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Two back to back presentations on different aspects of working with genetic data! Reduce, Reuse, Recycle: Are Whole Exome Sequencing Data Sufficient to Identify Copy Number Variations? Our First Presenter : Steven Bentley I have conducted my PhD on rare hereditary forms of Parkinsonism in Queensland families at Griffith University. This study has identified a number of novel candidate genetic mutations that may be sufficient to cause disease, however further investigations are underway. I also work for the Griffith DNA Sequencing Facility, in both technical and bioinformatic roles. The First Presentation: Whole exome sequencing (WES) produces a lot of data, however it is still quite expensive to perform. Due to the expense and high data content, reusing the data is an appealing idea. Algorithms are released each year to genotype Copy Number Variations (CNVs) from WES data. However, while there is an increase in published articles using WES data for initial CNV discovery analyses, the question remains if these data are sufficient to reliably call CNVs as a first pass? This study suggests single-end data from the Ion Proton were not sufficient for this purpose. Integrating cancer research with machine learning – achievements and challenges Our Second Presenter: Dr Maren Westermann is a machine learning engineer at Max Kelsen. She works on the Immunotherapy Outcome Prediction (IOP) project that combines whole-genome sequencing and machine learning to improve the success rates of immunotherapy in cancer patients. Maren has a strong background in the biological sciences. After completing a Bachelor’s and Master’s degree in Biology at the University of Giessen, Germany, she graduated with a PhD from The University of Queensland. After finishing her PhD, Maren became interested in machine learning and its potential to provide solutions for previously unsolvable problems. She started educating herself in machine learning making use of MOOCs. Our Second Presentation: In Australia, about 3 in 10 deaths are caused by cancer, making it one of the most common fatal diseases. The development of cancer has been linked to mutations of the genome. Dr Maren Westermann will give an overview of state of the art machine learning models applied to whole-genome sequencing data of cancer patients and highlight the challenges and constraints that are faced by the cancer research community. Our Sponsor this month: AARNet Pty Ltd (APL) is the not for profit company that operates Australia's Academic and Research Network (AARNet). The shareholders are 38 Australian universities and the CSIRO. AARNet provides high capacity Internet and other communications services for the nation's research and education community, including universities, health and other research organisations, schools, vocational training providers and cultural institutions. AARNet serves over one million end users who access the network for teaching, learning and research. For further information, please visit: www.aarnet.edu.au (http://www.aarnet.edu.au/) The event is at the Thoughtworks offices, level 19, 127 Creek Street, with official kick-off at 5:30 pm.