The genomic data generated from next-generation sequencing machines doesn't amount to much more than alphabet soup if it's not subjected to significant computational processing and statistical analysis. For the data to be useful, the trick is to turn those As, Ts, Gs, and Cs into a manageable description of disease risks and other genetic predispositions. That requires a lot of computational power and time—already a significant bottleneck for some genomic analysis companies.
Several companies are looking to the cloud as a way to help them analyze all the data. The idea is that researchers can send their data to a Web-hosted analysis service that will process raw data into a genetic profile. However, the data files generated by sequencing machines are so massive that the mundane issue of uploading large files to the cloud becomes its own issue. The strategy of a Redwood City, California-based startup called Bina Technologies is to divide and conquer: give customers an in-house data-crunching machine that will turn a mountain of raw sequence into easily shared genetic profiles. Those profiles can then be quickly uploaded to Bina Technologies' cloud-hosted site for data management, sharing, and aggregation.