Study level

  • Master of Philosophy
  • Honours

Faculty/School

Topic status

In progress.

Research centre

Supervisors

Professor Colleen Nelson
Position
Professor
Division / Faculty
Faculty of Health
Dr Anja Rockstroh
Position
Postdoctoral Research Fellow
Division / Faculty
Faculty of Health

Overview

At the Australian Prostate Cancer Research Centre QLD, we are interested in the cellular adaptive response processes leading to therapy resistance in advanced prostate cancer.

A focus area of our research is studying the transcriptome changes in prostate cancer cell lines, xenograft models and patient samples using RNA sequencing technologies.

By integrating our large in-house repository of RNAseq data sets with publicly available studies, this project will further explore the cellular heterogeneity of prostate tumours and the plasticity of cancer cells in response to treatment.

This includes assessing the differential expression of annotated and novel, coding and non-coding genes and transcript variants, investigating alternative splicing events and the presence of fusion transcripts, imputing DNA alterations like mutations and copy number variations, as well as assessing the influence of the tumour microenvironment.

A more defined project proposal will be developed together with the candidate, based on their individual interests.

Research activities

The project will provide comprehensive skills for RNAseq data analysis of in-house generated data as well as public studies mined from large repositories and data bases.

The analysis pipeline will start at raw data quality control, data clean up, read alignment and count estimation, which require setting up and running scripts in a Linux High Performance Cluster environment.

This is followed by various downstream analysis pipelines that involve the use of R packages, Python libraries and purpose-built tool kits.

Outcomes

This project aims to obtain a better understanding of the dynamic molecular alterations that occur during treatment-induced adaptive processes and drive mechanisms of therapy resistance.

These insights will help to uncover novel vulnerabilities and avenues of intervention for the treatment of advanced prostate cancer.

Skills and experience

You should have:

  • a keen interest in computational biology (RNAseq data analysis and scripting is required)

Prior skills in R or Python scripting and working in a Linux/HPC environment are beneficial but not mandatory.

Keywords

Contact

Contact the supervisor for more information.