ClassifieR 2.0: expanding interactive gene expression-based stratification to prostate and high-grade serous ovarian cancer
ClassifieR 2.0 combines molecular subtyping of prostate cancer and HGSOC with commonly used sample annotation tools in a single, user-friendly platform, allowing scientists without bioinformatics training to explore prostate and HGSOC transcriptional data without the need for extensive bioinformatics knowledge or manual data handling to operate various packages. Our sigInfer method within ClassifieRp enables the inference of commercially available gene signatures for prostate cancer, while ClassifieRov incorporates consensus molecular subtyping for HGSOC. Overall, ClassifieR 2.0 aims to make molecular subtyping more accessible to the wider research community. This is crucial for increased understanding of the molecular heterogeneity of these cancers and developing personalised treatment strategies.
Authors
- Aideen McCabe (ORCID: https://orcid.org/0000-0002-1218-3671)
- Gerard P. Quinn (ORCID: https://orcid.org/0000-0001-8784-0157)
- Ross G. Murphy (ORCID: https://orcid.org/0000-0001-8583-8635)
- Kellie Dean (ORCID: https://orcid.org/0000-0002-3213-8181)
- Simon S. McDade (ORCID: https://orcid.org/0000-0002-3024-4773)
- Suneil Jain (ORCID: https://orcid.org/0000-0001-7429-4791)
- Micheál Ó. Dálaigh
Institutions
- Queen's University Belfast (GB)
- Belfast Health and Social Care Trust (GB)
- University of Ulster (GB)
- Science Foundation Ireland (IE)
- Ollscoil na Gaillimhe – University of Galway (IE)
- University College Cork (IE)
- Department for Infrastructure (GB)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-11-21
- DOI
- https://doi.org/10.1186/s12859-024-05981-6
- Citations
- 1
- Primary Topic
- Cancer, Lipids, and Metabolism
- Type
- article
- Field-Weighted Citation Impact
- 0.29
Funders
- Movember Foundation
- Cancer Research UK
- Science Foundation Ireland
- Medical Research Council