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dc.contributor.authorLiu, Mingmingen_US
dc.date.accessioned2016-12-16T07:00:21Z
dc.date.available2016-12-16T07:00:21Z
dc.date.issued2015-06-24en_US
dc.identifier.othervt_gsexam:5906en_US
dc.identifier.urihttp://hdl.handle.net/10919/73703
dc.description.abstractWith the development of sequencing technologies, more and more sequence variants are available for investigation. Different types of variants in the human genome have been identified, including single nucleotide polymorphisms (SNPs), short insertions and deletions (indels), and large structural variations such as large duplications and deletions. Of great research interest is the functional effects of these variants. Although many programs have been developed to predict the effect of SNPs, few can be used to predict the effect of indels or multiple variants, such as multiple SNPs, multiple indels, or a combination of both. Moreover, fine grained prediction of the functional outcome of variants is not available. To address these limitations, we developed a prediction framework, HMMvar, to predict the functional effects of coding variants (SNPs or indels), using profile hidden Markov models (HMMs). Based on HMMvar, we proposed HMMvar-multi to explore the joint effects of multiple variants in the same gene. For fine grained functional outcome prediction, we developed HMMvar-func to computationally define and predict four types of functional outcome of a variant: gain, loss, switch, and conservation of function.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectGenetic variationen_US
dc.subjectIndelen_US
dc.subjectSNPen_US
dc.subjectHidden Markov Modelen_US
dc.titlePredicting the Functional Effects of Human Short Variations Using Hidden Markov Modelsen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairZhang, Liqingen_US
dc.contributor.committeememberHeath, Lenwood S.en_US
dc.contributor.committeememberWu, Xiaoweien_US
dc.contributor.committeememberHu, Jianjunen_US
dc.contributor.committeememberWatson, Layne T.en_US


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