The Bioinformatics Core Facility in our Cancer Research Division provides a range of services and knowledge for data analysis

Bioinformatics Consulting Core Facility 

The Bioinformatics Core aims to provide all levels of bioinformatics support to our research laboratories and the Victorian Comprehensive Cancer Centre (VCCC). The team of bioinformaticians and postdoctoral scientists at the core work alongside laboratory and clinical researchers and contribute to their experimental design, grant applications and the analysis and publication of genomic and transcriptomic data. Data types analysed by the core include whole-genome and whole-exome sequencing, targeted re-sequencing, radiological images, RNA-sequencing, single-cell RNA sequencing, ChIP-sequencing, NanoString and various types of microarray data. The core also develops and maintains software infrastructure required for bioinformatics processing, including pipelines and cloud provision systems. 

Cost recovery 

Fees apply to project work. Support to larger projects is available via EFT (part salary) contribution, while smaller projects are charged via a fee-for-service model. The core has a fixed pricing structure, which we update quarterly, for the most common analysis services. For custom or larger-scale analyses, we will provide a quotation after we understand the requirements and priorities of the project. 

We provide consultation services free of charge for our staff. 

Types of bioinformatics services 

Project work 

Bioinformatics needs are often unique, and different laboratories may require different levels of support from the core. The team at the Bioinformatics Core can help to analyse project requirements, discuss solutions with the investigators, and subsequently perform the analysis. The team also has extensive experience in generating publication-quality plots that you can use in articles and publishing data as per journal requirements. 

Grant applications 

Bioinformaticians at the core can contribute to grant applications by generating pilot data and describing the infrastructure and bioinformatics analyses available to support the proposed research. 


The core can provide consultation on experimental designs and analyses options. For research groups that perform bioinformatics analyses by themselves, the core can share knowledge and help troubleshoot as needed. 

Methods/software development 

Existing tools often fail to answer unique research questions, giving rise to the need for new bioinformatics workflows and/or methods. The team at the Bioinformatics Core has a record of accomplishment of building robust, highly cited bioinformatics software, and can discuss collaboration opportunities with research groups who are interested. 

Deep machine learning & Artificial Intelligence

The core is committed to aiding cancer research through the development and application of deep learning (DL) and other machine learning techniques. We have been working on DL projects that involve whole-slide images, radiological images, and multi-omics data, and are keen to explore further cancer application areas.  

Pipeline development 

Behind the scenes, the core actively develops and maintains analysis pipelines for efficient processing of sequencing data. We also actively collaborate in the maintenance of open-source software for building portable pipelines, and are currently leading the development of Janis. Bioinformatics pipelines are an integral part of high-throughput genomic facilities and are considered by us as a fundamental software-level infrastructure. Our bioinformaticians within and outside the core have had the pipelines available to them and they have used them extensively. 

Bioinformatics partners 

The Bioinformatics Core works closely with the Molecular Genomics Core Facility, Research Computing Facility, Digital & Healthcare Innovations, Computational Biology laboratories and other lab-based bioinformaticians. 

The bioinformatics team 

  • Dr Jason Li (BCompSc, BEng (Hons), PhD): Senior Core Manager/Research Fellow. Primary research interests in knowledge discovery from large genomic and transcriptomic datasets using machine learning techniques and software automation. Niche expertise: Artificial Intelligence systems, Bioinformatics Business Management. This email address is being protected from spambots. You need JavaScript enabled to view it. 
  • Franco Caramia (BCompSc, MBioinf): PhD student and Senior Bioinformatician. Primary research interests in differential expression analysis, data normalisation, population association studies, systems biology, biological network analysis and the role of chromosome X inactivation in cancer. Niche expertise: cancer sex disparity; large genomic datasets (TCGA). This email address is being protected from spambots. You need JavaScript enabled to view it. 
  • Dr Niko Thio (BEng, MSc IT, PhD): Senior Bioinformatics/Software Engineer. Specialised in single-cell and bulk RNAseq analysis, mass cytometry (CyTOF) data analysis, general QC and data pre-processing, with interests in spatial data analysis, end-to-end data consolidation, analytics, and visualisation. Niche expertise: single-cell/bulk RNAseq analysis, Nanostring mRNA, and custom software development. This email address is being protected from spambots. You need JavaScript enabled to view it.This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Richard Lupat (BSc, MPhil): Senior Bioinformatics Software Engineer. Specialised in developing reproducible and scalable bioinformatics workflows on commercial cloud and high performance computing facilities. Niche expertise: Automation and validation of clinical bioinformatics workflows. This email address is being protected from spambots. You need JavaScript enabled to view it. 
  • Maia Zethoven (BSc, MSc): Bioinformatician. Primary research interests in single-cell RNA-seq analysis and clustering/subtyping analysis of transcriptomic datasets. Niche expertise: Variant Annotation, Single-cell RNA seq; VC pipeline validation & accreditation. This email address is being protected from spambots. You need JavaScript enabled to view it.  
  • Rashindrie Perera (BSc (Hons)): Machine Learning Scientist. Primary research interests in developing deep learning methods to process multi-gigapixel histology images for cancer prognostication. Niche expertise: Computer vision, Medical image processing, Few-shot Learning. This email address is being protected from spambots. You need JavaScript enabled to view it. 
  • Michelle Meier (M Syst.Biol., B. Biol.): Bioinformatician. Primary research interests in single-cell data integration. Niche expertise: omics pre-processing, scATAC-seq analysis, scRNA-seq analysis. This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Patrick Crock (BSc, MBMedSc): Bioinformatician. Primary research interests in bulk and single-cell RNA-seq analysis, differential gene expression, spatial/clustering analyses, and investigation of the tumour microenvironment. Niche expertise: scRNA­-seq; tissue spatial analysis. This email address is being protected from spambots. You need JavaScript enabled to view it.

Recent collaborations 

  • Lupat, R. Perera, S. Loi and J. Li, "Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes," in IEEE Access, vol. 11, pp. 10912-10924, 2023, doi: 10.1109/ACCESS.2023.3240515.
  • Amarasinghe KC, Lopes J, Beraldo J, Kiss N, Bucknell N, Everitt S, Jackson P, Litchfield C, Denehy L, Blyth BJ, Siva S, MacManus M, Ball D, Li J* (joint senior author), Hardcastle N*. A Deep Learning Model to Automate Skeletal Muscle Area Measurement on Computed Tomography Images. Front Oncol. 2021 May 7;11:580806. doi: 10.3389/fonc.2021.580806. PMID: 34026597; PMCID: PMC8138051.
  • Haupt S, Caramia F, Klein SL, Rubin JB, Haupt Y. Sex disparities matter in cancer development and therapy. Nat Rev Cancer. 2021 Jun;21(6):393-407. doi: 10.1038/s41568-021-00348-y. Epub 2021 Apr 20
  • Haupt S, Caramia F, Herschtal A, Soussi T, Lozano G, Chen H, Liang H, Speed TP, Haupt Y. Identification of cancer sex-disparity in the functional integrity of p53 and its X chromosome network. Nat Commun. 2019 Nov 26;10(1):5385. doi: 10.1038/s41467-019-13266-3
  • Lupat R, Franklin M, Thomas E, Kesumadewi J, Yu J, Bhuyan M, Papenfuss T, Park D, Pope B, Li JJanis: A Python framework for Portable Pipelines. Zenodo. 2021. doi: 10.5281/zenodo.4427231