Machine learning analysis of mutagenesis datasets

Machine learning analysis of mutagenesis datasets

Project details

Deep mutational scanning (DMS) is a method that allows the functional consequences of all possible amino acid changes in a protein to be determined easily and cheaply using mutagenesis and high-throughput sequencing (Fowler and Fields, Nat Meth 2014 11(8):801-807). Use of DMS is expanding rapidly, and many datasets have already been generated on a variety of clinically relevant protein targets. There are diverse opportunities for developing and applying computational methods to these datasets to learn about protein evolution and protein variants that carry disease risk. The student will develop expertise in advanced machine learning and statistical methods and learn how to make biological inferences using complex, multi-dimensional datasets.

About our research group

The Papenfuss lab uses mathematics, statistics and computation to understand the evolution of cancer, as well as other diseases. This frequently entails the development of novel bioinformatics, computational and mathematical methods to make sense of complex biological data, for example tools for discovering genomic rearrangements. The lab comprises mathematicians, statisticians, computer scientists, physicists, as well as biologists. Our work is enabled by the Institutes new high-performance computing resources.

The lab is joint between the Institute and the Peter MacCallum Cancer Centre. We collaborate widely, and this project is a collaboration with Professor David Thomas and Dr Alan Rubin at the Institute.



Professor Tony Papenfuss

Tony Papenfuss
Head, Computational Biology; Laboratory Head
Dr Alan Rubin
Bioinformatics division

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