Edited by Anne S.Y. Cheung and Rolf H. Weber
Chapter 11: International genomic cloud computing: ‘mining’ the terms of service
The genomic research community is facing a big data challenge. The cost of genetic sequencing is falling faster than that of storage and bandwidth, and thanks to advanced technologies for genetic sequencing and analysis, more data have been generated than ever before. Consider that the average human whole-genome sequence contains approximately three billion data points (known as ‘base pairs’) and generates roughly 100 gigabytes of data, and that the whole genome of a tumour and a matching normal tissue sample consumes one terabyte of uncompressed data. A project utilizing thousands of genomes (not to mention phenotypic data and the linking of local data with online public data) for disease research would quite quickly generate petabytes of data. Yet, genetic sequencing machines currently are incapable of generating a single string containing billions of properly organized nucleotides. Instead, they produce shorter, fragmented and unordered sections, each only a few hundred or thousand nucleotides long. Researchers must rely on technicians and computers to properly organize them. At the same time, the current reality is that the amount of genomic data and associated clinical data needed to procure the statistical power required to advance biomedical research and clinical practice exceeds the technical capacity of any single site or server. This is especially the case in rare disease research or cohort studies that look at genetic factors responding to specialized treatments.
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