The Oshlack Lab uses computational and statistical techniques 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.
We work on many aspects of sequencing analysis and applications in disease and development. We are best known for our work in transcriptomics which spans nearly 20 years of research and technology development.
Variants of the transcriptome
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.
Single cell sequencing analysis
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.
Long read sequencing
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.
JAFFAL: detecting fusion genes with long-read transcriptome sequencing (2022) Genome Biology 10.1186/s13059-021-02588-5
MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data (2021) Genome Biology 10.1186/s13059-021-02507-8
Clustering trees: a visualization for evaluating clusterings at multiple resolutions (2018), Gigascience 10.1093/gigascience/giy083
Splatter: simulation of single-cell RNA sequencing data (2017) 10.1186/s13059-017-1305-0
Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database (2017) 10.1101/206573
STRetch: detecting and discovering pathogenic short tandem repeats expansions (2017) 10.1101/159228
A full list of publications can be found here: https://orcid.org/0000-0001-9788-5690