Konstantinos Sidiropoulos

The Reactome pathway knowledgebase

The Reactome Knowledgebase (www.reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations—an extended version of a classic metabolic map, in a single consistent data model. Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression pattern surveys or somatic mutation catalogues from tumour cells. Over the last two years we redeveloped major components of the Reactome web interface to improve usability, responsiveness and data visualization. A new pathway diagram viewer provides a faster, clearer interface and smooth zooming from the entire reaction network to the details of individual reactions. Tool performance for analysis of user datasets has been substantially improved, now generating detailed results for genome-wide expression datasets within seconds. The analysis module can now be accessed through a RESTFul interface, facilitating its inclusion in third party applications. A new overview module allows the visualization of analysis results on a genome-wide Reactome pathway hierarchy using a single screen page. The search interface now provides auto-completion as well as a faceted search to narrow result lists efficiently.

Open Targets: a platform for therapeutic target identification and validation

We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

A Simple algorithm to monitor HR for real time treatment applications

As the demand for effective and reliable telecare systems increases rapidly over the last years, novel ideas applied on existing consumer products enables the development of innovative solutions that could enhance the users wellbeing. In this research, we are going to demonstrate the potential of a system that enables users to monitor their own heart beat rate in real time and use specialised software for personal health coaching. In this paper we will explain and demonstrate how to extract heart beat rate information from a user using the camera of a commercially available mobile phone which will enable us to supply the users of the system with vital information and utilize interactive tools useful for personal health coaching. Our industrial partner Health Smart Limited have filed a patent [1] for this application, they retain the full intellectual property of this project.

Reactome pathway analysis: a high-performance in-memory approach

BackgroundReactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples.ResultsHere, we present a new high-performance in-memory implementation of the well-established over-representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data structures are used to improve performance and minimise the memory footprint. The first step, finding out whether an identifier in the user’s sample corresponds to an entity in Reactome, is addressed using a radix tree as a lookup table. The second step, modelling the proteins, chemicals, their orthologous in other species and their composition in complexes and sets, is addressed with a graph. The third and fourth steps, that aggregate the results and calculate the statistics, are solved with a double-linked tree.ConclusionThrough the use of highly optimised, in-memory data structures and algorithms, Reactome has achieved a stable, high performance pathway analysis service, enabling the analysis of genome-wide datasets within seconds, allowing interactive exploration and analysis of high throughput data. The proposed pathway analysis approach is available in the Reactome production web site either via the AnalysisService for programmatic access or the user submission interface integrated into the PathwayBrowser. Reactome is an open data and open source project and all of its source code, including the one described here, is available in the AnalysisTools repository in the Reactome GitHub (https://github.com/reactome/).

Reactome enhanced pathway visualization

Motivation Reactome is a free, open‐source, open‐data, curated and peer‐reviewed knowledge base of biomolecular pathways. Pathways are arranged in a hierarchical structure that largely corresponds to the GO biological process hierarchy, allowing the user to navigate from high level concepts like immune system to detailed pathway diagrams showing biomolecular events like membrane transport or phosphorylation. Here, we present new developments in the Reactome visualization system that facilitate navigation through the pathway hierarchy and enable efficient reuse of Reactome visualizations for users’ own research presentations and publications. Results For the higher levels of the hierarchy, Reactome now provides scalable, interactive textbook‐style diagrams in SVG format, which are also freely downloadable and editable. Repeated diagram elements like ‘mitochondrion’ or ‘receptor’ are available as a library of graphic elements. Detailed lower‐level diagrams are now downloadable in editable PPTX format as sets of interconnected objects. Availability and implementation http://reactome.org Contact fabregat@ebi.ac.uk or hhe@ebi.ac.uk

Reactome graph database: Efficient access to complex pathway data

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.

Development and evaluation of a PDA-based teleradiology terminal in thyroid nodule diagnosis

We developed a wireless personal digital assistant (PDA)-based teleradiology terminal which allowed a secure connection to the hospitals Picture Archiving and Communication System (PACS) through the DICOM protocol. Ten members of the hospitals medical staff completed a questionnaire about its mobility, usability, stability, performance and diagnostic efficiency in a real health-care environment. There was a high degree of satisfaction with the systems mobility (mean score 4.1, SD 1.0, on a five-point scale), usability (mean score 4.2, SD 1.1), stability (mean score 3.9, SD 0.4) and performance (mean score 4.2, SD 0.6). The system was evaluated as a tool for providing assistance in diagnosing thyroid nodules from ultrasound images. A total of 144 ultrasound images with thyroid nodules were assessed by an expert. Six image quality attributes were evaluated. The physician concluded that the ultrasound thyroid images on the PDA screen were of similar quality to those displayed on a diagnostic visual display unit screen. However, the expert found difficulties in diagnosing microcalcification, internal echo texture and vascularity. The PDA terminal provided rapid, secure and convenient portable access to PACS images and the image quality was sufficient for diagnostic interpretation of ultrasound images of the thyroid.

Remote monitoring of electromagnetic signals and seismic events using smart mobile devices

This study presents the design and development of a novel mobile wireless system to be used for monitoring seismic events and related electromagnetic signals, employing smart mobile devices like personal digital assistants (PDAs) and wireless communication technologies such as wireless local area networks (WLANs), general packet radio service (GPRS) and universal mobile telecommunications system (UMTS). The proposed system enables scientists to access critical data while being geographically independent of the sites of data sources, rendering it as a useful tool for preliminary scientific analysis.

Assessing the performance of four different categories of histological criteria in brain tumours grading by means of a computer-aided diagnosis image analysis system

Brain tumours are considered one of the most lethal and difficult to treat forms of cancer, with unknown aetiology and lack of any realistic screening. In this study, we examine, whether the combination of descriptive criteria, used by expert histopathologists in assessing histologic tissue samples, and quantitative image analysis features may improve the diagnostic accuracy of brain tumour grading. Data comprised 61 cases of brain cancers (astrocytomas, oligodendrogliomas, meningiomas) collected from the archives of the University Hospital of Patras, Greece. Incorporating physicians descriptive criteria and image analysiss quantitative features into a discriminant function, a computer‐aided diagnosis system was designed for discriminating low‐grade from high‐grade brain tumours. Physicians descriptive features, when solely used in the system, proved of high discrimination accuracy (93.4%). When verbal descriptive features were combined with quantitative image analysis features in the system, discrimination accuracy improved to 98.4%. The generalization of the proposed system to unseen data converged to an overall prediction accuracy of 86.7% ± 5.4%. Considering that histological grading affects treatment selection and diagnostic errors may be notable in clinical practice, the utilization of the proposed system may safeguard against diagnostic misinterpretations in every day clinical practice.

Reactome diagram viewer: data structures and strategies to boost performance

Abstract Motivation Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community. Results The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partitioning data structure minimizes CPU workload, enabling the introduction of new features that further enhance user experience. Through the use of highly optimized data structures and algorithms, Reactome has boosted the performance and usability of the new pathway diagram viewer, providing a robust, scalable and easy-to-integrate solution to pathway visualization. As graph-based visualization of complex data is a frequent challenge in bioinformatics, many of the individual strategies presented here are applicable to a wide range of web-based bioinformatics resources. Availability and implementation Reactome is available online at: https://reactome.org. The diagram viewer is part of the Reactome pathway browser (https://reactome.org/PathwayBrowser/) and also available as a stand-alone widget at: https://reactome.org/dev/diagram/. The source code is freely available at: https://github.com/reactome-pwp/diagram. Supplementary information Supplementary data are available at Bioinformatics online.