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--- open_projects [2025/07/23 15:52] – add Michael Hall's project project
+++ open_projects [2025/10/17 17:30] (current) – project
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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 ===
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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.
-=== 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 ===