Reuse of design pattern measurements for health data

Research using health data is challenged by its heterogeneous nature, description and storage. The COVID-19 outbreak made clear that rapid analysis of observations such as clinical measurements across a large number of healthcare providers can have enormous health benefits. This has brought into focus the need for a common model of quantitative health data that enables data exchange and federated computational analysis. The application of ontologies, Semantic Web technologies and the FAIR principles is an approach used by different life science research projects, such as the European Joint Programme on Rare Diseases, to make data and metadata machine readable and thereby reduce the barriers for data sharing and analytics and harness health data for discovery. Here, we show the reuse of a pattern for measurements to model diverse health data, to demonstrate and raise visibility of the usefulness of this pattern for biomedical research

Semantic Web integration of Cheminformatics resources with the SADI framework

<p>Abstract</p> <p>Background</p> <p>The diversity and the largely independent nature of chemical research efforts over the past half century are, most likely, the major contributors to the current poor state of chemical computational resource and database interoperability. While open software for chemical format interconversion and database entry cross-linking have partially addressed database interoperability, computational resource integration is hindered by the great diversity of software interfaces, languages, access methods, and platforms, among others. This has, in turn, translated into limited reproducibility of computational experiments and the need for application-specific computational workflow construction and semi-automated enactment by human experts, especially where emerging interdisciplinary fields, such as systems chemistry, are pursued. Fortunately, the advent of the Semantic Web, and the very recent introduction of RESTful Semantic Web Services (SWS) may present an opportunity to integrate all of the existing computational and database resources in chemistry into a machine-understandable, unified system that draws on the entirety of the Semantic Web.</p> <p>Results</p> <p>We have created a prototype framework of Semantic Automated Discovery and Integration (SADI) framework SWS that exposes the QSAR descriptor functionality of the Chemistry Development Kit. Since each of these services has formal ontology-defined input and output classes, and each service consumes and produces RDF graphs, clients can automatically reason about the services and available reference information necessary to complete a given overall computational task specified through a simple SPARQL query. We demonstrate this capability by carrying out QSAR analysis backed by a simple formal ontology to determine whether a given molecule is drug-like. Further, we discuss parameter-based control over the execution of SADI SWS. Finally, we demonstrate the value of computational resource envelopment as SADI services through service reuse and ease of integration of computational functionality into formal ontologies.</p> <p>Conclusions</p> <p>The work we present here may trigger a major paradigm shift in the distribution of computational resources in chemistry. We conclude that envelopment of chemical computational resources as SADI SWS facilitates interdisciplinary research by enabling the definition of computational problems in terms of ontologies and formal logical statements instead of cumbersome and application-specific tasks and workflows.</p

Assaying Rho GTPase–dependent processes in Dictyostelium discoideum

The model organism D. discoideum is well-suited to investigate basic questions of molecular and cell biology, particularly those related to the structure, regulation and dynamics of the cytoskeleton, signal transduction, cell-cell adhesion and development. D. discoideum cells make use of Rho-regulated signaling pathways to reorganize the actin cytoskeleton during chemotaxis, endocytosis and cytokinesis. In this organism the Rho family encompasses 20 members, several belonging to the Rac subfamily, but there are no representatives of the Cdc42 and Rho subfamilies. Here we present protocols suitable for monitoring the actin polymerization response and the activation of Rac upon stimulation of aggregation competent cells with the chemoattractant cAMP, and for monitoring the localization and dynamics of Rac activity in live cells

Adding a Little Reality to Building Ontologies for Biology

BACKGROUND: Many areas of biology are open to mathematical and computational modelling. The application of discrete, logical formalisms defines the field of biomedical ontologies. Ontologies have been put to many uses in bioinformatics. The most widespread is for description of entities about which data have been collected, allowing integration and analysis across multiple resources. There are now over 60 ontologies in active use, increasingly developed as large, international collaborations. There are, however, many opinions on how ontologies should be authored; that is, what is appropriate for representation. Recently, a common opinion has been the "realist" approach that places restrictions upon the style of modelling considered to be appropriate. METHODOLOGY/PRINCIPAL FINDINGS: Here, we use a number of case studies for describing the results of biological experiments. We investigate the ways in which these could be represented using both realist and non-realist approaches; we consider the limitations and advantages of each of these models. CONCLUSIONS/SIGNIFICANCE: From our analysis, we conclude that while realist principles may enable straight-forward modelling for some topics, there are crucial aspects of science and the phenomena it studies that do not fit into this approach; realism appears to be over-simplistic which, perversely, results in overly complex ontological models. We suggest that it is impossible to avoid compromise in modelling ontology; a clearer understanding of these compromises will better enable appropriate modelling, fulfilling the many needs for discrete mathematical models within computational biology

The Biomolecular Interaction Network Database and related tools 2005 update

The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machine-readable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues

Improving the performance of DomainDiscovery of protein domain boundary assignment using inter-domain linker index

BACKGROUND: Knowledge of protein domain boundaries is critical for the characterisation and understanding of protein function. The ability to identify domains without the knowledge of the structure – by using sequence information only – is an essential step in many types of protein analyses. In this present study, we demonstrate that the performance of DomainDiscovery is improved significantly by including the inter-domain linker index value for domain identification from sequence-based information. Improved DomainDiscovery uses a Support Vector Machine (SVM) approach and a unique training dataset built on the principle of consensus among experts in defining domains in protein structure. The SVM was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. RESULTS: Improved DomainDiscovery is compared with other methods by benchmarking against a structurally non-redundant dataset and also CASP5 targets. Improved DomainDiscovery achieves 70% accuracy for domain boundary identification in multi-domains proteins. CONCLUSION: Improved DomainDiscovery compares favourably to the performance of other methods and excels in the identification of domain boundaries for multi-domain proteins as a result of introducing support vector machine with benchmark_2 dataset