Developing machine learning tools to understand transcriptional regulation in plants
| dc.contributor.author | Song, Qi | en |
| dc.contributor.committeechair | Li, Song | en |
| dc.contributor.committeemember | Grene, Ruth | en |
| dc.contributor.committeemember | Heath, Lenwood S. | en |
| dc.contributor.committeemember | Haak, David C. | en |
| dc.contributor.committeemember | Zhang, Liqing | en |
| dc.contributor.department | Genetics, Bioinformatics, and Computational Biology | en |
| dc.date.accessioned | 2019-09-10T08:00:54Z | en |
| dc.date.available | 2019-09-10T08:00:54Z | en |
| dc.date.issued | 2019-09-09 | en |
| dc.description.abstract | Abiotic stresses constitute a major category of stresses that negatively impact plant growth and development. It is important to understand how plants cope with environmental stresses and reprogram gene responses which in turn confers stress tolerance. Recent advances of genomic technologies have led to the generation of much genomic data for the model plant, Arabidopsis. To understand gene responses activated by specific external stress signals, these large-scale data sets need to be analyzed to generate new insight of gene functions in stress responses. This poses new computational challenges of mining gene associations and reconstructing regulatory interactions from large-scale data sets. In this dissertation, several computational tools were developed to address the challenges. In Chapter 2, ConSReg was developed to infer condition-specific regulatory interactions and prioritize transcription factors (TFs) that are likely to play condition specific regulatory roles. Comprehensive investigation was performed to optimize the performance of ConSReg and a systematic recovery of nitrogen response TFs was performed to evaluate ConSReg. In Chapter 3, CoReg was developed to infer co-regulation between genes, using only regulatory networks as input. CoReg was compared to other computational methods and the results showed that CoReg outperformed other methods. CoReg was further applied to identified modules in regulatory network generated from DAP-seq (DNA affinity purification sequencing). Using a large expression dataset generated under many abiotic stress treatments, many regulatory modules with common regulatory edges were found to be highly co-expressed, suggesting that target modules are structurally stable modules under abiotic stress conditions. In Chapter 4, exploratory analysis was performed to classify cell types for Arabidopsis root single cell RNA-seq data. This is a first step towards construction of a cell-type-specific regulatory network for Arabidopsis root cells, which is important for improving current understanding of stress response. | en |
| dc.description.abstractgeneral | Abiotic stresses constitute a major category of stresses that negatively impact plant growth and development. It is important to understand how plants cope with environmental stresses and reprogram gene responses which in turn confers stress tolerance to plants. Genomics technology has been used in past decade to generate gene expression data under different abiotic stresses for the model plant, Arabidopsis. Recent new genomic technologies, such as DAP-seq, have generated large scale regulatory maps that provide information regarding which gene has the potential to regulate other genes in the genome. However, this technology does not provide context specific interactions. It is unknown which transcription factor can regulate which gene under a specific abiotic stress condition. To address this challenge, several computational tools were developed to identify regulatory interactions and co-regulating genes for stress response. In addition, using single cell RNA-seq data generated from the model plant organism Arabidopsis, preliminary analysis was performed to build model that classifies Arabidopsis root cell types. This analysis is the first step towards the ultimate goal of constructing cell-typespecific regulatory network for Arabidopsis, which is important for improving current understanding of stress response in plants. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:21945 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/93512 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | regulatory network | en |
| dc.subject | Machine learning | en |
| dc.subject | genomics | en |
| dc.title | Developing machine learning tools to understand transcriptional regulation in plants | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Genetics, Bioinformatics, and Computational Biology | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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