Thesis Topic Details

Topic ID:
3414
Title:
High Performance Computing of Gene Networks in Breast Cancer
Supervisor:
Warren Kaplan
Research Area:
Bioinformatics, Genome informatics, High-Performance-Computing
Associated Staff
Assessor:
Topic Details
Status:
Active
Type:
Research
Programs:
BINF
Group Suitable:
No
Industrial:
No
Pre-requisites:
A keen interest in bioinformatics and genomics is required for this project and a background in the same subjects would be desirable but not essential
Description:
High throughput genome sequencing has bought biologists a place at the table of Big Data and Extremely High Performance Computing. Where it took 10 years and cost almost $3 Billion to sequence the first Human Genome - we now can sequence a Human Genome in under 2 days for less than $5000. These dramatic performance and price changes have made extremely large genome sequencing projects by multinational consortia feasible. One of the largest of these projects is ENCODE which has re-written many of the rules of biology and the release of the these results dominated the scientific literature in late 2012.

Gene regulation is an essential part of cellular development and cell specific differentiation. Cancer is a complex genetic disease that is is caused by (among other things) the disregulation of genes; in particular, the networks of key molecular control proteins known as transcription factors.

Recently, the ENCODE project has provided large amounts of genetic, epigenetic and transcriptomic data that provide a remarkable snapshot of cell-type specific gene regulation. This project will concentrate on the use of a state-of-the-art method to build transcription factor networks in breast cancer. Using these networks and experimental data generated at the Garvan we will investigate breast cancer specific gene networks and also look at comparisons with other cell types (both normal and cancerous) to extend our understanding of the gene disregulation that leads to the onset of breast cancer.

This project will primarily involve the development of software to generate transcriptional networks and identify differences between those in normal and cancerous cells. For this, you will use our 1200 core large memory cluster at the Garvan Institute.
Comments:
Please email me at:
w[dot]kaplanATgarvan[dot]org[dot]au
or phone 9355 5758
http://pwbc.garvan.unsw.edu.au/wolfpack/
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