  Cloud computing is a cost-effective way for start-up life sciences
laboratories to store and manage their data. However, in many instances the
data stored over the cloud could be redundant which makes cloud-based data
management inefficient and costly because one has to pay for every byte of data
stored over the cloud. Here, we tested efficient management of data generated
by an electron cryo microscopy (cryoEM) lab on a cloud-based environment. The
test data was obtained from cryoEM repository EMPIAR. All the images were
subjected to an in-house parallelized version of principal component analysis.
An efficient cloud-based MapReduce modality was used for parallelization. We
showed that large data in order of terabytes could be efficiently reduced to
its minimal essential self in a cost-effective scalable manner. Furthermore,
on-spot instance on Amazon EC2 was shown to reduce costs by a margin of about
27 percent. This approach could be scaled to data of any large volume and type.
