Eukaryotic gene prediction

dc.contributor.advisorAgaian, Sos
dc.contributor.authorJleed, Hitham H.
dc.contributor.committeeMemberAkopian, David
dc.contributor.committeeMemberWang, Yufeng
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractSince the completion of the Human Genome Program in 21st century, there is an enormous amount of genomic and protein data available for use in public databases needed to be understood and analyzed. Gene prediction is one of the challenges in the analysis of newly sequenced genomes, and it is a basic step to an understanding of the genome. Thus, genomic DNA researches have rapidly increased to predict accurate genome structures of the DNA sequences, such as the promoter and coding regions. However, it is needed to find accurate and fast tools to analyze genomic sequences and annotate genes. In this thesis, a new eukaryotic gene prediction will be introduced using digital signal processing tools that can improve the efficiency of promoter recognition and coding regions prediction. First, a new nucleotide orthogonal signal mapping scheme is introduced that maps the nucleotide sequences into discrete signals. Second, we develop a new algorithm that recognizes promoters in given eukaryotic genomic DNA sequences. Finally, we design a new algorithm that identifies the coding regions in eukaryotic DNA sequences, improving the coding prediction by heightening the protein coding region and smothering the non-coding region. We will compare the proposed system with commonly used techniques based on computer simulation. This system improves the accuracy of identifying the promoter and predicting the coding regions.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent96 pages
dc.subject.classificationElectrical engineering
dc.subject.classificationBiomedical engineering
dc.titleEukaryotic gene prediction
dcterms.accessRightspq_closed and Computer Engineering of Texas at San Antonio of Science


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