Transformational Bioinformatics

Denis Bauer

Denis Bauer
Group Leader
Transformational Bioinformatics

Cloud-native, industry-transforming Bioinformatics

We are cloud-natives, developing novel bioinformatics solutions for research and industry using BigData infrastructure and machine learning.

The impending digital disruption through the rise of Artificial Intelligence and the exponential increase in data volumes poses a substantial challenge for the Health and LifeScience discipline. Preparing Australia for this, the Transformational Bioinformatics group develops novel bioinformatics solutions for research and industry.

We partner with international cloud providers, Universities and Startups to bring health innovation to Australia. We specifically focus on machine learning for population-scale ‘omics (genomics, transcriptomics, methylomics) analysis as well as genome engineering for health and biosecurity applications.

Read more at https://bioinformatics.csiro.au/

Our Solutions

We develop bespoke future-ready solutions for our collaborators and partners. We are also the creators of two open-source bioinformatics platform development projects:

  • VariantSpark – powering genomic insights
    VariantSpark is a Spark-based data analytical framework for population-scale ‘omics data. It can cluster patients by their genomic profile or identify disease associated genes in whole genome cohorts (thousands of samples with millions of variants each) in just 30 minutes. This allows – for the first time – personalized genomic insights at point-of-care, by e.g. finding patients-like-mine based on their genomic similarity to other patients in international studies or the health care system.
    Find out more from our Genomics Insights Team.
  • GT-Scan – the search engine for the genome
    Finding the optimal spot to edit is a computationally challenging optimisation problem, balancing efficient incorporation rate with location specificity. We therefore developed GT-Scan, a cloud-based framework recommending researchers the optimal target site for genome editing applications. It can identify all potential sites and process them efficiently by using server-less functions, which are capable of massively parallelising task in a web-service environment. This allows queries to remain constant in runtime (~1 min) despite the underlying complexity varying drastically (e.g. 100-100K binding site candidates), ideally catering for time critical clinical workflows.
    Find out more from our Digital Genome Engineering team.