Dr Shivashankar Nagaraj’s research group’s overall focus is on the development and application of bioinformatics approaches that utilize large amounts of data to solve problems in complex diseases including cancer.
We take a holistic approach, integrating Indigenous Health research and training with outreach programs in an effort to ensure that our work guarantees access to high-quality healthcare for all.
Specifically, we develop computational approaches for the large-scale integrative analysis of Omic biological datasets pertaining to three related human health disciplines.
Our studies aim to define the genetic architecture of these Indigenous populations and the association between these genetic factors and the incidence of serious chronic diseases, thereby helping to develop a precision medicine approach that will enable accurate diagnosis and inform targeted treatment efforts.
We are developing a new algorithm that will guide pre-transfusion compatibility testing and thereby improve the safety of blood transfusions. This Next-Generation Sequencing-based algorithm is scalable and can be customized for any population. This research has important implications for the fields of Indigenous Health, Oncology, and Organ transplantation.
We apply Artificial Intelligence (AI) approaches to improve patient outcomes, leveraging multi-omic datasets (e.g. Epithelial to Mesenchymal Transition (EMT), determine the tissue of origin for cancers of unknown primary (CUP)).
Nagaraj lab research group is a part of the newly formed Centre for Genomics and Personalized Health (http://www.qut.edu.au/research/centre-for-genomics) and is a within the Schools of Biomedical Sciences(https://www.qut.edu.au/health/schools/school-of-biomedical-sciences).
Nagaraj lab is recruiting PhD candidates to perform cutting-edge Genomics research at QUT.
The student in this will be trained both computational, Clinical and experimental aspects of this project. There is also strong emphasis on communication, leadership skills and attendance to national and international conference within the group for highest level of exposure to HDR students.
Life expectancy of Aboriginal and Torres Strait Islander Australians is much lower than other Australians, in part due to an apparent genetic predisposition to chronic diseases. Better understanding of this genetic contribution has the potential to improve early detection and target prevention strategies. This project will use whole genome sequencing (WGS) to define the genetic architecture of the Indigenous Australians and its association to serious chronic diseases, helping to develop a precision medicine approach that will enable accurate diagnosis and inform targeted treatments.
This project builds on the most comprehensive chronic disease profiling performed in any Indigenous community, the longest follow-up, treatment and prevention trials, and documentation of endpoints. The PhD research work could ultimately lead to development of tests for early detection of chronic disease, to protocols of personalised management, and ultimately, better health outcomes for Indigenous Australians.
Project 1: Genomic architecture of chronic disease in Australia’s First Peoples
The overall goal of this project is to understand the landscape of Indigenous genomes and define its architecture using
nearly 500 whole genomes from Australia’s First Peoples. The student will be trained in analysis if next-generation sequence datasets including copy number variation and analysis of variants using ACMG guidelines. The project will create a global variant map and study the association of variants to chronic diseases. Functional validation experimental analysis of the effects of protein variants identified may be undertaken through collaboration with experimental biologists.
Project 2: Deep learning and integration of clinical datasets with Indigenous genomic datasets
The proposed research aims to use advanced data analytics such as AI for integration of clinical and Indigenous genomic datasets. Given the complex nature of the chronic diseases, advanced Machine Learning methods will allow accurate modelling of genotypic interactions with disease phenotypes. These models can generate individual risk scores, stratifying individuals and guiding preventative intervention efforts. An individual’s polygenic risk score can be compared with others to yield odds ratios (OR), indicating relative disease risk based solely on genotypes, which could translate into screening and therapeutic applications.
Two scholarships are available.
- QUT Scholarship: candidates will receive a tax-exempt living allowance of $28,092 per annum for three years, with an additional top-up of up to $5,000 per annum considered for excellent candidates.
- Faculty Scholarship: candidates will receive a tax-exempt living allowance of $28,092 per annum for three years, with an additional top-up of up to $5,000 per annum considered for excellent candidates.
Our ideal candidates hold a MSc or equivalent in a relevant discipline (e.g. Bioinformatics, Statistics, Computer Science, Data Science, Genomics, Clinical Genomics) with strong analytical and programming skills. Excellent oral and written communication skills, motivation and the ability to work as part of a team is also required. Candidates must also meet the academic eligibility requirements for admission to the PhD.
- Experience in various high-performance computing (HPC) clusters
- Exposure to Artificial Intelligence and Machine Learning
QUT and the School are committed to equity and diversity among our staff and students and we actively encourage applications from those who bring diversity to the university. Aboriginal and Torres Strait Islander students are strongly encouraged to apply.
Your application must be submitted to Dr Shivashankar Nagaraj at firstname.lastname@example.org and must include:
- a cover letter
- curriculum vitae
- a brief summary of your research experience (one page)
- details of two referees.
Application closing date and time
Applications close 11:59pm AEST, Wednesday 28 October 2020.
For additional information please contact Dr Shivashankar Nagaraj at email@example.com