Malaria remains a global scourge to human life and existence responsible for up to 500 million cases and 3 million deaths annually. Currently there is no feasible and effective vaccine against these parasites and widespread drug resistance to numerous malaria chemotherapeutics has bolstered the need to design and establish novel malaria drug and vaccine strategies. An avenue for such discovery occurred in October 2002 with the landmark publication of the complete genomes of Plasmodium falciparum, the most debilitating of malaria in humans, and the rodent malaria, Plasmodium yoelii. Given the vast wealth of information provided by such large-scale sequencing projects, including numerous additional malaria genomes within the past five years, it is essential that current annotations and predicted gene structures be accurate when presented to the malaria research community at large. While the human malaria genome deserved the majority of attention, the accuracy of the rodent model genome is imperative for the traditional pipeline of drug/vaccine development and validity studies. As with all current genome projects, the gene predictions present in the P. yoelii genome can be improved and using pre-existing genomics-based datasets, like sequenced expressed sequenc tag (EST) libraries, is a proven method for reannotation. Generous predictions, including those findings from our prior NIH AREA support, suggest that 20% of the predicted gene structures are currently erroneous. This renewal project will utilize basic bioinformatics, comparative genomics and established large-scale experimental datasets to manually curate and refine the remaining annotated P. yoelii gene structures with a focus on exon/intron boundaries and improved contig assemblies. Exon/intron boundaries and contig gaps not resolved by bioinformatic methods will be targeted and validated using traditional molecular methods. Results from these studies would benefit malaria researchers and vaccine developers such that they can accurately identify novel vaccine and drug targets using correct gene structures in the rodent malaria model system. The generated data will become immediately available and accessible on the free online genomic resource, PlasmoDB (www.plasmodb.org), for dissemination to the greater malaria research community.
Malaria is one of the most severe public health problems worldwide and a leading cause of death and disease in many developing countries. This project will use existing large-scale genomic datasets to refine the current gene structures for the rodent malaria, Plasmodium yoelii. An improved curation of this model organism's genome would yield a more efficient and economic pipeline for novel vaccine and drug discovery.
