cPath: open source software for collecting, storing, and querying biological pathways

BACKGROUND: Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required. RESULTS: We have developed cPath, an open source database and web application for collecting, storing, and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists via a customizable web interface, and export pathway data via a web service to third-party software, such as Cytoscape, for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identifier mapping service for linking identical interactors and linking to external resources; built-in support for PSI-MI and BioPAX standard pathway exchange formats; a web service interface for searching and retrieving pathway data sets; and thorough documentation. The cPath software is freely available under the LGPL open source license for academic and commercial use. CONCLUSION: cPath is a robust, scalable, modular, professional-grade software platform for collecting, storing, and querying biological pathways. It can serve as the core data handling component in information systems for pathway visualization, analysis and modeling

Genetic risk factors for cerebrovascular disease in children with sickle cell disease: design of a case-control association study and genomewide screen

BACKGROUND: The phenotypic heterogeneity of sickle cell disease is likely the result of multiple genetic factors and their interaction with the sickle mutation. High transcranial doppler (TCD) velocities define a subgroup of children with sickle cell disease who are at increased risk for developing ischemic stroke. The genetic factors leading to the development of a high TCD velocity (i.e. cerebrovascular disease) and ultimately to stroke are not well characterized. METHODS: We have designed a case-control association study to elucidate the role of genetic polymorphisms as risk factors for cerebrovascular disease as measured by a high TCD velocity in children with sickle cell disease. The study will consist of two parts: a candidate gene study and a genomewide screen and will be performed in 230 cases and 400 controls. Cases will include 130 patients (TCD ≥ 200 cm/s) randomized in the Stroke Prevention Trial in Sickle Cell Anemia (STOP) study as well as 100 other patients found to have high TCD in STOP II screening. Four hundred sickle cell disease patients with a normal TCD velocity (TCD < 170 cm/s) will be controls. The candidate gene study will involve the analysis of 28 genetic polymorphisms in 20 candidate genes. The polymorphisms include mutations in coagulation factor genes (Factor V, Prothrombin, Fibrinogen, Factor VII, Factor XIII, PAI-1), platelet activation/function (GpIIb/IIIa, GpIb IX-V, GpIa/IIa), vascular reactivity (ACE), endothelial cell function (MTHFR, thrombomodulin, VCAM-1, E-Selectin, L-Selectin, P-Selectin, ICAM-1), inflammation (TNFα), lipid metabolism (Apo A1, Apo E), and cell adhesion (VCAM-1, E-Selectin, L-Selectin, P-Selectin, ICAM-1). We will perform a genomewide screen of validated single nucleotide polymorphisms (SNPs) in pooled DNA samples from 230 cases and 400 controls to study the possible association of additional polymorphisms with the high-risk phenotype. High-throughput SNP genotyping will be performed through MALDI-TOF technology using Sequenom's MassARRAY™ system. DISCUSSION: It is expected that this study will yield important information on genetic risk factors for the cerebrovascular disease phenotype in sickle cell disease by clarifying the role of candidate genes in the development of high TCD. The genomewide screen for a large number of SNPs may uncover the association of novel polymorphisms with cerebrovascular disease and stroke in sickle cell disease