A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model

Development of high-throughput technologies, such as Next-generation sequencing, allows thousands of experiments to be performed simultaneously while reducing resource requirement. Consequently, a massive amount of experiment data is now rapidly generated. Nevertheless, the data are not readily usab...

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Main Authors: Watthanai Pinthong, Panya Muangruen, Prapat Suriyaphol, Dumrong Mairiang
Format: Article
Language:English
Published: PeerJ Inc. 2016-07-01
Series:PeerJ
Subjects:
Online Access:https://peerj.com/articles/2248.pdf
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spelling doaj-d1b4193df0e6439ca5f6db5ea578d3972020-11-24T23:01:19ZengPeerJ Inc.PeerJ2167-83592016-07-014e224810.7717/peerj.2248A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a modelWatthanai Pinthong0Panya Muangruen1Prapat Suriyaphol2Dumrong Mairiang3Department of Anatomy, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, ThailandSiriraj Information Technology Department, Faculty of Medicine Siriraj Hospital, Mahidol University, , ThailandDivision of Bioinformatics and Data Management for Research, Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, ThailandMedical Biotechnology Research Laboratory, The National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathumthani, ThailandDevelopment of high-throughput technologies, such as Next-generation sequencing, allows thousands of experiments to be performed simultaneously while reducing resource requirement. Consequently, a massive amount of experiment data is now rapidly generated. Nevertheless, the data are not readily usable or meaningful until they are further analysed and interpreted. Due to the size of the data, a high performance computer (HPC) is required for the analysis and interpretation. However, the HPC is expensive and difficult to access. Other means were developed to allow researchers to acquire the power of HPC without a need to purchase and maintain one such as cloud computing services and grid computing system. In this study, we implemented grid computing in a computer training center environment using Berkeley Open Infrastructure for Network Computing (BOINC) as a job distributor and data manager combining all desktop computers to virtualize the HPC. Fifty desktop computers were used for setting up a grid system during the off-hours. In order to test the performance of the grid system, we adapted the Basic Local Alignment Search Tools (BLAST) to the BOINC system. Sequencing results from Illumina platform were aligned to the human genome database by BLAST on the grid system. The result and processing time were compared to those from a single desktop computer and HPC. The estimated durations of BLAST analysis for 4 million sequence reads on a desktop PC, HPC and the grid system were 568, 24 and 5 days, respectively. Thus, the grid implementation of BLAST by BOINC is an efficient alternative to the HPC for sequence alignment. The grid implementation by BOINC also helped tap unused computing resources during the off-hours and could be easily modified for other available bioinformatics software.https://peerj.com/articles/2248.pdfBasic Local Alignment Search Tools (BLAST)Grid computingData-intensive methodsBerkeley Open Infrastructure for Network Computing (BOINC)Next-generation sequencing (NGS)
collection DOAJ
language English
format Article
sources DOAJ
author Watthanai Pinthong
Panya Muangruen
Prapat Suriyaphol
Dumrong Mairiang
spellingShingle Watthanai Pinthong
Panya Muangruen
Prapat Suriyaphol
Dumrong Mairiang
A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model
PeerJ
Basic Local Alignment Search Tools (BLAST)
Grid computing
Data-intensive methods
Berkeley Open Infrastructure for Network Computing (BOINC)
Next-generation sequencing (NGS)
author_facet Watthanai Pinthong
Panya Muangruen
Prapat Suriyaphol
Dumrong Mairiang
author_sort Watthanai Pinthong
title A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model
title_short A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model
title_full A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model
title_fullStr A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model
title_full_unstemmed A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model
title_sort simple grid implementation with berkeley open infrastructure for network computing using blast as a model
publisher PeerJ Inc.
series PeerJ
issn 2167-8359
publishDate 2016-07-01
description Development of high-throughput technologies, such as Next-generation sequencing, allows thousands of experiments to be performed simultaneously while reducing resource requirement. Consequently, a massive amount of experiment data is now rapidly generated. Nevertheless, the data are not readily usable or meaningful until they are further analysed and interpreted. Due to the size of the data, a high performance computer (HPC) is required for the analysis and interpretation. However, the HPC is expensive and difficult to access. Other means were developed to allow researchers to acquire the power of HPC without a need to purchase and maintain one such as cloud computing services and grid computing system. In this study, we implemented grid computing in a computer training center environment using Berkeley Open Infrastructure for Network Computing (BOINC) as a job distributor and data manager combining all desktop computers to virtualize the HPC. Fifty desktop computers were used for setting up a grid system during the off-hours. In order to test the performance of the grid system, we adapted the Basic Local Alignment Search Tools (BLAST) to the BOINC system. Sequencing results from Illumina platform were aligned to the human genome database by BLAST on the grid system. The result and processing time were compared to those from a single desktop computer and HPC. The estimated durations of BLAST analysis for 4 million sequence reads on a desktop PC, HPC and the grid system were 568, 24 and 5 days, respectively. Thus, the grid implementation of BLAST by BOINC is an efficient alternative to the HPC for sequence alignment. The grid implementation by BOINC also helped tap unused computing resources during the off-hours and could be easily modified for other available bioinformatics software.
topic Basic Local Alignment Search Tools (BLAST)
Grid computing
Data-intensive methods
Berkeley Open Infrastructure for Network Computing (BOINC)
Next-generation sequencing (NGS)
url https://peerj.com/articles/2248.pdf
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