Wormbase-ParaSite

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Flatworms and roundworms are diverse groups of organisms and include those responsible for serious human, veterinary and plant diseases. Their global impact is hard to measure but annual human morbidity is estimated to be equivalent to at least 50 million productive years of life (c.f. 85M for HIV/AIDS), and agricultural losses from plant parasitic nematodes can be measured in hundreds of millions of dollars. Despite their impact on human health, a recent report highlighted that parasitic helminths attract only $77M per annum in research expenditure (cf. $1.1billion for HIV/AIDS). Due to the wide range of pathology caused in various host species, no single model species can capture the range of disease-causing mechanisms involved. Parasitologists are therefore inherently interested in making comparisons between many different species. Access to genomic-scale datasets has revolutionized molecular and cell biological studies of protozoan pathogens, advancing basic and applied research, but this is only now starting to happen for parasitic worms. Major sequencing programmes are now underway and large scale functional genomics datasets are beginning to emerge (e.g. RNA-Seq is becoming commonplace). Studies of genomic variation in multiple isolates, to address clinical, epidemiological or applied agricultural questions, are the obvious next steps. While the emergence of new genome-scale datasets is immensely exciting, genomes are often produced in a relatively poor states of assembly and annotation compared to existing reference genomes. Moreover, the prevailing paradigm for organising genomic information (essentially, the genome browser and the underlying data models that support this) are relatively poorly fitted to the exploration of hundreds of highly fragmented genomes with limited functional characterisation. In this application, we propose the creation of a new resource to organise, classify and allow the exploration hundreds of worm genomes, facilitating the exploitation of sequence-based data for understanding and ultimately controlling worm-induced pathology.The propose resource will be called WormBase-ParaSite, and will be strategically aligned with WormBase (the main resource for the model nematode Caenorhadbitis elegans). Specifically the resource will provide: (a) Gene structures and functional annotation for unannotated worm genomes. (b) Comparative genomic analysis, visualisation and querying. (c) Methods for exploration and data mining the complete data set, through an intuitive query-building interface accessible to research scientists. (d) A platform for the visualisation of the results of high-throughput sequencing experiments in the context of other annotation that enables functional genomics and variation studies. (e) An infrastructure for accepting and integrating functional annotation submitted from the community engaged in worm research.The project will complement the existing scope (and leverage the existing content) of WormBase, which has supplied biologists working on the model worm Caenorhabditis elegans with an invaluable information resource since the genome of this species was one of the first to be deciphered. It will provide additional capacity and tools for the handling of a massively increased quantity of genomes, with a clear focus on the information that is most relevant to parasitologists. The resource will also provide a home for data from the flatworms, such as flukes and tapeworms, which are outside the scope of WormBase.