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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. (More)


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Paper citation in several formats:
Heinzlreiter, P.; Perkins, J.; Torreño, Ó.; Karlsson, J.; Ranea, J.; Mitterecker, A.; Blanca, M. and Trelles, O. (2014). A Cloud-based GWAS Analysis Pipeline for Clinical Researchers. In Proceedings of the 4th International Conference on Cloud Computing and Services Science - CLOSER; ISBN 978-989-758-019-2; ISSN 2184-5042, SciTePress, pages 387-394. DOI: 10.5220/0004802103870394

author={Paul Heinzlreiter. and James Richard Perkins. and Óscar Torreño. and Johan Karlsson. and Juan Antonio Ranea. and Andreas Mitterecker. and Miguel Blanca. and Oswaldo Trelles.},
title={A Cloud-based GWAS Analysis Pipeline for Clinical Researchers},
booktitle={Proceedings of the 4th International Conference on Cloud Computing and Services Science - CLOSER},


JO - Proceedings of the 4th International Conference on Cloud Computing and Services Science - CLOSER
TI - A Cloud-based GWAS Analysis Pipeline for Clinical Researchers
SN - 978-989-758-019-2
IS - 2184-5042
AU - Heinzlreiter, P.
AU - Perkins, J.
AU - Torreño, Ó.
AU - Karlsson, J.
AU - Ranea, J.
AU - Mitterecker, A.
AU - Blanca, M.
AU - Trelles, O.
PY - 2014
SP - 387
EP - 394
DO - 10.5220/0004802103870394
PB - SciTePress