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Virginia Tech

Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models

Abstract

dc:description.abstract

With 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.

Degree

thesis:*
Name thesis:degree_name
Ph. D.
Level thesis:degree_level
doctoral
Discipline thesis:degree_discipline
Computer Science and Applications
Department dc:contributor.department
Computer Science
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2015

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Liu, Mingming
Chair dc:contributor.committeechair
  • Zhang, Liqing
Committee members dc:contributor.committeemember
  • Heath, Lenwood S.
  • Wu, Xiaowei
  • Hu, Jianjun
  • Watson, Layne T.

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:5906
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/73703

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Liu, Mingming. Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models. doctoral thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/73703