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Title: gene regulatory element prediction with bayesian networks
Keywords: Gene regulation, Bayesian networks, Transcription factor binding sites, Motif finding, Gene promoter, Enhancers or cis-regulatory modules
Issue Date: 4-Jun-2009
Citation: VIPIN NARANG (2009-06-04). gene regulatory element prediction with bayesian networks. ScholarBank@NUS Repository.
Abstract: While computational advances have enabled sequencing of genomes at a rapid rate, annotation of functional elements in genomic sequences is lagging far behind. Of particular importance is the identification of sequences that regulate gene expression. This dissertation contributes to the computational modeling and detection of three very important regulatory elements in eukaryotic genomes, viz. transcription factor binding motifs, gene promoters and cis-regulatory modules (enhancers). Position specificity of transcription factor binding sites is the main insight used to enhance the modeling and detection performance in all three applications. The first application is the development of an in-silico tool called LocalMotif for the discovery of transcription factor binding motifs in regulatory sequences when the motif is localized relative to an anchoring point such as the transcription start site or the center of the ChIP sequences. The second application is the development of a statistical model called BayesProm for promoter sequences based on the positional densities of oligonucleotides in promoter sequences in a continuous naC/ve Bayes classifier. The third application concerns a probabilistic graphical model called Modulexplorer that learns de-novo the combinations of transcription factor binding motifs in enhancers that control the expression of genes in a certain tissue at a certain development stage. This is the first research to learn an in silico model for several different types of enhancers and use it to predict similar novel enhancers.
Appears in Collections:Ph.D Theses (Open)

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