The Alicia Oshlack lab researches bioinformatics and computational biology
Computational and statistical techniques are used to analyse and explore the transcriptome and genome. The group has a diverse and complementary skill set that includes statistics, computer science, genetics, software engineering, molecular biology and physics. We work on a range of projects from developing data analysis tools and statistical methods through to collaborative analysis of biological systems. We have major applications in discovering cancer drivers using transcriptomic approaches.
Cancer samples have highly rearranged genomes with mutations occurring at all scales. The most basic functional read-out of the genome is the transcriptome and some of these mutation will result in altered transcript which may be important in cancer. We work on identifying the many types of mutations of the transcriptome in cancer samples from fusion genes to spicing variants. We identify driver mutations in specific cancer samples and develop generalised methods for discovery of these mutations.
Recent technology development has given us the ability to sequence RNA and DNA from individual cell within a tissue. These data sets are large, complex and noisy and while some analysis is becoming standard practise there remain many challenges. We work on the analysis of collaborative data sets and well as new methods for analysis tasks that are novel or not yet robust in order to get new, high-quality information from these data. The single cell analysis team is lead by Dr Jovana Maksimovic.
Until recently only sequencing of short fragmented DNA was possible. Now technologies are giving us sequencing of unlimited length which allows us to get full length transcripts. We are developing new methods to make use of this long read data to understand the complete transcriptome.