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A case study for cloud based high throughput analysis of NGS data using the globus genomics system
Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis chal...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720014/ https://www.ncbi.nlm.nih.gov/pubmed/26925205 http://dx.doi.org/10.1016/j.csbj.2014.11.001 |
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author | Bhuvaneshwar, Krithika Sulakhe, Dinanath Gauba, Robinder Rodriguez, Alex Madduri, Ravi Dave, Utpal Lacinski, Lukasz Foster, Ian Gusev, Yuriy Madhavan, Subha |
author_facet | Bhuvaneshwar, Krithika Sulakhe, Dinanath Gauba, Robinder Rodriguez, Alex Madduri, Ravi Dave, Utpal Lacinski, Lukasz Foster, Ian Gusev, Yuriy Madhavan, Subha |
author_sort | Bhuvaneshwar, Krithika |
collection | PubMed |
description | Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis challenge that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis. These capabilities are implemented using the “Globus Genomics” system, which is an enhanced Galaxy workflow system made available as a service that offers users the capability to process and transfer data easily, reliably and quickly to address end-to-endNGS analysis requirements. The Globus Genomics system is built on Amazon 's cloud computing infrastructure. The system takes advantage of elastic scaling of compute resources to run multiple workflows in parallel and it also helps meet the scale-out analysis needs of modern translational genomics research. |
format | Online Article Text |
id | pubmed-4720014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-47200142016-02-26 A case study for cloud based high throughput analysis of NGS data using the globus genomics system Bhuvaneshwar, Krithika Sulakhe, Dinanath Gauba, Robinder Rodriguez, Alex Madduri, Ravi Dave, Utpal Lacinski, Lukasz Foster, Ian Gusev, Yuriy Madhavan, Subha Comput Struct Biotechnol J Research Article Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis challenge that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis. These capabilities are implemented using the “Globus Genomics” system, which is an enhanced Galaxy workflow system made available as a service that offers users the capability to process and transfer data easily, reliably and quickly to address end-to-endNGS analysis requirements. The Globus Genomics system is built on Amazon 's cloud computing infrastructure. The system takes advantage of elastic scaling of compute resources to run multiple workflows in parallel and it also helps meet the scale-out analysis needs of modern translational genomics research. Research Network of Computational and Structural Biotechnology 2014-11-07 /pmc/articles/PMC4720014/ /pubmed/26925205 http://dx.doi.org/10.1016/j.csbj.2014.11.001 Text en © 2014 Bhuvaneshwar et al. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology. http://creativecommons.org/licenses/by/3.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Research Article Bhuvaneshwar, Krithika Sulakhe, Dinanath Gauba, Robinder Rodriguez, Alex Madduri, Ravi Dave, Utpal Lacinski, Lukasz Foster, Ian Gusev, Yuriy Madhavan, Subha A case study for cloud based high throughput analysis of NGS data using the globus genomics system |
title | A case study for cloud based high throughput analysis of NGS data using the globus genomics system |
title_full | A case study for cloud based high throughput analysis of NGS data using the globus genomics system |
title_fullStr | A case study for cloud based high throughput analysis of NGS data using the globus genomics system |
title_full_unstemmed | A case study for cloud based high throughput analysis of NGS data using the globus genomics system |
title_short | A case study for cloud based high throughput analysis of NGS data using the globus genomics system |
title_sort | case study for cloud based high throughput analysis of ngs data using the globus genomics system |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720014/ https://www.ncbi.nlm.nih.gov/pubmed/26925205 http://dx.doi.org/10.1016/j.csbj.2014.11.001 |
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