Authors:
Paul Heinzlreiter
1
;
James Richard Perkins
2
;
Óscar Torreño
3
;
Johan Karlsson
4
;
Juan Antonio Ranea
5
;
Andreas Mitterecker
6
;
Miguel Blanca
2
and
Oswaldo Trelles
3
Affiliations:
1
RISC Software GmbH and Leibniz Supercomputing Centre (LRZ), Austria
;
2
University Hospital-IBIMA, Spain
;
3
RISC Software GmbH and University of Málaga, Austria
;
4
University of Málaga and Integromics S.L., Spain
;
5
University of Málaga, Spain
;
6
RISC Software GmbH and Joh. Kepler University Linz, Austria
Keyword(s):
Cloud Computing, Bioinformatics, Biomedicine.
Related
Ontology
Subjects/Areas/Topics:
Big Data Cloud Services
;
Cloud Application Architectures
;
Cloud Computing
;
Cloud Scenarios
;
Collaboration and e-Services
;
Data Engineering
;
e-Business
;
Enterprise Information Systems
;
Fundamentals
;
Mobile Software and Services
;
Ontologies and the Semantic Web
;
Platforms and Applications
;
Service Discovery
;
Services Science
;
Software Agents and Internet Computing
;
Software Engineering
;
Software Engineering Methods and Techniques
;
Telecommunications
;
Web Services
;
Wireless Information Networks and Systems
Abstract:
The cost of obtaining genome-scale biomedical data continues to drop rapidly, with many hospitals and universities
being able to produce large amounts of data. Managing and analysing such ever-growing datasets
is becoming a crucial issue. Cloud computing presents a good solution to this problem due to its flexibility
in obtaining computational resources. However, it is essential to allow end-users with no experience to take
advantage of the cloud computing model of elastic resource provisioning. This paper presents a workflow
that allows the end-user to perform the core steps of a genome wide association analysis where raw gene-
expression data is quality assessed. A number of steps in this process are computationally intensive and vary
greatly depending on the size of the study, from a few samples to a few thousand. Therefore cloud computing
provides an ideal solution to this problem by enabling scalability due to elastic resource provisioning. The key
contributions of thi
s paper are a real world application of cloud computing addressing a critical problem in
biomedicine through parallelization of the appropriate parts of the workflow as well as enabling the end-user
to concentrate on data analysis and biological interpretation of results by taking care of the computational
aspects.
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