Dr Sara Ballouz | UNSW Research

Keywords

chronic fatigue syndrome (cfs)

network analysis

Fields of Research (FoR)

Bioinformatics and computational biology, Bioinformatic methods development

Biography

My central scientific interest has been to understand the genetic architecture of disease. With data from the genome, transcriptome, epigenome and proteome increasing exponentially, robust tools and practices need to be established to analyse this deluge, in particular if to be applied to personalized medicine.

I obtained my PhD from the University of New South Wales and the Victor Chang Cardiac Research Institute in 2013, working with Drs...view more

I obtained my PhD from the University of New South Wales and the Victor Chang Cardiac Research Institute in 2013, working with Drs Merridee Wouters and Bruno Gaeta. I then moved to Cold Spring Harbor Laboratory for my postdoctoral training with Dr Jesse Gillis. In 2020, I started my own group at the Garvan-Weizmann Centre for Cellular Genomics at the Garvan Institute of Medical Research.

My Qualifications

Education

2013 - PhD, Victor Chang Cardiac Research Institute and the University of New South Wales, Sydney - Australia
2008 - BE Bioinformatics First Class Honours, University of New South Wales, Sydney – Australia
2008 - BSc Genetics, University of New South Wales, Sydney – Australia

My Research Activities

• Transcriptomics (single-cell and bulk)

• Co-expression

• X-linked disorders

• Sex differences in disease

• Meta-analysis

• Personalized medicine

My Research Supervision

Supervision keywords

sex differences

bioinformatics

computational biology

x chromosome inactivation

autoimmune diseases

chronic fatigue syndrome (cfs)

single-cell analysis

gene expression

network analysis

Areas of supervision

Broad: Bioinformatics, Genomics and transcriptomics, Computational Biology, Functional Genomics

Biology and disease: Autoimmune disease, X-linked disorders, myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), long-COVID, X-chromosome inactivation, sex differences in disease

Methods: Co-expression and network analysis, differential expression analysis, gene set enrichment, candidate disease gene prediction, functional gene analysis, meta-analysis

Data: RNA-seq, single-cell analysis