Peter Georgeson

Bioinformatician

Melbourne Bioinformatics

Biography

I am a PhD student embedded at Dan Buchanan’s Colorectal Oncogenomics Group. The focus of my PhD research is the application of tumour mutational signatures to understanding and detecting the underlying aetiology driving different sub-types of colorectal cancer.

I am also involved with a number of applied research projects continuing from my previous role at Melbourne Bioinformatics. These include:

Melbourne Genomics Health Alliance, a consortium bringing genomics to the clinic.
Human Genomics Group, a group focussed on applying novel approaches to large scale data-analytics problems.
The Pan Prostate Genomics Consortium, a project to analyse a large cohort of WGS prostate cancer samples across numerous collaborating institutions.

I have an ongoing interest in genomics, particularly cancer genomics, statistical methods, and machine learning.

My background prior to bioinformatics is software engineering. I ran a software consulting company that developed a variety of mobile and web apps. See Supernifty for further details.

Interests

Cancer Genomics
Bioinformatics
Statistical Methods
Machine Learning

Education

Master of Science (Computer Science), 2016

The University of Melbourne, Australia
Bachelor of Computer Engineering (Hons I)

The University of Newcastle, Australia
Bachelor of Mathematics

The University of Newcastle, Australia

Selected Publications

All publications

Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics

Genetic variant effect prediction algorithms are used extensively in clinical genomics and research to determine the likely consequences of amino acid substitutions on protein function. We derive three independent, functionally determined human mutation datasets, UniFun, BRCA1-DMS and TP53-TA, and employ them, alongside previously described datasets, to assess the pre-eminent variant effect prediction tools. Apparent accuracies of variant effect prediction tools were influenced significantly by the benchmarking dataset. Benchmarking with the assay-determined datasets yielded considerably lower accuracy than observed for other, potentially more conflicted datasets.

Khalid Mahmood, Chol-hee Jung, Gayle Philip, Peter Georgeson, Jessica Chung, Bernard J. Pope, Daniel J. Park

Human Genomics

Details PDF Paper

Single nucleotide-level mapping of DNA double-strand breaks in human HEK293T cells

Constitutional biological processes involve the generation of DNA double-strand breaks (DSBs). We re-analyse public RAFT data to derive sites of DSBs at the single-nucleotide level across the built genome for human HEK293T cells. This refined mapping, combined with accessory ENCODE data tracks and ribosomal DNA-related sequence annotations, will likely be of value for the design of clinically relevant targeted assays such as those for cancer susceptibility, diagnosis, treatment-matching and prognostication.

Bernard J. Pope, Khalid Mahmood, Chol-hee Jung, Peter Georgeson, Daniel J. Park

Genomics Data

Details Paper

UNDR ROVER - a fast and accurate variant caller for targeted DNA sequencing

ROVER, a DNA variant caller which identifies genetic variants from PCR-targeted massively parallel sequencing (MPS) datasets generated by the Hi-Plex protocol. ROVER permits stringent filtering of sequencing chemistry-induced errors by requiring reported variants to appear in both reads of overlapping pairs above certain thresholds of occurrence. ROVER was developed in tandem with Hi-Plex and has been used successfully to screen for genetic mutations in the breast cancer predisposition gene PALB2. UNDR ROVER provides the same rapid and accurate genetic variant calling as its predecessor with greatly reduced computational costs.

Daniel J. Park, Roger Li, Edmund Lau, Peter Georgeson, Tú Nguyen-Dumont, Bernard J. Pope

BMC Bioinformatics

Details PDF Code Paper