Dr Nasser Bagheri (Senior Research Fellow and co-lead (VIDEA) lab, Centre for Mental Health Research)

Title: Visualising Health Care Ecosystem: From Biologic to Population Health Data to Inform Policy and Practice

Abstract: The Visual and Decision Analysis (VIDEA) lab is a new service and research organisation at the Centre for Mental Health Research at ANU. We are using new and advanced technologies and methods (e.g. machine learning, artificial intelligence, network analysis, etc) with data science capacity to visualise pattern of mental healthcare across communities to provide better communication with policy makers and stakeholders in public health sector. Our existing capacity in machine learning enables us to apply this unique expertise to analyse and visualise complex healthcare data sets.

Additionally, another arm of VIDEA Lab is capacity of geospatial analysis and spatial visualisation of healthcare patterns using GIS (Geographical Information Systems) technology. VIDEA Lab has extensive expertise in medical geography and spatial epidemiology to support spatial analysis at Centre for Mental Health Research.

Mr Renzo Balboa (PhD Student, NCIG)

Title: Characterising the Diversity of Alu repeats in Humans

Abstract: More than half of the human genome is comprised of repeat sequences, with a large number annotated as transposable elements and long duplicated regions. Alu elements are primate-specific ~300bp repeat sequences which are considered to be the most successful transposable elements in the human genome. In any individual, it is believed that there are ~1.1 million copies of Alu repeats, comprising ~11% of the human genome. Currently, the Alu retrotransposition rate is estimated to be at ~1 in 20 live births. Evolutionary characteristics of Alu elements, such as high polymorphism rates and neutral evolutionary mechanisms are useful for understanding human diversity and has potential use in the context of precision medicine and population genetics. The extent of Alu polymorphisms, however, are currently understudied at a global population scale. To address this, we have used whole genome sequence data from 150 populations across the world to comprehensively annotate Alu variations at an individual level. We detect ~1.3 million Alu across all individuals, and find that shared elements carry a largely similar profile. Our results also suggest that ~15% of Alu elements are currently not accounted for in the human reference genome. To the best of our knowledge, this is the first comprehensive survey of Alu elements in diverse individuals, which will pave way for understanding their roles in DNA regulation and subsequently in health and disease.

Key words: Infinite alleles model, Alu and SINE retrotransposition elements

Seminars held fortnightly from 2-3pm on Mondays at various locations, light refreshments will follow.

All welcome to attend.