At the Australian Prostate Cancer Reseach 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 cells and xenograft models using short read RNA sequencing.
NextGen sequencing ia a fast progressing field and multiple approaches for sample preparation, processing and data analysis are being used, each with unique advantages and disadvantages. The proposed project at our centre encompases a comprehensive head-to-head comparison and biological interpretation of data derived from two fundamentally different approaches for RNA sequencing library preparation: polyA-enrichment and ribo-depletion. Insights obtained from this comparison will highlight the strengths and weaknesses of each approach, uncover biases that influence the biological interpretation of the results and inform the decision making process to determine the optimal approach for future experiments.
We will be analysing two pre-existing raw data sets derived from the same experimental setup, but processed using two different library preparation approaches. An established pipeline on the QUT High Performace Computer Cluster will be used to process the raw data through quality control, alignment and count estimation. This part of the analysis will provide basic insights into using a command line interface on a Linux system and the 'nextflow' workflow framework.
Downstream analyses will include, but are not limited to, differential gene expression and functional enrichment analyses, as well as data visualisation. These steps are heavily based on R scripting and the use of R packages as well as a variety of online tools.
This research will be carried out at the Translational Research Institute.
Research activities include:
- literature search and review
- data analysis
- presenting results at a group seminar.
Skills and experience
You will have a keen interest in:
- RNAseq data analysis
Prior skills in R or Python scripting and working in a Linux/HPC environment are beneficiaul but not mandatory.
Contact the supervisor for more information.