Software

A score system for quality evaluation of RNA sequence tags: an improvement for gene expression profiling

Daniel G Pinheiro^1,3 , Pedro AF Galante^4,5 , Sandro J de Souza⁴ , Marco A Zago^2,3 and Wilson A Silva Jr^1,3

¹  Departamento de Genética, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil

²  Departamento de Clínica Médica, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil

³  Centro de Terapia Celular, Hemocentro de Ribeirão Preto, Ribeirão Preto, SP, Brazil

⁴  Ludwig Institute for Cancer Research, São Paulo, SP, Brazil

⁵  Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA

author email corresponding author email

BMC Bioinformatics 2009, 10:170doi:10.1186/1471-2105-10-170

Published:	6 June 2009

Abstract

Background

High-throughput molecular approaches for gene expression profiling, such as Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS) or Sequencing-by-Synthesis (SBS) represent powerful techniques that provide global transcription profiles of different cell types through sequencing of short fragments of transcripts, denominated sequence tags. These techniques have improved our understanding about the relationships between these expression profiles and cellular phenotypes. Despite this, more reliable datasets are still necessary. In this work, we present a web-based tool named S3T: Score System for Sequence Tags, to index sequenced tags in accordance with their reliability. This is made through a series of evaluations based on a defined rule set. S3T allows the identification/selection of tags, considered more reliable for further gene expression analysis.

Results

This methodology was applied to a public SAGE dataset. In order to compare data before and after filtering, a hierarchical clustering analysis was performed in samples from the same type of tissue, in distinct biological conditions, using these two datasets. Our results provide evidences suggesting that it is possible to find more congruous clusters after using S3T scoring system.

Conclusion

These results substantiate the proposed application to generate more reliable data. This is a significant contribution for determination of global gene expression profiles. The library analysis with S3T is freely available at http://gdm.fmrp.usp.br/s3t/ webcite. S3T source code and datasets can also be downloaded from the aforementioned website.