Lab Genomic data gives researchers enormous power to uncover how genomic changes drive cancer formation, metastasis, drug response, and recurrence. However, one of the challenges of identifying genetic abnormalities is that the cancer “drivers,” which promote tumor growth, naturally occur in a long-tail distribution. This means that many patients with cancer have causal genomic changes that only occur in a very small percentage of cancers. Characterizing these rare events requires big data.
The Cancer Genome Atlas, better known as the TCGA, began as a small pilot and has grown to become a precious resource for researchers and physicians in the field of cancer research. TCGA currently covers 33 cancer types, and harbors over 20,000 individual tumor samples, each contains a wealth of genetic, proteomic, histologic and clinical data. Exploring such unprecedented amounts of data presents promising avenues for cancer research, yet mining it without comprehensive computational skills is an almost impossible task. Luckily, several tools have recently been developed to aid ‘non-programmer’ researchers in exploring and analyzing TCGA data with ease and elegance.
Here are some of the best and most innovative tools to mine TCGA data: