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Efficient computational methods for applications in genomics
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Tillämpad beräkningsvetenskap.ORCID-id: 0000-0002-6212-539x
2019 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Uppsala University, 2019.
Serie
IT licentiate theses / Uppsala University, Department of Information Technology, ISSN 1404-5117 ; 2019-006
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Forskningsprogram
Beräkningsvetenskap
Identifikatorer
URN: urn:nbn:se:uu:diva-396409OAI: oai:DiVA.org:uu-396409DiVA, id: diva2:1367712
Veileder
Prosjekter
eSSENCETilgjengelig fra: 2019-11-04 Laget: 2019-11-04 Sist oppdatert: 2019-11-11bibliografisk kontrollert
Delarbeid
1. An empirical evaluation of genotype imputation of ancient DNA
Åpne denne publikasjonen i ny fane eller vindu >>An empirical evaluation of genotype imputation of ancient DNA
2019 (engelsk)Rapport (Annet vitenskapelig)
Serie
Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2019-008
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-396336 (URN)
Prosjekter
eSSENCE
Tilgjengelig fra: 2019-11-04 Laget: 2019-11-04 Sist oppdatert: 2019-11-11bibliografisk kontrollert
2. Evaluation of methods handling missing data in PCA on genotype data: Applications for ancient DNA
Åpne denne publikasjonen i ny fane eller vindu >>Evaluation of methods handling missing data in PCA on genotype data: Applications for ancient DNA
2019 (engelsk)Rapport (Annet vitenskapelig)
Serie
Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2019-009
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-396346 (URN)
Prosjekter
eSSENCE
Tilgjengelig fra: 2019-11-04 Laget: 2019-11-04 Sist oppdatert: 2019-11-11bibliografisk kontrollert
3. BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data
Åpne denne publikasjonen i ny fane eller vindu >>BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data
Vise andre…
2018 (engelsk)Inngår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 19, s. 240:1-11, artikkel-id 240Artikkel i tidsskrift (Fagfellevurdert) Published
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Identifikatorer
urn:nbn:se:uu:diva-360033 (URN)10.1186/s12859-018-2241-z (DOI)000436517200001 ()29940842 (PubMedID)
Prosjekter
eSSENCE
Tilgjengelig fra: 2018-06-26 Laget: 2018-09-09 Sist oppdatert: 2019-11-11bibliografisk kontrollert
4. SWEEP: Accelerating scientific research through scalable serverless workflows
Åpne denne publikasjonen i ny fane eller vindu >>SWEEP: Accelerating scientific research through scalable serverless workflows
Vise andre…
2019 (engelsk)Inngår i: Companion Proc. 12th International Conference on Utility and Cloud Computing, New York: ACM Press, 2019, s. 43-50Konferansepaper, Publicerat paper (Fagfellevurdert)
sted, utgiver, år, opplag, sider
New York: ACM Press, 2019
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-396405 (URN)10.1145/3368235.3368839 (DOI)978-1-4503-7044-8 (ISBN)
Konferanse
UCC 2019
Prosjekter
eSSENCE
Tilgjengelig fra: 2019-12-02 Laget: 2019-11-04 Sist oppdatert: 2019-12-05bibliografisk kontrollert

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