{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113190"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113190","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Computational methods for inferring regulatory mechanisms from sequence and expression variation","abstract":"With the rapid development and decreasing cost of sequencing technologies, more and more novel genetic variants are being detected. Numerous human genetic and bioinformatic studies associate genotype data to phenotype information and provide increasing number of phenotype-related variants. Despite the large number of associations been detected, we are not even close to a complete understanding of the mechanisms how the genetic variants contribute to phenotypic variation. With the vast majority of the genetic variants from Genome-wide Association Study (GWAS) located on the non-coding region of human genome, it is crucial to understand the gene regulatory mechanisms to be able to interpret the variants. Therefore, the goal of my dissertation is to unveil the molecular mechanisms in the context of human disease using the genetic variants associated with the diseases of interest and also to get a better understanding of gene expression regulation which may in turn improve our interpretation of sequence variants. In this dissertation, Chapter 2 introduces a pipeline through which we associated transcription factors (TFs) with drug response variation. The pipeline involves a novel computational model that predicts TF binding strength for given DNA sequences. Chapter 3 addresses the variant set characterization task where the goal is to rank biological pathways for association with a given set of variants. For this, we developed a computational tool which applies “Random Walk with Restarts” algorithm on a network composed of single-nucleotide polymorphisms (SNP), genes and pathways to associate pathways to a given set of variants. In Chapter 4, we analyze the mechanisms of another group of gene expression regulators: long non-coding RNAs or lncRNAs. With lncRNA-mRNA connections mapped out, non-coding variants can be annotated from more functional categories. To enable this, we modeled the expression of protein coding genes using lncRNAs with potential regulatory mechanisms and created a high confidence lncRNA-mRNA regulatory network.","abstract_html":"With the rapid development and decreasing cost of sequencing technologies, more and more novel genetic variants are being detected. Numerous human genetic and bioinformatic studies associate genotype data to phenotype information and provide increasing number of phenotype-related variants. Despite the large number of associations been detected, we are not even close to a complete understanding of the mechanisms how the genetic variants contribute to phenotypic variation. With the vast majority of the genetic variants from Genome-wide Association Study (GWAS) located on the non-coding region of human genome, it is crucial to understand the gene regulatory mechanisms to be able to interpret the variants. Therefore, the goal of my dissertation is to unveil the molecular mechanisms in the context of human disease using the genetic variants associated with the diseases of interest and also to get a better understanding of gene expression regulation which may in turn improve our interpretation of sequence variants. In this dissertation, Chapter 2 introduces a pipeline through which we associated transcription factors (TFs) with drug response variation. The pipeline involves a novel computational model that predicts TF binding strength for given DNA sequences. Chapter 3 addresses the variant set characterization task where the goal is to rank biological pathways for association with a given set of variants. For this, we developed a computational tool which applies “Random Walk with Restarts” algorithm on a network composed of single-nucleotide polymorphisms (SNP), genes and pathways to associate pathways to a given set of variants. In Chapter 4, we analyze the mechanisms of another group of gene expression regulators: long non-coding RNAs or lncRNAs. With lncRNA-mRNA connections mapped out, non-coding variants can be annotated from more functional categories. To enable this, we modeled the expression of protein coding genes using lncRNAs with potential regulatory mechanisms and created a high confidence lncRNA-mRNA regulatory network.","abstract_has_math":false,"creators":["Xie, Xiaoman"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Biophysics & Quant Biology","degree_department":null,"school":null,"contributors":["Sinha, Saurabh","Belmont, Andrew S","Han, Hee-Sun","Zhao, Sihai Dave"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T22:35:14Z","date_published":"2022-01-12T22:35:14Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Gene Regulation","Variant Interpretation","Biological Pathways","Regression Analysis"],"languages":["en"],"rights":["Copyright 2021 Xiaoman Xie"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113190","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sinha, Saurabh","Belmont, Andrew S","Han, Hee-Sun","Zhao, Sihai Dave"]},{"key":"dc:creator","label":"Author","values":["Xie, Xiaoman"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T22:35:14Z","2024-01-12T22:35:30Z","2021-07-15","2021-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biophysics & Quant Biology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Gene Regulation","Variant Interpretation","Biological Pathways","Regression Analysis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Xiaoman Xie"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113190"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["With the rapid development and decreasing cost of sequencing technologies, more and more novel genetic variants are being detected. Numerous human genetic and bioinformatic studies associate genotype data to phenotype information and provide increasing number of phenotype-related variants. Despite the large number of associations been detected, we are not even close to a complete understanding of the mechanisms how the genetic variants contribute to phenotypic variation. With the vast majority of the genetic variants from Genome-wide Association Study (GWAS) located on the non-coding region of human genome, it is crucial to understand the gene regulatory mechanisms to be able to interpret the variants. Therefore, the goal of my dissertation is to unveil the molecular mechanisms in the context of human disease using the genetic variants associated with the diseases of interest and also to get a better understanding of gene expression regulation which may in turn improve our interpretation of sequence variants. In this dissertation, Chapter 2 introduces a pipeline through which we associated transcription factors (TFs) with drug response variation. The pipeline involves a novel computational model that predicts TF binding strength for given DNA sequences. Chapter 3 addresses the variant set characterization task where the goal is to rank biological pathways for association with a given set of variants. For this, we developed a computational tool which applies “Random Walk with Restarts” algorithm on a network composed of single-nucleotide polymorphisms (SNP), genes and pathways to associate pathways to a given set of variants. In Chapter 4, we analyze the mechanisms of another group of gene expression regulators: long non-coding RNAs or lncRNAs. With lncRNA-mRNA connections mapped out, non-coding variants can be annotated from more functional categories. To enable this, we modeled the expression of protein coding genes using lncRNAs with potential regulatory mechanisms and created a high confidence lncRNA-mRNA regulatory network.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Xiaoman Xie, accepted the attached license on 2021-07-13 at 15:37.","The student, Xiaoman Xie, submitted this Dissertation for approval on 2021-07-13 at 16:25.","This Dissertation was approved for publication on 2021-07-15 at 15:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16917 on 2022-01-12 at 12:54:57","Made available in DSpace on 2022-01-12T22:35:14Z (GMT). No. of bitstreams: 4 XIE-DISSERTATION-2021.pdf: 5340785 bytes, checksum: 54450f35b3fbdfd90db5d86bee77d308 (MD5) AppendixC.xlsx: 1953665 bytes, checksum: 60323ce6d2db09f745a89079007b9d98 (MD5) LICENSE.txt: 4208 bytes, checksum: ac666c0871717ad12166589394efe4bb (MD5) PROQUEST_LICENSE.txt: 4554 bytes, checksum: 48abd5ca525104e11905c307ba30cb41 (MD5) Previous issue date: 2021-07-15","Embargo set by: Seth Robbins for item 121116 Lift date: 2024-01-12T22:35:30Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Computational methods for inferring regulatory mechanisms from sequence and expression variation"]}]}],"canonical_facts":{"dc:contributor":["Sinha, Saurabh","Belmont, Andrew S","Han, Hee-Sun","Zhao, Sihai Dave"],"dc:creator":["Xie, Xiaoman"],"dc:date":["2022-01-12T22:35:14Z","2024-01-12T22:35:30Z","2021-07-15","2021-08"],"dc:description":["With the rapid development and decreasing cost of sequencing technologies, more and more novel genetic variants are being detected. Numerous human genetic and bioinformatic studies associate genotype data to phenotype information and provide increasing number of phenotype-related variants. Despite the large number of associations been detected, we are not even close to a complete understanding of the mechanisms how the genetic variants contribute to phenotypic variation. With the vast majority of the genetic variants from Genome-wide Association Study (GWAS) located on the non-coding region of human genome, it is crucial to understand the gene regulatory mechanisms to be able to interpret the variants. Therefore, the goal of my dissertation is to unveil the molecular mechanisms in the context of human disease using the genetic variants associated with the diseases of interest and also to get a better understanding of gene expression regulation which may in turn improve our interpretation of sequence variants. In this dissertation, Chapter 2 introduces a pipeline through which we associated transcription factors (TFs) with drug response variation. The pipeline involves a novel computational model that predicts TF binding strength for given DNA sequences. Chapter 3 addresses the variant set characterization task where the goal is to rank biological pathways for association with a given set of variants. For this, we developed a computational tool which applies “Random Walk with Restarts” algorithm on a network composed of single-nucleotide polymorphisms (SNP), genes and pathways to associate pathways to a given set of variants. In Chapter 4, we analyze the mechanisms of another group of gene expression regulators: long non-coding RNAs or lncRNAs. With lncRNA-mRNA connections mapped out, non-coding variants can be annotated from more functional categories. To enable this, we modeled the expression of protein coding genes using lncRNAs with potential regulatory mechanisms and created a high confidence lncRNA-mRNA regulatory network.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Xiaoman Xie, accepted the attached license on 2021-07-13 at 15:37.","The student, Xiaoman Xie, submitted this Dissertation for approval on 2021-07-13 at 16:25.","This Dissertation was approved for publication on 2021-07-15 at 15:37.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16917 on 2022-01-12 at 12:54:57","Made available in DSpace on 2022-01-12T22:35:14Z (GMT). No. of bitstreams: 4 XIE-DISSERTATION-2021.pdf: 5340785 bytes, checksum: 54450f35b3fbdfd90db5d86bee77d308 (MD5) AppendixC.xlsx: 1953665 bytes, checksum: 60323ce6d2db09f745a89079007b9d98 (MD5) LICENSE.txt: 4208 bytes, checksum: ac666c0871717ad12166589394efe4bb (MD5) PROQUEST_LICENSE.txt: 4554 bytes, checksum: 48abd5ca525104e11905c307ba30cb41 (MD5) Previous issue date: 2021-07-15","Embargo set by: Seth Robbins for item 121116 Lift date: 2024-01-12T22:35:30Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113190"],"dc:language":["en"],"dc:rights":["Copyright 2021 Xiaoman Xie"],"dc:subject":["Gene Regulation","Variant Interpretation","Biological Pathways","Regression Analysis"],"dc:title":["Computational methods for inferring regulatory mechanisms from sequence and expression variation"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Biophysics & Quant Biology"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:53Z"}