The Bioinformatics Core Facility in the Cancer Research Division at Peter Mac provides a range of services and know-how for data analysis.
Bioinformatics Consulting Core Facility
The aim of the Bioinformatics Core is to provide all levels of bioinformatics support to research laboratories within Peter Mac and, more broadly, within 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.
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 is updated quarterly, for the most common analysis services. For custom or larger-scale analyses, a quotation will be provided after understanding the requirements and priorities of the project.
Consultation services are available free of charge for Peter Mac staff.
Types of services
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 can be used in articles, as well as publishing data as per journal requirements.
Bioinformaticians at the core can contribute to grant applications by generating pilot data, and helping to describe in the application the infrastructure and bioinformatics analyses that are 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 know-how and help troubleshoot as needed.
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 track record of building robust, highly cited bioinformatics software, and can discuss collaboration opportunities with research groups who are interested.
Deep machine learning
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.
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. The pipelines are available to and have been extensively used by all bioinformaticians at Peter Mac, both within and outside the core.
The Bioinformatics Core works closely with the Molecular Genomics Core Facility, Research Computing Facility, Papenfuss Lab, and other lab-based bioinformaticians at Peter Mac.
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: Bioinformatics Business Management.
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).
Dr Niko Thio (BEng, MSc IT, PhD): Senior Software Engineer. Specialised in software development and management, with interests in end-to-end consolidation, analytics and visualisation. Niche expertise: Sequencing facility LIMS; sequencing data processing.
Richard Lupat (BSc, MPhil): Senior Bioinformatics Software Engineer. Specialised in bioinformatics pipeline development and automation for next generation sequencing data. Niche expertise: Clinical sequencing automation; Cloud computing.
Maia Zethoven (BSc, MSc): Bioinformatician. Primary research interests in single-cell RNA-seq analysis and clustering/subtyping analysis of transcriptomic datasets. Niche expertise: Single-cell RNA seq; VC pipeline validation & accreditation.
Rashindrie Perera (BSc(Hons)): PhD student and Data Analyst. Primary research interests in developing deep learning methods to process multi-gigapixel histology images for cancer prognostication. Niche expertise: Computer vision and machine learning.
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.
Michelle Meier (BSc, MSc): Bioinformatician. Primary research interests in bulk, single-cell-RNA and ATAC seq analysis, differential gene expression, trajectory analysis and omics integration. Niche expertise: single cell and bulk RNA-seq analysis.
Cancer Sex Disparity
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.
Tumour-resident T-cells in Breast Cancer
Savas P, Virassamy B, Ye C, Salim A, Mintoff CP, Caramia F, Salgado R, Byrne DJ, Teo ZL, Dushyanthen S, Byrne A, Wein L, Luen SJ, Poliness C, Nightingale SS, Skandarajah AS, Gyorki DE, Thornton CM, Beavis PA, Fox SB; Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab), Darcy PK, Speed TP, Mackay LK, Neeson PJ, Loi S. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med. 2018 Jul;24(7):986-993. doi: 10.1038/s41591-018-0078-7. Epub 2018 Jun 25. Erratum in: Nat Med. 2018 Dec;24(12):1941.
Lupat R, Franklin M, Thomas E, Kesumadewi J, Yu J, Bhuyan M, Papenfuss T, Park D, Pope B, Li J. Janis: A Python framework for Portable Pipelines. Zenodo. 2021. doi: 10.5281/zenodo.4427231.