Computational Methods for High-Throughput Small RNA Analysis in Plants

Lionel Monteiro Morgado

    Research output: ThesisThesis fully internal (DIV)

    1566 Downloads (Pure)

    Abstract

    In plants, millions of uncharacterized small RNAs (sRNAs) can be found for which experimental validation poses an impractical laborious task. On the other hand, it is nowadays relatively easy and cheap to capture sRNA sequences at a genome-wide scale using deep sequencing approaches. Computational methods have been devised to perform preliminary studies of populations of sRNAs and guide downstream experiments. Nonetheless, sRNA biology is complex and demands a battalion of independent computational methods which currently cannot be found in one unifying framework necessary for a thorough examination, while many critical algorithms are inaccurate or remain to be devised. In special, sRNAs that guide epigenetic mechanisms such as DNA methylation are estimated to be among the most abundant in plants, but despite the importance of this category, there is a lack of tools available in the public domain for their identification.
    In this thesis, novel computational methods for sRNA categorization are introduced, as well as an integrative framework for general sRNA analysis. The new software, developed for high-throughput sRNA analysis, was applied to real problems in biology bringing new insights to sRNA-mediated epigenetics.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • University of Groningen
    Supervisors/Advisors
    • Jansen, Ritsert, Supervisor
    • Johannes, Frank, Co-supervisor
    • Kok, Jan, Assessment committee
    • Sibon, Ody, Assessment committee
    • de Ridder, Dick, Assessment committee, External person
    Award date26-Mar-2018
    Place of Publication[Groningen]
    Publisher
    Print ISBNs978-94-034-0541-4
    Electronic ISBNs978-94-034-0540-7
    Publication statusPublished - 2018

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