Bioinformatics

We help Burnet's researchers get the best from data with training, consultation and analysis, accelerating the development of therapies and evidence-based recommendations across the Institute.

Group Heads

Dr Mark Ziemann

About

We help Burnet's researchers extract meaningful results from their data. We provide training, consultation and data analysis services. We also conduct meta-research to improve the reliability of bioinformatics.

Advances in genomic sequencing and other ’omics means researchers must be skilled in working with large datasets to extract the best results.

Our main aim is to enhance Burnet’s data analytics capacity. We achieve this through:

one-on-one training sessions, workshops and provision of learning materials

working directly with other working groups at the institute

providing consultation and conducting data analysis.

We’re developing data resources and software tools to help researchers extract usable information from their raw data sets.

We specialise in integrating complex data types, including protein expression, epigenetics and metabolic datasets. We provide data analytics skills and expertise to many working groups across Burnet. This data is used to understand the effect of mutations, trace disease outbreaks, and how genes are switched on and off.

We also focus on research reliability, involving systematic assessments of published research works to understand whether they are reproducible and have used validated methods.

Current projects

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Getting to the heart of cardiovascular disease in viral infections

This project aims to understand the mechanisms of how viral infections can potentiate the development of CVD, with a particular focus on the role monocytes may play in this process.

Impact of a vaginal microbiota metabolite on epithelium integrity and HIV susceptibility

We investigate how a factor produced by optimal microbiota augments the cervicovaginal mucosal barrier to prevent HIV acquisition.

Towards reliable pathway enrichment

Pathway enrichment analysis is crucial for understanding genomic data, but misuse threatens the research. We aim to improve enrichment analysis in the scientific literature.

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