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Philippe Rocca-Serra

Publications and source records attributed to Philippe Rocca-Serra.

15 recordsLinked to original sources
PubMed2006

A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB.

BACKGROUND: Sharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGE-ML by the MGED Society. However, the complexity of the MAGE-ML format has made its use impractical for laboratories lacking dedicated bioinformatics support. RESULTS: We propose a simple tab-delimited, spreadsheet-based format, MAGE-TAB, which will become a part of the MAGE microarray data standard and can be used for annotating and communicating microarray data in a MIAME compliant fashion. CONCLUSION: MAGE-TAB will enable laboratories without bioinformatics experience or support to manage, exchange and submit well-annotated microarray data in a standard format using a spreadsheet. The MAGE-TAB format is self-contained, and does not require an understanding of MAGE-ML or XML.

Computational Biology↗
PubMed2006

The use of concept maps during knowledge elicitation in ontology development processes--the nutrigenomics use case.

BACKGROUND: Incorporation of ontologies into annotations has enabled 'semantic integration' of complex data, making explicit the knowledge within a certain field. One of the major bottlenecks in developing bio-ontologies is the lack of a unified methodology. Different methodologies have been proposed for different scenarios, but there is no agreed-upon standard methodology for building ontologies. The involvement of geographically distributed domain experts, the need for domain experts to lead the design process, the application of the ontologies and the life cycles of bio-ontologies are amongst the features not considered by previously proposed methodologies. RESULTS: Here, we present a methodology for developing ontologies within the biological domain. We describe our scenario, competency questions, results and milestones for each methodological stage. We introduce the use of concept maps during knowledge acquisition phases as a feasible transition between domain expert and knowledge engineer. CONCLUSION: The contributions of this paper are the thorough description of the steps we suggest when building an ontology, example use of concept maps, consideration of applicability to the development of lower-level ontologies and application to decentralised environments. We have found that within our scenario conceptual maps played an important role in the development process.

Algorithms↗
PubMed2006

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 NCICB's 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.

Computational Biology↗
PubMed2006

A strategy capitalizing on synergies: the Reporting Structure for Biological Investigation (RSBI) working group.

In this article we present the Reporting Structure for Biological Investigation (RSBI), a working group under the Microarray Gene Expression Data (MGED) Society umbrella. RSBI brings together several communities to tackle the challenges associated with integrating data and representing complex biological investigations, employing multiple OMICS technologies. Currently, RSBI includes environmental genomics, nutrigenomics and toxicogenomics communities, where independent activities are underway to develop databases and establish data communication standards within their respective domains. The RSBI working group has been conceived as a "single point of focus" for these communities, conforming to general accepted view that duplication and incompatibility should be avoided where possible. This endeavour has aimed to synergize insular solutions into one common terminology between biologically driven standardisation efforts and has also resulted in strong collaborations and shared understanding between those in the technological domain. Through extensive liaisons with many standards efforts, several threads have been woven with the hope that ultimately technology-centered standards and their specific extensions into biological domains of interest will not only stand alone, but will also be able to function together, as interchangeable modules.

Databases, Genetic↗
PubMed2006

Annotation of environmental OMICS data: application to the transcriptomics domain.

Researchers working on environmentally relevant organisms, populations, and communities are increasingly turning to the application of OMICS technologies to answer fundamental questions about the natural world, how it changes over time, and how it is influenced by anthropogenic factors. In doing so, the need to capture meta-data that accurately describes the biological "source" material used in such experiments is growing in importance. Here, we provide an overview of the formation of the "Env" community of environmental OMICS researchers and its efforts at considering the meta-data capture needs of those working in environmental OMICS. Specifically, we discuss the development to date of the Env specification, an informal specification including descriptors related to geographic location, environment, organism relationship, and phenotype. We then describe its application to the description of environmental transcriptomic experiments and how we have used it to extend the Minimum Information About a Microarray Experiment (MIAME) data standard to create a domain-specific extension that we have termed MIAME/Env. Finally, we make an open call to the community for participation in the Env Community and its future activities.

Ecology↗
PubMed2006

Development of FuGO: an ontology for functional genomics investigations.

The development of the Functional Genomics Investigation Ontology (FuGO) is a collaborative, international effort that will provide a resource for annotating functional genomics investigations, including the study design, protocols and instrumentation used, the data generated and the types of analysis performed on the data. FuGO will contain both terms that are universal to all functional genomics investigations and those that are domain specific. In this way, the ontology will serve as the "semantic glue" to provide a common understanding of data from across these disparate data sources. In addition, FuGO will reference out to existing mature ontologies to avoid the need to duplicate these resources, and will do so in such a way as to enable their ease of use in annotation. This project is in the early stages of development; the paper will describe efforts to initiate the project, the scope and organization of the project, the work accomplished to date, and the challenges encountered, as well as future plans.

Biomedical Research↗
PubMed2005

Chemical effects in biological systems--data dictionary (CEBS-DD): a compendium of terms for the capture and integration of biological study design description, conventional phenotypes, and 'omics data.

A critical component in the design of the Chemical Effects in Biological Systems (CEBS) Knowledgebase is a strategy to capture toxicogenomics study protocols and the toxicity endpoint data (clinical pathology and histopathology). A Study is generally an experiment carried out during a period of time for the purpose of obtaining data, and the Study Design Description captures the methods, timing, and organization of the Study. The CEBS Data Dictionary (CEBS-DD) has been designed to define and organize terms in an attempt to standardize nomenclature needed to describe a toxicogenomics Study in a structured yet intuitive format and provide a flexible means to describe a Study as conceptualized by the investigator. The CEBS-DD will organize and annotate information from a variety of sources, thereby facilitating the capture and display of toxicogenomics data in biological context in CEBS, i.e., associating molecular events detected in highly-parallel data with the toxicology/pathology phenotype as observed in the individual Study Subjects and linked to the experimental treatments. The CEBS-DD has been developed with a focus on acute toxicity studies, but with a design that will permit it to be extended to other areas of toxicology and biology with the addition of domain-specific terms. To illustrate the utility of the CEBS-DD, we present an example of integrating data from two proteomics and transcriptomics studies of the response to acute acetaminophen toxicity (A. N. Heinloth et al., 2004, Toxicol. Sci. 80, 193-202).

Acetaminophen↗
PubMed2005

Defining best practice for microarray analyses in nutrigenomic studies.

Microarrays represent a powerful tool for studies of diet-gene interactions. Their use is, however, associated with a number of technical challenges and potential pitfalls. The cost of microarrays continues to drop but is still comparatively high. This, coupled with the complex logistical issues associated with performing nutritional microarray studies, often means that compromises have to be made in the number and type of samples analysed. Additionally, technical variations between array platforms and analytical procedures will almost inevitably lead to differences in the transcriptional responses observed. Consequently, conflicting data may be produced, important effects may be missed and/or false leads generated (e.g. apparent patterns of differential gene regulation that ultimately prove to be incorrect or not significant). This is likely to be particularly true in the field of nutrition, in which we expect that many dietary bioactive agents at nutritionally relevant concentrations will elicit subtle changes in gene transcription that may be critically important in biological terms but will be difficult to detect reliably. Thus, great care should always be taken in designing and executing microarray studies. This article seeks to provide an overview of both the main practical and theoretical considerations in microarray use that represent potential sources of technical variation and error. Wherever possible, recommendations are made on what we propose to be the best approach. The overall aims are to provide a basic framework of advice for researchers who are new to the use of microarrays and to promote a discussion of standardisation and best practice in the field.

Animals↗
PubMed2004

The ArrayExpress gene expression database: a software engineering and implementation perspective.

MOTIVATION: The lack of microarray data management systems and databases is still one of the major problems faced by many life sciences laboratories. While developing the public repository for microarray data ArrayExpress we had to find novel solutions to many non-trivial software engineering problems. Our experience will be both relevant and useful for most bioinformaticians involved in developing information systems for a wide range of high-throughput technologies. RESULTS: ArrayExpress has been online since February 2002, growing exponentially to well over 10,000 hybridizations (as of September 2004). It has been demonstrated that our chosen design and implementation works for databases aimed at storage, access and sharing of high-throughput data. AVAILABILITY: The ArrayExpress database is available at http://www.ebi.ac.uk/arrayexpress/. The software is open source. CONTACT: ugis@ebi.ac.uk.

Algorithms↗
PubMed2004

EnsMart: a generic system for fast and flexible access to biological data.

The EnsMart system (www.ensembl.org/EnsMart) provides a generic data warehousing solution for fast and flexible querying of large biological data sets and integration with third-party data and tools. The system consists of a query-optimized database and interactive, user-friendly interfaces. EnsMart has been applied to Ensembl, where it extends its genomic browser capabilities, facilitating rapid retrieval of customized data sets. A wide variety of complex queries, on various types of annotations, for numerous species are supported. These can be applied to many research problems, ranging from SNP selection for candidate gene screening, through cross-species evolutionary comparisons, to microarray annotation. Users can group and refine biological data according to many criteria, including cross-species analyses, disease links, sequence variations, and expression patterns. Both tabulated list data and biological sequence output can be generated dynamically, in HTML, text, Microsoft Excel, and compressed formats. A wide range of sequence types, such as cDNA, peptides, coding regions, UTRs, and exons, with additional upstream and downstream regions, can be retrieved. The EnsMart database can be accessed via a public Web site, or through a Java application suite. Both implementations and the database are freely available for local installation, and can be extended or adapted to 'non-Ensembl' data sets.

Animals↗
PubMed2003

ArrayExpress--a public repository for microarray gene expression data at the EBI.

ArrayExpress is a new public database of microarray gene expression data at the EBI, which is a generic gene expression database designed to hold data from all microarray platforms. ArrayExpress uses the annotation standard Minimum Information About a Microarray Experiment (MIAME) and the associated XML data exchange format Microarray Gene Expression Markup Language (MAGE-ML) and it is designed to store well annotated data in a structured way. The ArrayExpress infrastructure consists of the database itself, data submissions in MAGE-ML format or via an online submission tool MIAMExpress, online database query interface, and the Expression Profiler online analysis tool. ArrayExpress accepts three types of submission, arrays, experiments and protocols, each of these is assigned an accession number. Help on data submission and annotation is provided by the curation team. The database can be queried on parameters such as author, laboratory, organism, experiment or array types. With an increasing number of organisations adopting MAGE-ML standard, the volume of submissions to ArrayExpress is increasing rapidly. The database can be accessed at http://www.ebi.ac.uk/arrayexpress.

Animals↗
PubMedDate not supplied

ArrayExpress: a public database of gene expression data at EBI.

ArrayExpress is a public repository for microarray-based gene expression data, resulting from the implementation of the MAGE object model to ensure accurate data structuring and the MIAME standard, which defines the annotation requirements. ArrayExpress accepts data as MAGE-ML files for direct submissions or data from MIAMExpress, the MIAME compliant web-based annotation and submission tool of EBI. A team of curators supports the submission process, providing assistance in data annotation. Data retrieval is performed through a dedicated web interface. Relevant results may be exported to ExpressionProfiler, the EBI based expression analysis tool available online (http://www.ebi.ac.uk/arrayexpress).

Computational Biology↗