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.
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
Shoba
S
Ranganathan
Ranjeeta
R
Menon
Robin B
RB
Gasser
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