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    PREDICTION OF CHROMATIN STATES USING DNA SEQUENCE PROPERTIES

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    Rihab Bahabri Thesis.pdf
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    Rihab's Thesis
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    Type
    Thesis
    Authors
    Bahabri, Rihab R.
    Advisors
    Bajic, Vladimir B. cc
    Committee members
    Gao, Xin cc
    Moshkov, Mikhail cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2013-06
    Embargo End Date
    2014-06-11
    Permanent link to this record
    http://hdl.handle.net/10754/293891
    
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    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 became available to the public after the expiration of the embargo on 2014-06-11.
    Abstract
    Activities of DNA are to a great extent controlled epigenetically through the internal struc- ture of chromatin. This structure is dynamic and is influenced by different modifications of histone proteins. Various combinations of epigenetic modification of histones pinpoint to different functional regions of the DNA determining the so-called chromatin states. How- ever, the characterization of chromatin states by the DNA sequence properties remains largely unknown. In this study we aim to explore whether DNA sequence patterns in the human genome can characterize different chromatin states. Using DNA sequence motifs we built binary classifiers for each chromatic state to eval- uate whether a given genomic sequence is a good candidate for belonging to a particular chromatin state. Of four classification algorithms (C4.5, Naive Bayes, Random Forest, and SVM) used for this purpose, the decision tree based classifiers (C4.5 and Random Forest) yielded best results among those we evaluated. Our results suggest that in general these models lack sufficient predictive power, although for four chromatin states (insulators, het- erochromatin, and two types of copy number variation) we found that presence of certain motifs in DNA sequences does imply an increased probability that such a sequence is one of these chromatin states.
    Citation
    Bahabri, R. R. (2013). PREDICTION OF CHROMATIN STATES USING DNA SEQUENCE PROPERTIES. KAUST Research Repository. https://doi.org/10.25781/KAUST-WY88B
    DOI
    10.25781/KAUST-WY88B
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-WY88B
    Scopus Count
    Collections
    Theses; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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