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  • 1.
    Andrade, Jorge
    KTH, School of Biotechnology (BIO), Gene Technology.
    Grid and High-Performance Computing for Applied Bioinformatics2007Doctoral 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.

  • 2.
    Andrade, Jorge
    et al.
    KTH, School of Biotechnology (BIO), Gene Technology.
    Andersen, Malin
    KTH, School of Biotechnology (BIO), Gene Technology.
    Berglund, Lisa
    KTH, School of Biotechnology (BIO), Proteomics.
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Gene Technology.
    Applications of grid computing in genetics and proteomics2007In: Applied Parallel Computing: State Of The Art In Scientific Computing / [ed] Kagstrom, B; Elmroth, E; Dongarra, J; Wasniewski, J, 2007, Vol. 4699, p. 791-798Conference paper (Refereed)
    Abstract [en]

    The potential for Grid technologies in applied bioinformatics is largely unexplored. We have developed a model for solving computationally demanding bioinformatics tasks in distributed Grid environments, designed to ease the usability for scientists unfamiliar with Grid computing. With a script-based implementation that uses a strategy of temporary installations of databases and existing executables on remote nodes at submission, we propose a generic solution that do not rely on predefined Grid runtime environments and that can easily be adapted to other bioinformatics tasks suitable for parallelization. This implementation has been successfully applied to whole proteome sequence similarity analyses and to genome-wide genotype simulations, where computation time was reduced from years to weeks. We conclude that computational Grid technology is a useful resource for solving high compute tasks in genetics and proteomics using existing algorithms.

  • 3.
    Andrade, Jorge
    et al.
    KTH, School of Biotechnology (BIO), Gene Technology.
    Andersen, Malin
    KTH, School of Biotechnology (BIO), Gene Technology.
    Sillén, Anna
    Graff, Caroline
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Gene Technology.
    The use of grid computing to drive data-intensive genetic research2007In: European Journal of Human Genetics, ISSN 1018-4813, E-ISSN 1476-5438, Vol. 15, no 6, p. 694-702Article in journal (Refereed)
    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.

  • 4.
    Andrade, Jorge
    et al.
    KTH, School of Biotechnology (BIO), Gene Technology.
    Berglund, Lisa
    KTH, School of Biotechnology (BIO), Gene Technology.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Gene Technology.
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Gene Technology.
    Using Grid Technology for Computationally Intensive Applied Bioinformatics Analyses2006In: In Silico Biology, ISSN 1386-6338, Vol. 6, no 6, p. 495-504Article in journal (Refereed)
    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.

  • 5.
    Berglund, Lisa
    et al.
    KTH, School of Biotechnology (BIO).
    Andrade, Jorge
    KTH, School of Biotechnology (BIO).
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO).
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO).
    The epitope space of the human proteome2008In: Protein Science, ISSN 0961-8368, E-ISSN 1469-896X, Vol. 17, no 4, p. 606-613Article in journal (Refereed)
    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.

  • 6.
    Sandholm, Thomas
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC. KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV.
    Lai, Kevin
    Hewlett-Packard Laboratories, Information Dynamics Laboratory, Palo Alto.
    Andrade, Jorge
    KTH, School of Biotechnology (BIO), Gene Technology.
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Gene Technology.
    Market-Based Resource Allocation using Price Prediction in a high performance computing Grid for scientific applications2006In: Proceedings of the IEEE International Symposium on High Performance Distributed Computing 2006, 2006, Vol. 15th IEEE International Symposium, p. 132-143Conference 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.

  • 7.
    Sillén, Anna
    et al.
    Karolinska Institutet.
    Andrade, Jorge
    KTH, School of Biotechnology (BIO), Gene Technology.
    Lilius, Lena
    Karolinska Institutet.
    Forsell, Charlotte
    Karolinska Institutet.
    Axelman, Karin
    Karolinska Institutet.
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Gene Technology.
    Winblad, Bengt
    Karolinska Institutet.
    Graff, Caroline
    Karolinska Institutet.
    Expanded high-resolution genetic study of 109 Swedish families with Alzheimer's disease2008In: European Journal of Human Genetics, ISSN 1018-4813, E-ISSN 1476-5438, Vol. 16, no 2, p. 202-208Article in journal (Refereed)
    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.

  • 8. Sillén, Anna
    et al.
    Brohede, Jesper
    Forsell, Charlotte
    Lilius, Lena
    Andrade, Jorge
    KTH, School of Biotechnology (BIO).
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Proteomics.
    Kimura, Toru
    Winblad, Bengt
    Graff, Caroline
    Linkage Analysis of Autopsy-Confirmed Familial Alzheimer Disease Supports an Alzheimer Disease Locus in 8q242011In: Dementia and Geriatric Cognitive Disorders, ISSN 1420-8008, E-ISSN 1421-9824, Vol. 31, no 2, p. 109-118Article in journal (Refereed)
    Abstract [en]

    Background/Aims: We have previously reported the results of an extended genome-wide scan of Swedish Alzheimer disease (AD)-affected families; in this paper, we analyzed a subset of these families with autopsy-confirmed AD. Methods: We report the fine-mapping, using both microsatellite markers and single-nucleotide polymorphisms (SNPs), in the observed maximum logarithm of the odds (LOD)-2 unit (LODmax-2) region under the identified linkage peak, linkage analysis of the fine-mapping data with additionally analyzed pedigrees, and association analysis of SNPs selected from candidate genes in the linked interval. The subset was made on the criterion of at least one autopsy-confirmed AD case per family, resulting in 24 families. Results: Linkage analysis of a family subset having at least one autopsy-confirmed AD case showed a significant nonparametric single-point LOD score of 4.4 in 8q24. Fine-mapping under the linkage peak with 10 microsatellite markers yielded an increase in the multipoint (mpt) LOD score from 2.1 to 3.0. SNP genotyping was performed on 21 selected candidate transcripts of the LODmax-2 region. Both family-based association and linkage analysis were performed on extended material from 30 families, resulting in a suggestive linkage at peak marker rs6577853 (mpt LOD score = 2.4). Conclusion: The 8q24 region has been implicated to be involved in AD etiology.

  • 9. Sillén, Anna
    et al.
    Brohede, Jesper
    Lilius, Lena
    Forsell, Charlotte
    Andrade, Jorge
    KTH, School of Biotechnology (BIO), Gene Technology.
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Gene Technology.
    Ebise, Hayao
    Winblad, Bengt
    Graff, Caroline
    Linkage to 20p13 including the ANGPT4 gene in families with mixed Alzheimer's disease and vascular dementia2010In: Journal of Human Genetics, ISSN 1434-5161, E-ISSN 1435-232X, Vol. 55, no 10, p. 649-655Article in journal (Refereed)
    Abstract [en]

    This study aimed at identifying novel susceptibility genes for a mixed phenotype of Alzheimer's disease and vascular dementia. Results from a genome scan showed strongest linkage to 20p13 in 18 families, and subsequent fine mapping was performed with both microsatellites and single-nucleotide polymorphisms in 18 selected candidate transcripts in an extended sample set of 30 families. The multipoint linkage peak was located at marker rs2144151 in the ANGPT4 gene, which is a strong candidate gene for vascular disease because of its involvement in angiogenesis. Although the significance of the linkage decreased, we find this result intriguing, considering that we included additional families, and thus the reduced linkage signal may be caused by genetic heterogeneity.

1 - 9 of 9
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