Predicting the effect of non-coding structural variants in cancer

Predicting the effect of non-coding structural variants in cancer

Project details

This is an exciting new opportunity to use whole genome and transcriptome sequencing data from cancers to understand the role of non-coding rearrangements. Currently, non-coding structural variation is ignored in clinical sequencing and in cohort studies. There are virtually no tools to annotate or predict the effect of such mutations.  

This project will involve the exploratory analysis of several cohorts with matched whole genome and transcriptome sequencing: 

  1. Stafford Fox Rare Cancer cohort 
  2. ICGC Pan-Prostate Cancer Genome project (>1200 patients with WGS and ~700 with RNAseq) 
  3. TCGA 
  4. Other large-scale somatic and germline datasets 

The aim is to identify recurrent structural variants (SVs) that may impact gene expression (e.g. Rustad et al.) and to develop machine learning approaches to predict the effect of non-coding SV mutations for use in cases lacking RNAseq data. 

About our research group

The Papenfuss lab undertakes computational biology and bioinformatics research in the Bioinformatics division at WEHI.  

We develop and apply mathematical, statistical and computational approaches to make sense of different types of omics data from cancer and other diseases in order to drive discoveries. A key focus of the lab is using computational approaches to understanding how cancers are initiated and evolve as they progress and adapt to new environmental niches and in response to therapy.  

 

Email supervisors

 

Researchers:

Professor Tony Papenfuss

Tony Papenfuss
Professor
Tony
Papenfuss
Laboratory Head; Leader, Computational Biology Theme
Dr Justin Bedo
Dr
Justin
Bedo
Bioinformatics division

Project Type: