Article

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

BMC Bioinformatics 01/2005;
Source: DOAJ

ABSTRACT Abstract

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 ( Par allel P rocessing of EST s), 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.

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Keywords

affordable costs
 
collected information
 
complete analysis
 
data information content
 
efficient bioinformatic approaches
 
error-prone DNA sequences
 
EST analysis
 
EST data
 
execution time
 
functional genomic studies
 
hardware components
 
information content
 
large datasets
 
ParPEST
 
preliminary functional annotation
 
relational database
 
reliable analysis
 
reliable information
 
specific steps
 
suitable data warehouse