Cloud computing refers to the practice of using a network of remote servers hosted on the Internet to store, manage and process data. It is an alternative to the use of a local server or a personal computer. In a sense the network that forms the cloud can be thought of as an electricity grid. The key advantages are the ability to share data, to enable collaboration, and to offer greater protection of data should a piece of hardware crash.
The University of Warwick has developed a cloud-based microbial bioinformatics resource. The created database is said, by the developers, to be the largest of its type in the world. The project is called the ‘Cloud Infrastructure for Microbial Bioinformatics’ (CLIMB) project. The focus is with medical microbiology, where microorganisms that cause disease are analyzed. Social media coverage relating to the CLIMB project are posted via the MRC Climb (@MRCClimb) Twitter feed.
The CLIMB has been set-up through based on the idea of Professor Mark Pallen (University of Warwick). Interviewed by his university, the academic explains: “CLIMB represents a user-friendly, one-stop shop for sharing software and data between medical microbiologists in the academic and clinical arenas.”
The cloud already contains data from dozens of research groups. An important part of the system is virtualization, which enables scientists to work in a simulated computer environment populated by virtual machines. These are placed on top of physical hardware and resemble conventional servers. These provide large data storage capabilities and allow several biological databases to be integrated.
The primary data is taken from molecular sequencing of microbial genomes, which allow medical microbiologists to assess the most appropriate antimicrobials to treat a patient or to track epidemiological trends within the community. The use of such data is termed “bioinformatics.” Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data.
Bioinformatics has two aims: the implementation of computer programs to enable efficient access to information; and the creation of new algorithms and statistical tools to understand relationships between items of data within large data sets.
