The Eyras Group is working to understand the biology of RNA and cancer using computational methods.
It develops machine learning methods to identify new signatures of therapeutic vulnerability in cancer, and develops new algorithms to facilitate the systematic implementation of long-read sequencing for precision medicine, and for the study model and non-model organisms.
Enabling new technologies to study RNA biology
Differential RNA processing can characterise relevant physiological processes but remain largely unexplored for their role in disease onset and response to therapy. We investigate RNA processing alterations in cancer and how they can help to understand cell transformation, metastasis and therapy resistance. We develop methods to uncover new molecular mechanisms and to facilitate the analysis of patient samples at a massive scale.
RNA sequencing is becoming more affordable and easier to carry out systematically for many samples, even in small labs. In particular, long-read sequencing provides many advantages to study transcriptomes, but alternative splicing complexity and error rates present many challenges for the processing and interpretation of the data. We are developing algorithms to study RNA biology from long-read data, to enable the study of the transcriptome dynamics across individuals and in model as well as non-model species. We are especially interested in the application of these methods in cancer precision medicine.
We also develop machine learning methods to process and interpret high-throughput genomics data. These methods include applications to the study of long-read transcriptomics, the discovery of signatures of cancer progression and therapy resistance, and the selection of predictive variables from large heterogeneous datasets.
In summary, our aim is to develop new algorithms to enable new technologies for their application to study RNA and to address open questions in health, ecology, industry and society; and make them widely available to the scientific community.
A link to the software of the group is here.
You can read more about the group on the ANU website here.
Our research is organised into the following themes:
- RNA biology in cancer
- Algorithms for transcriptomics and alternative splicing
- Algorithms for long-read sequencing
- Machine learning in genomics
Highlight publications
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ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning. Genome Biology (2019) Nov 29; 20(1):260. |
ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning. |
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Genome Biology (2018). 19(1):40 |
SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions. |
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The functional impact of alternative splicing in cancer. Cell Reports (2017) 20(9):2215-2226. |
The functional impact of alternative splicing in cancer. |
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Genome Research (2016). 26(6), 732-744. |
Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks. |
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Nucleic Acids Research (2015). 43(3):1345-56. |
Detection of recurrent alternative splicing switches in tumor samples reveals novel signatures of cancer. |