Malaria in humans is caused by six species of Plasmodium parasites, of which the nuclear genome sequences for the two Plasmodium ovale spp., P. ovale curtisi and P. ovale wallikeri, and Plasmodium malariae have not yet been analyzed. Here we present an analysis of the nuclear genome sequences of these three parasites, and describe gene family expansions therein. Plasmodium ovale curtisi and P. ovale wallikeri are genetically distinct but morphologically indistinguishable and have sympatric ranges through the tropics of Africa, Asia and Oceania. Both P. ovale spp. show expansion of the surfin variant gene family, and an amplification of the Plasmodium interspersed repeat (pir) superfamily which results in an approximately 30% increase in genome size. For comparison, we have also analyzed the draft nuclear genome of P. malariae, a malaria parasite causing mild malaria symptoms with a quartan life cycle, long-term chronic infections, and wide geographic distribution. Plasmodium malariae shows only a moderate level of expansion of pir genes, and unique expansions of a highly diverged transmembrane protein family with over 550 members and the gamete P25/27 gene family. The observed diversity in the P. ovale wallikeri and P. ovale curtisi surface antigens, combined with their phylogenetic separation, supports consideration that the two parasites be given species status.

Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.

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Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate host disease phenotypes with infectious agents. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http://patho.phenomebrowser.net/ , and the data are freely available through a public SPARQL endpoint.

Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate phenotypes with infectious disease. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http: //patho.phenomebrowser.net/, and the data is freely available through a public SPARQL endpoint.