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 that has emerged recently. Currently, non-coding structural variation is ignored in clinical sequencing and in cohort studies. There are hardly any 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. Rare cancer cohort
  2. ICGC Pan-Prostate Cancer Genome project (>1200 patients with WGS and ~700 with RNAseq)
  3. TCGA
  4. Potentially other big somatic and germline datasets

The aim is to identify recurrent structural variants (SVs) that may impact gene expression (e.g. https://www.biorxiv.org/content/10.1101/2019.12.18.881086v1.full) and to develop machine learning approaches to predict the effect of non-coding SV mutations for use in cases lacking RNAseq data.

Many rare cancers have low tumour mutation burden with few (often one or no) known drivers. The role of SVs in these cancers could be highly important.

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About our research group

The Papenfuss Lab is a computational biology and bioinformatics research laboratory in the Bioinformatics Division at the Walter and Eliza Hall Institute of Medical Research. We apply mathematics, statistics and computational approaches to make sense of genomics data from human disease, especially related to the evolution of cancer.

 

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: