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 If you are a potential supervisor, [[supervisor_instructions:click here]]
+=== 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.
+=== De novo identification of insertion sequences with De Bruijn graphs ===
+Contacts: Leah Roberts l.roberts3@uq.edu.au, Tom Stanton tom.stanton@monash.edu
+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.
+Unfortunately, as IS are small and repetitive throughout the genome, they cause issues for short-read de novo assemblers, most notably collapsed repeats. Because of this, it can be difficult to determine the exact location of IS throughout the genome.
+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.
+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).
+=== Machine Learning to predict plasmids from bacterial isolates ===
+Contacts: Leah Roberts l.roberts3@uq.edu.au, Zamin Iqbal zi245@bath.ac.uk
+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.
+We have developed a plasmid network that allows users to predict the types of plasmids in their bacterial samples based on gene presence/absence. This project would build upon this work by creating a machine learning framework (Random Forest) that can predict plasmid presence from short-read bacteria.
+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).
+=== 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 ===