{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/50726"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/50726","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Statistical methods for modeling RNA-Seq short-read data","abstract":"This thesis explores various methods for analyzing data generated using the next-generation sequencing technology, RNA-Seq. Two methods are developed which attempt to accurately calculate RNA expression, the first using a penalized regression approach to remove bias based on nucleotide composition, as well as a second which demonstrates the use of variation as an estimate of gene expression. Another method is developed which utilizes RNA-Seq gene expression data to identify genomic regulatory elements using a semi-parametric model with multiple responses considered simultaneously. Lastly, a method is established which identifies differentially expressed genes in timecourse data using a functional ANOVA mixed-effect model.","abstract_html":"This thesis explores various methods for analyzing data generated using the next-generation sequencing technology, RNA-Seq. Two methods are developed which attempt to accurately calculate RNA expression, the first using a penalized regression approach to remove bias based on nucleotide composition, as well as a second which demonstrates the use of variation as an estimate of gene expression. Another method is developed which utilizes RNA-Seq gene expression data to identify genomic regulatory elements using a semi-parametric model with multiple responses considered simultaneously. Lastly, a method is established which identifies differentially expressed genes in timecourse data using a functional ANOVA mixed-effect model.","abstract_has_math":false,"creators":["Dalpiaz, David"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Ma, Ping","Douglas, Jeffrey A.","Simpson, Douglas G.","Zhong, Wenxuan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-09-16T17:25:54Z","date_published":"2014-09-16T17:25:54Z","updated_at":"2026-07-22T22:25:41Z","subjects":["RNA-Seq","Gene expression","Penalized likelihood","Differential expression"],"languages":["en"],"rights":["Copyright 2014 David Dalpiaz"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/50726","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ma, Ping","Douglas, Jeffrey A.","Simpson, Douglas G.","Zhong, Wenxuan"]},{"key":"dc:creator","label":"Author","values":["Dalpiaz, David"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-09-16T17:25:54Z","2014-08","2014-09-16"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"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":["RNA-Seq","Gene expression","Penalized likelihood","Differential expression"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2014 David Dalpiaz"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/50726"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis explores various methods for analyzing data generated using the next-generation sequencing technology, RNA-Seq. Two methods are developed which attempt to accurately calculate RNA expression, the first using a penalized regression approach to remove bias based on nucleotide composition, as well as a second which demonstrates the use of variation as an estimate of gene expression. Another method is developed which utilizes RNA-Seq gene expression data to identify genomic regulatory elements using a semi-parametric model with multiple responses considered simultaneously. Lastly, a method is established which identifies differentially expressed genes in timecourse data using a functional ANOVA mixed-effect model.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-06-30T18:41:19Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Dalpiaz_David.pdf: 4086197 bytes, checksum: 3e9f78da4a602eeec9f2ca333dc9e299 (MD5)","Made available in DSpace on 2014-09-16T17:25:54Z (GMT). No. of bitstreams: 2 David_Dalpiaz.pdf: 4086197 bytes, checksum: 3e9f78da4a602eeec9f2ca333dc9e299 (MD5) license.txt: 4063 bytes, checksum: 4b5b2be68d678e4cc7d7d7288aabc005 (MD5)"]},{"key":"dc:title","label":"Title","values":["Statistical methods for modeling RNA-Seq short-read data"]}]}],"canonical_facts":{"dc:contributor":["Ma, Ping","Douglas, Jeffrey A.","Simpson, Douglas G.","Zhong, Wenxuan"],"dc:creator":["Dalpiaz, David"],"dc:date":["2014-09-16T17:25:54Z","2014-08","2014-09-16"],"dc:description":["This thesis explores various methods for analyzing data generated using the next-generation sequencing technology, RNA-Seq. Two methods are developed which attempt to accurately calculate RNA expression, the first using a penalized regression approach to remove bias based on nucleotide composition, as well as a second which demonstrates the use of variation as an estimate of gene expression. Another method is developed which utilizes RNA-Seq gene expression data to identify genomic regulatory elements using a semi-parametric model with multiple responses considered simultaneously. Lastly, a method is established which identifies differentially expressed genes in timecourse data using a functional ANOVA mixed-effect model.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2014-06-30T18:41:19Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Dalpiaz_David.pdf: 4086197 bytes, checksum: 3e9f78da4a602eeec9f2ca333dc9e299 (MD5)","Made available in DSpace on 2014-09-16T17:25:54Z (GMT). No. of bitstreams: 2 David_Dalpiaz.pdf: 4086197 bytes, checksum: 3e9f78da4a602eeec9f2ca333dc9e299 (MD5) license.txt: 4063 bytes, checksum: 4b5b2be68d678e4cc7d7d7288aabc005 (MD5)"],"dc:identifier":["http://hdl.handle.net/2142/50726"],"dc:language":["en"],"dc:rights":["Copyright 2014 David Dalpiaz"],"dc:subject":["RNA-Seq","Gene expression","Penalized likelihood","Differential expression"],"dc:title":["Statistical methods for modeling RNA-Seq short-read data"],"dc:type":["text"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:41Z"}