2006
DOI: 10.1007/s10723-006-9045-5
|View full text |Cite
|
Sign up to set email alerts
|

GeneGrid: Architecture, Implementation and Application

Abstract: 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 … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
4
0
1

Year Published

2009
2009
2023
2023

Publication Types

Select...
4
2
1

Relationship

0
7

Authors

Journals

citations
Cited by 7 publications
(5 citation statements)
references
References 17 publications
(20 reference statements)
0
4
0
1
Order By: Relevance
“…Therefore, both computing models became popular in scientific applications for which a large pool of computational resources allows various computationally intensive problems to be solved. In the domain of Life sciences there are many dedicated cloud-based and grid-based tools that allow us to solve problems, such as whole genome and metagenome sequence analysis [1], gene detection [45], identification of peptide sequences from spectra in mass spectrometry [44], analysis of data from DNA microarray experiments [33], mapping nextgeneration sequence data to the human genome and other reference genomes, for use in a variety of biological analyses including SNP discovery, genotyping and personal genomics [13,57], gene sequence analysis and protein characterization [28], 3D ligand binding site comparison and similarity searching of a structural proteome [24], molecular docking [4,8], protein structure similarity searching and structural alignment [25,[50][51][52], and many others.…”
Section: Related Workmentioning
confidence: 99%
“…Therefore, both computing models became popular in scientific applications for which a large pool of computational resources allows various computationally intensive problems to be solved. In the domain of Life sciences there are many dedicated cloud-based and grid-based tools that allow us to solve problems, such as whole genome and metagenome sequence analysis [1], gene detection [45], identification of peptide sequences from spectra in mass spectrometry [44], analysis of data from DNA microarray experiments [33], mapping nextgeneration sequence data to the human genome and other reference genomes, for use in a variety of biological analyses including SNP discovery, genotyping and personal genomics [13,57], gene sequence analysis and protein characterization [28], 3D ligand binding site comparison and similarity searching of a structural proteome [24], molecular docking [4,8], protein structure similarity searching and structural alignment [25,[50][51][52], and many others.…”
Section: Related Workmentioning
confidence: 99%
“…It was developed in Perl, except the job submission and monitoring of job sets tools in Java [11]. Furthermore, GeneGrid, a Grid computing framework for the creation of a virtual Bioinformatics laboratory, based on Globus and Open Grid Services Architecture (OGSA) to integrate the heterogeneous bioinformatics applications and database spanning multiple administrative domains and locations [13]. Recently, there is innovative technology to extend the Grid infrastructure to the Volunteer Grid empowered by BONIC to execute the large scale bioinformatics applications [14].…”
Section: Related Workmentioning
confidence: 99%
“…Grid technology is now being used to deploy large infrastructures outside the computer science domain. In the biomedical field there are numerous examples of grid infrastructures [7][8][9][10][11][12][13][14][15][16][17][18].…”
Section: Introductionmentioning
confidence: 99%