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    Evaluating the Application of Allele Frequency in the Saudi Population Variant Detection

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    Name:
    Final_Master_Thesis_Sakhaa_2020 .pdf
    Size:
    4.329Mb
    Format:
    PDF
    Embargo End Date:
    2021-04-20
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    Type
    Thesis
    Authors
    Alsaedi, Sakhaa cc
    Advisors
    Hoehndorf, Robert cc
    Committee members
    Gao, Xin cc
    Gojobori, Takashi cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-04-26
    Embargo End Date
    2021-04-20
    Permanent link to this record
    http://hdl.handle.net/10754/662641
    
    Metadata
    Show full item record
    Access Restrictions
    At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2021-04-20.
    Abstract
    Human Mendelian disease in Saudi Arabia is both significant and challenging. Next-generation sequencing (NGS) has resulted in important discoveries of the genetic variants responsible for inherited disease. However, the success of clinical genomics using NGS requires accurate and consistent identification of rare genome variants. Rarity is one very important criterion for pathogenicity. Here we describe a model to detect variants by analyzing allele frequencies of a Saudi population. This work will enhance the opportunity to improve variant calling workflow to gain robust frequency estimates in order to better detect rare and unusual variants which are frequently associated with inherited disease.
    Citation
    Alsaedi, S. (2020). Evaluating the Application of Allele Frequency in the Saudi Population Variant Detection. KAUST Research Repository. https://doi.org/10.25781/KAUST-7K6DI
    DOI
    10.25781/KAUST-7K6DI
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-7K6DI
    Scopus Count
    Collections
    Theses; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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