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Plasmodium falciparum is the parasite responsible for more than 90% of deaths that occur due to malaria. Organization and mining of 'omic' (genomic, proteomic, transcriptomic, interactomic) data can improve our understanding of P. falciparum biology and help in the fight against malaria. PlasmoID (Plasmodium Information Discovery) is a tool developed for the dynamic exploration of the parasite's 'omic' landscape. Diverse computational and curated P. falciparum protein-protein interaction datasets, as well as binary relationships involving protein-small molecule entities, manually curated protein-protein relationships derived from the published literature and protein-protein interactions based on metabolic pathways are included in the PlasmoID database. The graphical user interface is designed as a plug-in to Cytoscape, an open-source network visualization tool. Important features of this plug-in include a synchronized tabular representation of any network loaded on the canvas, ability to find the shortest path between a pair of nodes in the database, search and expansion of entities from the database, and the ability to add new entities to the database through the interface. Malaria researchers can now seamlessly interrogate heterogeneous 'omic' datasets as well as add proprietary data to generate and visualize P. falciparum pathway and cell process network models. PlasmoID can be downloaded from http://pfalciparum.atc.tcs.com/PlasmoID.

It is getting familiar that pathway information greatly contributes to elucidate the molecular basis of human disease with large-scale biological data. We developed a pathway database for molecular pathology in periodontitis named 'Pathogenic Pathway Database for Periodontitis'. Periodontitis is an inflammation disease in periodontal tissue and associated with an increased health risk of angina, myocardial infarction, and fetal cardiovascular events. Despite accumulation of biomedical research on periodontitis pathology at the molecular level, there has been no systematization for biological pathways in periodontitis. We checked 185 reference papers and extracted causal relationships among molecules as well as pathways representing molecular etiology. We also built an ontology that systematizes conceptual terms associated with the pathways. Besides pathways, our ontology provides cellular and tissue specific contextual information that is required to represent pathology at the cellular and tissue specific levels. We implemented in our database an integrated viewer for the association between pathways and ontology. Pathogenic Pathway Database for Periodontitis is freely available at http://bio-omix.tmd.ac.jp/disease/perio/.

The application of in silico tools for the development of T-cell vaccines is crucial. Yet, due to myriad of polymorphisms of human T-lymphocytic antigen challenges, such therapeutic opportunities present unique roadblocks. There is an obvious advantage in using immunoinformatics (i.e., significantly decreasing cost related to laboratory expenses). A previous publication looked at random binding and nonbinding peptides in order to test the practicality of using such in silico tools to obtain possible immunogenic peptides. The present in silico study applied the same basic approaches to an applicable problem that was to identify promiscuous peptide vaccine candidates for hepatitis C virus (HCV) infection. The data sets used, included the proteins HCV E1, E2 and P7 as the binders (non-self antigens) and the GAD65 and ICA69, which have an association with diabetes, as non-binders (self-antigens). The in silico tools utilized were ProPred, MHC2PRED, and RANKPEP. The resulting differences were identifiable in each of the statistical parameters examined. Variations in the outcomes were evident by the dissimilarities found among the major indices of evaluation Sensitivity, Specificity, Accuracy, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Matthews's correlation coefficient (MCC) of the percentages of the predicted promiscuous peptides to HLA-DRB1*0101, *0301, and *0401. The conclusion from this study indicates that more work needs to be done in order to enhance the predictability of programs for the identification of peptide vaccine candidates for HCV. Such programs should not be solely relied upon without in vitro assay verification.