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 If you are a potential supervisor, [[supervisor_instructions:click here]]
+=== The Barrier Atlas: Cross-Tissue Insights into Homeostasis and Dysfunction ===
+Contact: Amanda Oliver (Amanda.Oliver@qimrb.edu.au)
+Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of human tissue biology, revealing cellular diversity across organs and disease states. Building on existing datasets profiling millions of cells, this project aims to construct a unified single-cell atlas of barrier tissues, including the lung and gut, to uncover shared and tissue-specific mechanisms that maintain immune balance at the body’s environmental interfaces. The student will develop and apply computational pipelines for large-scale data integration, quality control, cell type annotation, and spatial and microbial mapping across millions of cells and thousands of samples. Advanced methods such as gene regulatory network inference, deep learning, and foundation models will be used to explore cross-tissue immune regulation and barrier dysfunction. By combining single-cell, spatial, and microbiome data, the project will deliver the first cross-tissue atlas of barrier biology, providing new insights into diseases such as inflammatory bowel disease and chronic respiratory disorders.
+Suitable for Masters, or PhD students. Strong bioinformatics skills using Python or R are essential; experience with single-cell or spatial transcriptomics and knowledge of immunology or barrier tissue biology is highly desirable.
+=== The Escape of Human Genomic Data into Public Repositories ===
+Contact: Michael Hall (michael.hall1@uq.edu.au)
+Public sequencing repositories (e.g. SRA) are growing rapidly, but many studies involving human clinical samples may inadvertently include identifiable host DNA—even when ethics approvals explicitly prohibit this. This project investigates the extent and implications of such data leakage.
+Objectives:
+	•	Identify publicly available datasets from clinical pathogen/metagenomic sequencing studies
+	•	Quantify residual human genomic content using a variety of approaches and references
+	•	Benchmark human read detection approaches (e.g. host depletion vs k-mer-based methods)
+	•	Assess potential identifiability using forensic markers (e.g. Illumina Infinium SNPs, CODIS loci)
+	•	Explore the role of ethics language, technical variability, and population bias (e.g. African vs European genomes) in leakage rates
+Skills you’ll gain:
+	•	Handling and processing large sequencing datasets
+	•	Working knowledge of alignment and k-mer classification tools (e.g. minimap2, kraken) and human read detection pipelines
+	•	Experience in reproducible bioinformatics analysis and privacy-aware genomic research
+	•	Insight into the intersection of ethics, bioinformatics, and public data governance
+This project is ideal for students interested in clinical genomics, privacy, ethics, or data-driven policy impact. Familiarity with the command line is necessary. Knowledge of Python would be great, but not required—we can build those skills as you go!
+=== Decoding the relationships between DNA replication, genome architecture, chromatin organisation ===
+Contact: Dr Mathew Jones (mathew.jones@uq.edu.au)
+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.
+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? 2. How are these processes disrupted in cancer and impacted by cancer therapies. The project will assess the impact of genomic features on replication using nanopore sequencing data generated by the Jones lab’s and their artificial intelligence assay for assessing DNA replication in human cells (https://doi.org/10.1101/2022.09.22.509021) and publicly available Hi-C, Repli-Seq, CUT & RUN, ChIP-seq, scSeq, datasets (e.g., GEO, ENCODE).
+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.
+=== Pangenomes to predict bacterial transmission in healthcare settings ===
+Contacts: Leah Roberts l.roberts3@uq.edu.au, Michael Hall michael.hall2@unimelb.edu.au
+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, as the shared regions between the strains becomes a very small proportion of the total genomic content.
+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.
+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).
+=== Investigation of the effect of the circadian rhythm on the genetic control of gene expression ===
+Contact: Sonia shah <sonia.shah@imb.uq.edu.au>, Solal Chauquet <uqschauq@uq.edu.au >
+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.
+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.
+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 ===
+Contact info: Daniel Hwang <d.hwang@uq.edu.au>
+Project description: Human perception of taste and smell plays a key role in food preferences and choices. There is a large and growing body of work suggesting that taste and smell (together known as "chemosensory perception") determine eating behaviour and dietary intake, a primary risk factor of chronic conditions such as obesity, cardiometabolic disorders, and cancer. Evidence to date is largely based on observational studies that are susceptible to confounding and reverse causation, leaving the "causal effects" of chemosensory perception on food consumption unclear. If their relationship is truly causal, flavour modification may represent a tangible way of modifying food consumption in a way that benefits public health outcomes. This project aims to: (i) elucidate the genetic architecture underlying individual differences in taste and smell perception, (ii) use this information to assess their causal effects on eating behaviour, and (iii) create a sensory-food causal network mapping individual sensory qualities (i.e. sweet taste, bitter taste, and more) to individual food items.
+=== Increasing drug success rate in human clinical trials using genomics ===
+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.
+Project significance: Findings from this project could potentially identify new therapeutic applications for these compounds or unknown side effects, and ultimately informing future human clinical trials.
+Contact: Sonia Shah <sonia.shah@imb.uq.edu.au>
+Supervisors: You will be working with a multidisciplinary team of supervisors Prof Dave Evans, Dr Sonia Shah, Prof Glenn King, Assoc/Prof Nathan Palpant
+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), Dr Olga Kondrashova (Olga.Kondrashova@qimrberghofer.edu.au)
+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, where it ensures that stem cells divide only when needed, preserving regenerative capacity. However, quiescence is also adopted by cancer stem cells to evade chemo- and radiotherapies that preferentially kill fast-dividing cells. Single-cell data promises to uncover the molecular regulation of quiescent stem cells in health and disease but the identification of these cells within these datasets is either reliant on expert knowledge and manual curation or is currently impossible, due to a lack of marker genes.
+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, it is unknown whether such a model would generalise to cancerous tissue, where increased variability in transcriptomic states often degrades the distinction between cell types.
+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.
+=== Understanding sex-specific cardiovascular disease risk ===
+Contact info: Dr Sonia Shah (sonia.shah@imb.uq.edu.au), Dr Clara Jiang (j.jiang@uq.edu.au)
+Description: Cardiovascular diseases (CVD) account for 35% of female deaths globally (29% in Australia). However, CVDs remain under-studied, under-diagnosed and under-treated in women. This sex disparity is partly due to the lack of knowledge of female-specific risk factors. This project involves statistical analysis of large-scale health and genetic data to identify sex-specific CVD risk factors and underlying mechanisms.
+Requirements: A background in genetics and computational data analysis is preferable.
 === De-risking the drug development pipeline by finding biomarkers of drug action ===
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 The project is embedded in the [[https://imb.uq.edu.au/research-groups/nefzger|Nefzger lab with a major focus on “Cellular reprogramming and Ageing”]]. The applicant will be closely working with [[https://imb.uq.edu.au/profile/11267/marina-naval-sanchez|Dr. Naval-Sanchez]]  as the main supervisor.
+=== Decoding Transcription Factor Dosage Effects on Cell State Transitions with DoseH-Seq ===
+Contact info: Dr Christian Nefzger (c.nefzger@imb.uq.edu.au), Ralph Patrick (ralph.patrick@imb.uq.edu.au) and Marina Naval-Sanchez (m.navalsanchez@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, TF dosage effects may also underlie seemingly contradictory effects linked to overactivation of certain TFs in cancer contexts, including of the Nfi family (Becker-Santos, 2017, The Lancet Discovery Science, PMID: 28596133).
+Single-cell RNA+ATAC-seq is a uniquely powerful assay to measure the impact of TF levels on cell regulatory architecture; however, no tools currently exist to directly study TF dosage effects on temporal cell state transitions. To address these gaps, we developed Dosage and Hashtag sequencing (DoseH-seq), an expansion of the 10x Genomics single-nucleus (sn)RNA+ATAC-seq assay that enables sensitive detection of lentiviral perturbations (e.g., TFs) linked to a heterogeneously expressed promoter. In combination with sample hash tagging, multiple temporal, and dosage states, for theoretically any number of genes of interest, can be profiled. This allows detection of TF dosage-dependent effects on temporal cell state transitions, chromatin architecture, co-factor expression, and the rewiring of TF networks at high-resolution. Compatibility with BGI sequencing technology enables the generation of low-cost datasets.We demonstrate the utility of DoseH-seq by tracking the dosage effects of somatic transcription factor, Nfix, during reprogramming towards pluripotency. Contrary to the current dogma, we find that Nfi overexpression can act either as a reprogramming roadblock or as a reprogramming booster, depending on TF dosage and context. These insights may help resolve the TF’s paradoxical role in cancer. DoseH-seq represents a powerful tool for elucidating, and ultimately controlling, both desired and pathological cell state transitions.
+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.