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    Distinguishing the Transcription Regulation Patterns in Promoters of Human Genes with Different Function or Evolutionary Age

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    Tanvir Alam Thesis.pdf
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    Type
    Thesis
    Authors
    Alam, Tanvir cc
    Advisors
    Bajic, Vladimir B. cc
    Committee members
    Gao, Xin cc
    Zhang, Xiangliang cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2012-07
    Embargo End Date
    2013-07-30
    Permanent link to this record
    http://hdl.handle.net/10754/244611
    
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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 2013-07-30.
    Abstract
    Distinguishing transcription regulatory patterns of different gene groups is a common problem in various bioinformatics studies. In this work we developed a methodology to deal with such a problem based on machine learning techniques. We applied our method to two biologically important problems related to detecting a difference in transcription regulation of: a/ protein-coding and long non-coding RNAs (lncRNAs) in human, as well as b/ a difference between primate-specific and non-primate-specific long non-coding RNAs. Our method is capable to classify RNAs using various regulatory features of genes that transcribe into these RNAs, such as nucleotide frequencies, transcription factor binding sites, de novo sequence motifs, CpG islands, repetitive elements, histone modification marks, and others. Ten-fold cross-validation tests suggest that our model can distinguish protein-coding and non-coding RNAs with accuracy above 80%. Twenty-fold cross-validation tests suggest that our model can distinguish primate-specific from non-primate-specific promoters of lncRNAs with accuracy above 80%. Consequently, we can hypothesize that transcription of the groups of genes mentioned above are regulated by different mechanisms. Feature selection techniques allowed us to reduce the number of features significantly while keeping the accuracy around 80%. Consequently, we can conclude that selected features play significant role in transcription regulation of coding and non-coding genes, as well as primate-specific and non-primate-specific lncRNA genes.
    Citation
    Alam, T. (2012). Distinguishing the Transcription Regulation Patterns in Promoters of Human Genes with Different Function or Evolutionary Age. KAUST Research Repository. https://doi.org/10.25781/KAUST-85H4D
    DOI
    10.25781/KAUST-85H4D
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-85H4D
    Scopus Count
    Collections
    MS Theses; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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