The Andrews group - Genome Informatics

Genome data science and interpretation of personal genome information

A data revolution is underway in modern biological science, now that obtaining the genome sequence for an individual organism has become routine and increasingly affordable due to exponential advances in sequencing technologies.  As biology, and increasingly medicine, become data-rich disciplines, the challenge becomes how to use this abundant information to produce knowledge.  A great many human diseases, including cancer, have a genetic basis - which will be better understood through mining of genomic information.  Understanding the molecular pathology of these diseases – and the new and precision treatments of these diseases - will be the major dividend of the genomic era.

High performance computing now has a central place in biological science, and we are biologists turned computer scientists that control and analyse genomic data to create information.

Our team has a multidisciplinary background and draws experience from large, international public genome projects. Our systems run in a high-performance computing infrastructure provided by the National Computation Infrastructure.  We contribute much of the bioinformatics and computational biology work of both the Centre for Personalised Immunology and Canberra Clinical Genomics.  We developed the mouse exome informatics system that drives the Missense Mutation Library.

Our focus remains on developing information systems that allow processing of, often massive, genomic data to produce targeted information to better understand the genetic basis of disease.  We are collaborative by nature, as our work is a component of large, multidisciplinary projects that bring together researchers working in medicine and genomics, data scientists, computational biologists, clinicians and patients.

Projects:

  • Relative contribution of mutations to disease, including pleiotropy, variable penetrance, mutational load, gene networks and complex disease
  • The functional contribution of mutation in related genes and severity of resultant disease phenotype
  • 'Big data' information systems to aggregate and mine very large volumes of human genome to identify causal variation specific to disease
  • Human mutation as 'devolution' for insight into recent functional evolution

Updated:  21 June 2018/Responsible Officer:  Director, JCSMR/Page Contact:  Web Manager