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The Human EST Ontology Explorer: a tissue-oriented visualization system for ontologies distribution in human EST
Ivan Merelli1, Andrea Caprera, Alessandra Stella
1Istituto Tecnologie Biomediche-Consiglio Nazionale delle Ricerche, Via Fratelli Cervi 93, Segrate (MI), Italy. ivan.merelli@itb.cnr.it
The Human EST Ontology Explorer (HEOE) tool analyzes over eight million human Expressed Sequenced Tags (ESTs) to compare gene expression across healthy and diseased tissues. It provides novel insights into Gene Ontology (GO) category and metabolic pathway involvement for ESTs.
Area of Science:
- Bioinformatics
- Genomics
- Molecular Biology
Background:
- NCBI dbEST contains over 8 million human Expressed Sequenced Tags (ESTs), a valuable resource for gene expression studies.
- Existing web resources allow browsing EST data and comparing gene expression levels across libraries.
- A gap exists in tools for analyzing quantitative EST distributions within Gene Ontology (GO) categories and metabolic pathways.
Purpose of the Study:
- To present the Human EST Ontology Explorer (HEOE), a web facility for comparing gene expression levels among diverse human tissue libraries.
- To enable examination of EST distributions in GO categories and their involvement in metabolic pathways.
Main Methods:
- Large-scale BLAST annotation of EST sequences utilizing grid computing.
- Inference of library-specific EST distributions within the GO Graph.
- Implementation of a pathway-based search interface for metabolic pathway representation.
- Integration of EST processing using Perl scripts and storage in a MySQL database.
- Development of a PHP-based web interface for data visualization and comparison.
Main Results:
- The HEOE provides library-dependent statistics on GO Direct Acyclic Graph (DAG) distributions at each GO hierarchical level.
- It allows simultaneous visualization, retrieval, and comparison of data from different libraries.
- Statistically significant differences in GO categories among selected libraries can be computed.
- The tool was tested for grid scalability using a large-scale human EST dataset.
Conclusions:
- HEOE offers a complementary approach to existing resources for inspecting EST expression levels.
- The analysis procedure is adaptable for inclusion of additional libraries, expanding tissue representation.
- The HEOE currently includes analysis from 70 non-normalized libraries, covering healthy and unhealthy tissues.
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