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Open Access Short Report

Methodology optimizing SAGE library tag-to-gene mapping: application to Leishmania

Sondos Smandi, Fatma Z Guerfali, Mohamed Farhat, Khadija Ben-Aissa, Dhafer Laouini, Lamia Guizani-Tabbane, Koussay Dellagi and Alia Benkahla*

Author Affiliations

Laboratoire d'Immuno-Pathologie, Vaccinologie et Génétique Moléculaire (LIVGM), WHO Collaborating Center for Research and Training in Leishmaniasis, Institut Pasteur de Tunis, 13 place Pasteur BP74 1002, Tunis, Tunisia

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BMC Research Notes 2012, 5:74  doi:10.1186/1756-0500-5-74

Published: 27 January 2012

Abstract

Background

Leishmaniasis are widespread parasitic-diseases with an urgent need for more active and less toxic drugs and for effective vaccines. Understanding the biology of the parasite especially in the context of host parasite interaction is a crucial step towards such improvements in therapy and control. Several experimental approaches including SAGE (Serial analysis of gene expression) have been developed in order to investigate the parasite transcriptome organisation and plasticity. Usual SAGE tag-to-gene mapping techniques are inadequate because almost all tags are normally located in the 3'-UTR outside the CDS, whereas most information available for Leishmania transcripts is restricted to the CDS predictions. The aim of this work is to optimize a SAGE libraries tag-to-gene mapping technique and to show how this development improves the understanding of Leishmania transcriptome.

Findings

The in silico method implemented herein was based on mapping the tags to Leishmania genome using BLAST then mapping the tags to their gene using a data-driven probability distribution. This optimized tag-to-gene mappings improved the knowledge of Leishmania genome structure and transcription. It allowed analyzing the expression of a maximal number of Leishmania genes, the delimitation of the 3' UTR of 478 genes and the identification of biological processes that are differentially modulated during the promastigote to amastigote differentiation.

Conclusion

The developed method optimizes the assignment of SAGE tags in trypanosomatidae genomes as well as in any genome having polycistronic transcription and small intergenic regions.