Cloud and heterogeneous computing solutions exist today for the emerging big data problems in biology

In their commentary on our Review (Computational solutions to large-scale data management and analysis. Nature Rev. Genet. 11, 647–657 (2010))¹, Trelles et al. (Big data, but are we ready? Nature Rev. Genet. 8 Feb 2011 (doi:10.1038/nrg2857-c1))² claim that processing 1 petabyte (PB) of data on a 1,000 node Amazon instance would take 750 days and cost US$6 million. They argue that, in their current form, cloud and heterogeneous computing will soon be incapable of processing the growing volume of biological data. This assessment is at odds with the facts.

As an alternative to cloud and heterogeneous computing, the authors suggest using large-scale multi-socket computers. These systems create the illusion of a 'single' computer from the underlying cluster. However, for the big-data problems processed in embarrassingly parallel ways, this type of architecture is not needed and just imposes unnecessary costs. We presented a spectrum of possible solutions and, importantly, the pros and cons of each system¹. Providers like Amazon offer more than just a high-performance computing (HPC) resource; they deliver a low-cost, ubiquitous, user-friendly, standardized platform for developing HPC applications. Acquiring a multi-million dollar computational cluster is no longer a prerequisite to doing great 'big-data' science; one just needs a great idea and the skills to execute. We argue that increasing access will hasten the pace of innovation in informatics algorithm development — a priority for future research that was identified in the recent report by the President's Council of Advisors on Science and Technology (PCAST)^4,5.