MOTIVATION: Molecular complexes play a major role in the regulation of biological pathways. The Biological Pathway Exchange format (BioPAX) facilitates the integration of data sources describing interactions some of which involving complexes. The BioPAX specification explicitly prevents complexes to have any component that is another complex (unless this component is a black-box complex whose composition is unknown). However, we observed that the well-curated Reactome pathway database contains such recursive complexes of complexes. We propose reproductible and semantically rich SPARQL queries for identifying and fixing invalid complexes in BioPAX databases, and evaluate the consequences of fixing these nonconformities in the Reactome database. RESULTS: For the Homo sapiens version of Reactome, we identify 5833 recursively defined complexes out of the 14 987 complexes (39%). This situation is not specific to the Human dataset, as all tested species of Reactome exhibit between 30% (Plasmodium falciparum) and 40% (Sus scrofa, Bos taurus, Canis familiaris, and Gallus gallus) of recursive complexes. As an additional consequence, the procedure also allows the detection of complex redundancies. Overall, this method improves the conformity and the automated analysis of the graph by repairing the topology of the complexes in the graph. This will allow to apply further reasoning methods on better consistent data. AVAILABILITY AND IMPLEMENTATION: We provide a Jupyter notebook detailing the analysis https://github.com/cjuigne/non_conformities_detection_biopax.

SUMMARY: PAX2GRAPHML is an open-source Python library that allows to easily manipulate BioPAX source files as regulated reaction graphs described in.graphml format. The concept of regulated reactions, which allows connecting regulatory, signaling and metabolic levels, has been used. Biochemical reactions and regulatory interactions are homogeneously described by regulated reactions involving substrates, products, activators and inhibitors as elements. PAX2GRAPHML is highly flexible and allows generating graphs of regulated reactions from a single BioPAX source or by combining and filtering BioPAX sources. Supported by the graph exchange format .graphml, the large-scale graphs produced from one or more data sources can be further analyzed with PAX2GRAPHML or standard Python and R graph libraries. AVAILABILITY AND IMPLEMENTATION: https://pax2graphml.genouest.org.

To be easily constructed, shared and maintained, complex in silico bioinformatics analysis are structured as workflows. Furthermore, the growth of computational power and storage demand from this domain, requires workflows to be efficiently executed. However, workflow performances usually rely on the ability of the designer to extract potential parallelism. But atomic bioinformatics tasks do not often exhibit direct parallelism which may appears later in the workflow design process.

The reliability is a major factor that limits the success of promising service oriented architecture in bioinformatics. To evaluate services reliability and availability and create indicators of quality of service, we have defined an XML format designed to exchange service status reports. We demonstrate the interest of sharing status reports between service consumers and producers by providing two example applications, a workflow management system plug-in and a Web report generator. Furthermore, we describe the framework that we use to distribute quality of service management features, QBIOS. QBIOS enables service test case creation, scheduled execution and monitoring. We illustrate the use of our open test strategy at the level of a group of scientific partners sharing bioinformatics services.