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Bio and health informatics meets cloud : BioVLab as an example

Heejoon Chae1, Inuk Jung2, Hyungro Lee1, Suresh Marru3, Seong-Whan Lee4 and Sun Kim256*

Author Affiliations

1 School of Informatics and Computing, Indiana University, Bloomington, Indiana, USA

2 Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea

3 Pervasive Technology Institute, , Bloomington, Indiana, USA

4 Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea

5 Computer Science Department, Seoul National University, Seoul, Korea

6 Bioinformatics Institute, Seoul National University, Seoul, Korea

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Health Information Science and Systems 2013, 1:6  doi:10.1186/2047-2501-1-6

Published: 4 February 2013


The exponential increase of genomic data brought by the advent of the next or the third generation sequencing (NGS) technologies and the dramatic drop in sequencing cost have driven biological and medical sciences to data-driven sciences. This revolutionary paradigm shift comes with challenges in terms of data transfer, storage, computation, and analysis of big bio/medical data. Cloud computing is a service model sharing a pool of configurable resources, which is a suitable workbench to address these challenges. From the medical or biological perspective, providing computing power and storage is the most attractive feature of cloud computing in handling the ever increasing biological data. As data increases in size, many research organizations start to experience the lack of computing power, which becomes a major hurdle in achieving research goals. In this paper, we review the features of publically available bio and health cloud systems in terms of graphical user interface, external data integration, security and extensibility of features. We then discuss about issues and limitations of current cloud systems and conclude with suggestion of a biological cloud environment concept, which can be defined as a total workbench environment assembling computational tools and databases for analyzing bio/medical big data in particular application domains.

Bioinformatics; Cloud computing; Big data; Workflow; User interface; Data integration; Analysis; Security