Hidden Markov models for the analysis of next-generation-sequencing data

Aaron Sebastian Taudt

    Research output: ThesisThesis fully internal (DIV)

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    Modern day biology increasingly relies on the production and analysis of huge amounts of digital data. This data is produced by new experimental techniques which allow ever deeper insights into the mechanisms by which our cells function. One such technique is “Next Generation Sequencing”. It was developed in the 2000s and is now widely applied to the study of all phenomena which involve the DNA sequence. The presented thesis describes novel algorithms for the analysis of Next Generation Sequencing (NGS) data. More specifically, four different algorithms are presented. These algorithms were developed for different varieties of NGS experiments, each of which allows a unique perspective into the cell.
    Chapter 1 gives an introduction to Next Generation Sequencing and explains in more detail how the same technology can be used in a modified form to investigate different types of sequence related phenomena.
    Chapter 2 presents a model for the analysis of copy number gains and losses in single cells from NGS data, and this method has been applied to investigate the role of aneuploidy in Alzheimer’s disease, as well as the role of smaller copy number aberrations in cancer cells. In Chapter 3 an extension of the model from Chapter 2 is presented.
    Chapter 4 and 5 present models for the analysis of epigenetic modifications on the DNA, and these models can help to increase our knowledge of how totipotent stem cells differentiate into specialized cell types, and how the environment can affect gene expression.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • University of Groningen
    • de Haan, Gerald, Supervisor
    • Colomé Tatché, Maria, Co-supervisor
    • Korbel, Jan O., Assessment committee, External person
    • Eggen, Bart, Assessment committee
    • Ossowski, Stephan, Assessment committee, External person
    Award date15-Oct-2018
    Place of Publication[Groningen]
    Publication statusPublished - 2018

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