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Integrating Epigenetic Priors For Improving Computational Identification of Transcription Factor Binding Sites

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dc.contributor.advisorGrigull, Jorg
dc.creatorShoukat, Affan
dc.date.accessioned2016-09-20T16:26:44Z
dc.date.available2016-09-20T16:26:44Z
dc.date.copyright2015-09-17
dc.date.issued2016-09-20
dc.date.updated2016-09-20T16:26:44Z
dc.degree.disciplineApplied and Industrial Mathematics
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractTranscription factors and histone modifications play critical roles in tissue-specific gene expression. Identifying binding sites is key in understanding the regulatory interactions of gene expression. Nave computational approaches uses solely DNA sequence data to construct models known as Position Weight Matrices. However, the various assumptions and the lack of background genomic information leads to a high false positive rate. In an attempt to improve the predictive performance of a PWM, we use a Hidden Markov Model to incorporate chromatin structure, in particular histone modifications. The HMM captures physical interactions between distinct HMs. Indeed, the integration of sequence based PWM models and chromatin modifications improve the predictive ability of the integrative model.
dc.identifier.urihttp://hdl.handle.net/10315/32106
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectBioinformatics
dc.subject.keywordsTranscription factor binding sites
dc.subject.keywordsMathematical modelling
dc.subject.keywordsPosition weight matrices
dc.titleIntegrating Epigenetic Priors For Improving Computational Identification of Transcription Factor Binding Sites
dc.typeElectronic Thesis or Dissertation

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