This article is part of the supplement: A Semantic Web for Bioinformatics: Goals, Tools, Systems, Applications
Ontology-based, Tissue MicroArray oriented, image centered tissue bank
1 Istituto di Tecnologie Biomediche – Consiglio Nazionale delle Ricerche, Segrate (Milan) 20090, Italy
2 Parco Tecnologico Padano, Cascina Codazza (Lodi) 26900, Italy
3 Biolab - Department of Computer Science, Control Systems and Telecommunications (DIST) - University of Genoa, Genoa 16100, Italy
BMC Bioinformatics 2008, 9(Suppl 4):S4 doi:10.1186/1471-2105-9-S4-S4Published: 25 April 2008
Tissue MicroArray technique is becoming increasingly important in pathology for the validation of experimental data from transcriptomic analysis. This approach produces many images which need to be properly managed, if possible with an infrastructure able to support tissue sharing between institutes. Moreover, the available frameworks oriented to Tissue MicroArray provide good storage for clinical patient, sample treatment and block construction information, but their utility is limited by the lack of data integration with biomolecular information.
In this work we propose a Tissue MicroArray web oriented system to support researchers in managing bio-samples and, through the use of ontologies, enables tissue sharing aimed at the design of Tissue MicroArray experiments and results evaluation. Indeed, our system provides ontological description both for pre-analysis tissue images and for post-process analysis image results, which is crucial for information exchange. Moreover, working on well-defined terms it is then possible to query web resources for literature articles to integrate both pathology and bioinformatics data.
Using this system, users associate an ontology-based description to each image uploaded into the database and also integrate results with the ontological description of biosequences identified in every tissue. Moreover, it is possible to integrate the ontological description provided by the user with a full compliant gene ontology definition, enabling statistical studies about correlation between the analyzed pathology and the most commonly related biological processes.