A Special Issue on Data Standards Foreword - Centre for Ecology ...

OMICS A Journal of Integrative BiologyVolume 10, Number 2, 2006©Mary Ann Liebert, Inc.ForewordA <strong>Special</strong> <strong>Issue</strong> on Data StandardsDAWN FIELD 1 and SUSANNA-ASSUNTA SANSONE 2ABSTRACTThis special issue arose as a response to the ongoing proliferation of grass roots, communitydriveninternational data standardization activities. It is our hope that giving a range of participantsin such activities the opportunity to contribute up-to-date descriptions of their efforts willhelp foster dialogue, raise awareness, and make a call for action on behalf of this rapidly growingcommunity. This issue is not an exhaustive review of activities, but provides a snapshot of arange of activities at different stages of maturity and their growing interactions. It is organizedinto four groups of papers on data standardization activites in the fields of genomics, the postgenomictechnologies of transcriptomics, proteomics and metabolomics and integrative activites,and other initiatives. Each invited piece contains details of the current status, future prospects,and successes and challenges of these efforts, making this issue a resource for those wishing totrack, participate, or conform to any of these standardization activities, as well as those wishingto initiate new activities. In this foreword, we provide a brief background to the practice andmethodology being adopted in the development of OMICS standards, review the content of thisspecial issue, and attempt to highlight the growing interactions and synergies between groups.INTRODUCTIONTHE INTRODUCTION of OMICS technologies into the life sciences has heralded tremendous opportunitiesbut also significant challenges. Among these recognized challenges is the need to manage the vast quantitiesof raw data generated by these high-throughput methods in an effective manner such that it can beproperly analyzed and re-analyzed, scrutinized for the sake of peer-review publication, compared, and disseminatedthrough public databases for the benefit of the wider scientific community.THE NEED FOR OMICS DATA STANDARDSThe topic of OMICS data standards has come to the fore, especially because the science of OMICS is distinguishedby a concern with the formidable task of characterizing not just one, a handful, or some of a particulartype of molecule but all molecules within a biological sample. Molecular data sets of this scale areunprecedented in the study of biological systems. This sheer volume of data is further compounded by the1 Molecular Evolution & Bioinformatics Section, Oxford Centre for Ecology and Hydrology, Oxford, United Kingdom.2 EMBL, EBI (European Bioinformatics Institute), Wellcome Trust Genome Campus, Hinxton, Cambridge, UnitedKingdom.84

FIELD AND SANSONETABLE 1.AN EVER INCREASING NUMBER OF STANDARDIZATION ACTIVITIESCommunity effort and website Standardization activities Citation in this issueGenomic researchThe Genomic Standards Consortium (GSC): Content (Minimal (Field et al., 2006;www.genomics.ceh.ac.uk/genomecatalogue/ Information about a Morrison et al.,Genome Sequence: MIGS), 2006a)syntax, and semanticsInternational Nucleotide Sequence Database Content (INSDC (Cochrane et al.,Collaboration (INSDC): www.insdc.org Third-Party Annotation 2006)Submission Guidelines)Genome Reviews: Content (review of (Sterk et al., 2006)www.ebi.ac.uk/GenomeReviews/standardization within theGenome Reviewsdatabase) and syntaxGSC: Chair of Organelles Working Group: Content (call for (Boore, 2006)www.genomics.ceh.ac.uk/genomecataloguestandardization ofdescriptions of organelles)Post-genomic standardizationMGED Society: Content (Minimal (Ball and Brazma,www.mged.org Information about a 2006)Microarray Experiment:MIAME), syntax, andsemanticsHUPO–Proteomics Standards Initiative (PSI) Content (Minimal (Taylor et al., 2006)http://psidev.sourceforge.net86Information about aProteomics Experimentguidelines: MIAPE),syntax, and semanticsExperimental Standards for Proteomics Content (call for (Hogan et al., 2006)development of standardexperimental mixtures ofproteins)Metabolomics Society–MSI (Metabolomics Content, syntax, and (Fiehn et al., 2006)Standards Initiative):semanticswww.metabolomicssociety.orgIntegration activitiesReporting Structures for Biological Investigations Contributions to content (Sansone et al.,Working Group (RSBI): and semantics 2006)www.mged.org/Workgroups/rsbi/rsbi.html“Env” Community led by the Environmental Content (MIAME/Env (Morrison et al.,Genomics Working Group (EGWG): checklist), syntax, and 2006b)http://envgen.nox.ac.uk/miame/miame_env.html semanticsThe Functional Genomics Experiment Object Syntax (Jones et al., 2006)Model (FuGE): http://fuge.sourceforge.net/National Center for BioMedical Ontology (cBIO): Semantics (Rubin et al., 2006)www.bioontology.org/Functional Genomics Investigation Ontology Semantics (Whetzel et al.,(FuGO): 2006)http://fugo.sourceforge.net/Other initiativesMISFISHIE Working Group: Content (MI Specification (Deutsch et al.,http://mged.sourceforge.net/misfishie/ for In Situ Hybridiation 2006)and ImmunohistochemistryExperiments: MISFISHIE),syntax, and semantics(Continued)

A SPECIAL ISSUE ON DATA STANDARDSTABLE 1.AN EVER INCREASING NUMBER OF STANDARDIZATION ACTIVITIES (CONT’D)Community effort and website Standardization activities Citation in this issueFlow Cytometry experiment community: Content (MI about a Flow (Spidlen et al.,www.flowcyt.org Cytometry Experiment: 2006)MI-FACE), syntax, andsemanticsPhylogenetics Community Content (MI About a (Leebens-Mack, et al.,Phylogenetic Analysis: 2006)MIAPA)Generation Challenges Project (GCP): Syntax and semantics (Bruskiewich et al.,www.generationcp.org/bioinformatics.php 2006)Taxonomic Names/Concepts sub-group of the Content and syntax (Kennedy et al.,International Working Group on Taxonomic (Taxonomic Concept 2006)Databases:Schema)http://tdwg.napier.ac.uk/TCS_1.01/v101.xsdRaman Analysis (RA) Content (Discussion of the (Huang and Spiers,application of RA in 2006)microbiology)This table summarizes the community-based contributions in this issue. For each, we list the formal (or informal) nameof the community, the area of standardization covered (i.e., content, syntax and semantics), and its citation in this issue.When a community has defined an “MI” checklist, its full name is given. Readers can obtain full information about eachinitiative and its output from the listed websites.source, providing guidelines for researchers reporting their experiments, as well as journal editors, fundingbodies, scientific organizations, technology vendors, and regulatory bodies.MIAME, however, as all MI checklists, is a data content standard, not a format standard. It is not enoughto specify that certain minimum information should be provided. It is essential that a standard transmissionformat exists for the data. The next step is the conceptualization and implementation of the minimal requirements,for example through the creation of an Object Model (OM), which is then translated into a comunicationformat that facilitates the exchange of data.While the data format provides the “syntax” of how to report the data, the “semantics,” or meanings, ofthe resulting annotations must also receive attention. This is best done as sets of controlled vocabularies orontologies. A controlled vocabulary is a way to insert an interpretive layer of semantics between terms usedby different experimentalists to describe a sample treatment or an instrument’s parameter, to better representthe original intention of the terms use. An ontology is an explicit formal representation of the knowledgein a subject area, which includes controlled vocabularies for referring to the concepts and logical statementsthat describe what the concepts are and how they can or cannot be related to each other. Theincorporation of ontologies into the annotations of data and metadata can provide semantics for featuresrelevant to the interpretation, analysis, and integration of the experiment.Standards for data content (“MI” checklists), syntax (file formats), and semantics (ontology) make informationmore accessible (facilitating comparison, reposition and exchange of data) and enable the extractionof maximum value from data sets (enabling an assessment of the quality and relevance of a piece of work)(Quackenbush, 2004). The reuse of the same semantics and syntax benefits the entire scientific communityby simplifying the job of data integration, but also eases the task of software developers, vendors, and equipmentmanufacturers by reducing time and costs for implementing standards-compliant products.THE PRACTICE OF GAINING ACCEPTANCETo be of use, standards must gain widespread or absolute acceptance within a community. This meansthey must be relevant and widely used. Managing this process of consensus-building from start to finishtakes time, resources, and expertise. To be successful, initiatives must overcome not only technological bar-87

FIELD AND SANSONEReporting Structure the Biological Investigations (RSBI)FIG. 1. General proposed structure to use existing checklists in a modular way]. The Emergence of multiple MinimalInformation (MI) checklists. This figure illustrates the MI checklists in this issue. The MI trend is likely to progressand more checklists are yet to come. It is essential, however, that these are not created in isolation. Their formulationshould attempt to anticipate the needs of functional genomics and systems biology and their design should allow thedifferent checklists to function together as interchangeable modules. The RSBI framework proposes a way to facilitatethis coordination by restructuring the formulation of these checklists (Sansone et al., this issue). Readers should notethat this is not an exhaustive list of activities and communities working to define “MI” checklist are continually emerging(e.g. see the checklists for the “Minimal Information about an RNAi experiment” [MIARE] at www.rnaiglobal.org/gti.html and the “Minimal Information about a Cellular Assay” [MIACA] at http://miaca.sourceforge.net/).riers, but also any sociological barriers that may stand in the way of building a consensus view of the informationessential for describing a particular type of data, as well as resistance of data generators to providethis information. Such issues should never be underestimated, and underscore the need to attain significantbuy-in, an important theme of the contributions in this issue.Gaining community buy-in can be a long process, and the development of data standards is an iterativeprocess. Ideally, the creation and implementation of data standards is a highly interdisciplinary activity. Thekey stakeholders who must be included from the onset are the end users, and when standardization is technology-driven,as in the case of post-genomic technologies such as transcriptomics, this includes vendors.The end-users group is primarily comprised of researchers but also includes members of regulatory bodiesand, when data are submitted as part of a publication, journal editors. Because the practical aspects of standardsdevelopment are highly technical, practitioners of OMICS standards development projects are commonlymembers of computer science departments, among which software engineers abound. Successful projectstherefore also need to make sure they strike the right balance between leveraging the expertise ofdevelopers with the expert knowledge of researchers generating and analyzing the resulting data.REAL-WORLD EXAMPLES: THE CONTENTS OF THIS SPECIAL ISSUEThe entire biological community is now benefiting from the successes of standardization activities, whichare calling for new levels of attention to be paid to proper data management. The contents of this issue are88

A SPECIAL ISSUE ON DATA STANDARDSorganized into a call for action and contributions, which fall into the general categories of genomics, postgenomics,integrative activities, and other initiatives. The technologies used in OMICS are at different levelsof maturity, and likewise, the reporting practices for publishing and disseminating data generated usingthese methods are at different stages of development. Some, like the MIAME specification developed bythe Microarray Gene Expression Data (MGED) Society are in wide use (Ball and Brazma, this issue), andothers like the issue of standardizing the use of Raman analysis (RA) in environmental microbiology arebeing discussed for the first time (Huang and Spiers, this issue). All of these invited pieces provide detailsthat are not already found on project websites or within core documents, and are written to encourage transparency,participation, and the adoption of the resulting standards.It should be noted that this issue is not an exhaustive resource of standardization activities (Fig. 1). Forexample, as high-throughput technologies are widely used in industry and are being considered by regulatoryagencies to develop policy, a range of methodologies has come under intense scrutiny. Agreement onstandardization of data will do little good if experimental protocols prove inconsistent. There is growingappreciation of the importance of controlling for unwanted sources of variation in complex OMICS studies,and the production and analysis of standard materials is now the focus of many initiatives, some ofwhich have been reviewed elsewhere (Sansone et al., 2004). This issue only contains one contribution onthe need for complex experimental standards, namely for proteomics (Hogan et al., this issue) to complementthe ongoing effort in the transcriptomics domain (Baker et al., 2005; ERCC, 2005) and elsewhere.A COMMUNITY’S CALL FOR ACTIONAs a further introduction to this issue, we have invited Cath Brooksbank and John Quackenbush to formalizea call to action for further recognition of the importance of global OMICS standardization activities—past,present, and future. In their commentary, these authors eloquently describe the history of activitiesin this area and point out that Herculean efforts are often accomplished “on the side” and withoutformal funding, simply because the lack of standardization is an unacceptable state of affairs for OMICSresearchers and is proving repeatedly to be a significant bottleneck in the collection, sharing, and integrationof data (Brooksbank and Quackenbush, this issue). As they put it, a “quiet revolution” has been brewingsince the early 1990s, which is now full-blown and deserves resources that match its contributions. Theynote the successes of a variety of communities, most notably the Gene Ontology (GO) Consortium (Ashburneret al., 2000) and the MGED Society (Ball and Brazma, this issue), and point out the promise of futurestandardization efforts to justify an open call to the community for further support and acceptance ofthese activities. These authors specifically call for journals to uphold the highest expectations for the reportingof data in the primary literature and for funding bodies to provide critical resources for these projects.Only through a combination of grass-roots development efforts, community adoption, and formal supportfrom journals and funding bodies will it be possible to achieve the highest standards of reporting andcompliance in the shortest time span for the benefit of the entire scientific community.GENOMIC RESEARCHThe five invited papers in this category describe the acquisition and handling of information describing completegenome sequences. The genomic revolution started in earnest with the sequencing of the first bacterialgenome (Fleischmann et al., 1995). We now have over 300 publicly available genome sequences from bacteriain our complete genome collection as well as a growing number of larger genomes, including the human genome.This wealth of information has drawn attention to the benefits of describing this collection in more detail for thesake of comparative analyses and the difficulties of collecting such metadata (Martiny and Field, 2006).The first contribution in this category is a meeting report from the Genomic Standards Consortium (GSC)describing their second exploratory workshop at the European Bioinformatics Institute (EBI) (Field et al.,this issue). This international group is organizing itself to work towards developing the Minimal Informationabout a Genome Sequence (MIGS) specification. Participants at this workshop included members of89

FIELD AND SANSONEthe International Nucleotide Sequence Database Collaboration (INSDC) and the Genome Reviews database,who have contributed pieces describing evidence standards in experimental and inferential INSDC ThirdParty Annotation data (Cochrane et al., this issue) and the standardization of comparative genomic data andinformation within the Genome Reviews database (Sterk et al., this issue). Jeff Boore, chair of the organellesworking group within the GSC, also elaborates on issues surrounding the standardization of genomic databasesto accurately describe and allow data mining of organellar genomes (Boore, this issue). Finally, membersof the GSC also elaborate on the concept of sample in a piece that explores how the GSC can bestwork towards its goals within the existing framework of the INSDC and the wider functional genomic community(Morrison et al., this issue). This is an obvious example where interest in describing one aspect ofan OMICS experiment (sample) can have an impact across a range of standardization activities and underscoresthe need for tight linkages between initiatives (Fig. 1).POST-GENOMIC STANDARDIZATIONThe post-genomic technologies of transcriptomics, proteomics, and metabolomics have emerged from thesuccesses of the initial genomic revolution to provide primary OMICS methods of characterizing sets of moleculesproduced by genomes. This issue contains updates from the authorities devising standards for all threeof these “core” OMICS technologies. Catherine Ball and Alvis Brazma provide a historical overview of emergenceand activities of the MGED Society and describe how the standardization activities arose from theneeds of the research community and how they have evolved over time (Ball and Brazma, this issue). ChrisTaylor leads a paper on the development of proteomics standards by members of the Proteomics StandardsInitiative (PSI) within the HUman Proteome Organisation (HUPO) (Taylor et al., this issue). As already mentioned,this issue also includes one example of a call for standardization of experimental standards, in thiscase for the development of reference datasets representing complex mixtures of proteins for proteomics research(Hogan et al., this issue). Finally, metabolomic and metabonomic data is also growing in importancewithin the OMICS context, as such technologies allow the metabolites of cells to be characterized and studiedin response to an unlimited number of parameters. In this issue, leaders of the Metabolomics StandardsInitiative (MSI), working under the Metabolomics Society umbrella, make an open call for participation inthe newly established efforts to standardize the reporting of this type of data (Fiehn et al., this issue).INTEGRATION ACTIVITIESIronically, the emergence of multiple standards has made the need for harmonization between groups developingstandards a critical issue—namely to avoid duplication of effort between standardization activitiesand accelerate the way in which new standards are built (Sansone et al., 2004). For example, the conceptof “sample,” as described in this issue (Morrison et al., this issue), is one of many cross-cutting conceptsthat should be shared among standards. This issue contains descriptions of five projects providing integrativeroles. The first is the Reporting Structures for Biological Investigations (RSBI), a group working underthe umbrella of MGED, but also working towards greater harmony between a wide range of standardizationactivities in different domains of applications namely nutrigenomics, ecotoxicogenomics andenvironmental genomics (Sansone et al., this issue). In this issue, the “Env” community, which contributesthe environmental genomic component of the RSBI working group, makes its first open call for participation(Morrison et al., this issue). The Env community, organized around the Environmental Genomics WorkingGroup (EGWG), is interested in the annotation and exchange of data from environmental OMICS studies.Such metadata includes details on the location, phenotype, and environmental conditions of the biologicalsample used and has been formalized as a community-level extension of MIAME, called MIAME/Env(Morrison et al., this issue).As the number of checklists from different initiatives grow, it will be essential to monitor the overlap ofthe information to be collected and to capitalize on synergies that make the generation of new checklistsand the future integration of data simpler. The RSBI working group is therefore an important source of case90

FIELD AND SANSONEings are available and helping projects to build momentum and produce outputs in a timely fashion. Thepractical keys to success are often stable releases of standards—stable releases are required to allow workingimplementations to be produced. Other themes include the importance of use cases, taking the time toproperly define scope, and attention to the significant technological details each project must surmount.What we have witnessed over the past decade is the rise of a family of high throughput technologies.While each method holds great promise, it represents but one way to investigate biological systems andtrue knowledge is far more likely to come from the interpretation of data won through the application ofmany technologies. It is clear that data standards will have a special role to play in realizing this goal, andthe broader OMICS community has shown that it is ready to rise to the occasion. We hope that from theindividual pockets of the community leading data standardization activities will emerge an inclusive, joinedupOMICS standardization community. Already, it is increasingly common for key individuals to be membersof two or more standardization efforts and this trend is increasing. The development of a high-profile,vibrant, interdisciplinary community will have the collective expertise and connections to help push forwardthe long-term vision of OMICS data integration and interpretation.ACKNOWLEDGMENTSIt has been our pleasure to put together this special issue. We would like to thank Eugene Kolker, Editor-in-Chief,for inviting us to do so and thereby providing these communities with a unique forum in whichto report ongoing activities. This invitation came about as a result of Eugene’s attendance at the GSC workshop,which is included in this issue (Field et al., 2006, this issue), and we would like to thank NERC forfunding. We would also like to thank all of the contributing authors.REFERENCESASHBURNER, M., BALL, C.A., BLAKE, J.A., et al. (2000). Gene ontology: tool for the unification of biology. TheGene Ontology Consortium. Nat Genet 25, 25–29.ASHBURNER, M., BALL, C.A., BLAKE, J.A., et al. (2006). Gene ontology: tool for the unification of biology. TheGene Ontology Consortium Database resources of the National Center for Biotechnology Information. Nucleic AcidsRes 34, D173–D180.BAKER, S.C., BAUER, S.R., BEYER, R.P., et al. (2005). The External RNA Controls Consortium: a progress report.Nat Methods 2, 731–734.BALL, C.A., and BRAZMA, A. (2006). MGED standards: work in progress. OMICS (this issue).BOORE, J.L. (2006). Requirements and standards for organelle genome databases. OMICS (this issue).BRAZMA, A., HINGAMP, P., QUACKENBUSH, J., et al. (2001). Minimum information about a microarray experiment(MIAME)—toward standards for microarray data. Nat Genet 29, 365–371.BROOKSBANK, C., and QUACKENBUSH, J. (2006). Data standards: a call to action. OMICS (this issue).BRUSKIEWICH, R., DAVENPORT, G., HAZEKAMP, T., et al. (2006). The Generation Challenge Programme (GCP):standards for crop data. OMICS (this issue).COCHRANE, G., BATES, K., APWEILER, R., et al. (2006). Evidence standards in experimental and inferential INSDCThird Party Annotation data. OMICS (this issue).DEUTSCH, E.W., BALL, C.A., BOYA, S., et al. (2006). Minimum information specification for in situ hybridizationand immunohistochemistry experiments (MISFISHIE). OMICS (this issue).EILBECK, K., LEWIS, S.E., MUNGALL, C.J., et al. (2005). The Sequence Ontology: a tool for the unification ofgenome annotations. Genome Biol 6, R44.ERCC. (2005). Proposed methods for testing and selecting the ERCC external RNA controls. BMC Genomics 6, 150.FIEHN, O., KRISTAL, B., OMMEN, B.V., et al. (2006). Establishing reporting standards for metabolomic and metabonomicstudies: a call for participation. OMICS (this issue).FIELD, D., MORRISON, N., SELENGUT, J., et al. (2006). eGenomics: cataloging our complete genome collection II.OMICS (this issue).FLEISCHMANN, R.D., ADAMS, M.D., WHITE, O., et al. (1995). Whole-genome random sequencing and assemblyof Haemophilus influenzae Rd. Science 269, 496–512.92

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