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Susanna-Assunta Sansone

Publications and source records attributed to Susanna-Assunta Sansone.

17 recordsLinked to original sources
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

Establishing reporting standards for metabolomic and metabonomic studies: a call for participation.

Metabolite concentrations in cellular systems are very much dependent on the physiological, environmental, and genetic status of an organism and are regarded as the ultimate result of cellular regulation, resulting in the visible phenotypes. Therefore, the comprehensive analysis of metabolite levels and fluxes renders a suitable tool for assessing the degree of perturbation in biological systems. Lessons derived from development of other OMICS areas (genomics, proteomics, and transcriptomics) have shown that large-scale comparisons and interpretations will require the re-use of data over long periods of time and by multiple laboratories with different expertise and backgrounds. Reaching this goal will require standardization of reporting structures of metabolomic studies for journal publication purposes, for regulatory deposition, and for database dissemination. An initiative by the Metabolomics Society is presented that aims to define important aspects of metabolomic workflows. These include biological study designs, chemical analysis, and data processing, as well as the ontologies that are necessary in this framework.

Animals↗
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↗
PubMed2006

Meeting report: Validation of toxicogenomics-based test systems: ECVAM-ICCVAM/NICEATM considerations for regulatory use.

This is the report of the first workshop "Validation of Toxicogenomics-Based Test Systems" held 11-12 December 2003 in Ispra, Italy. The workshop was hosted by the European Centre for the Validation of Alternative Methods (ECVAM) and organized jointly by ECVAM, the U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM), and the National Toxicology Program (NTP) Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM). The primary aim of the workshop was for participants to discuss and define principles applicable to the validation of toxicogenomics platforms as well as validation of specific toxicologic test methods that incorporate toxicogenomics technologies. The workshop was viewed as an opportunity for initiating a dialogue between technologic experts, regulators, and the principal validation bodies and for identifying those factors to which the validation process would be applicable. It was felt that to do so now, as the technology is evolving and associated challenges are identified, would be a basis for the future validation of the technology when it reaches the appropriate stage. Because of the complexity of the issue, different aspects of the validation of toxicogenomics-based test methods were covered. The three focus areas include a) biologic validation of toxicogenomics-based test methods for regulatory decision making, b) technical and bioinformatics aspects related to validation, and c) validation issues as they relate to regulatory acceptance and use of toxicogenomics-based test methods. In this report we summarize the discussions and describe in detail the recommendations for future direction and priorities.

Animal Testing Alternatives↗
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

Database development in toxicogenomics: issues and efforts.

The marriage of toxicology and genomics has created not only opportunities but also novel informatics challenges. As with the larger field of gene expression analysis, toxicogenomics faces the problems of probe annotation and data comparison across different array platforms. Toxicogenomics studies are generally built on standard toxicology studies generating biological end point data, and as such, one goal of toxicogenomics is to detect relationships between changes in gene expression and in those biological parameters. These challenges are best addressed through data collection into a well-designed toxicogenomics database. A successful publicly accessible toxicogenomics database will serve as a repository for data sharing and as a resource for analysis, data mining, and discussion. It will offer a vehicle for harmonizing nomenclature and analytical approaches and serve as a reference for regulatory organizations to evaluate toxicogenomics data submitted as part of registrations. Such a database would capture the experimental context of in vivo studies with great fidelity such that the dynamics of the dose response could be probed statistically with confidence. This review presents the collaborative efforts between the European Molecular Biology Laboratory-European Bioinformatics Institute ArrayExpress, the International Life Sciences Institute Health and Environmental Science Institute, and the National Institute of Environmental Health Sciences National Center for Toxigenomics Chemical Effects in Biological Systems knowledge base. The goal of this collaboration is to establish public infrastructure on an international scale and examine other developments aimed at establishing toxicogenomics databases. In this review we discuss several issues common to such databases: the requirement for identifying minimal descriptors to represent the experiment, the demand for standardizing data storage and exchange formats, the challenge of creating standardized nomenclature and ontologies to describe biological data, the technical problems involved in data upload, the necessity of defining parameters that assess and record data quality, and the development of standardized analytical approaches.

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↗