DNA based algorithms for complex infection diagnosis

Themes: Software technology & Intelligent Systems, Life Science & Health

Proof of principle

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Diagnostics and biological techniques for tuberculosis (TB) and other bacterial pathogens currently cannot efficiently classify complex infections. Our data-driven approach for strain identification will lead to more effective diagnosis of patients - especially those with complex multi-strain infections.


We develop algorithms to match genetic information from a patient sample onto a classification database built from known pathogenic strains. Specific patterns within the sample's genetic information can be used to identify and quantify constituent strains and their antibiotic resistances.


Sequencing as a diagnostic tool is being adopted more widely in the clinic. Cost is the major barrier to widespread adoption, but this is rapidly dropping. Our research can immediately impact on how we tackle patients with complex infections.

Dr. Thomas Abeel


Christine Anyansi MSc