Advanced in silico analysis of expressed sequence tag (EST) data for parasitic nematodes of major socio-economic importance--fundamental insights toward biotechnological outcomes.
Parasitic nematodes infect humans, other animals and plants, and impose a significant public health and economic burden worldwide due to the diseases that they cause. A better understanding of parasite genomes, host-parasite relationships and the molecular biology of parasites themselves will enable the rational development of diagnostic tests and/or safe anti-parasitic compounds, following the functional annotation of parasite genomic sequences. With only a few completely sequenced nematode genomes, expressed sequence tag (EST) datasets provide a low-cost alternative ("poor man's genome") to whole genome sequences and a glimpse of the transcriptome of an organism. EST data require a number of computational methods for their pre-processing, clustering, assembly and annotation to yield biologically relevant information. In this article, we review the steps involved in EST data analysis, the development of new semi-automated bioinformatic pipelines and their application to parasitic nematodes of major socio-economic significance, focused on identifying molecules involved in key biological processes or pathways that might serve as targets for new drugs or vaccines.
Ranganathan
Shoba
S
Department of Chemistry and Biomolecular Sciences and Australian Research Council (ARC) Centre of Excellence in Bioinformatics, Macquarie University, Sydney, New South Wales 2109, Australia. shoba.ranganathan@mq.edu.au
Menon
Ranjeeta
R
Gasser
Robin B
RB
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D055808:Drug Discovery; D020224:Expressed Sequence Tags; D006801:Humans; D009348:Nematoda / Q000235:genetics*; D009349:Nematode Infections / Q000469:parasitology