Abstract: The vast amount of expression sequence tags (EST) data in the public databases provides an important resource for comparative and functional genomic studies and, moreover, represents a useful information for a reliable annotation of genomic sequences. ESTs are short and errorprone sequences often including artifactual contamination, nevertheless they represent the framework from which fundamental information for both bioinformatic and experimental analysis is derived. Therefore, EST data need suitable preprocessing to provide high quality information. Because of the advances in biotechnologies, large EST datasets are daily released to the scientific community and fast and efficient bioinformatic approaches are the first step to support these data management and analysis. Therefore the requirements for this kind of analysis are suitable computational tools based on advanced technologies. We describe here a pipeline analysis for ESTs clustering, assembling and annotation by parallel computing that optimizes execution time for the processing of large data sets.