Chris F. Taylor

Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project

The Minimum Information for Biological and Biomedical Investigations (MIBBI) project aims to foster the coordinated development of minimum-information checklists and provide a resource for those exploring the range of extant checklists.

A systematic approach to modeling, capturing, and disseminating proteomics experimental data

Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.

The metabolomics standards initiative (MSI)

In 2005, the Metabolomics Standards Initiative has been formed. An outline and general introduction is provided to inform about the history, structure, working plan and intentions of this initiative. Comments on any of the suggested minimal reporting standards are welcome to be sent to the open email list Msi-workgroups-feedback@lists.sourceforge.net

PRIDE: a public repository of protein and peptide identifications for the proteomics community

PRIDE, the ‘PRoteomics IDEntifications database’ () is a database of protein and peptide identifications that have been described in the scientific literature. These identifications will typically be from specific species, tissues and sub-cellular locations, perhaps under specific disease conditions. Any post-translational modifications that have been identified on individual peptides can be described. These identifications may be annotated with supporting mass spectra. At the time of writing, PRIDE includes the full set of identifications as submitted by individual laboratories participating in the HUPO Plasma Proteome Project and a profile of the human platelet proteome submitted by the University of Ghent in Belgium. By late 2005 PRIDE is expected to contain the identifications and spectra generated by the HUPO Brain Proteome Project. Proteomics laboratories are encouraged to submit their identifications and spectra to PRIDE to support their manuscript submissions to proteomics journals. Data can be submitted in PRIDE XML format if identifications are included or mzData format if the submitter is depositing mass spectra without identifications. PRIDE is a web application, so submission, searching and data retrieval can all be performed using an internet browser. PRIDE can be searched by experiment accession number, protein accession number, literature reference and sample parameters including species, tissue, sub-cellular location and disease state. Data can be retrieved as machine-readable PRIDE or mzData XML (the latter for mass spectra without identifications), or as human-readable HTML.

The MGED Ontology: a resource for semantics-based description of microarray experiments

MOTIVATION The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. RESULTS Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. AVAILABILITY The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICBs Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). CONTACT Stoeckrt@pcbi.upenn.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

The Metabolomics Standards Initiative

In 2005, the Metabolomics Standards Initiative has been formed. An outline and general introduction is provided to inform about the history, structure, working plan and intentions of this initiative. Comments on any of the suggested minimal reporting standards are welcome to be sent to the open email list Msi-workgroups-feedback@lists.sourceforge.net

ISA software suite

Summary: The first open source software suite for experimentalists and curators that (i) assists in the annotation and local management of experimental metadata from high-throughput studies employing one or a combination of omics and other technologies; (ii) empowers users to uptake community-defined checklists and ontologies; and (iii) facilitates submission to international public repositories. Availability and Implementation: Software, documentation, case studies and implementations at http://www.isa-tools.org Contact: isatools@googlegroups.com

RDML: structured language and reporting guidelines for real-time quantitative PCR data

The XML-based Real-Time PCR Data Markup Language (RDML) has been developed by the RDML consortium (http://www.rdml.org) to enable straightforward exchange of qPCR data and related information between qPCR instruments and third party data analysis software, between colleagues and collaborators and between experimenters and journals or public repositories. We here also propose data related guidelines as a subset of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) to guarantee inclusion of key data information when reporting experimental results.

The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics

The Functional Genomics Experiment data model (FuGE) has been developed to facilitate convergence of data standards for high-throughput, comprehensive analyses in biology. FuGE models the components of an experimental activity that are common across different technologies, including protocols, samples and data. FuGE provides a foundation for describing entire laboratory workflows and for the development of new data formats. The Microarray Gene Expression Data society and the Proteomics Standards Initiative have committed to using FuGE as the basis for defining their respective standards, and other standards groups, including the Metabolomics Standards Initiative, are evaluating FuGE in their development efforts. Adoption of FuGE by multiple standards bodies will enable uniform reporting of common parts of functional genomics workflows, simplify data-integration efforts and ease the burden on researchers seeking to fulfill multiple minimum reporting requirements. Such advances are important for transparent data management and mining in functional genomics and systems biology.

Guidelines for reporting the use of mass spectrometry in proteomics

Joeri Borstlap1, Glyn Stacey2, Andreas Kurtz3, Anja Elstner3, Alexander Damaschun1, Begoña Arán4 & Anna Veiga4,5 1CellNet Initiative, Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité– Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. 2The UK Stem Cell Bank, National Institute for Biological Standards and Control, Blanch Lane, South Mimms, Potters Bar, Hertfordshire, EN6 3QG, UK. 3Cell Therapy Group, BerlinBrandenburg Center for Regenerative Therapies (BCRT), Charité–Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. 4Banc de Linies Cellulars, Centre de Medicina Regenerativa de Barcelona (CMRB), C/Dr. Aiguader 88, 08003-Barcelona, Spain. 5Institut Universitari Dexeus, Passeig de la Bonanova 67, 08017-Barcelona, Spain. e-mail: joeri.borstlap@b-crt.de