Computational toxicology using the OpenTox application programming interface and Bioclipse

BACKGROUND: Toxicity is a complex phenomenon involving the potential adverse effect on a range of biological functions. Predicting toxicity involves using a combination of experimental data (endpoints) and computational methods to generate a set of predictive models. Such models rely strongly on being able to integrate information from many sources. The required integration of biological and chemical information sources requires, however, a common language to express our knowledge ontologically, and interoperating services to build reliable predictive toxicology applications. FINDINGS: This article describes progress in extending the integrative bio- and cheminformatics platform Bioclipse to interoperate with OpenTox, a semantic web framework which supports open data exchange and toxicology model building. The Bioclipse workbench environment enables functionality from OpenTox web services and easy access to OpenTox resources for evaluating toxicity properties of query molecules. Relevant cases and interfaces based on ten neurotoxins are described to demonstrate the capabilities provided to the user. The integration takes advantage of semantic web technologies, thereby providing an open and simplifying communication standard. Additionally, the use of ontologies ensures proper interoperation and reliable integration of toxicity information from both experimental and computational sources. CONCLUSIONS: A novel computational toxicity assessment platform was generated from integration of two open science platforms related to toxicology: Bioclipse, that combines a rich scriptable and graphical workbench environment for integration of diverse sets of information sources, and OpenTox, a platform for interoperable toxicology data and computational services. The combination provides improved reliability and operability for handling large data sets by the use of the Open Standards from the OpenTox Application Programming Interface. This enables simultaneous access to a variety of distributed predictive toxicology databases, and algorithm and model resources, taking advantage of the Bioclipse workbench handling the technical layers

XMPP for cloud computing in bioinformatics supporting discovery and invocation of asynchronous web services

Background: Life sciences make heavily use of the web for both data provision and analysis. However, the increasing amount of available data and the diversity of analysis tools call for machine accessible interfaces in order to be effective. HTTP-based Web service technologies, like the Simple Object Access Protocol (SOAP) and REpresentational State Transfer (REST) services, are today the most common technologies for this in bioinformatics. However, these methods have severe drawbacks, including lack of discoverability, and the inability for services to send status notifications. Several complementary workarounds have been proposed, but the results are ad-hoc solutions of varying quality that can be difficult to use. Results: We present a novel approach based on the open standard Extensible Messaging and Presence Protocol (XMPP), consisting of an extension (IO Data) to comprise discovery, asynchronous invocation, and definition of data types in the service. That XMPP cloud services are capable of asynchronous communication implies that clients do not have to poll repetitively for status, but the service sends the results back to the client upon completion. Implementations for Bioclipse and Taverna are presented, as are various XMPP cloud services in bio- and cheminformatics. Conclusion: XMPP with its extensions is a powerful protocol for cloud services that demonstrate several advantages over traditional HTTP-based Web services: 1) services are discoverable without the need of an external registry, 2) asynchronous invocation eliminates the need for ad-hoc solutions like polling, and 3) input and output types defined in the service allows for generation of clients on the fly without the need of an external semantics description. The many advantages over existing technologies make XMPP a highly interesting candidate for next generation online services in bioinformatics

An eScience-Bayes strategy for analyzing omics data

<p>Abstract</p> <p>Background</p> <p>The omics fields promise to revolutionize our understanding of biology and biomedicine. However, their potential is compromised by the challenge to analyze the huge datasets produced. Analysis of omics data is plagued by the curse of dimensionality, resulting in imprecise estimates of model parameters and performance. Moreover, the integration of omics data with other data sources is difficult to shoehorn into classical statistical models. This has resulted in <it>ad hoc </it>approaches to address specific problems.</p> <p>Results</p> <p>We present a general approach to omics data analysis that alleviates these problems. By combining eScience and Bayesian methods, we retrieve scientific information and data from multiple sources and coherently incorporate them into large models. These models improve the accuracy of predictions and offer new insights into the underlying mechanisms. This "eScience-Bayes" approach is demonstrated in two proof-of-principle applications, one for breast cancer prognosis prediction from transcriptomic data and one for protein-protein interaction studies based on proteomic data.</p> <p>Conclusions</p> <p>Bayesian statistics provide the flexibility to tailor statistical models to the complex data structures in omics biology as well as permitting coherent integration of multiple data sources. However, Bayesian methods are in general computationally demanding and require specification of possibly thousands of prior distributions. eScience can help us overcome these difficulties. The eScience-Bayes thus approach permits us to fully leverage on the advantages of Bayesian methods, resulting in models with improved predictive performance that gives more information about the underlying biological system.</p

Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research

A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase

A confidence predictor for logD using conformal regression and a support-vector machine

Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service