Coin Laboratory: Cancer Bioinformatics
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Professor Lachlan Coin
Research Overview
The Coin Group develops genomic and transcriptomic tools to develop biomarkers for rapid characterization of disease state and prediction of drug susceptibility, with the aim of decreasing the time taken from hospital admission to administering the right treatment. We also develop biomarkers for measuring treatment response. We currently focus on infectious disease as well as cancer.
We utilize approaches from high-dimensional statistics, information theory and machine learning, including deep neural networks. We aim to implement streaming algorithms, which process data as soon as it is generated, providing real-time inference and visualization of both the most likely predicted disease state, as well as uncertainty in those predictions, which decrease as more data is collected.
Much of our current research utilizes real-time nanopore sequencing, based on its unique ability to generate sequence data in real-time, as well as its capability to sequence native DNA and RNA. We have emerging interests in single-cell long-read RNA and DNA sequencing, particularly as they relate to improving diagnostic and prognostic tools.
In infectious disease, we collaborate with chemists and materials scientists to develop techniques for isolating bacteria from complex biological matrices. We apply these approaches in conjunction with real-time nanopore sequencing to sequence bacterial genomes directly from clinical sample.
We have ongoing interests in evolution of complex regions of the genome, particularly highly repetitive regions. We are currently working utilizing long nanopore reads to characterize complex regions of tumour genomes inaccessible to short-read sequencing technologies. We are interested in the role in which complex rearrangements in these repetitive regions can influence phenotype, including drug resistance. We have also worked on using circulating tumour DNA to non-invasively characterise copy number variation in primary tumours.
Staff
Professor Lachlan Coin: Group leader
Dr Chenxi Zhou: Postdoctoral researcher
Dr Devika Ganesamoorthy: Postdoctoral researcher
Dr Miranda Pitt: Postdoctoral researcher
Ms Jessie Chang: PhD student
Mr Daniel Rawlinson: Research assistant
Collaborators
Dr Mark Blaskovich, University of Queensland
Professor Matt Cooper, University of Queensland
Dr Seweryn Bialasiewicz, Queensland Paediatric Infectious Disease Laboratory
Dr Krispin Hajkowicz, Royal Brisbane and Women’s Hospital
Dr Luregn Schlapbach, Queensland Children’s Hospital
Dr Scott Bell, Prince Charles Hospital
Dr Erin Price, University of Sunshine Coast
Dr Derek Sarovich, University of Sunshine Coast
Professor Emma McBryde, James Cook University
Professor Claire Wainwright, Queensland Children’s Hospital
Dr Peter Simpson, University of Queensland
Dr Chris Coulter, Queensland Mycobacterial Reference Laboratory
Dr Arnold Bainomugisa , Queensland Mycobacterial Reference Laboratory
Dr Sushil Pandey, Queensland Mycobacterial Reference Laboratory
International
Professor Mike Levin, Imperial College London, United Kingdom
Dr Myrsini Kaforou, Imperial College London, United Kingdom
Professor John Chambers, Nanyang Technical University, Singapore
Dr Martin Smith, University of Montreal, Canada
Dr Eva Novoa, Centre for Genomic Regulation, Spain
Dr Evelyn Lavu, Central Public Health Laboratory at Port Moresby General Hospital
Funding
NHMRC
ARC
MRFF
Research Projects
- Genomic tools for rapid diagnosis of bacterial infection using host and bacterial biomarkers
- Genomic tools for diagnosis of the presence of a solid tumour and for prediction of tumour subtype and likely treatment response from circulating tumour DNA
- Characterisation of complex structural variation in bacterial and human genomes
Faculty Research Themes
Cancer, Infection and Immunology
School Research Themes
Key Contact
For further information about this research, please contact Head of Laboratory Professor Lachlan Coin
Department / Centre
Clinical Pathology , UMCCR | Home
Unit / Centre
Coin Laboratory: Cancer Bioinformatics
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