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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+ | === Decoding the relationships between DNA replication, | ||
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+ | Contact: Dr Mathew Jones (mathew.jones@uq.edu.au) | ||
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+ | The human genome is packaged into chromatin and assembled into 3D self-interacting chromatin domains that regulate gene expression and coordinate the process of DNA replication. Understanding the relationships between genome structure and function is one of the outstanding challenges in modern biology. Changes in the 3D structure of the genome can cause copying errors (genetic mutations) during DNA replication that results in diseases such as cancer and advanced aging. Decoding the relationships between the genomic landscape and cellular processes such as DNA replication has the potential to inform the development of novel treatments that can treat cancer and extend longevity. | ||
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+ | In this project we are seeking talented and enthusiastic postgraduate students to tackle two fundamental questions: 1. How does the epigenome and the 3D organisation of the genome regulate DNA replication? | ||
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+ | Bioinformatics and Computer Science students with skills in R, Python and C++ that are familiar with software suites for the comparison, manipulation and annotation of genomic features are encouraged to contact Dr Mathew Jones (mathew.jones@uq.edu.au) to learn more about the projects available. | ||
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+ | === De novo identification of insertion sequences with De Bruijn graphs === | ||
+ | |||
+ | Contacts: Leah Roberts l.roberts3@uq.edu.au, | ||
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+ | Insertion sequences (IS) are small DNA elements that can replicate and move throughout a bacterial genome independently. This ability often results in their insertion upstream of or within genes, which consequently leads to large effects on gene expression within the bacteria. These changes in expression can affect a multitude of phenotypes, including resistance to antibiotics and virulence. As such, is it necessary that we characterise where these IS move to within the genome. | ||
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+ | Unfortunately, | ||
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+ | This project aims to develop an end-to-end pipeline for de novo IS discovery using De Bruijn graphs, and quantify in a collection of bacterial genomes the effect of IS insertions on phenotype. | ||
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+ | This project is suitable for an honours or Masters student. Some background in command line, HPC and python is highly desirable. In this project, you will learn about bacterial genomics and pipeline managers (e.g. Snakemake) in addition to bioinformatic tool development and testing. This project will be based at UQCCR (Herston Campus) and co-supervised by Dr Tom Stanton (Monash University). | ||
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+ | |||
+ | === Machine Learning to predict plasmids from bacterial isolates === | ||
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+ | Contacts: Leah Roberts l.roberts3@uq.edu.au, | ||
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+ | Plasmids play a key role in gene exchange between bacteria and often carry gene conferring resistance to antibiotics and survival in hospital environments. However, they are difficult to fully characterise from short-read whole genome sequencing data alone. This is because plasmids are typically full of repeat sequences which can cause problems for short-reads assemblers. Long-read sequencing can solve this issue, however this technology is currently not routinely used in healthcare settings. | ||
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+ | We have developed a plasmid network that allows users to predict the types of plasmids in their bacterial samples based on gene presence/ | ||
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+ | This project is suitable for an honours or Masters student. Background in command line, HPC and python is highly desirable. This project will be based at UQCCR (Herston Campus) and co-supervised by Prof Zamin Iqbal (University of Bath, UK). | ||
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+ | |||
+ | === Pangenomes to predict bacterial transmission in healthcare settings === | ||
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+ | Contacts: Leah Roberts l.roberts3@uq.edu.au, | ||
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+ | Predicting whether two bacterial isolates are the same (and thereby inferring if transmission has occurred) has traditionally been performed by identifying and counting single nucleotide variants (SNVs). To do this, a reference genome is usually selected, and isolate reads are mapped to the reference to identify SNVs in regions shared between all isolates. However, for large datasets of very diverse bacterial strains, a single reference genome is usually insufficient, | ||
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+ | We propose a novel method using pangenome reference graphs to better identify and discriminate transmission of bacterial pathogens. This project would start to build test datasets and develop novel workflows for predicting transmission from pangenome graphs. | ||
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+ | This project is suitable for an honours, Masters, or PhD student. Background in command line, HPC and python is highly desirable. This project will be based at UQCCR (Herston Campus) and co-supervised by Dr Michael Hall (University of Melbourne). | ||
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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. | ||
+ | |||
+ | === 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 === | ||
+ | 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 === | ||
+ | |||
+ | Contact info: Dr Sonia Shah (sonia.shah@imb.uq.edu.au), | ||
+ | |||
+ | 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, | ||
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+ | === Machine learning integration of sequencing and imaging data in cancer research === | ||
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+ | Cancer is a complex disease that is difficult to treat due to the high level of variation within a tumor and between patients. To better understand cancer complexity at the tissue level, we use a combination of techniques such as single-cell sequencing, spatial transcriptomics, | ||
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+ | Contact: Quan Nguyen < | ||
=== Resolving molecular trajectories of differentiation pathways of adaptive immune cells in chronic infection generating long-term memory === | === Resolving molecular trajectories of differentiation pathways of adaptive immune cells in chronic infection generating long-term memory === | ||
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This project is looking for bioinformatics Masters students (ideally 16 units, but we consider 8 unit applicants as well. **Students placed overseas who want to conduct a project remotely are welcome**). We also consider Phd students. | This project is looking for bioinformatics Masters students (ideally 16 units, but we consider 8 unit applicants as well. **Students placed overseas who want to conduct a project remotely are welcome**). We also consider Phd students. | ||
+ | === (Deep) Learning the regulatory grammar of ageing | ||
- | === Trans-ancestry conditional analyses of genome-wide association studies === | + | Contact info: Dr Christian Nefzger (c.nefzger@imb.uq.edu.au), |
- | Contact: Dr Loic Yengo (l.yengo@imb.uq.edu.au) | + | Aging is a gradual process of functional and homeostatic decline in living systems and the greatest risk factor for virtually all degenerative diseases. At a cellular level, epigenetic changes in the non-coding part of the genome play a major role in this functional decline. The laboratory has access to deep profiling of age-related chromatin accessibility changes (ATAC-seq) with matched gene expression (RNA-seq) from 22 purified primary cell types across 11 tissues providing a roadmap of distinct regulatory elements including promoters and enhancers impacted by ageing. |
- | The experimental design of genome-wide association studies (GWAS) consists in testing | + | Recently, machine learning and deep learning methods to understand |
- | This project aims at developing a COJO algorithm | + | The project aims to: |
- | The ideal candidate will have a good understanding | + | 1. Compare available machine learning Convolutional Neural Networks (CNNS) algorithms ([[http:// |
+ | |||
+ | 2. Statistically associate phenotypic variants from GWAs studies impacting | ||
+ | The ideal candidate should have an interest in machine/ | ||
+ | The project is embedded in the [[https:// | ||
- | === DNA sequence analysis to investigate why prevalence of adverse effects to ACE inhibitor medication differs across ancestries === | ||
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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, | ||
+ | === Decoding Transcription Factor Dosage Effects on Cell State Transitions with DoseH-Seq === | ||
+ | Contact info: Dr Christian Nefzger (c.nefzger@imb.uq.edu.au), | ||
+ | |||
+ | Cell identity is controlled by different combinations of transcription factors (TFs) that bind to genomic regulatory elements to regulate gene expression. TF activity is not binary in most instances but graded and in response to TF dosage levels (e.g., Naqvi et al., Nat Genet., 2023, PMID: 37024583). For this reason, TFs are strongly enriched for haploinsufficient disease associations (Seidman et al, 2002, J. Clin. Invest. PMID: 11854316; Van de Lee et al., 2020, Trends Genet., PMID: 32451166) and TF dosage and stoichiometry strongly affects reprogramming outcomes (e.g., Polo et al, 2012, Cell, PMID: 32939092; An et al., 2019, Cell Reports, PMID: 31722212). Furthermore, | ||
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+ | Single-cell RNA+ATAC-seq is a uniquely powerful assay to measure the impact of TF levels on cell regulatory architecture; | ||
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+ | 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. | ||
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+ | === Trans-ancestry conditional analyses of genome-wide association studies === | ||
+ | |||
+ | Contact: Dr Loic Yengo (l.yengo@imb.uq.edu.au) | ||
+ | |||
+ | 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, | ||
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+ | 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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=== Comparing algorithms to estimate polygenic effects === | === Comparing algorithms to estimate polygenic effects === | ||
- | Contact info: Ben Hayes (b.hayes@uq.edu.au), | + | Contact info: Ben Hayes (b.hayes@uq.edu.au), |
With the advent of new genomic technologies comes the need to develop new statistical and computational algorithms that can handle large amounts of data in Animal Science. Within the Bayesian paradigm, current methods to estimate polygenic effects for complex traits rely mostly on Gibbs sampling. These approaches are not necessarily scalable to big datasets as the computation time grows more than linearly with sample size. This means that huge computational resources, in terms of RAM memory and/or computing time, need to be used to fit such models. The aim of this project is to compare the performance of alternative Markov chain Monte Carlo (MCMC) algorithms when estimating polygenic effects for complex traits in tropically adapted beef cattle. In addition to Gibbs sampling, at least two MCMC algorithms will be compared: Hamiltonian Monte Carlo and Variational Inference. The student will also learn the basics of Bayesian Statistics and High Performance Computing at UQ. | With the advent of new genomic technologies comes the need to develop new statistical and computational algorithms that can handle large amounts of data in Animal Science. Within the Bayesian paradigm, current methods to estimate polygenic effects for complex traits rely mostly on Gibbs sampling. These approaches are not necessarily scalable to big datasets as the computation time grows more than linearly with sample size. This means that huge computational resources, in terms of RAM memory and/or computing time, need to be used to fit such models. The aim of this project is to compare the performance of alternative Markov chain Monte Carlo (MCMC) algorithms when estimating polygenic effects for complex traits in tropically adapted beef cattle. In addition to Gibbs sampling, at least two MCMC algorithms will be compared: Hamiltonian Monte Carlo and Variational Inference. The student will also learn the basics of Bayesian Statistics and High Performance Computing at UQ. | ||
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The project as available on an ongoing basis for honours or Masters of Bioinformatics students, full time. | The project as available on an ongoing basis for honours or Masters of Bioinformatics students, full time. | ||
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+ | === Develop tools to impute methylation sites from low-coverage sequencing === | ||
+ | Contact info: Loan Nguyen (t.nguyen3@uq.edu.au) | ||
+ | |||
+ | In humans, the methylation state of CpG sites changes with age and can therefore be utilized as an accurate biomarker for aging. In cattle, biological age prediction based on methylation status could provide key information for genetic improvement programs. Additionally, | ||
+ | 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, | ||
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+ | Available for semester 1, 2 and summer | ||
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+ | === Differential methylated regions related to puberty in Brahman cattle === | ||
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+ | 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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+ | This project is designed for students who are studying for Masters of Molecular Biology, Masters of Biotechnology, | ||
+ | |||
+ | Available for semester 1, 2 and summer | ||
=== 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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- | === Shared genetics and functional mechanisms underlying female reproductive disorders and related diseases === | ||
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- | The human endometrium plays a vital role in female fertility, embryo implantation, | ||
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- | Available all year, for Master of Bioinformatics students; suitable for one or two semesters, full-time. | ||
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- | === Integrating omic datasets to interrogate endometriosis risk regions === | ||
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- | Human endometrium is a highly specialised and complex tissue that plays vital role in female fertility, embryo implantation and pregnancy. Endometriosis is a disease occurring in 7-10% of women whereby tissue similar to that of the endometrium grows outside the uterus. Large scale genetic studies have identified 14 genomic regions associated with endometriosis. Recent investigations have also found associations between genetic variants and gene expression and methylation in the endometrium. The aim of this project is to integrate genetic, expression and methylation data with transcript level data from >300 endometrial samples to investigate genetic and epigenetic mechanisms regulating genes in endometriosis risk regions. Data from publically available databases such as ENCODE and Roadmap will also be downloaded to functionally annotate regions. Mapping out regulatory mechanisms in these risk regions will help prioritise target genes for functional analysis. Students with a basic background in statistics and/or bioinformatics are encouraged to apply. | ||
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- | Available all year, for Master of Bioinformatics students; suitable for one or two semesters, full-time. | ||
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- | === Bioinformatics analysis/ transcriptomics of chemokines from Barramundi during bacterial infection === | ||
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- | Contact: Stuart Kellie (SCMB), s.kellie@uq.edu.au; | ||
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- | === Finding new phages in the genomes of gut bacteria | ||
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- | Contact: Rosalind Gilbert, ros.gilbert@daf.qld.gov.au, | ||
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- | Viruses infecting bacteria (bacteriophages or phages) are highly abundant in microbial ecosystems such as those found in the gut. This project will involve finding and annotating novel bacteriophages present as prophage elements within the genome sequences of gut-associated bacteria, for example, those infecting the genera often found in ruminant livestock (for example, Ruminococcus, | ||
- | Available all year, for Master of Bioinformatics or Honours students; suitable for 2 unit (one day per week, 1 semester), 4 unit (2 to 3 days per week, 1 semester) projects | ||
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- | === Analysis of candidate genes for motor neuron disease === | ||
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- | Contact: Dr Marie Mangelsdorf, | ||
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- | Motor neuron disease (MND) is a late onset neurodegenerative disease in which the motor neurons that control muscle movement die, leading to paralysis and death usually within 3 years of diagnosis. There is no treatment. MND may be both familial or sporadic. More than 20 genes for MND have been identified largely through analysis of familial cases and for most genes, sporadic cases have also been shown to harbour mutations in the same genes. Currently mutations in these genes account for ~60% of familial cases, and 10% of sporadic cases. We are undertaking multi-faceted genomics approach of sporadic cases in order to uncover the genetic causes of sporadic MND. One aspect of this study is whole exome sequencing (WES) of sporadic cases. The Honours project will validate and investigate sequence variants in candidate genes identified by WES. Techniques used to determine pathogenicity of the variants will include polymerase chain reaction, Sanger sequencing, molecular cloning, tissue culture and microscopy. | ||
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- | Available all year, for Master of Bioinformatics students; suitable for one semester, full-time. | ||
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- | === Bioinformatics analysis to characterise species of gnathiid isopods === | ||
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- | Contact: Jess Morgan, Jessica.morgan@uq.edu.au, | ||
- | I am looking for a motivated student to help me investigate applying bioinformatics techniques to characterise species of gnathiid isopods that parasitise fish. Specifically, | ||
- | Available for Master of Bioinformatics students; suitable for one or two semesters, full-time | ||
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- | === Identification and characterization of short open reading frames === | ||
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- | Contact: Joseph Rothnagel, j.rothnagel@uq.edu.au, | ||
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- | Short peptides (sPEPs) that are encoded by short Open Reading Frames (sORFs) are surprisingly common in eukaryote genomes. Recent bioinformatic and ribosomal footprinting studies have identified several thousand sORFs with coding potential and several sPEPs have been identified by mass spectrometry. | ||
- | Available all year, for Master of Bioinformatics students; suitable for one semester, full-time. | ||
=== Reconstruction of ancestral proteins === | === Reconstruction of ancestral proteins === |
open_projects.1705273923.txt.gz · Last modified: 2024/01/15 10:12 by project