University of Illinois Urbana-Champaign

Modeling transcriptomic dynamics and epigenomic conservation

Cao, Xiaoyi

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Xiaoyi_Cao.pdf

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https://hdl.handle.net/2142/42326

Description

Title
Modeling transcriptomic dynamics and epigenomic conservation
Author(s)
Cao, Xiaoyi
Issue Date
2013-02-03T19:35:26Z
Director of Research (if dissertation) or Advisor (if thesis)
Zhong, Sheng
Doctoral Committee Chair(s)
Zhong, Sheng
Committee Member(s)
  • Jakobsson, Eric
  • Ma, Jian
  • Price, Nathan D.
Department of Study
School of Molecular & Cell Bio
Discipline
Biophysics & Computnl Biology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2013-02-03T19:35:26Z
Keyword(s)
  • Gene clustering
  • Temporal patterns clustering
  • Temporal patterns comparison
  • Epigenomic comparison
Abstract
Gene regulatory networks control gene expression during various biological processes, including two sets with similarity: differentiation processes of embryonic stem (ES) cells and embryo development processes. This thesis work centers on two aspects in analyses of such gene expression patterns: temporal models for gene expression patterns and comparative analysis of epigenomic contributions to gene expressions across biological processes or across species evolutionarily. We presented a comparative model for different biological process based on a new model of clustering of temporal gene expression patterns. With this method, we are able to compare different differentiation processes via internal or external stimulation and infer the underlying mechanism. With the improvement of data resolution and the appearance of single-cell time-course expression data, we further make our clustering model time-variant to better analyze these datasets in developmental process. The time-variant model has dynamic cluster structure in the various time-points of the biological process instead of static ones. It also includes feature selection, which enable us to select the genes with expression levels dependent to clustering results. By applying this model on a single-cell embryo developmental dataset, we are able to infer early cell fate decision and the core transcriptional factors in this process. The contribution of epigenomics in gene regulatory network of ES cells is also becoming a major topic. We provide a comparative approach by utilizing epigenomic information together with gene expression from different species. A integration and visualization tool is also developed to boost analyses of such cross-species data. From the analyses across three mammalian species, it appears that epigenomic information is more conserved in different species than what was expected.
Graduation Semester
2012-12
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http://hdl.handle.net/2142/42326
Copyright and License Information
Copyright 2012 Xiaoyi Cao

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