Bioinformatics Group


The group was established in 2004 by Prof Shoba Ranganathan originally under the auspices of the Macquarie University Biotechnology Research Institute (MUBRI) and with support from the Division of Environmental and Life Sciences. We are currently in the Department of Chemistry and Biomolecular Sciences, Macquarie University.

We are a collegial and energetic group of researchers dedicated to making breakthrough discoveries in biological and biomedical sciences by applying genetic, genomic and computational approaches to address and solve the problems faced by researchers in Molecular, Cellular and Biomedical Sciences, while advancing cutting-edge computational biology research, at the highest levels of excellence.

PhD Scholarships

Domestic PhD scholarships are available for 2013 enrollment – eligible Australian citizens or residents interested in doing a Bioinformatics PhD should contact us by email – successful candidates commence on or before 13 December 2013.

Areas of Interest and Research Focus

Our key areas of interest are computational structural biology, sequence variations, biodiversity informatics and cheminformatics.


Computational structural biology

Our research focuses on:

  • applying computational methods to identify potential T cell epitopes for vaccine design
  • mapping disease causing mutations (especially in human) to protein structures for genotype-phenotype correlations
  • identifying the characteristics for structural domain interfaces to predict potential protein interactions

Variations at genome, transcriptome and proteome levels

We are looking at:

  • alternative splicing events by whole genome comparisons
  • characterizing background variations from deleterious changes
  • biomarkers discovery from a Boolean based integrated approach


Biodiversity informatics and cheminformatics

Bioinformatics approaches are applied to study:

  • Australian aboriginal medicinal plant relevance and distribution
  • bioactive characterization
  • cheminformatics studies of drugs, toxins and metabolites for predicting novel therapeutics