The integration of multi-modal molecular data promises to deliver a more comprehensive understanding of cancer. By integrating diverse data modalities, such as genomics, transcriptomics, epigenomics, proteomics, and clinical data, researchers can uncover novel insights and identify potential biomarkers or therapeutic targets.
This project will involve analysis of big public data such as The Cancer Genome Atlas (TCGA) and several multi-omics datasets generated by collaborators from patient cohorts. The effect of removing unwanted variation, such as library size differences, batch effects and tumor purity will be explored (Molania, et al, Nature Biotechnology, 2023). This will improve the reliability of the data and deliver more accurate integration. The student will then develop and test new methods to effectively integrate genomics, transcriptomics, epigenomics, proteomics and clinical data.
Our lab develops and applies new bioinformatics methods to drive discoveries in cancer and translate these into improved outcomes for patients.
Our approach is to use mathematics, statistics and computing to make sense of omics data, especially related to cancer evolution spanning initiation, progression and outcome.
The main focus of our computational biology research is rare cancers, melanoma and prostate cancer, but our methodological research is also relevant to cancer in general and other diseases. We have a strong track record in developing methods for analysing genome sequencing data, especially associated with chromosomal rearrangements. A major focus of our research is the development and application of new computational and machine learning tools to analyse large, complex multiomics datasets.