Standards and infrastructure for managing experimental metadata

*See also the "related presentation":http://precedings.nature.com/documents/3145/version/1*

We present an infrastructure that leverages synergistic reporting standards and ontologies^1,2,3,4,5^ to create a common structured representation and storage mechanism for experimental metadata from biological and biomedical investigations ranging from simple single-assay studies to complex, methodologically diverse multi-assay studies. 

The infrastructure’s components include: a data capture and editing tool (_ISAcreator_); validator (_ISAvalidator_); database (_BioInvestigation Index_); and converter (_ISAconverter_); and a BioConductor analysis package (_R-ISApackage_). The components are designed for local installation, and can work independently, or as unified system.

View the "public instance":http://www.ebi.ac.uk/bioinvindex running at EBI and/or "download the components":http://isatab.sf.net for your local use.

*References*
1. Taylor CF, Field D, Sansone SA,… Rocca-Serra P et al. (2008) The MIBBI Project. _Nature Biotechnology_ Aug;26(8):889-896. "http://www.mibbi.org":http://www.mibbi.org

2. Smith B, Ashburner M, Rosse C,… Rocca-Serra P, …Sansone SA et al. (2007) The OBO Foundry. _Nature Biotechnology_ Nov;25(11):1251-5. "http://www.obofoundry.org":http://www.obofoundry.org

3. Ontology for Biomedical Investigations (OBI) "http://obi-ontology.org":http://obi-ontology.org 

4. Sansone SA, Rocca-Serra P, Brandizi M,… Taylor CF et al. (2008) The First MGED RSBI (ISA-TAB) Workshop. _OMICS_. Jun;12(2):143-9. "http://isatab.sf.net":http://isatab.sf.net

5. Jones AR, Miller M, Aebersold R,… Sansone SA et al. (2007) The Functional Genomics Experiment model (FuGE). _Nature Biotechnology_ Oct;25(10):1127-1133. "http://fuge.sf.net":http://fuge.sf.ne

graph2tab, a library to convert experimental workflow graphs into tabular formats

Motivations: Spreadsheet-like tabular formats are ever more popular in the biomedical field as a mean for experimental reporting. The problem of converting the graph of an experimental workflow into a table-based representation occurs in many such formats and is not easy to solve

KnetMiner:A comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

Generating new ideas and scientific hypotheses is often the result of extensive literature and database reviews, overlaid with scientists’ own novel data and a creative process of making connections that were not made before. We have developed a comprehensive approach to guide this technically challenging data integration task and to make knowledge discovery and hypotheses generation easier for plant and crop researchers. KnetMiner can digest large volumes of scientific literature and biological research to find and visualise links between the genetic and biological properties of complex traits and diseases. Here we report the main design principles behind KnetMiner and provide use cases for mining public datasets to identify unknown links between traits such grain colour and pre-harvest sprouting in Triticum aestivum, as well as, an evidence-based approach to identify candidate genes under an Arabidopsis thaliana petal size QTL. We have developed KnetMiner knowledge graphs and applications for a range of species including plants, crops and pathogens. KnetMiner is the first open-source gene discovery platform that can leverage genome-scale knowledge graphs, generate evidence-based biological networks and be deployed for any species with a sequenced genome. KnetMiner is available at http://knetminer.org

The BioSample Database (BioSD) at the European Bioinformatics Institute.

ABSTRACT The BioSample Databas

The EBI RDF platform: linked open data for the life sciences

Motivation: Resource description framework (RDF) is an emerging technology for describing, publishing and linking life science data. As a major provider of bioinformatics data and services, the European Bioinformatics Institute (EBI) is committed to making data readily accessible to the community in ways that meet existing demand. The EBI RDF platform has been developed to meet an increasing demand to coordinate RDF activities across the institute and provides a new entry point to querying and exploring integrated resources available at the EBI. Availability: http://www.ebi.ac.uk/rdf Contact: [email protected]

The Genopolis Microarray Database

<p>Abstract</p> <p>Background</p> <p>Gene expression databases are key resources for microarray data management and analysis and the importance of a proper annotation of their content is well understood.</p> <p>Public repositories as well as microarray database systems that can be implemented by single laboratories exist. However, there is not yet a tool that can easily support a collaborative environment where different users with different rights of access to data can interact to define a common highly coherent content. The scope of the Genopolis database is to provide a resource that allows different groups performing microarray experiments related to a common subject to create a common coherent knowledge base and to analyse it. The Genopolis database has been implemented as a dedicated system for the scientific community studying dendritic and macrophage cells functions and host-parasite interactions.</p> <p>Results</p> <p>The Genopolis Database system allows the community to build an object based MIAME compliant annotation of their experiments and to store images, raw and processed data from the Affymetrix GeneChip^®platform. It supports dynamical definition of controlled vocabularies and provides automated and supervised steps to control the coherence of data and annotations. It allows a precise control of the visibility of the database content to different sub groups in the community and facilitates exports of its content to public repositories. It provides an interactive users interface for data analysis: this allows users to visualize data matrices based on functional lists and sample characterization, and to navigate to other data matrices defined by similarity of expression values as well as functional characterizations of genes involved. A collaborative environment is also provided for the definition and sharing of functional annotation by users.</p> <p>Conclusion</p> <p>The Genopolis Database supports a community in building a common coherent knowledge base and analyse it. This fills a gap between a local database and a public repository, where the development of a common coherent annotation is important. In its current implementation, it provides a uniform coherently annotated dataset on dendritic cells and macrophage differentiation.</p

DC-ATLAS: a systems biology resource to dissect receptor specific signal transduction in dendritic cells

BACKGROUND: The advent of Systems Biology has been accompanied by the blooming of pathway databases. Currently pathways are defined generically with respect to the organ or cell type where a reaction takes place. The cell type specificity of the reactions is the foundation of immunological research, and capturing this specificity is of paramount importance when using pathway-based analyses to decipher complex immunological datasets. Here, we present DC-ATLAS, a novel and versatile resource for the interpretation of high-throughput data generated perturbing the signaling network of dendritic cells (DCs). RESULTS: Pathways are annotated using a novel data model, the Biological Connection Markup Language (BCML), a SBGN-compliant data format developed to store the large amount of information collected. The application of DC-ATLAS to pathway-based analysis of the transcriptional program of DCs stimulated with agonists of the toll-like receptor family allows an integrated description of the flow of information from the cellular sensors to the functional outcome, capturing the temporal series of activation events by grouping sets of reactions that occur at different time points in well-defined functional modules. CONCLUSIONS: The initiative significantly improves our understanding of DC biology and regulatory networks. Developing a systems biology approach for immune system holds the promise of translating knowledge on the immune system into more successful immunotherapy strategies