One of the great challenges in biology is to understand the relationship between an individual’s genotype and their phenotype. The availability of whole genome sequences has made this challenge more pertinent than ever with the potential outcomes having profound consequences in terms of both revolutionizing medical science and influencing how we live our lives. Remarkably, the functions of only a tiny fraction of the genome are understood and the majority of the information encoded within the genome cannot currently be interpreted.
Our Lab combines cutting edge genomics and informatics technologies to better understand how the genome stores information and how this information controls development and, when perturbed, leads to disease. The overall objective of the laboratory is to unlock the clinical value in the noncoding regions of the genome.
One of the key approaches in unravelling the functions of different parts of the genome is by examining how, when and where it is transcribed into RNA. The advent of next generation sequencing has made it possible to define a cell’s complete transcriptome. By examining how the transcriptome changes in response to different environments and between different individuals, we can finally begin to appreciate how modifications to the genome are manifested in the transcriptome and ultimately the individual’s phenotype. This approach opens an entirely new window in understanding the molecular basis of disease.
In conjunction with expert clinical collaborators, our Lab integrates genomic technologies to examine a diversity of high impact diseases, including melanoma, squamous skin cell carcinoma, oesophageal cancer, osteoporosis, and developmental delay. The close clinical collaborations provide avenues for rapid translation of findings into improved patient care through the development of new diagnostics and identification of novel therapeutic targets.
Dr Brian Gloss. Brian is interested in transcriptional complexity in health and disease. He is currently involved in projects investigating coding and noncoding contribution, using a variety of sequencing technologies, to hematopoiesis, autoimmunity and congenital translocation-based disease. He is particularly intrigued by the rapidity and variety of cellular transcriptional processes and responses and is investigating these in mammalian development and cancer.
Dr Nenad Bartonicek. Nenad is a postdoctoral researcher with background in computational biology of noncoding RNAs. His interests lie in understanding the alien world of transcribed genome through novel computational methods and sequencing technologies. He may also lose it if someone mentions "placenta" or "testis”, especially if in the same sentence with “transgenerational information transfer". Currently intrigued by noncoding RNAs and repeats involved in cancer genome stability and metastasis.
Dr Daniel Thomson. Daniel is a postdoctoral researcher in the Genome Informatics laboratory. His research interests are on understanding RNA biology in the context of cancer research; focusing on microRNAs, pseudogenes and long noncoding RNAs using molecular biology, genomics and bioinformatics approaches.
Jesper Maag. Jesper is a 3rd year PhD student investigating the role of noncoding elements in memory formation and oesophageal adenocarcinoma development. Jesper primarily employs transcriptomics-based approaches to explore these processes, and has a paricular interest in understanding the interplay between the epigenome and the transcriptome.
Xiu Cheng Quek. Quek is a 3rd year PhD student focused on unravelling cancer progression using transcriptomic studies conducted on squamous cell carcinoma and hypermutated cell models. Also, an enthusiastic web developer with interests in data visualization.
Mahdi Zeraati. Mahdi is a 3rd year PhD student. He is exploring the regulatory function of RNA G-quadruplexes (G4s) in cancer development using molecular biology and bioinformatics tools. He is also generating antibodies against RNA and DNA G4s to be used as biological probes in the G4 research field.
Beth Signal. Beth is a 2nd year PhD student. Her research involves using temporal and single cell transcriptome profiling to understand mechanisms involved in cellular differentiation. She is also interested in applications of machine learning methodologies to transcriptome research.
James Torpy. James has a background in immunology and has recently joined the bioinformatics field with an interest in cancer transcriptomics. He is currently building pipelines for RNA-seq data set analysis and developing applets for these pipelines for use in cloud computing. James will soon be undertaking a PhD project to investigate the role of long non-coding RNAs transcribed from centromeric regions and their regulation of chromosomal segregation in cancer.
Laboratory Administered Websites
lncRNAdb 2.0. The reference database for functional long noncoding RNAs.
In the News
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What it might take to unravel the ‘lean mean machine’ that is cancer - Feb 22, 2010