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Title:Integration of bioinformatics approaches to study transcriptome patterns associated with complex traits at the gene and isoform levels
Author(s):Zhang, Pan
Director of Research:Rodriguez-Zas, Sandra Luisa
Doctoral Committee Chair(s):Rodriguez-Zas, Sandra Luisa
Doctoral Committee Member(s):Caetano-Anolles, Gustavo; Villamil, Maria Bonita; Yan, Huihuang
Department / Program:Illinois Informatics Institute
Discipline:Informatics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):RNA-seq, differential expression, alternative splicing, co-expression network, Gaussian Markov random fields
Abstract:The study of the molecules associated with complex traits such as diseases can advance the understanding of traits and help identify biomarkers and potential remedies. In this study, high-throughput RNA-seq technology, statistical models and bioinformatics approaches were used to profile the transcriptome and uncover the underlying molecular mechanisms associated with activity addiction and opioid-induced hyperalgesia with brain region or central nervous system region dependencies considered. Functional category enrichment, reconstruction of molecular interactions, and regulatory networks added systematic insights into the dysregulation of the transcriptome. In addition, co-expression networks at various aggregate levels (e.g., gene family, gene, or transcript isoform) were estimated using Gaussian Markov random fields based on the abundance of mRNA. Multiple criteria were used to benchmark the inferred networks against the pathways in Kyoto Encyclopedia of Genes and Genomes reference. Our findings suggest that the inference of networks using granular information can enhance the network performance, especially when high splicing variation is involved.
Issue Date:2021-03-23
Type:Thesis
URI:http://hdl.handle.net/2142/110638
Rights Information:Copyright 2021 Pan Zhang
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05


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