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This article is part of the supplement: Eleventh International Conference on Bioinformatics (InCoB2012): Bioinformatics

Open Access Proceedings

TranSeqAnnotator: large-scale analysis of transcriptomic data

Ranjeeta Menon1, Gagan Garg1, Robin B Gasser2 and Shoba Ranganathan13*

Author Affiliations

1 Department of Chemistry and Biomolecular Sciences and ARC Centre of Excellence, Macquarie University, Sydney, NSW 2109, Australia

2 Department of Veterinary Sciences, The University of Melbourne, Werribee, VIC 3030, Australia

3 Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597

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BMC Bioinformatics 2012, 13(Suppl 17):S24  doi:10.1186/1471-2105-13-S17-S24

Published: 13 December 2012

Abstract

Background

The transcriptome of an organism can be studied with the analysis of expressed sequence tag (EST) data sets that offers a rapid and cost effective approach with several new and updated bioinformatics approaches and tools for assembly and annotation. The comprehensive analyses comprehend an organism along with the genome and proteome analysis. With the advent of large-scale sequencing projects and generation of sequence data at protein and cDNA levels, automated analysis pipeline is necessary to store, organize and annotate ESTs.

Results

TranSeqAnnotator is a workflow for large-scale analysis of transcriptomic data with the most appropriate bioinformatics tools for data management and analysis. The pipeline automatically cleans, clusters, assembles and generates consensus sequences, conceptually translates these into possible protein products and assigns putative function based on various DNA and protein similarity searches. Excretory/secretory (ES) proteins inferred from ESTs/short reads are also identified. The TranSeqAnnotator accepts FASTA format raw and quality ESTs along with protein and short read sequences and are analysed with user selected programs. After pre-processing and assembly, the dataset is annotated at the nucleotide, protein and ES protein levels.

Conclusion

TranSeqAnnotator has been developed in a Linux cluster, to perform an exhaustive and reliable analysis and provide detailed annotation. TranSeqAnnotator outputs gene ontologies, protein functional identifications in terms of mapping to protein domains and metabolic pathways. The pipeline is applied to annotate large EST datasets to identify several novel and known genes with therapeutic experimental validations and could serve as potential targets for parasite intervention. TransSeqAnnotator is freely available for the scientific community at http://estexplorer.biolinfo.org/TranSeqAnnotator/ webcite.