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Learn to analyse genomics data from cancer samples with hands-on practical exercises in mutation calling, driver gene identification, mutational signature analysis and RNA deconvolution.
Cancer is one of the leading causes of death in the world. Across Asia, 9.82 million new cases and over 5.44 million deaths were reported in 2022, accounting for approximately 50% of global cases and mortality. Its burden and outcome are influenced by baseline genetics, as well as different environmental and medical factors, that vary across human populations. These factors leave specific footprints in the genome of cells, which can be studied through bioinformatics approaches. However, studies on cancer genetic susceptibility and interventions are largely based in the Global North, resulting in disproportionate representation of Asian datasets and research development. Therefore, there is an urgent need to address the widening global disparities in cancer genomics research to include more global populations.
This Cancer Genome Analysis course will provide Asian-based scientists with the requisite skills in processing, analysing, and interpreting data from cancer genomes. Participants will be equipped with the essential informatics skills and knowledge required to begin analysing next generation sequencing data and carry out some of the most common types of analysis in somatic genome studies. Participants will benefit from hands-on practical exercises in mutation calling, driver gene identification, mutational signature analysis and RNA deconvolution. By analysing real-world datasets, students will gain valuable skills that they can later apply to grow their cancer genome research capability, and drive the advancement of cancer genomics in Asia. Guest seminars will highlight translational applications of genomics in oncology, ethical aspects, plus opportunities and challenges of careers in cancer genomics. This will contribute directly to the much-needed capacity building for cancer research, and scientists will be able to strengthen the application of genomics in clinical practice, public health and policy needs.
The course will be led by experts in cancer genomics based in Asia, Latin America and USA as well as research and industry experts based in Asia.
The course is open to scientists who are involved in cancer research including PhD students, postdoctoral researchers, clinical scientists and medical professionals.
To train scientists to analyse genomics data from cancer samples. Applying standardised and publicly available datasets, participants will benefit from hands-on practical exercises in mutation calling, driver gene identification, mutational signature analysis and RNA deconvolution.
Topics to be covered
- Data formats and organisation in cancer NGS studies
- Somatic mutation calling
- Driver gene identification and oncoplots
- Mutational signature analysis
- Structural Variants
By the end of the course, participants should be able to:
- Perform QC assessment of somatic NGS data
- Explain the algorithmic concepts behind somatic variant calling
- Perform mutation calling on cancer sequencing files
- Identify driver genes associated with tumorigenesis
- Generate oncoplots to summarise the impact of mutations on cancer-associated genes
- Identify mutational signatures active in cancer samples
Participants are required to take the following pre-course modules (This will be provided as online self-paced study.):
- Unix/Linux
- Sample collection, preparation, storage and processing
- Molecular diagnostic applications and limitations
- Next-generation sequencing technologies
- Online databases for bioinformatics and cancer genomics
Course Runs
| Course Date | Course Title | Location | Citation DOI |
|---|---|---|---|
| 26 November–1 December 2023 | Cancer Genome Analysis - Latin America & the Caribbean | Universidad de la República, Montevideo, Uruguay | Citation DOI |
| 30 March–4 April 2025 | Cancer Genome Analysis - Asia | Khon Kaen University, Thailand | Citation DOI |
The course data are free to reuse and adapt with appropriate attribution. All course data in these repositories are licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
Each course landing page is assigned a DOI via Zenodo, providing a stable and citable reference. These DOIs can be found on the respective course landing pages and can be included in CVs or research publications, offering a professional record of the course contributions.
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