Exploiting Data Reduction Principles in Cloud-Based Data Management for Cryo-Image Data

TL;DR

This paper demonstrates that applying data reduction techniques like PCA in a cloud environment can significantly decrease storage costs for cryo-EM data, with potential scalability to other large datasets.

Contribution

The study introduces a scalable, cost-effective cloud-based data reduction method using PCA and MapReduce for cryo-EM data management.

Findings

Data reduction reduces storage costs by approximately 27%.

Efficient PCA implementation enables handling terabyte-scale cryo-EM data.

Method is scalable to various large-volume data types.

Abstract

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.