open_projects
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If you are a potential supervisor, [[supervisor_instructions: | If you are a potential supervisor, [[supervisor_instructions: | ||
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+ | === 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 | ||
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+ | === Developing quiescent stem cell classifier using single cell transcriptomics === | ||
+ | Contact info: Dr Lachlan Harris (Lachlan.Harris@qimrberghofer.edu.au), | ||
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+ | Quiescence is a reversible state of cell-cycle arrest, sometimes referred to as the “G0” phase of the cell-cycle. It is an adaptive feature of most adult stem cell populations, | ||
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+ | The most common classifiers that define cell-cycle stages (G1/S/G2/M) in single-cell RNA-sequencing data (scRNA- seq) were trained on populations of actively cycling cells. Therefore, these tools cannot identify quiescent stem cells in “G0” phase of the cell-cycle. It is an outstanding question as to whether there are sufficient transcriptomic similarities across quiescent stem cells from different tissue types to build a generalisable model to discriminate these cellular populations. Furthermore, | ||
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+ | This project aims to develop a broadly applicable quiescent classifier. As a first step towards this, this project will seek to 1) contribute to the curation of datasets and isolation of tissue-agnostic and tissue-specific feature sets that define quiescent stem cells and 2) compare methods for training quiescent classifiers and for determining the most salient features. | ||
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+ | === Understanding sex-specific cardiovascular disease risk === | ||
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+ | Contact info: Dr Sonia Shah (sonia.shah@imb.uq.edu.au), | ||
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+ | Description: | ||
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+ | Requirements: | ||
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+ | === De-risking the drug development pipeline by finding biomarkers of drug action === | ||
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+ | Supervisor: Dr Nathan Palpant (n.palpant@uq.edu.au) | ||
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+ | Greater than 90% of drugs fail to advance into clinical approval. Genetic evidence supporting a drug-target-indication can improve the success by greater than 50%. This project aims to make use of consortium-level data resources (UKBiobank, Human Cell Atlas, ENCODE etc) to identify genetic links between genetic targets and phenotypes to help facilitate the translation of drugs from healthy individuals (Phase 1 clinical trial assessing safety) into sick patients (Phase 2 clinical trial assessing efficacy). Finding orthogonal biomarkers of drug action in healthy individuals is critical to de-risk drug dosing when transitioning from Phase 1 to Phase 2 trials. Using ASIC1a as a candidate drug being developed to treat heart attacks, we aim to develop a functionally validated computational pipeline to predict orthogonal biomarkers of ASIC1a inhibitor drug action in healthy individuals to help inform dosing in human clinical trials. Computationally predicted biomarkers will be validated using genetic knockout animals and pharmacological inhibitors of ASIC1a. Collectively, | ||
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+ | === Parsing the genome into functional units to understand the genetic basis of cell identity and function === | ||
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+ | Supervisor: Dr Nathan Palpant (n.palpant@uq.edu.au) | ||
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+ | The billions of bases in the genome are shared among all cell types and tissues in the body. Understanding how regions of the genome control the diverse functions of cells is fundamental to understanding evolution, development, | ||
=== Machine learning integration of sequencing and imaging data in cancer research === | === Machine learning integration of sequencing and imaging data in cancer research === | ||
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- | === Trans-ancestry conditional analyses of genome-wide association studies | + | === Decoding Transcription Factor Dosage Effects on Cell State Transitions with DoseH-Seq === |
- | Contact: Dr Loic Yengo (l.yengo@imb.uq.edu.au) | + | Contact |
- | The experimental design | + | Cell identity is controlled by different combinations |
- | This project aims at developing | + | Single-cell RNA+ATAC-seq is a uniquely powerful assay to measure the impact |
- | The ideal candidate | + | The applicant would help drive method establishment around our novel DoseH-seq technique and support analysis to understand TFs dosage effects with established data sets. Ideal candidate will be able to efficiently program in R or Python. This project is looking for bioinformatics Masters students (ideally 16 units, but we consider 8 unit applicants as well. We also consider PhD students. |
+ | === Trans-ancestry conditional analyses of genome-wide association studies === | ||
- | === DNA sequence analysis to investigate why prevalence of adverse effects to ACE inhibitor medication differs across ancestries === | + | Contact: Dr Loic Yengo (l.yengo@imb.uq.edu.au) |
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- | Contact: Dr Sonia Shah (s.shah1@uq.edu.au) | + | |
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- | The angiotensin converting enzyme (ACE) is a component of the renin-angiotensin pathway which regulates blood pressure. It is a target for blood pressure lowering medication (ACE inhibitors). The efficacy and occurrence of adverse side-effects from ACE inhibitor treatment is different amongst difference ancestries. | + | |
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- | This project will analyse exome sequence data of the ACE gene in different ancestries to determine if there are differences in structure across different ancestries, which may explain the ancestry differences in ACE inhibitor adverse effets. | + | |
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- | The ideal candidate will have knowledge and experience in bioinformatics, | + | |
+ | The experimental design of genome-wide association studies (GWAS) consists in testing the association between a large number of DNA polymorphisms and a trait of interest. Classically, | ||
+ | This project aims at developing a COJO algorithm to simultaneously perform variants selection and meta-analyses of multiple GWAS from participants of diverse ancestries. The research will include: (i) developing and comparing algorithms, (ii) testing the impact of violations of model assumptions through simulations and (iii) writing a C++ based software implementing this algorithm. Application of this research can improve our ability to discover genes involved in the susceptibility of common diseases. | ||
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+ | The ideal candidate will have a good understanding of the multiple linear regression model and will be able to efficiently program in R/Python and C++. | ||
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In this project, students will use cutting edge data sources including reduce representation bisulphite sequencing data, whole genome bisulphite sequencing, long read sequencing and human methylation data to develop a tool to impute methylation sites from low coverage ONT sequence data. | In this project, students will use cutting edge data sources including reduce representation bisulphite sequencing data, whole genome bisulphite sequencing, long read sequencing and human methylation data to develop a tool to impute methylation sites from low coverage ONT sequence data. | ||
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This project is designed for students who are studying for Masters of Molecular Biology, Masters of Biotechnology, | This project is designed for students who are studying for Masters of Molecular Biology, Masters of Biotechnology, | ||
Available for semester 1, 2 and summer | Available for semester 1, 2 and summer | ||
- | ==== Differential methylated regions related to puberty in Brahman cattle === | + | === Differential methylated regions related to puberty in Brahman cattle === |
Puberty is a complex whole-body phenomenon that affects bone growth. In this study, we investigated how puberty in Bos indicus females affects methylation profiles in the epiphyseal growth plate, the cartilage that is essential to bone growth in long bones. Student will analyse nanopore sequencing data of 12 samples (6 pre-puberty and 6 post-puberty) to call methylation and identify the differentially methylated regions between these two groups. | Puberty is a complex whole-body phenomenon that affects bone growth. In this study, we investigated how puberty in Bos indicus females affects methylation profiles in the epiphyseal growth plate, the cartilage that is essential to bone growth in long bones. Student will analyse nanopore sequencing data of 12 samples (6 pre-puberty and 6 post-puberty) to call methylation and identify the differentially methylated regions between these two groups. | ||
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Available for semester 1, 2 and summer | Available for semester 1, 2 and summer | ||
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=== CRISPR === | === CRISPR === | ||
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Available all year, for Master of Bioinformatics students; suitable for one semester, full-time. | Available all year, for Master of Bioinformatics students; suitable for one semester, full-time. | ||
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- | === The transcriptional landscape of cardiovascular differentiation === | ||
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- | Contact info: Nathan Palpant (n.palpant@uq.edu.au) | ||
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- | Project description: | ||
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- | Availability, | ||
=== Machine learning and data integration in bioinformatics === | === Machine learning and data integration in bioinformatics === | ||
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Availability all year, for bioinformatics students with problem-solving skills, Honours or Masters. | Availability all year, for bioinformatics students with problem-solving skills, Honours or Masters. | ||
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=== Reconstruction of ancestral proteins === | === Reconstruction of ancestral proteins === |
open_projects.1705905639.txt.gz · Last modified: 2024/01/22 17:40 by project