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  • 1.
    Ausmees, Kristiina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Efficient computational methods for applications in genomics2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    During the last two decades, advances in molecular technology have facilitated the sequencing and analysis of ancient DNA recovered from archaeological finds, contributing to novel insights into human evolutionary history. As more ancient genetic information has become available, the need for specialized methods of analysis has also increased. In this thesis, we investigate statistical and computational models for analysis of genetic data, with a particular focus on the context of ancient DNA.

    The main focus is on imputation, or the inference of missing genotypes based on observed sequence data. We present results from a systematic evaluation of a common imputation pipeline on empirical ancient samples, and show that imputed data can constitute a realistic option for population-genetic analyses. We also discuss preliminary results from a simulation study comparing two methods of phasing and imputation, which suggest that the parametric Li and Stephens framework may be more robust to extremely low levels of sparsity than the parsimonious Browning and Browning model.

    An evaluation of methods to handle missing data in the application of PCA for dimensionality reduction of genotype data is also presented. We illustrate that non-overlapping sequence data can lead to artifacts in projected scores, and evaluate different methods for handling unobserved genotypes.

    In genomics, as in other fields of research, increasing sizes of data sets are placing larger demands on efficient data management and compute infrastructures. The last part of this thesis addresses the use of cloud resources for facilitating such analysis. We present two different cloud-based solutions, and exemplify them on applications from genomics.

    List of papers
    1. An empirical evaluation of genotype imputation of ancient DNA
    Open this publication in new window or tab >>An empirical evaluation of genotype imputation of ancient DNA
    2019 (English)Report (Other academic)
    Series
    Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2019-008
    National Category
    Computational Mathematics Genetics
    Identifiers
    urn:nbn:se:uu:diva-396336 (URN)
    Projects
    eSSENCE
    Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2019-11-11Bibliographically approved
    2. Evaluation of methods handling missing data in PCA on genotype data: Applications for ancient DNA
    Open this publication in new window or tab >>Evaluation of methods handling missing data in PCA on genotype data: Applications for ancient DNA
    2019 (English)Report (Other academic)
    Series
    Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2019-009
    National Category
    Computational Mathematics Genetics
    Identifiers
    urn:nbn:se:uu:diva-396346 (URN)
    Projects
    eSSENCE
    Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2019-11-11Bibliographically approved
    3. BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data
    Open this publication in new window or tab >>BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data
    Show others...
    2018 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 19, p. 240:1-11, article id 240Article in journal (Refereed) Published
    National Category
    Software Engineering Genetics
    Identifiers
    urn:nbn:se:uu:diva-360033 (URN)10.1186/s12859-018-2241-z (DOI)000436517200001 ()29940842 (PubMedID)
    Projects
    eSSENCE
    Available from: 2018-06-26 Created: 2018-09-09 Last updated: 2019-11-11Bibliographically approved
    4. SWEEP: Accelerating scientific research through scalable serverless workflows
    Open this publication in new window or tab >>SWEEP: Accelerating scientific research through scalable serverless workflows
    Show others...
    2019 (English)In: Companion Proc. 12th International Conference on Utility and Cloud Computing, New York: ACM Press, 2019, p. 43-50Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    New York: ACM Press, 2019
    National Category
    Software Engineering
    Identifiers
    urn:nbn:se:uu:diva-396405 (URN)10.1145/3368235.3368839 (DOI)978-1-4503-7044-8 (ISBN)
    Conference
    UCC 2019
    Projects
    eSSENCE
    Available from: 2019-12-02 Created: 2019-11-04 Last updated: 2019-12-05Bibliographically approved
  • 2.
    Ausmees, Kristiina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Evaluation of methods handling missing data in PCA on genotype data: Applications for ancient DNA2019Report (Other academic)
  • 3.
    Ausmees, Kristiina
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    John, Aji
    Toor, Salman Z.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Nettelblad, Carl
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data2018In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 19, p. 240:1-11, article id 240Article in journal (Refereed)
  • 4.
    Ausmees, Kristiina
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Sanchez-Quinto, Federico
    Jakobsson, Mattias
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Human Evolution.
    Nettelblad, Carl
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    An empirical evaluation of genotype imputation of ancient DNA2019Report (Other academic)
  • 5. John, Aji
    et al.
    Ausmees, Kristiina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Muenzen, Kathleen
    Kuhn, Catherine
    Tan, Amanda
    SWEEP: Accelerating scientific research through scalable serverless workflows2019In: Companion Proc. 12th International Conference on Utility and Cloud Computing, New York: ACM Press, 2019, p. 43-50Conference paper (Refereed)
1 - 5 of 5
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