What is the Cancer Genomics Cloud? A cloud platform by Seven Bridges, funded by the NCI, that gives researchers on-demand access to massive cancer genomic datasets.
Cancer research has a data problem, and quite a good one. Technologies like next-generation sequencing now generate enormous volumes of genomic data, far more than any single institution can store or process locally. This is were Cloud Computing comes into play.
Bringing the tools to the data
The Cancer Genomics Cloud (CGC) hosts datasets like The Cancer Genome Atlas (TCGA), which maps the genomic profiles of 33 cancer types. Rather than downloading terabytes of data, researchers bring their analysis tools to the data, running workflows directly in the cloud. No data transfer, no local supercomputer needed, no upfront investment just pay what you need.
Why it matters
Three words: democratisation, collaboration, reproducibility.
Previously, only very well funded institutions could run large-scale genomic analyses. With a “pay what you need” model, a small research group anywhere in the world can query the same datasets as a major cancer centre. Teams at different institutions can share the same cloud workspace, and containerised workflows mean that any analysis can be replicated and extended by others, solving a persistent challenge in computational biology.
The CGC is an exciting example of how PaaS (Platform as a Service) can remove infrastructure as a barrier to scientific discovery.
Final thoughts
Cloud computing is not just a technical convenience, it is reshaping who gets to do science and at what scale. Platforms like the CGC paved the way for what is now standard practice in large-scale genomic research. Today, AWS, Google Cloud, and Azure are the default infrastructure for institutions with the means to adopt them — and the rapid growth of the market suggests the rest of the field is catching up fast.
References
Lau, J.W. et al. (2017). The Cancer Genomics Cloud: Collaborative, reproducible, and democratized — a new paradigm in large-scale computational research. Cancer Research, 77(21), e3–e6. https://doi.org/10.1158/0008-5472.CAN-17-0387