Development of a Grid Infrastructure for Functional Genomics

Journal of Grid Computing, 2006

The emergence of Grid computing technology has opened up an unprecedented opportunity for biologists to share and access data, resources and tools in an integrated environment leading to a greater chance of knowledge discovery. GeneGrid is a Grid computing framework that seamlessly integrates a myriad of heterogeneous resources spanning multiple administrative domains and locations. It provides scientists an integrated environment for the streamlined access of a number of bioinformatics programs and databases through a simple and intuitive interface. It acts as a virtual bioinformatics laboratory by allowing scientists to create, execute and manage workflows that represent bioinformatics experiments. A number of cooperating Grid services interact in an orchestrated manner to provide this functionality. This paper gives insight into the de-tails of the architecture, components and implementation of GeneGrid.

Current Bioinformatics, 2008

The acquisition of biological data, ranging from molecular characterization and simulations (e.g. protein folding dynamics), to systems biology endeavors (e.g. whole organ simulations) all the way up to ecological observations (e.g. as to ascertain climate change's impact on the biota) is growing at unprecedented speed. The use of computational and networking resources is thus unavoidable. As the datasets become bigger and the acquisition technology more refined, the biologist is empowered to ask deeper and more complex questions. These, in turn, drive a runoff effect where large research consortia emerge that span beyond organizations and national boundaries. Thus the need for reliable, robust, certified, curated, accessible, secure and timely data processing and management becomes entrenched within, and crucial to, 21 st century biology. Furthermore, the proliferation of biotechnologies and advances in biological sciences has produced a strong drive for new informatics solutions, both at the basic science and technological levels. The previously unknown situation of dealing with, on one hand, (potentially) exabytes of data, much of which is noisy, has large experimental errors or theoretical uncertainties associated with it, or on the other hand, large quantities of data that require automated computationally intense analysis and processing, have produced important innovations in web and grid technology. In this paper we present a trace of these technological changes in Web and Grid technology, including details of emerging infrastructures, standards, languages and tools, as they apply to bioinformatics, computational biology and systems biology. A major focus of this technological review is to collate up-to-date information regarding the design and implementation of various bioinformatics Webs, Grids, Web-based grids or Grid-based webs in terms of their infrastructure, standards, protocols, services, applications and other tools. This review, besides surveying the current state-of-the-art, will also provide a road map for future research and open questions.

2008

To develop software infrastructure that will provide support for discovery, characterization, integrated access, and management of diverse and disparate collections of information sources, analysis methods, and applications in biomedical research. Design: An enterprise Grid software infrastructure, called caGrid version 1.0 (caGrid 1.0), has been developed as the core Grid architecture of the NCI-sponsored cancer Biomedical Informatics Grid (caBIG{trademark}) program. It

Journal of Grid …, 2012

Biomedical researchers can leverage Grid computing technology to address their increasing demands for data- and compute-intensive data analysis. However, usage of existing Grid infrastructures remains difficult for them. The e-infrastructure for biomedical science (e-BioInfra) is a platform with services that shield middleware complexities, in particular workflow management and monitoring. These services can be invoked from a web-based interface, called e-BioInfra Gateway, to perform large scale data analysis experiments, such that the biomedical researchers can focus on their own research problems. The gateway was designed to simplify usage both by biomedical researchers and e-BioInfra administrators, and to support straightforward extensions with new data analysis methods. In this paper we present the architecture and implementation of the gateway, also showing statistics for its usage. We also share lessons learned during the gateway development and operation. The gateway is currently used in several biomedical research projects and in teaching medical students the principles of data analysis.

BMC bioinformatics, 2006

Grid computing has great potential to become a standard cyberinfrastructure for life sciences which often require high-performance computing and large data handling which exceeds the computing capacity of a single institution. This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing "resourceome" for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. Extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in...

2007

Future Generation Computer Systems, 2009

In past years, researchers from many domains have discovered Grid technology which opens up new possibilities in solving problems that are difficult to handle with traditional cluster computing. With the rapidly increasing number of partially or completely sequenced genomes, computational genome annotation is a particularly challenging task in computational biology. In this paper, we describe how we adapted the gene-finding tool AUGUSTUS to Grid computing in the context of the German MediGRID project. The gridification process starts with providing security requirements and running the application manually using Grid middleware. Afterwards, the application is described as a workflow of successive program executions, which are automatically distributed to appropriate Grid resources by a workflow engine. Finally, we show how a convenient graphical user interface for end users is created by means of a portal framework.

Briefings in Bioinformatics, 2008

The torrent of data emerging from the application of new technologies to functional genomics and systems biology can no longer be contained within the traditional modes of data sharing and publication with the consequence that data is being deposited in, distributed across and disseminated through an increasing number of databases. The resulting fragmentation poses serious problems for the model organism community which increasingly rely on data mining and computational approaches that require gathering of data from a range of sources. In the light of these problems, the European Commission has funded a coordination action, CASIMIR (coordination and sustainability of international mouse informatics resources), with a remit to assess the technical and social aspects of database interoperability that currently prevent the full realization of the potential of data integration in mouse functional genomics. In this article, we assess the current problems with interoperability, with particular reference to mouse functional genomics, and critically review the technologies that can be deployed to overcome them. We describe a typical use-case where an investigator wishes to gather data on variation, genomic context and metabolic pathway involvement for genes discovered in a genome-wide screen. We go on to develop an automated approach involving an in silico experimental workflow tool, Taverna, using web services, BioMart and MOLGENIS technologies for data retrieval. Finally, we focus on the current impediments to adopting such an approach in a wider context, and strategies to overcome them.

Studies in health …, 2008

Source: PubMed CITATIONS 7 READS 30 Abstract caGrid is a middleware system which combines the Grid computing, the service oriented architecture, and the model driven architecture paradigms to support development of interoperable data and analytical resources and federation of such resources in a Grid environment. The functionality provided by caGrid is an essential and integral component of the cancer Biomedical Informatics Grid (caBIG ™ ) program. This program is established by the National Cancer Institute as a nationwide effort to develop enabling informatics technologies for collaborative, multiinstitutional biomedical research with the overarching goal of accelerating translational cancer research. Although the main application domain for caGrid is cancer research, the infrastructure provides a generic framework that can be employed in other biomedical research and healthcare domains. The development of caGrid is an ongoing effort, adding new functionality and improvements based on feedback and use cases from the community. This paper provides an overview of potential future architecture and tooling directions and areas of improvement for caGrid and caGrid-like systems. This summary is based on discussions at a roadmap workshop held in February with participants from biomedical research, Grid computing, and high performance computing communities.

The e-infrastructure for bioscience (e-BioInfra) is a platform integrating various services and middleware to facilitate access to grid resources for biomedical researchers at the Academic Medical Center of the University of Amsterdam. In the past six years the user interfaces with the e-BioInfra have evolved from command-line interfaces to a Java desktop application, and later to an easy-to-use web application for selected biomedical data analysis. This evolution represents improvements to accommodate the requirements of a broader range of biomedical researchers and applications. In this paper we present the current user interfaces and analyse their usage considering the typical biomedical data analysis on the e-BioInfra, the roles assumed by the users in the various phases of data analysis life-cycle, and the user profiles. We observe that in order to support a wide spectrum of user profiles, with different expertise and requirements, a platform must offer a variety of user interfaces addressed to each user profile.