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Improving Bioinformatic Methods For Studying Gene Regulation In Health And Disease
Funder
National Health and Medical Research Council
Funding Amount
$463,652.00
Summary
New methods for analysing genome-wide data will be developed to ease the data analysis bottleneck that currently exists in medical research. Modelling variation in gene expression from single cells, in screens designed to uncover gene function and assays that measure the factors that turn genes on or off will be the focus. Free software will be developed and made available to researchers worldwide to help them interpret the large and complex data sets that are now routine in genomic medicine.
High-throughput genetic assays are commonly used to study the molecular basis of disease and such technology requires sophisticated data analysis methods that account for significant biological and experimental complexity. Specialized methods will be developed in free public software that will greatly benefit future genetic profiling studies.
Investigating Widespread Regulation Of Gene Expression Through Intron Retention
Funder
National Health and Medical Research Council
Funding Amount
$363,026.00
Summary
We recently discovered a hidden type of gene regulation that appears to be altered in diverse cancers including leukaemia, melanoma and colon cancer. We will explore this widely relevant mechanism using molecular and computational tools. We created the only computer program able to detect this type of regulation and will now share our discovery with cancer scientists through cloud computing technology.
Statistical Methods For Identifying Structural Variation In Tumour Genomes Using Next Generation Sequencing
Funder
National Health and Medical Research Council
Funding Amount
$243,458.00
Summary
New DNA sequencing technology can sequence a tumour genome affordably in 2 weeks. This re-sequencing data can be used to find small mutations and large-scale chromosomal rearrangements that together are the drivers of cancer. These may one day be used to guide cancer therapy. This project will develop new algorithms for finding mutations and apply these to discover the genetic basis of drug resistance in a model lymphoma system.