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This paper presents the approach adopted by the EGI-ACE project for the setup and delivery of Data Spaces for various scientific domains. The work was implemented by members of the EGI e-infrastructure and of several European Research Infrastructures in the context of the European Open Science Cloud programme. Our results are several Data Space services that enable the reuse and exploitation of open, scientific big data for compute intensive use cases. The paper illustrates the EGI-ACE approach through two examples: (1) EMSO ERIC Data Portal for seafloor and water column research and (2) ENES Data Space for climate research.
The EGI-ACE project created a number of Data Spaces for various scientific domains, allowing for easy access and reuse of open, scientific big data for compute intensive use cases. The aim of the European Commission's strategy for data is to create a common European Data Space, which will bring together relevant data infrastructures and governance frameworks to facilitate data sharing while ensuring control for individuals and companies generating the data. The Data Spaces created within EGI-ACE contribute towards achieving this goal, publishing curated data from international scientific communities 'in the cloud' and offering them for scalable exploitation and reuse through domain-specific tools and environments that offer data visualization, analysis, and mapping. The paper provides an overview of the technical and policy implementation approaches of the EGI-ACE Data Spaces and their potential future role in the single market for data in Europe. The paper presents the generic approach that EGI-ACE followed for the implementation of scientific data spaces, and illustrates this with two examples, one from climate sciences (ENES), one from marine sciences (EMSO).
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This paper describes a protein tertiary structure prediction service implemented in a Grid Environment. The service has been used for predicting the dicarboxylate carrier (DIC) of Saccharomyces cerevisiae by using the homology modelling approach. The visualization of the predicted model is made possible by using an interactive virtual reality environment based on X3D and Ajax3d technologies.
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Biología Computacional , Sistemas de Computación , Bases de Datos de Proteínas , Estructura Terciaria de Proteína/genética , Simulación por Computador , Bases de Datos como Asunto , Transportadores de Ácidos Dicarboxílicos/genética , Humanos , Italia , Desarrollo de Programa , Saccharomyces cerevisiae/genética , Programas InformáticosRESUMEN
We present an integrated Grid system for the prediction of protein secondary structures, based on the frequent automatic update of proteins in the training set. The predictor model is based on a feed-forward multilayer perceptron (MLP) neural network which is trained with the back-propagation algorithm; the design reuses existing legacy software and exploits novel grid components. The predictor takes into account the evolutionary information found in multiple sequence alignment (MSA); the information is obtained running an optimized parallel version of the PSI-BLAST tool, based on the MPI Master-Worker paradigm. The training set contains proteins of known structure. Using Grid technologies and efficient mechanisms for running the tools and extracting the data, the time needed to train the neural network is dramatically reduced, whereas the results are comparable to a set of well-known predictor tools.
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Internet , Modelos Químicos , Modelos Moleculares , Estructura Secundaria de Proteína , Proteínas/química , Proteínas/ultraestructura , Análisis de Secuencia de Proteína/métodos , Algoritmos , Inteligencia Artificial , Simulación por Computador , Programas Informáticos , Interfaz Usuario-ComputadorRESUMEN
This paper describes the evolution of the main services of the ProGenGrid (Proteomics & Genomics Grid) system, a distributed and ubiquitous grid environment ("virtual laboratory"), based on Workflow and supporting the design, execution and monitoring of "in silico" experiments in bioinformatics.ProGenGrid is a Grid-based Problem Solving Environment that allows the composition of data sources and bioinformatics programs wrapped as Web Services (WS). The use of WS provides ease of use and fosters re-use. The resulting workflow of WS is then scheduled on the Grid, leveraging Grid-middleware services. In particular, ProGenGrid offers a modular bag of services and currently is focused on the biological simulation of two important bioinformatics problems: prediction of the secondary structure of proteins, and sequence alignment of proteins. Both services are based on an enhanced data access service.
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Acceso a la Información , Biología Computacional , Informática Médica , Internet , Italia , Solución de Problemas , ProteómicaRESUMEN
In this paper we describe the ProGenGrid (Proteomics and Genomics Grid) system, developed at the CACT/ISUFI of the University of Lecce which aims at providing a virtual laboratory where e-scientists can simulate biological experiments, composing existing analysis and visualization tools, monitoring their execution, storing the intermediate and final output and finally, if needed, saving the model of the experiment for updating or reproducing it. The tools that we are considering are software components wrapped as Web Services and composed through a workflow. Since bioinformatics applications need to use high performance machines or a high number of workstations to reduce the computational time, we are exploiting a Grid infrastructure for interconnecting wide-spread tools and hardware resources. As an example, we are considering some algorithms and tools needed for drug design, providing them as services, through easy to use interfaces such as the Web and Web service interfaces built using the open source gSOAP Toolkit, whereas as Grid middleware we are using the Globus Toolkit 3.2, exploiting some protocols such as GSI and GridFTP.