{"id":{"repo_id":"missouri","oai_identifier":"oai:mospace.umsystem.edu:10355/90090"},"canonical_url":"https://search.dev.ndltd.org/etd/missouri/oai:mospace.umsystem.edu:10355/90090","repository":{"repo_id":"missouri","name":"University of Missouri","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Development of a multi-omics approach to identify highly correlated transcriptomic, proteomic and metabolic signatures in maize B73 and FR697 drought stressed nodal roots","abstract":"Maize is one of the most important crops grown in the continental US and worldwide, and as such, major interest is directed towards understanding the impact of drought conditions on maize growth and development. Nodal roots, which develop from the base of the stem and produce the framework of the mature root system, can continue to grow under water stress conditions that inhibit the growth of the leaves and stem. To better understand the molecular mechanisms that led to this remarkable ability, we analyzed multiomics (transcriptome, proteome, metabolome) datasets generated from the growth zone of nodal roots collected from the reference inbred line B73 and from inbred line FR697, which exhibits a relatively greater ability to maintain root elongation under water-stressed conditions. We developed an informatics analytics pipeline consisting of a discriminatory multiomics data integration approach combining sparse Generalized Canonical Correlation Analysis (sGCCA) and generalized Partial Least Square analysis (PLS) to incorporate all datasets into one holistic global network and form clusters spanning all omics levels. Significant elements from these clusters were connected to various observations associated with water stress in the root tip samples and reinforced by their roles in biological pathways. We also generated an annotated \"SuperTranscriptome\" assembly from Pacbio Iso-Seq and RNA-Seq datasets to serve as a representative assembly for the FR697 genotype. The results were incorporated into the KBCommons maize database for storage and analysis from various viewpoints. To visualize interactions between the many elements, we are also developing a suite of 3D visualization, collectively called the \"KBCommons Omics Studio\", integrated with the KBCommons framework. Using these methods, we showcase possible biomarkers related to drought stress and allied observations. Supported by NSF Plant Genome Program IOS #1444448.","abstract_html":"Maize is one of the most important crops grown in the continental US and worldwide, and as such, major interest is directed towards understanding the impact of drought conditions on maize growth and development. Nodal roots, which develop from the base of the stem and produce the framework of the mature root system, can continue to grow under water stress conditions that inhibit the growth of the leaves and stem. To better understand the molecular mechanisms that led to this remarkable ability, we analyzed multiomics (transcriptome, proteome, metabolome) datasets generated from the growth zone of nodal roots collected from the reference inbred line B73 and from inbred line FR697, which exhibits a relatively greater ability to maintain root elongation under water-stressed conditions. We developed an informatics analytics pipeline consisting of a discriminatory multiomics data integration approach combining sparse Generalized Canonical Correlation Analysis (sGCCA) and generalized Partial Least Square analysis (PLS) to incorporate all datasets into one holistic global network and form clusters spanning all omics levels. Significant elements from these clusters were connected to various observations associated with water stress in the root tip samples and reinforced by their roles in biological pathways. We also generated an annotated "SuperTranscriptome" assembly from Pacbio Iso-Seq and RNA-Seq datasets to serve as a representative assembly for the FR697 genotype. The results were incorporated into the KBCommons maize database for storage and analysis from various viewpoints. To visualize interactions between the many elements, we are also developing a suite of 3D visualization, collectively called the "KBCommons Omics Studio", integrated with the KBCommons framework. Using these methods, we showcase possible biomarkers related to drought stress and allied observations. Supported by NSF Plant Genome Program IOS #1444448.","abstract_has_math":false,"creators":["Sen, Sidharth"],"institution":"University of Missouri--Columbia","degree_name":"Ph. D.","degree_level":"Doctoral","degree_discipline":"Informatics (MU)","degree_department":null,"school":null,"contributors":[],"advisors":["Joshi, Trupti"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021","date_published":"2021","updated_at":"2026-07-24T03:09:05Z","subjects":[],"languages":["eng","English"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.32469/10355/90090"],"render_values":[{"text":"https://doi.org/10.32469/10355/90090","href":"https://doi.org/10.32469/10355/90090","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10355/90090","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Joshi, Trupti"]},{"key":"dc:creator","label":"Author","values":["Sen, Sidharth"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-05-09T16:06:57Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-05-09T16:06:57Z"]},{"key":"dc:date.issued","label":"Date","values":["2021"]},{"key":"dc:publisher","label":"Institution","values":["University of Missouri--Columbia"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Informatics (MU)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. 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To better understand the molecular mechanisms that led to this remarkable ability, we analyzed multiomics (transcriptome, proteome, metabolome) datasets generated from the growth zone of nodal roots collected from the reference inbred line B73 and from inbred line FR697, which exhibits a relatively greater ability to maintain root elongation under water-stressed conditions. We developed an informatics analytics pipeline consisting of a discriminatory multiomics data integration approach combining sparse Generalized Canonical Correlation Analysis (sGCCA) and generalized Partial Least Square analysis (PLS) to incorporate all datasets into one holistic global network and form clusters spanning all omics levels. Significant elements from these clusters were connected to various observations associated with water stress in the root tip samples and reinforced by their roles in biological pathways. We also generated an annotated \"SuperTranscriptome\" assembly from Pacbio Iso-Seq and RNA-Seq datasets to serve as a representative assembly for the FR697 genotype. The results were incorporated into the KBCommons maize database for storage and analysis from various viewpoints. To visualize interactions between the many elements, we are also developing a suite of 3D visualization, collectively called the \"KBCommons Omics Studio\", integrated with the KBCommons framework. Using these methods, we showcase possible biomarkers related to drought stress and allied observations. Supported by NSF Plant Genome Program IOS #1444448."]},{"key":"dc:title","label":"Title","values":["Development of a multi-omics approach to identify highly correlated transcriptomic, proteomic and metabolic signatures in maize B73 and FR697 drought stressed nodal roots"]}]}],"canonical_facts":{"dc:contributor.advisor":["Joshi, Trupti"],"dc:creator":["Sen, Sidharth"],"dc:date.accessioned":["2022-05-09T16:06:57Z"],"dc:date.available":["2022-05-09T16:06:57Z"],"dc:date.issued":["2021"],"dc:description.abstract":["Maize is one of the most important crops grown in the continental US and worldwide, and as such, major interest is directed towards understanding the impact of drought conditions on maize growth and development. Nodal roots, which develop from the base of the stem and produce the framework of the mature root system, can continue to grow under water stress conditions that inhibit the growth of the leaves and stem. To better understand the molecular mechanisms that led to this remarkable ability, we analyzed multiomics (transcriptome, proteome, metabolome) datasets generated from the growth zone of nodal roots collected from the reference inbred line B73 and from inbred line FR697, which exhibits a relatively greater ability to maintain root elongation under water-stressed conditions. We developed an informatics analytics pipeline consisting of a discriminatory multiomics data integration approach combining sparse Generalized Canonical Correlation Analysis (sGCCA) and generalized Partial Least Square analysis (PLS) to incorporate all datasets into one holistic global network and form clusters spanning all omics levels. Significant elements from these clusters were connected to various observations associated with water stress in the root tip samples and reinforced by their roles in biological pathways. We also generated an annotated \"SuperTranscriptome\" assembly from Pacbio Iso-Seq and RNA-Seq datasets to serve as a representative assembly for the FR697 genotype. The results were incorporated into the KBCommons maize database for storage and analysis from various viewpoints. To visualize interactions between the many elements, we are also developing a suite of 3D visualization, collectively called the \"KBCommons Omics Studio\", integrated with the KBCommons framework. Using these methods, we showcase possible biomarkers related to drought stress and allied observations. 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