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Biomedical subjects

Mario Aversano

Publications and source records attributed to Mario Aversano.

2 recordsLinked to original sources

TomatEST database: in silico exploitation of EST data to explore expression patterns in tomato species.

TomatEST is a secondary database integrating expressed sequence tag (EST)/cDNA sequence information from different libraries of multiple tomato species. Redundant EST collections from each species are organized into clusters (gene indices). A cluster consists of one or multiple contigs. Multiple contigs in a cluster represent alternatively transcribed forms of a gene. The set of stand-alone EST sequences (singletons) and contigs, representing all the computationally defined 'Transcript Indices', are annotated according to similarity versus protein and RNA family databases. Sequence function description is integrated with the Gene Ontologies and the Enzyme Commission identifiers for a standard classification of gene products and for the mapping of the expressed sequences onto metabolic pathways. Information on the origin of the ESTs, on their structural features, on clusters and contigs, as well as on functional annotations are accessible via a user-friendly web interface. Specific facilities in the database allow Transcript Indices from a query be automatically classified in Enzyme classes and in metabolic pathways. The 'on the fly' mapping onto the metabolic maps is integrated in the analytical tools. The TomatEST database website is freely available at http://biosrv.cab.unina.it/tomatestdb.

Computational Biology↗

ParPEST: a pipeline for EST data analysis based on parallel computing.

BACKGROUND: Expressed Sequence Tags (ESTs) are short and error-prone DNA sequences generated from the 5' and 3' ends of randomly selected cDNA clones. They provide an important resource for comparative and functional genomic studies and, moreover, represent a reliable information for the annotation of genomic sequences. Because of the advances in biotechnologies, ESTs are daily determined in the form of large datasets. Therefore, suitable and efficient bioinformatic approaches are necessary to organize data related information content for further investigations. RESULTS: We implemented ParPEST (Parallel Processing of ESTs), a pipeline based on parallel computing for EST analysis. The results are organized in a suitable data warehouse to provide a starting point to mine expressed sequence datasets. The collected information is useful for investigations on data quality and on data information content, enriched also by a preliminary functional annotation. CONCLUSION: The pipeline presented here has been developed to perform an exhaustive and reliable analysis on EST data and to provide a curated set of information based on a relational database. Moreover, it is designed to reduce execution time of the specific steps required for a complete analysis using distributed processes and parallelized software. It is conceived to run on low requiring hardware components, to fulfill increasing demand, typical of the data used, and scalability at affordable costs.

Algorithms↗