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Grid and High-Performance Computing for Applied Bioinformatics
KTH, School of Biotechnology (BIO), Gene Technology.
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 [en]
Grid computing, bioinformatics, genomics, proteomics
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-4573ISBN: 978-91-7178-782-8 (print)OAI: oai:DiVA.org:kth-4573DiVA: diva2:12929
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
List of papers
1. Using Grid Technology for Computationally Intensive Applied Bioinformatics Analyses
Open this publication in new window or tab >>Using Grid Technology for Computationally Intensive Applied Bioinformatics Analyses
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.

Keyword
BLAST, Distributed computing, Grid
National Category
Industrial Biotechnology
Identifiers
urn:nbn:se:kth:diva-7795 (URN)17518760 (PubMedID)2-s2.0-34250677669 (Scopus ID)
Note
QC 20100622Available from: 2007-12-10 Created: 2007-12-10 Last updated: 2017-12-14Bibliographically approved
2. The epitope space of the human proteome
Open this publication in new window or tab >>The epitope space of the human proteome
2008 (English)In: Protein Science, ISSN 0961-8368, E-ISSN 1469-896X, Vol. 17, no 4, 606-613 p.Article in journal (Refereed) Published
Abstract [en]

In the post-genome era, there is a great need for protein-specific affinity reagents to explore the human proteome. Antibodies are suitable as reagents, but generation of antibodies with low cross-reactivity to other human proteins requires careful selection of antigens. Here we show the results from a proteomewide effort to map linear epitopes based on uniqueness relative to the entire human proteome. The analysis was based on a sliding window sequence similarity search using short windows (8, 10, and 12 amino acid residues). A comparison of exact string matching (Hamming distance) and a heuristic method (BLAST) was performed, showing that the heuristic method combined with a grid strategy allows for whole proteome analysis with high accuracy and feasible run times. The analysis shows that it is possible to find unique antigens for a majority of the human proteins, with relatively strict rules involving low sequence identity of the possible linear epitopes. The implications for human antibody-based proteomics efforts are discussed.

Keyword
proteomics; antigen; epitope; sequence similarity; antibody; grid; B-CELL EPITOPES; ANTIGENIC DETERMINANTS; DATA-BANK; PREDICTION; ANTIBODIES; MYOGLOBIN; PEPTIDES; SEQUENCE; LIBRARIES; PROTEINS
National Category
Industrial Biotechnology
Identifiers
urn:nbn:se:kth:diva-8255 (URN)10.1110/ps.073347208 (DOI)000254197800002 ()2-s2.0-41649104373 (Scopus ID)
Note
QC 20100705Available from: 2008-04-22 Created: 2008-04-22 Last updated: 2017-12-14Bibliographically approved
3. The use of grid computing to drive data-intensive genetic research
Open this publication in new window or tab >>The use of grid computing to drive data-intensive genetic research
Show others...
2007 (English)In: European Journal of Human Genetics, ISSN 1018-4813, E-ISSN 1476-5438, Vol. 15, no 6, 694-702 p.Article in journal (Refereed) Published
Abstract [en]

In genetics, with increasing data sizes and more advanced algorithms for mining complex data, a point is reached where increased computational capacity or alternative solutions becomes unavoidable. Most contemporary methods for linkage analysis are based on the Lander-Green hidden Markov model (HMM), which scales exponentially with the number of pedigree members. In whole genome linkage analysis, genotype simulations become prohibitively time consuming to perform on single computers. We have developed 'Grid-Allegro', a Grid aware implementation of the Allegro software, by which several thousands of genotype simulations can be performed in parallel in short time. With temporary installations of the Allegro executable and datasets on remote nodes at submission, the need of predefined Grid run-time environments is circumvented. We evaluated the performance, efficiency and scalability of this implementation in a genome scan on Swedish multiplex Alzheimer's disease families. We demonstrate that 'Grid-Allegro' allows for the full exploitation of the features available in Allegro for genome-wide linkage. The implementation of existing bioinformatics applications on Grids (Distributed Computing) represent a cost-effective alternative for addressing highly resource-demanding and data-intensive bioinformatics task, compared to acquiring and setting up clusters of computational hardware in house (Parallel Computing), a resource not available to most geneticists today.

Keyword
grid, bioinformatics, genome-wide, linkage analysis, genotype simulation
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:kth:diva-7797 (URN)10.1038/sj.ejhg.5201815 (DOI)000246792100012 ()2-s2.0-34249727262 (Scopus ID)
Note
QC 20101004Available from: 2007-12-10 Created: 2007-12-10 Last updated: 2017-12-14Bibliographically approved
4. Expanded high-resolution genetic study of 109 Swedish families with Alzheimer's disease
Open this publication in new window or tab >>Expanded high-resolution genetic study of 109 Swedish families with Alzheimer's disease
Show others...
2008 (English)In: European Journal of Human Genetics, ISSN 1018-4813, E-ISSN 1476-5438, Vol. 16, no 2, 202-208 p.Article in journal (Refereed) Published
Abstract [en]

Alzheimer's disease (AD) is a neurodegenerative disease that affects approximately 20 million persons all over the world. There are both sporadic and familial forms of AD. We have previously reported a genome-wide linkage analysis on 71 Swedish AD families using 365 genotyped microsatellite markers. In this study, we increased the number of individuals included in the original 71 analysed families besides adding 38 new families. These 109 families were genotyped for 1100 novel microsatellite markers. The present study reports on the linkage data generated from the non-overlapping genotypes from the first genome scan and the genotypes of the present scan, which results in a total of 1289 successfully genotyped markers at an average density of 2.85 cM on 468 individuals from 109 AD families. Non-parametric linkage analysis yielded a significant multipoint LOD score in chromosome 19q13, the region harbouring the major susceptibility gene APOE, both for the whole set of families (LOD = 5.0) and the APOE epsilon 4-positive subgroup made up of 63 families (LOD = 5.3). Other suggestive linkage peaks that were observed in the original genome scan of 71 Swedish AD families were not detected in this extended analysis, and the previously reported linkage signals in chromosomes 9, 10 and 12 were not replicated.

Keyword
linkage analysis; familial dementia; APOE; Alzheimer's disease; genome scan; lod score
National Category
Industrial Biotechnology
Identifiers
urn:nbn:se:kth:diva-7798 (URN)10.1038/sj.ejhg.5201946 (DOI)000252426500011 ()2-s2.0-38349170135 (Scopus ID)
Note
QC 20100622Available from: 2007-12-10 Created: 2007-12-10 Last updated: 2017-12-14Bibliographically approved
5. Market-Based Resource Allocation using Price Prediction in a high performance computing Grid for scientific applications
Open this publication in new window or tab >>Market-Based Resource Allocation using Price Prediction in a high performance computing Grid for scientific applications
2006 (English)In: Proceedings of the IEEE International Symposium on High Performance Distributed Computing 2006, 2006, Vol. 15th IEEE International Symposium, 132-143 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present the implementation and analysis of a market-based resource allocation system for computational Grids. Although Grids provide a way to share resources and take advantage of statistical multiplexing, a variety of challenges remain. One is the economically efficient allocation of resources to users from disparate organizations who have their own and sometimes conflicting requirements for both the quantity and quality of services. Another is secure and scalable authorization despite rapidly changing allocations.

Our solution to both of these challenges is to use a market-based resource allocation system. This system allows users to express diverse quantity- and quality-of-service requirements, yet prevents them from denying service to other users. It does this by providing tools to the user to predict and tradeoff risk and expected return in the computational market. In addition, the system enables secure and scalable authorization by using signed money-transfer tokens instead of identity-based authorization. This removes the overhead of maintaining and updating access control lists, while restricting usage based on the amount of money transferred We examine the performance of the system by running a bioinformatics application on a fully operational implementation of an integrated Grid market.

Keyword
Computational methods, Marketing, Multiplexing, Quality of service, Resource allocation, Statistical methods, Computational market, Computing grid, Market based resource allocation system, Quantity of service
National Category
Industrial Biotechnology Information Science
Identifiers
urn:nbn:se:kth:diva-7799 (URN)10.1109/HPDC.2006.1652144 (DOI)000239086500011 ()2-s2.0-33845897137 (Scopus ID)
Conference
The IEEE International Symposium on High Performance Distributed Computing 2006
Note
QC 20100622Available from: 2007-12-10 Created: 2007-12-10 Last updated: 2010-11-16Bibliographically approved

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