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Motif Selection: Identification of Gene Regulatory Elements using Sequence Coverage Based Models and Evolutionary Algorithms

Al-Ouran, Rami
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449003717

Abstract Details

2015, Doctor of Philosophy (PhD), Ohio University, Electrical Engineering & Computer Science (Engineering and Technology).
The accuracy of identifying transcription factor binding sites (motifs) has increased with the use of technologies such as chromatin immunoprecipitation followed by sequencing (ChIP-seq), but this accuracy remains low enough that bioinformaticians and biologists struggle in choosing the right methods for identifying such regulatory elements. Current motif discovery methods typically produce lengthy lists of putative transcription factor binding sites, and a significant challenge lies in how to mine these lists to select a manageable set of candidate sites for experimental validation. Additionally, despite the importance of covering large numbers of genomic sequences, current motif discovery methods do not consider the sequence coverage percentage. To address the aforementioned problems, the motif selection problem is introduced and solved using a coverage based model greedy algorithm and a multi-objective evolutionary algorithm. The motif selection problem aims to produce a concise list of significant motifs which is both accurate and covers a high percentage of the genomic input sequences. The proposed motif selection methods were evaluated using ChIP-seq data from the ENCyclopedia of DNA Elements (ENCODE) project. In addition, the proposed methods were used to identify putative transcription factor binding sites in two case studies: stage specific binding sites in Brugia malayi, and tissue specific binding sites in hydroxyproline-rich glycoprotein (HRGP) genes in Arabidopsis thaliana.
Lonnie Welch (Advisor)
Bioinformatics; Computer Science
Motif selection; motif discovery; ENCODE

Recommended Citations

Citations

  • Al-Ouran, R. (2015). Motif Selection: Identification of Gene Regulatory Elements using Sequence Coverage Based Models and Evolutionary Algorithms [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449003717

    APA Style (7th edition)

  • Al-Ouran, Rami. Motif Selection: Identification of Gene Regulatory Elements using Sequence Coverage Based Models and Evolutionary Algorithms. 2015. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449003717.

    MLA Style (8th edition)

  • Al-Ouran, Rami. "Motif Selection: Identification of Gene Regulatory Elements using Sequence Coverage Based Models and Evolutionary Algorithms." Doctoral dissertation, Ohio University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449003717

    Chicago Manual of Style (17th edition)

ohiou1449003717
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© 2015, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.