Trends in life science grid: from computing grid to knowledge grid.

Akihiko Konagaya

doi:10.1186/1471-2105-7-s5-s10

Abstract

BackgroundGrid 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.ResultsThis 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.ConclusionExtending 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 a community.

Highlights

Bioinformatics applications often require high-performance computing and large data handling which exceeds the computing capacity of a single institution [1]
Grid computing is a promising information technology which meets the above requirements, and has great potential to become a standard cyberinfrastructure for life sciences [10,11]
"We suggest that the full set of bioinformatics resources– the resourceome–should be explicitly characterized and organized." noted Russ Altman in his article [8]

Summary

Results

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 from sharing explicit knowledge on computers and from community formulation for sharing tacit knowledge among a community

Conclusion

Introduction

Konagaya A: OBIGrid

45. Konagaya A: Bioinformatics Ontology

52. Konagaya A