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Using Grid Technology for Computationally Intensive Applied Bioinformatics Analyses
KTH, School of Biotechnology (BIO), Gene Technology.
KTH, School of Biotechnology (BIO), Gene Technology.
KTH, School of Biotechnology (BIO), Gene Technology.ORCID iD: 0000-0001-8993-048X
KTH, School of Biotechnology (BIO), Gene Technology.ORCID iD: 0000-0003-0996-1644
2006 (English)In: In Silico Biology, ISSN 1386-6338, Vol. 6, no 6, 495-504 p.Article in journal (Refereed) Published
Abstract [en]

For several applications and algorithms used in applied bioinformatics, a bottle neck in terms of computational time may arise when scaled up to facilitate analyses of large datasets and databases. Re-codification, algorithm modification or sacrifices in sensitivity and accuracy may be necessary to accommodate for limited computational capacity of single work stations. Grid computing offers an alternative model for solving massive computational problems by parallel execution of existing algorithms and software implementations. We present the implementation of a Grid-aware model for solving computationally intensive bioinformatic analyses exemplified by a blastp sliding window algorithm for whole proteome sequence similarity analysis, and evaluate the performance in comparison with a local cluster and a single workstation. Our strategy involves temporary installations of the BLAST executable and databases on remote nodes at submission, accommodating for dynamic Grid environments as it avoids the need of predefined runtime environments (preinstalled software and databases at specific Grid-nodes). Importantly, the implementation is generic where the BLAST executable can be replaced by other software tools to facilitate analyses suitable for parallelisation. This model should be of general interest in applied bioinformatics. Scripts and procedures are freely available from the authors.

Place, publisher, year, edition, pages
2006. Vol. 6, no 6, 495-504 p.
Keyword [en]
BLAST, Distributed computing, Grid
National Category
Industrial Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-7795PubMedID: 17518760Scopus ID: 2-s2.0-34250677669OAI: oai:DiVA.org:kth-7795DiVA: diva2:12924
Note
QC 20100622Available from: 2007-12-10 Created: 2007-12-10 Last updated: 2012-03-20Bibliographically approved
In thesis
1. Grid and High-Performance Computing for Applied Bioinformatics
Open this publication in new window or tab >>Grid and High-Performance Computing for Applied Bioinformatics
2007 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

The beginning of the twenty-first century has been characterized by an explosion of biological information. The avalanche of data grows daily and arises as a consequence of advances in the fields of molecular biology and genomics and proteomics. The challenge for nowadays biologist lies in the de-codification of this huge and complex data, in order to achieve a better understanding of how our genes shape who we are, how our genome evolved, and how we function.

Without the annotation and data mining, the information provided by for example high throughput genomic sequencing projects is not very useful. Bioinformatics is the application of computer science and technology to the management and analysis of biological data, in an effort to address biological questions. The work presented in this thesis has focused on the use of Grid and High Performance Computing for solving computationally expensive bioinformatics tasks, where, due to the very large amount of available data and the complexity of the tasks, new solutions are required for efficient data analysis and interpretation.

Three major research topics are addressed; First, the use of grids for distributing the execution of sequence based proteomic analysis, its application in optimal epitope selection and in a proteome-wide effort to map the linear epitopes in the human proteome. Second, the application of grid technology in genetic association studies, which enabled the analysis of thousand of simulated genotypes, and finally the development and application of a economic based model for grid-job scheduling and resource administration.

The applications of the grid based technology developed in the present investigation, results in successfully tagging and linking chromosomes regions in Alzheimer disease, proteome-wide mapping of the linear epitopes, and the development of a Market-Based Resource Allocation in Grid for Scientific Applications.

Place, publisher, year, edition, pages
Stockholm: KTH, 2007
Series
Trita-BIO-Report, ISSN 1654-2312 ; 2007:9
Keyword
Grid computing, bioinformatics, genomics, proteomics
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-4573 (URN)978-91-7178-782-8 (ISBN)
Public defence
2007-12-21, FD5, AlbaNova, oslagstullsbacken 21, Stockholm, 10:00
Opponent
Supervisors
Note
QC 20100622Available from: 2007-12-10 Created: 2007-12-10 Last updated: 2012-03-20Bibliographically approved

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Uhlén, Mathias

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