Genomic Medicine Grid Juan Pedro Sánchez Merino Instituto de Salud Carlos III

The completion of the HGP opened the possibility to understand the relationships between the structure of human genes, environmental factors and physiopathological processes Genomic Medicine deals with the application of genomic knowledge into clinical practice in order to facilitate the development of new diagnostic and therapeutic solutions Genomic Medicine

For example, Genomic Medicine will impact care provision in the following ways: –Clinical Diagnosis: new devices (biochips) enable to monitor simultaneously a large number of molecular parameters that can be used as diagnostic markers –Disease Reclassification: The gene expression profiles can be used to propose new disease classifications that could improve disease diagnoses and prognoses –Pharmacogenetics and Pharmacogenomics: Genomic Medicine will provide new technologies to understand the role of the genes in diseases and to apply this knowledge in the development and prescription of new drugs –Genetic epidemiology and Public Health: New genetic information technologies will make it possible to perform cost- effective genetic tests at the population level Developments in Genomics Affecting Care Delivery

IT Needs of Genomic Medicine Current research on genomic medicine is producing enormous amounts of data, which requires computational resources to make it available worldwide, and advanced computer tools to analyze them The realization of the Genomic Medicine concept requires the integration of biological and medical knowledge For this purpose, new methods and tools are required to bring together Medical Informatics and Bioinformatics In such approach, all levels of information (molecule to population) and the appropriate techniques and methods (from BI, MI, public health and epidemiology informatics) would be used

Biomedical Informatics: The Convergence of BI and MI Present informatics tools were not designed to effectively link genetic and clinical information BMI aims to solve this: Space where MI and BI meets and interact to convert the data that geneticists and molecular biologists produce into information that physicians and health professionals can use This vision requires the design and development of computer methods and tools for the effective integration of biomedical knowledge

The integration of biomedical information sources brings up a new problem domain with some specific challenges to be addressed: –The amount of data available and being produced is tremendous –There are many different sources of information spread over the Web –Data integration is difficult: wide range of formats and different semantics, some resources are available only through web interfaces –Coding and terminologies are not unified: difficult to discern quality and link related concepts –Medical coding systems are not ready for managing genetic information –Intellectual property rights, privacy and confidentiality issues and protection of the ownership of valuable data may hinder the exchange of contents –Results are often published in natural language formats

Semantic Integration of Biomedical Resources Very important challenge The biomedical resources are usually unrelated although the contents they hold are semantically connected. The main goals of semantic integration of biomedical resources are: –To allow an uniform access to biological, biomedical, bioinformatics, medical and clinical resources –To allow the discovering and exploitation of semantic relationships in data sources

Main Services of Semantic Integration Semantic Modelling of different biomedical concepts and resources using ontologies Semantic annotation of biomedical resources (to allow a continuous exchange between data sources and users) Discovering, browsing and querying of biomedical resources, offered both to humans and to computer programs Semantic modelling of medical documentation through different types of metadata (media-type, content- descriptive, document composition, etc)

Web Services. Allow the searching, calling and execution of distributed services. Could be used to implement some basic biomedical services and applications. Grid-based DBMS and metadata management systems that provide a secure, efficient and automatic data source management in a Grid environment. Support for Virtual Organizations clusters through basic Grid services, such as security, and tools and platforms for cooperation. Grid technologies in line with the semantic integration needs

Biomedical Grids for Genomic Medicine Applications There are many areas in informatics necessary to support Genomic Medicine (development of digital models, molecular imaging, etc.) Grid can contribute to the development of some of these areas by: –Supplying high computing power –Enabling seamless access and integration of complex data sources –Establishing collaborative Virtual Organizations

Technologies to store large amounts of phenotype, genotype and proteotype data Grid can support the development of models and simulations that integrate gene sequences, functions, pathophysiological processes and clinical manifestations in an unified abstraction E-learning tools: the new e-learning tools needs to share computational resources such as data files, simulations, etc, so they are candidates to exploit the integration and share features of the Grid technology In Molecular Imaging, Grid can provide the processing power needed by this area Expected Contributions

–In Development of Pharmacogenomics. Grid offers services that assist in the management of the diversity of information sources used in pharmacogenomics. –In the Developing of new clinical decision making tools Grid could help to integrate all the data used in decision-making and supply the computing power needed to run real time, complex interactive systems –Grid can facilitate the collaborative work through the set up of Virtual Organizations –Grid can help to integrate genetic data obtained in functional and comparative genomics into the clinical information systems. –In Genetic epidemiology, the information needed to perform such studies are spread in different sites. Grid can facilitate seamless access to all these resources.

Requirements of Biomedical Grids Biomedical Grids Requirements: –Must be able to: Store,integrate and analyze biomedical data –Provide processing power –Support the modelling, designing and execution of workflow biomedical applications by using ontologies, workflow languages, etc. –offer high-level tools for the extraction of knowledge from the data repositories available on the Grid –offer knowledge discovery services. –it uses metadata and ontologies to describe Grid resources, in order to enhance and automate service discovery and negotiation, application composition, information extraction and knowledge discovery Semantic Grid Knowledge Grid Biomedical Grid

Data Sources and Modelling Layer Data Sources and Modelling Layer Application composition and enactment Layer Application composition and enactment Layer Data analysis and knowledge layer Data analysis and knowledge layer Conceptual Layers of Biomedical Grids

Conceptual Layers Data Sources and Modelling Layer Data Sources and Modelling Layer Application composition and enactment Layer Application composition and enactment Layer Data analysis and knowledge layer Data analysis and knowledge layer Ontology-based modelling of biological/biomedical databases to allow easy access to information semantically correlated Modelling of distributed biomedical applications Main Tasks

Conceptual Layers Data Sources and Modelling Layer Data Sources and Modelling Layer Application composition and enactment Layer Application composition and enactment Layer Data analysis and knowledge layer Data analysis and knowledge layer Services that allow ontology-based querying and browsing for the search of resources (to be used in the composition of the applications)  Modelling of SW and DDBB Main Tasks Workflow-based modelling and scheduling of distributed applications on the Grid

Conceptual Layers Data Sources and Modelling Layer Data Sources and Modelling Layer Application composition and enactment Layer Application composition and enactment Layer Data analysis and knowledge layer Data analysis and knowledge layer Data analysis tools made by using the workflow technologies to do the extraction of useful knowledge Main Tasks

The Road Ahead for Grid-enabled Genomic Medicine Steps for the implantation of the grid in the area of Genomic Medicine: 1)Developing the specific semantic Grid services required for a knowledge integration environment 2)Deploying and testing of the first Grid middleware prototypes for the health sector 3)Developing, deploying and testing of the first Grid Genomic Medicine applications 4)Fostering and promotion of the Grid-culture by means of the education and training of the physicians, scientists and the rest of the staff involved in the Genomic-Medicine sector

INBIOMED PROJECT INBIOMED is a Research Thematic Network that groups >100 researchers in MI, BI, Genomic Epidemiology and Pharmacogenomics One of the main goals of the project is to develop a middleware for the integration and sharing of information and algorithms between research groups. Main features: –It has an unified model of information to describe the resources –To assist in the use of analysis tools –To allow for the development of complex applications based on the algorithms offered

Data Queries Data Process Process Algorithms Web Services Files Local DW Public DB Data Sources Client App Other INBIOMED Data Model (Ontology) Client Application Layer Web Services (Logic) Layer Data Sources Layer

ISCIII Grid Grid Middleware: InnerGrid Nitya ( 24 nodes Several Bioinformatics tools deployed: –BLAST –BussuB ( In–house development Retrieval of amplicons from Fasta files

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