open_projects
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If you are a potential supervisor, [[supervisor_instructions: | If you are a potential supervisor, [[supervisor_instructions: | ||
+ | === Investigation of the effect of the circadian rhythm on the genetic control of gene expression === | ||
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+ | Contact: Sonia shah < | ||
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+ | The circadian rhythm reflects the daily cycle of behaviours and metabolic processes organisms exhibit. A 24-hour gene expression pattern occurs at the molecular level, with genes activated either during the day or night. Different tissues all display circadian control, with some more affected than others. Within the liver, for example, 3000 genes are subjected to circadian control. This regulation is orchestrated by a small group of CLOCK genes, establishing feedback loops that result in rhythmic gene expression in every tissue. | ||
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+ | We know that gene expression can be influences by genetics variants, called expression quantitative trait loci (eQTL), and this may be one mechanism linking genetic variants to disease. As a result, large eQTL datasets have been generated to assist in understanding disease mechanisms. However, it remains unknown whether sample collection time can affect eQTL identification. This project therefore aims to identify the possible effects of the circadian rhythm on the genetic control of gene expression using the Genotype-Tissue expression (GTEx) dataset. | ||
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+ | During this project, you will run Python tools such as PEER and tensorQTL to identify eQTL within 49 tissues. You will subsequently investigate the associations identified and follow up on the role of the genes under circadian controls within different phenotypes. | ||
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+ | === Understanding the influence of taste and olfactory perception on eating behaviour and health conditions using big genetic data === | ||
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+ | Contact info: Daniel Hwang < | ||
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+ | Project description: | ||
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+ | === Increasing drug success rate in human clinical trials using genomics === | ||
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+ | Around 90% of drug candidates fail in human clinical trials largely due to lack of efficacy or safety concerns. This partly reflects the limitations of using in vitro and animal studies to predict the effect of compounds in humans. Recent studies highlight that drug targets backed by evidence from human genetic studies are 2 times more likely to make it to market. Human genetic data can also identify potential adverse side effects. Such information prior to embarking on human clinical trials could improve the success rate of a compound in human clinical trials and help avoid adverse outcomes for participants. This project will use statistical genomics analyses using publicly available human genomic data to predict efficacy as well as any safety concerns of compounds that are currently in the drug development pipeline. | ||
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+ | Project significance: | ||
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+ | Contact: Sonia Shah < | ||
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+ | Supervisors: | ||
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+ | Familiarity with computational analyses (e.g using R or python etc) is needed for this project. Some knowledge around genome-wide association studies and statistical genomics methods such as Mendelian randomisation analysis would be beneficial | ||
=== Developing quiescent stem cell classifier using single cell transcriptomics === | === Developing quiescent stem cell classifier using single cell transcriptomics === |
open_projects.1706751964.txt.gz · Last modified: 2024/02/01 12:46 by project