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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2016-07-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/2248.pdf |
| _version_ | 1827606534168772608 |
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| author | Watthanai Pinthong Panya Muangruen Prapat Suriyaphol Dumrong Mairiang |
| author_facet | Watthanai Pinthong Panya Muangruen Prapat Suriyaphol Dumrong Mairiang |
| author_sort | Watthanai Pinthong |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T06:38:38Z |
| format | Article |
| id | doaj.art-d1b4193df0e6439ca5f6db5ea578d397 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:38:38Z |
| publishDate | 2016-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-d1b4193df0e6439ca5f6db5ea578d3972023-12-03T10:55:00ZengPeerJ 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) |
| 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) |
| title | 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_short | 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 |
| 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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