education

Below you will find descriptions of projects that bioinformatics students may do in fulfilment of project courses in their respective degrees. Please contact the person listed for further detail.

If you are a potential supervisor, click here

Chronic virus infections are a major ongoing global problem that establish lifelong disease due to the development of persistent refractory infections and the lack of long-term effective antiviral therapies. Cytotoxic CD8+ T cells play a key role in destroying virus-infected cells and in they are required to generate immunological memory which provides long-term protective immunity. The transcriptional fate map of cells generated under conditions of chronic infection and how ‘stem cell-like’ features are encoded are not well understood. In this project, we aim to unravel the key transcription factors and molecular circuits that define the long-term generation and maintenance of stem-cell like memory responses during such infections. This study will use and develop computational and statistical tools to model the transcriptional landscape and models of T cell differentiation generating memory heterogeneity and stem-cell properties. The project will include implementing protocols for quality control analysis, normalization, and clustering, analysing gene networks underlying cell subpopulations, identifying key genetic regulators of cell states. We will explore how key factors can be manipulated to improve T cell immunotherapy for infections and cancer.

Contact: Gabrielle Belz g.belz@uq.edu.au

Lung protection depends in early defence mechanisms orchestrated by responses by stromal cells and innate immune cells. The differentiation, heterogeneity and functional regulation of these cells is not yet understood. Understanding these processes is crucial to map the temporal development of lung protective responses and pinpoint key check points that dictate reversible and irreversible stages of disease. Uncovering these features will maximize the capacity to uncover translational pathways for cell therapeutics and drug discovery. In this project, you will use and develop computational and statistical tools to study the transcriptional landscape of lung cell fibrosis and inflammation at bulk and single cell resolution. The project will include implementing protocols for quality control analysis, normalization, and clustering, analysing gene networks underlying cell subpopulations, identifying key genetic regulators of cell states, and helping develop novel strategies for studying and analysing single cell RNA-sequencing data to study biological questions.

Contact: Gabrielle Belz g.belz@uq.edu.au

This MRFF-funded project aims to study spatial transcriptomics and proteomics in challenging paediatric cancers, specifically sarcoma and neuroblastoma, which are a clinical challenge where new therapies haven’t been offered since the last 3 decades. We are particularly interested in the immunoevasive strategies that cancer cells use to highjack the anti-cancer immunity. By analysing the molecular landscape and spatial organization of tumour cells within the microenvironment, we aim to identify biomarkers, therapeutic targets, and understand treatment resistance. The outcomes could revolutionize diagnosis, treatment, and prognosis for these devastating paediatric cancers. Objective: Investigate spatial transcriptomics and proteomics in sarcoma and neuroblastoma to identify biomarkers, immune-suppression, therapeutic targets, and understand treatment resistance. Methodology: (1) Use spatially resolved RNA sequencing to capture the transcriptomic landscape and identify distinct cell populations. (2) Analyse protein expression patterns and spatial distribution within tumour tissues using mass spectrometry-based proteomics. (3) Integrate data with clinical, histopathological, and genomic information to identify molecular subtypes and potential therapeutic opportunities. Expected Outcomes: • Discover novel molecular markers and spatially distinct cell populations. • Enhance understanding of the tumour microenvironment and treatment response. • Identify potential therapeutic targets for personalized treatment strategies. Impact: This project has the potential to revolutionize diagnosis, treatment, and prognosis in hard-to-cure paediatric cancers. By unravelling spatial complexities, it aims to develop targeted therapies, improve patient stratification, and achieve better clinical outcomes. Collaborators: Dr. Arutha Kulasingue, Dr. Ahmed Mehdi & A/Prof. Fernando Guimaraes

Contact: f.guimaraes@uq.edu.au

This project aims to investigate the immune responses in bacterial pneumonia, influenza, COVID-19, and syncytial virus using spatial transcriptomics. By analysing existing sequencing data from these infections, we will explore the spatial organization of immune cells and gene expression profiles within lung tissue. The research aims to uncover unique immune signatures, molecular interactions, and potential therapeutic targets, improving our understanding and management of these respiratory infections. Objective: Analyse spatial transcriptomics data to understand and compare immune responses in bacterial pneumonia, influenza, COVID-19, and syncytial virus. Methodology: Apply spatial transcriptomics techniques to study immune cell organization and gene expression patterns within lung tissue; Conduct comparative analysis to identify distinct immune signatures and pathways across the infections; Investigate molecular interactions and signalling pathways associated with immune responses. Expected Outcomes: • Comparative understanding of immune responses in respiratory infections. • Identification of unique immune signatures and potential therapeutic targets. • Insights into molecular interactions and signalling pathways involved in the immune response. Impact: This research project advances our understanding of immune responses in respiratory infections, potentially leading to improved diagnostics, therapies, and management strategies for bacterial pneumonia, influenza, COVID-19, and syncytial virus. Collaborators: Dr. Arutha Kulasingue, Dr. Ahmed Mehdi & A/Prof. Fernando Guimaraes

Contact: f.guimaraes@uq.edu.au

A cancer diagnosis at any age is upsetting, but felt more harshly when the patient is a young child who has only started out in life. Compared to adult cancer patients, the window of opportunity to help child cancer patients is especially short. We need to create an early warning system for paediatric cancers. Specialized immune cells known as T-cells and B-cells use specific receptors to recognize tumour antigens and fight cancerous cells. My lab's vision is to harness these cells and their receptors to enable early cancer detection and disease monitoring. These specific adaptive immune receptors are essential for all aspects of the T- and B-cell’s life cycle, serving as natural ‘time-keepers’ of the immune response against cancer progression. We will create bespoke computational algorithms to explore the properties that define how effective these immune cells are in childhood cancer, perform high resolution gene expression profiling at the single-cell level and develop highly advanced computer models that can be used to detect adaptive immune receptors that are targeted towards cancer. The projects will be largely dry-lab based and the candidates should expect to be working as part of a team together with leading groups in Australia as well as international collaborative networks (Cambridge, Sanger, UK).

The projects will suit either an immunologist wanting to learn bioinformatics and/or a computer scientist who wants to apply their skills onto biological problems. An ideal candidate would have a background in immunology, computer science, and/or bioinformatics. Basic understanding of statistical methods and machine learning experience working with python/R is highly desirable.

Contact: Kelvin (Zewen) Tuong z.tuong@uq.edu.au

This work is the Australian contribution to an international project to resolve the tree-of-life called the Tree-of-Life Web Project (http://tolweb.org). Students will use common bioinformatics tools to mine our Illumina nucleotide sequence data & the Sequence Read Archive for mitochondrial genomes & ribosomal RNA gene-clusters. Then students will use Geneious to manipulate sequences, & other programs to predict (“reconstruct”) the evolutionary tree of the ticks. Virtual time machines! Have a look at the You Tube Channel of my Tick Mitochondrial Genome Network: https://www.youtube.com/channel/UCnBhfhYxjC4rsJmVpBwHT0g/featured

Mine with the tools of Bioinformaticsour Illumina nucleotide sequence data & the Sequence Read Archive for new and interesting genes in ticks that are associated with disease in humans our domestics animals, particularly dogs, and wildlife.

Contact info: Professor Stephen Barker SCMB s.barker@uq.edu.au

This project is designed for students who are studying for Masters of Molecular Biology, Masters of Biotechnology, & Masters of Bioinformatics (BIOX700x).

Available for semester 1, 2 and summer

Ticks are the “pests of our times” in that the current generation of Australians & indeed people all over the world are more concerned about the diseases associated than with ticks that with any other infectious disease. This is understandable since we know so little about how the climate influences why ticks live where they live (geographic-risk) & why ticks are abundant in some years yet rare in other years (variation in risk among years). Students will use computer models to predict how the climate influences why ticks live where they live (geographic-risk) & why ticks are abundant in some years yet rare in other years (variation in risk among years); and thus the potential changes to the geographic distributions & amount of disease caused by ticks, under a range of climate-change scenarios.

Contact info: Professor Stephen Barker SCMB s.barker@uq.edu.au

This project is designed for students who are studying for Masters of Molecular Biology, Masters of Biotechnology, & Masters of Bioinformatics (BIOX700x).

Available for semester 1, 2 and summer

Contact info: Dr Christian Nefzger (c.nefzger@imb.uq.edu.au) and Marina Naval-Sanchez (m.navalsanchez@imb.uq.edu.au)

On a cellular level, ageing appears to be a largely epigenetic phenomenon. To uncover transcription factors (TFs) and chromatin state changes that drive ageing in different cell types, we have generated a molecular atlas (RNAseq, ATACseq) comprised of dozens of mammalian cell types from both young and aged subjects. By pinpointing and analysing age-related changes to the TF network the project will reveal if there are TFs or TF families that drive ageing across different cell types or if ageing is a largely cell type specific process. The project will leverage bulk data and entail computational techniques related to quantification of TF activity levels. Integrative network analyses between transcriptional and chromatin state data will also be performed. The project aims to improve our understanding about the ageing process to ultimately find new ways that make aged cells work more efficiently.

The project will be well supervised. The students do not need to be directly familiar with the analysis procedures for this project, but the 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. Students placed overseas who want to conduct a project remotely are welcome). We also consider Phd students.

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, these associations are tested using a simple linear regression (i.e. one at a time) framework, which cannot distinguish associations from correlated variants. To solve that issue, conditional and joint (COJO) analyses leverage the correlation structure between polymorphisms to identify subsets of variants that are jointly associated with the trait of interest. Current implementations of COJO algorithms can be applied to GWAS performed in individuals of a single ancestry, where the correlation structure between variants is constant; but they cannot yet handle meta-analyses of GWAS from diverse ancestries (e.g. East-Asian, European).

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.

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++.

Contact: Dr Sonia Shah (s.shah1@uq.edu.au)

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.

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.

The ideal candidate will have knowledge and experience in bioinformatics, particularly DNA and protein sequence analysis and analysis of next generation sequence data. Though not necessary, experience with tools such as NCBI BLAST, samtools, vcftools, other sequence analysis packages in R will be advantageous.

Contact: Professor Stephen Barker, SCMB, UQ (s.barker@uq.edu.au); Dr Renfu Shao University of the Sunshine Coast, Qld (rshao@usc.edu.au)

Join the International Tick Tree of Life Project. Predict the evolutionary history (phylogeny) of ticks from mitochondrial and nuclear genomes. Students will use common bioinformatics tools to mine the Sequence Read Archive for mitochondrial genomes and rRNA gene clusters. Then the students will use Geneious to manipulate sequences, and other programs to predict (“reconstruct”) the evolutionary tree of the ticks. Virtual time machines!

Available all year for Master of Bioinformatics students; suitable for one or two semesters, full-time.

Contact info: Ulrike Kappler (u.kappler@uq.edu.au) and Birgitta Ebert (birgitta.ebert@uq.edu.au)

Haemophilus influenzae is a human respiratory pathogen that causes both acute respiratory tract diseases and increases the severity of chronic diseases such a bronchiectasis, chronic obstructive pulmonary disease and cystic fibrosis. Virulence of this bacterium depends strongly on its metabolic processes, and in this project we aim to produce a metabolic model for this bacterium that will allow us to investigate the effect of changes in flux through specific pathways on pathogen fitness.

Suitable for bioinformatics project or Masters students.

Contact info: Ben Hayes (b.hayes@uq.edu.au), for further information contact Elizabeth Ross (e.ross@uq.edu.au) and Loan Nguyen (t.nguyen3@uq.edu.au)

Genotyping by sequencing is a genetic screening method for characterising both novel and known single nucleotide polymorphisms (SNPs) and performing genotyping studies. Until recently short read sequence data was favoured for novel SNP discovery, however recent advances in portable long read sequencers may change this. Oxford Nanopore sequencing Technologies (ONT) offers a range of rapid real-time sequencing platforms, including sequencers that can fit in the palm of your hand. With the significant increase in read length as well as the absence of certain biases, Oxford Nanopore sequencing has become a popular option for genome characterisation. This project will implement the use of nanopore sequencing for SNP genotyping, structural variant identification, and calling methylation. Students will develop a deep understanding of cutting edge sequencing and genotyping methods, bioinformatics skills, project design, scientific communication and data management.

The project as available on an ongoing basis for honours or Masters of Bioinformatics students, full time. PhD projects are also available in this area.

Contact info: Ben Hayes (b.hayes@uq.edu.au), for further information contact Elizabeth Ross (e.ross@uq.edu.au) and Loan Nguyen (t.nguyen3@uq.edu.au)

New technology now allows the sequencing to hundreds of thousands of full length transcripts (expressed genes) from a single sample. A dataset of 10 tissues has been generated using Iso-seq - a method that can sequence the full length expressed isoforms in a sample. This project will analyse that Iso-seq data and identify novel isoforms including those for genes that are known to be of industry importance. This information will provide a deeper understanding of the genetic variation in Australian beef cattle and be used to inform large genome wide association studies and discovery of mutations controlling gene expression. The project focuses on bioinformatics and analysis skills in a fast developing area of research.

The project as available on an ongoing basis for honours or Masters of Bioinformatics students, full time.

Contact info: Ben Hayes (b.hayes@uq.edu.au), for further information contact Elizabeth Ross (e.ross@uq.edu.au)

Ruminants such as cattle are host to a vast array of microbial species which reside in a specialised chamber of their stomach called the rumen. Microbes in the rumen digest the feed which the animals eat. Cutting edge sequencing technologies now allow for accurate profiling of microbiome communities. This study will analyse the microbes that live inside the rumen of cattle fed a methane mitigating diet. Methane is a potent greenhouse gas that is produced as a by-product of ruminant digestion. The goal of this study is to utilize high throughput sequencing to identify the species of microbe that have a changed abundance in response to the diet and therefore increase our understanding of how the rumen microbiome can be manipulated to reduce methane emissions from ruminants.

The project as available on an ongoing basis for honours or Masters of Bioinformatics students, full time. PhD projects are also available in this area.

Contact info: Ben Hayes (b.hayes@uq.edu.au), for further information contact Roy Costilla (r.costilla@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.

The project as available on an ongoing basis for honours or Masters of Bioinformatics students, full time.

Contact info: Ben Hayes (b.hayes@uq.edu.au), for further information contact Bailey Engle (b.engle@uq.edu.au)

Fertility is an important but complex genetic trait influenced by a large number of genetic and environmental factors. In beef cattle, this combination of variables affects the pregnancy potential of a cow. The goal of this project is to assess genetic variation leading to different pregnancy outcomes in Brahman heifers. Students will use high density SNP genotypes and RNA-seq data to identify genes and genomic regions influencing positive pregnancy status in a very large genomic and phenotypic data set (up to 30,000 cattle). This project will develop skills in bioinformatics and data analysis, management of extremely large biological datasets, and applied quantitative genetics to understand and improve cow fertility.

The project as available on an ongoing basis for honours or Masters of Bioinformatics students, full time.

Contact: Dr Dimitri Perrin (dimitri.perrin@qut.edu.au) and Prof. Ernst Wolvetang (AIBN/UQ)

CRISPR-Cas9 revolutionised the field of gene editing and opened up new applications in health, agriculture, environment, etc. The default approach is to rely on a guide RNA (gRNA) engineered to bind at a specific location of interest in the genome, and on Cas9 to ‘follow’ this guide and make a cut at that position. A more recent approach is to use ‘base editors’ that combine enzymes modifying DNA with inactive Cas9 variants. This allows base changes without cut, DNA repair mechanism or donor template. This has a huge potential, but the performance of base editors is still incompletely characterised.

In this project, you will develop a computational method to design optimal gRNAs for base editors, and validate these results in the context of human induced pluripotent stem cells. You will investigate the efficiency of the guide, as well as characterise the off-target effects and discover determinants that influence the targeting specificity of gRNA. By doing so, the objective is to develop an optimised gRNA selection and design platform that can be used to accelerate human stem cell research.

Contact: Dr Sandie Degnan (s.degnan@uq.edu.au)

Aquaporins are pore-forming membrane channels that play critical roles in controlling the water contents of cells in all kingdoms of life. Their discovery in 1992 led to the 2003 Nobel Prize in Chemistry. Aquaporins are remarkable for permitting the transport of water, and sometimes other small solutes, across biological membranes, while at the same time being completely impermeable to charged species. Both their physiological functions and the physiological relevance of their selectivity to unconventional permeants are poorly understood, not least because of the paucity of data from non-model organisms living in diverse environments with respect to water availability or osmolality. This project will involve bioinformatic characterisation, and phylogenetic analysis, of aquaporins from marine animals representing phyletic lineages at the base of the animal tree, including sponges, ctenophores, cnidarians and placozoans. There will be opportunity to explore the possibility of acquisition by animals of aquaporins from bacteria, as has been demonstrated already in plants, and thus to help clarify fundamental issues about the evolutionary origin and diversification of animal aquaporins.

Available all year, for Master of Bioinformatics students; suitable for one semester, full-time.

Contact info: Nathan Palpant (n.palpant@uq.edu.au)

Project description: Analysing the transcriptional landscape of cardiovascular differentiation from stem cells at single cell resolution. Stem cells provide a mechanism for generating all cell types of the body. Understanding the mechanisms by which stem cells differentiation into these diverse cell types is central to utilizing them for understanding developmental biology and maximizing their translational potential for cell therapeutics and drug discovery. In this project, you will make use of and develop computational and statistical tools to study the transcriptional landscape of cardiovascular differentiation at single cell resolution. The project will include implementing protocols for quality control analysis, normalization, and clustering, analysing gene networks underlying cell subpopulations, identifying key genetic regulators of cell states, and helping develop novel strategies for studying and analysing single cell RNA-sequencing data to study biological questions.

Availability, requirements, etc: The project as available on an ongoing basis for honours or masters of Bioinformatics students, full time.

Contact: Mikael Bodén (m.boden@uq.edu.au)

Biological data at greater scale give statistical power to distinguish meaningful signals from mere noise or artefacts, i.e. to identify "drivers" and "determinants" of function and structure.

The application of machine learning is far-reaching: my group is involved in several collaborative projects ranging from mapping regulatory events during development, identification of cancer/disease drivers, and engineering of proteins for medical and industrial applications. With emerging technologies come new data types; there are challenges with combining them to compose an accurate representation of complex biology. If you have interest on using and/or developing methods applicable to such problems, there is scope to articulate specific projects.

Availability all year, for bioinformatics students with problem-solving skills, Honours or Masters.

Contact: Elizabeth Gillam (e.gillam@uq.edu.au), Yosephine Gumulya (y.gumulya@uq.edu.au), or Mikael Boden (m.boden@uq.edu.au). We are looking for a student interested in sequence analysis for purposes of reconstructing ancestral proteins, potentially to have existed many millions years ago. This work can be focused on the application of current methodologies to generate candidate ancestral sequences, and/or the development of computational methodologies to support the process of generating candidate ancestors or ancestral components. The former angle is short term (a #2 course or part of a longer project course) and is suited to candidates with an interest in sequence analysis for understanding evolution of protein function and protein engineering. The latter angle is longer term (semester long) and suits candidates with strong computational skills.

Available all year, for Master of Bioinformatics students; suitable for one semester (#2 – #8).

Contact: Richard J. Lewis (r.lewis@uq.edu.au) The one-year project would analyse the transcriptome from the venom duct of a cone snail species using advanced bioinformatic tools and prepare table and figures to visualise the data. If the study goes well the work by the student should end up as part of a publication. The following articles provide background to the project.

Dutertre S, Jin A-H, Vetter I, Hamilton B, Sunagar K, Lavergne V, Dutertre V, Fry BG, Antunes A, Venter DJ, Alewood PF, Lewis RJ (2014) Evolution of separate predation- and defence-evoked venoms in carnivorous cone snails. Nature Communications 5:3521.

Dutertre S, Jin AH, Kaas Q, Jones A, Alewood PF, Lewis RJ (2013) Deep venomics reveals the mechanism for expanded peptide diversity in cone snail venom. Mol Cell Proteomics 12:312-329.

Contact: Dr Stefano Meliga (s.meliga@uq.edu.au)

The Nanopatch is a high density microprojection array for epidermal and dermal delivery of vaccines. Application to mouse skin has resulted into immunogenicity comparable with intramuscular injection using less than 1/100 of the dose. However, the mechanisms underlying this low-dose potent response have not yet been fully understood. Experimental evidence suggests that enhanced immunogenicity is triggered by precise targeting of vaccine to skin antigen-presenting cells in conjunction with generation of controlled levels of cell damage. We offer student projects aiming at the numerical investigation of Nanopatch-mediated vaccine targeting, generation of inflammation and triggering of signalling pathways leading to immune response. The successful candidate will be developing and applying mathematical / statistical models to verify the mode-of-action hypotheses, and have the chance to drive the design of the next-generation delivery device. This project is computer-centred and will be taking place at AIBN. Requirements: Applied mathematical and statistical skills, programming ability (e.g. MATLAB, R), interest in working in a highly multidisciplinary team is a plus, knowledge of immunology is not mandatory.

Available all year.

Contact: Dr Cheong Xin Chan (c.chan1@uq.edu.au)

Symbiodiniaceae are a specialised group of dinoflagellate algae that grow symbiotically with diverse coral reef animals including corals and sponges. A modest, episodic increase in ocean water temperature can break down the coral-dinoflagellate symbiotic association (thus cause coral bleaching); unless this symbiosis is soon re-established, corals are at risk for starvation, disease and death. Our group is interested in the genome evolution of Symbiodiniaceae and their closely related species, specifically related to their evolutionary transition from free-living to symbiotic lifestyles, and its functional implications for the coral host and the health of coral reefs in light of global climate change. This project aims to discover genes and functions in these algae that are specific and/or relevant to environmental adaptation using a comparative genomic approach, using newly sequenced and existing data (Illumina, PacBio and Nanopore). Using native direct RNA sequencing, we are also investigating the impact of mRNA modification (i.e. epitranscriptome) on genome evolution of these ecologically important species.

This project is strictly computational based. The researcher will acquire skills in genomics and bioinformatics of non-model systems, specifically in the analysis of high-throughput sequencing data, comparative analysis of large-genome-scale data, functional annotation of genome sequences, and/or the use of machine learning in sequence analysis. The researcher will work as part of a team, and is expected to produce a report or oral presentation at the end of their project. Research outcomes may be included in a scholarly publication.

This project is available on an on-going basis, and is suitable for Masters students or advanced undergraduate students (year 3+) with a strong background in life sciences (i.e. biology and related subject areas), mathematics, and/or computer sciences. A background in genomics and/or bioinformatics is desirable but not essential. This project will require scripting (e.g. Python), high-performance computing in the UNIX environment, and/or R.

Contact: Dr Shiv Nagaraj (shiv.nagaraj@qut.edu.au)

Life expectancy of Aboriginal and Torres Strait Islander Australians is much lower than other Australians, in part due to an apparent genetic predisposition to chronic diseases. Better understanding of this genetic contribution has the potential to improve early detection and target prevention strategies. This project will use whole genome sequencing (WGS) to define the genetic architecture of the Indigenous Australians and its association to serious chronic diseases, helping to develop a precision medicine approach that will enable accurate diagnosis and inform targeted treatments. This project builds on the most comprehensive chronic disease profiling performed in any Indigenous community, the longest follow-up, treatment and prevention trials, and documentation of endpoints. The PhD research work could ultimately lead to development of tests for early detection of chronic disease, to protocols of personalised management, and ultimately, better health outcomes for Indigenous Australians.

Project Genomic architecture of chronic disease in Australia’s First Peoples

The overall goal of this project is to understand the landscape of Indigenous genomes and define its architecture using nearly 500 whole genomes from Australia’s First Peoples. The student will be trained in analysis if next-generation sequence datasets including copy number variation and analysis of variants using ACMG guidelines. The project will create a global variant map and study the association of variants to chronic diseases. Functional validation experimental analysis of the effects of protein variants identified may be undertaken through collaboration with experimental biologists.

This project is aligned with the QUT Annual Scholarship Round. When applying for admission to the PhD the applicant will be considered in the scholarship round provided that the application is submitted by the 20 September 2020.

If successful in the Annual Scholarship Round the candidate will receive a tax-exempt living allowance stipend of $28,092 per annum (indexed annually) for three years. International applicants successful in the Annual Scholarship Round will also receive a full Tuition Fee Sponsorship. There is also the opportunity for a $5000.00 per annum top-up scholarship for applicants of exceptional academic merit.

Application criteria:

Our ideal candidates hold an MSc or equivalent in a relevant discipline (e.g. Bioinformatics, Statistics, Computer Science, Data Science, Genomics etc) with strong analytical and programming skills. Excellent oral and written communication skills, motivation and the ability to work as part of a team is also required.

Aboriginal and Torres Strait Islander students are strongly encouraged to apply

Dr Qiongyi Zhao (q.zhao@uq.edu.au)

Extrachromosomal circular DNA (eccDNA) had previously been observed across different species of organisms from yeast to human. Circular DNA enrichment sequencing (CIDER-Seq) is a technique to enrich and accurately sequence circular DNA without the need for polymerase chain reaction amplification, cloning, and computational sequence assembly. In this study, we performed CIDER-Seq and generated the long-read (PacBio) sequencing data using post-mitotic mice neurons. Combined with the Illumina data that was generated previously, this project will require the candidate student to analyse both long-read (PacBio) and short-read (Illumina) sequencing data, to identify and characterize full-length eccDNAs in post-mitotic neurons.

The ideal candidate will have knowledge and experience in bioinformatics and analysis of next-generation sequencing data. The off-shore candidate needs to be able to access the UQ computing cluster or the local computing server to process the sequencing data.

Contact: Dr Kai Feng (k.feng@uq.edu.au) and prof Barry Dickson (b.dickson@uq.edu.au)

Understanding the logic of biological neural networks will not only shed light on how behaviours are generated by the nervous system but also inspire the design of more agile robots and more efficient artificial neural network architectures. Reconstruction of neural circuits using electron microscope has reached unprecedented large scale in the last few years, such that the connectome (i.e. the synaptic connections between all neurons) has now been determined for almost the entire brain and nerve cord of the fruit fly Drosophila (further reading: google “hemibrain”). This connectome consists of >10m synapses between >200000 neurons. Analysing the structure of this connectome will help to uncover the algorithms used by the brain to perform different kinds of computations. Connectomes can be analysed using methods similar to those developed for other biological networks, such as genomes and proteomes, which also seek to uncover motifs within a network of interacting components. We are specifically interested in the network motifs that generate the fly's motor patterns. Insights from these computational studies will create testable hypotheses to guide our experimental studies.

The project is suitable for one or two semesters, full time. International students working remotely from overseas are welcome. The ideal candidate should have interests in neuroscience and good skills in programming using Python or R. PhD scholarships in related areas are also available.

Contact: Dr Nic Waddell (nic.waddell@qimrberghofer.edu.au) and Dr Venkat Addala (Venkateswar.Addala@qimrberghofer.edu.au)

Next generation sequencing has allowed researchers to characterise the somatic landscape of cancer genomes, which has led to the discovery of biomarkers that may be predictive and prognostic to targeted therapies. However, the efficacy of current targeted therapies has failed to raise the overall survival curve in many tumour types. Immunotherapy has shown a promising benefit in treating many tumours and demonstrated remarkable responses in some patients even at recurrent, relapse and metastasis stage. The challenge now is to determine who and why some patients respond to treatment. Somatic mutations within the genomes of cancer cells may result in neoantigens that are presented on the tumour cell surface. These can then be seen by the immune system and killed by the patients immune system.

This project will test and develop bioinformatic approaches that can be applied to understand complex tumor-immune interactions. Specifically the project will use genome and RNAseq data to predict neoantigens and determine which of these be important in immunotherapy. The findings from this work are likely to shed new insight into tumour immunology and may predict which patients will respond to immunotherapy. This project requires basic knowledge of R or python.

The project is available throughout the year for students with bioinformatics knowledge (Honours or Masters). This project cannot be carried out remotely due to ethics restrictions on data sharing.