peptides, biodiversity analysis, treemap visualizationProgram session: Development of Resources, Tools or Databases Related to the GSC MissionAbstractUnipept (unipept.ugent.be) integrates a fast index of tryptic peptides built from UniProtKB (Wu et al. 2005) records with cleaned up information from the NCBI Taxonomy Database (Wheeler et al. 2004) to allow for biodiversity analysis of metaproteome samples. With Unipept, Users can submit tryptic peptides obtained from shotgun MS/MS experiments to which the application responds with a list of all UniProtKB records containing that peptide. The NCBI Taxonomy Database is used to compute the complete taxonomic lineage of every UniProtKB record in the result set. Subsequently, these lineages are combined to compute the common lineage of the submitted peptide. Of this common lineage, the most specific taxonomic node is determined as the lowest common ancestor (LCA) using a robust LCA scanning algorithm. The resulting information is visualized using an interactive JavaScript tree view that bundles all taxonomic lineages, accompanied with a comprehensible table that contains all matched UniProtKB records.Users can also submit a list of tryptic peptides. In this case, the LCA is calculated for every submitted peptide as described above. These LCAs are then bundled into a frequency table and visualized on the results page using an interactive treemap [Figure 6]. This treemap displays hierarchical data in a multilayer histogram-like graphical representation. The squares in the treemap each correspond to a taxonomic node in the NCBI taxonomy, with their size proportional to the number of peptides having that taxonomic node as their LCA. The cleaned up hierarchy of the NCBI taxonomy is used to tile the squares according to their occurrence in the taxonomic lineages. These squares are color coded according to their taxonomic ranks. This graphical representation allows users to see at a glance which organisms are present in a metaproteome sample and to what extent. The treemap is interactive and can be manipulated by clicking on individual nodes. This makes it possible for users to zoom in to an area of interest (e.g. Bacteria or Firmicutes). Figure 6.Ring chart visualization of treemap data.Full size imageWith complex samples containing a diverse range of taxa, the treemap representation quickly becomes cluttered. To resolve this problem, a new ring chart visualization was built into Unipept. Ring charts display the same data as the treemap, but as an interactive multi-level pie chart. The center of this pie chart represents the root node, with each ring around it stepping one level down the taxonomic hierarchy. The color of each slice is computed as the average of the colors of its children and slices without children are given random colors. Ring charts provide a more comprehensive view by displaying only four levels at a time. Users can see more levels by clicking on a slice of interest. The node that was clicked then becomes the center of the ring chart and the four levels below it are displayed. By clicking on the center of a ring chart, users can zoom out one level. By hovering the mouse over a slice, a tooltip is displayed that gives more information about the taxonomic node associated with the slice. The tooltip shows the number of peptides that have the taxon as their LCA, and the number of peptides whose LCA is the taxon or one of its descendants in the NCBI taxonomy. These visualizations make Unipept an essential tool for gaining novel insights into the biodiversity of complex metaproteome samples [7–9]. Markus GökerGSC15p09 Proposal for a Minimum Information on a         and   Institute  —   of  and    GermanyCorrespondence:   standards,  genome  gene  session: Development of Resources, Tools or Databases Related to the GSC Mission, Implementing Standards in Genomic and Metagenomic Research ProjectsAbstractThe      developed by   is  to  capture a large number of  by  or  of an    over  with   The   of  organisms such as   and   in    can be  in  of    to evaluate the response of  to diverse   these data    from the respiration  for  analysis and  of these parameters into  positive or negative  for     data are of use in genome  and the  of metabolic   pathways  from genome   the  or   to    or the  or  to   These hypotheses can be  using the     and   can  be    such as the   currently  the application of   to     standard  the metadata   and     data has not  been   this  of   not  genomic information but an  important type of  data, it makes  to establish such a  standard under the  of the    for a GSC project  to    is that the  standard for  the  metadata  be  in  with the   of    of  users  it to  environmental   standards for such experiments  be  in  with  and  have  and published  a  for the   software environment  that  tools for  the curve   the curve   associated metadata of organisms and experimental  as  as methods for analyzing these  complex data   and   is also possible to  and  these   and  in a standardized  format already  the data  among   would be  to include automated  of   in this  and it can serve as a software  for applying the novel    is  it would  be possible to      in standardized   Peter   a   for    and        and Peter  University, Ghent,    sequence       session: Development of Resources, Tools or Databases Related to the GSC  and  of sequencing   reference  is a   in  genome analysis   to the  growth in sequencing data, the development of fast and     with a    has    in recent  However, most   on   and are not   for the   produced by a growing number of sequencing  With the  of  generation sequencing      is  a  for   and more      developed  a new  for      a novel  of    and   techniques.  is based on the   approach and      to compute its     have    as their  used   but are   when  is  and also benefit from    when  is  has been   other    including  and  on the    using both  and   The results show    for all   However, for most experimental   is four to    than other          and  higher     at       and   National Institutes of Health, Bethesda,    BioSample,    Development of Resources, Tools or Databases Related to the GSC Mission, Implementing Standards in Genomic and Metagenomic Research   metadata  with sequence data to NCBI's  Data  is important for providing users with a complete  of the source of the biological  We  with the GSC to  the  of  metadata within sequence  to  in a  structured   metadata was  in  tools and  with a   This information is  captured in   designed  to    BioProject for   and  for   is a  that   to various  of a research   of projects  that include  to  and  information related to a specific  These  can be based on funding source, overall     and can support multiple   related or diverse   submitted data are linked by a common BioProject  which allows for  in  and  a   BioProject also   to    data, and  is a   in which to    information about biological source  used to generate experimental   provides a   of  for metadata that  be  when making data  to   In order to  collection of  useful      that drive  of specific information  to the sample  Currently,  and   are supported as  as   with GSC MIxS    are  for sample    to  sample    currently  as the  record for source metadata provided for  and BioProject   will  as the   of source information for most of NCBI's  Data  This will allow users to  all available data present in multiple   that are  from a sample with common  as  as view  in the  of their associated      of environment  terms in                  of Marine Biology,  and     for Marine    Systems Biology,    for  University of            is an  source,     tool  such   Standards  data  and facilitate   biological   structured information available in biological  can be used to this  the  potential    based on the  existing in the scientific literature is   of environment   such as      in  is a  for   environmental   is an  source,     tool  such  identification. 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