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NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks
BACKGROUND: Advances in Internet technologies have allowed life science researchers to reach beyond the lab-centric research paradigm to create distributed collaborations. Of the existing technologies that support distributed collaborations, there are currently none that simultaneously support data...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2211279/ https://www.ncbi.nlm.nih.gov/pubmed/18039379 http://dx.doi.org/10.1186/1751-0473-2-8 |
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author | Baker, Erich J Lin, Guan N Liu, Huadong Kosuri, Ravi |
author_facet | Baker, Erich J Lin, Guan N Liu, Huadong Kosuri, Ravi |
author_sort | Baker, Erich J |
collection | PubMed |
description | BACKGROUND: Advances in Internet technologies have allowed life science researchers to reach beyond the lab-centric research paradigm to create distributed collaborations. Of the existing technologies that support distributed collaborations, there are currently none that simultaneously support data storage and computation as a shared network resource, enabling computational burden to be wholly removed from participating clients. Software using computation-enable logistical networking components of the Internet Backplane Protocol provides a suitable means to accomplish these tasks. Here, we demonstrate software that enables this approach by distributing both the FASTA algorithm and appropriate data sets within the framework of a wide area network. RESULTS: For large datasets, computation-enabled logistical networks provide a significant reduction in FASTA algorithm running time over local and non-distributed logistical networking frameworks. We also find that genome-scale sizes of the stored data are easily adaptable to logistical networks. CONCLUSION: Network function unit-enabled Internet Backplane Protocol effectively distributes FASTA algorithm computation over large data sets stored within the scaleable network. In situations where computation is subject to parallel solution over very large data sets, this approach provides a means to allow distributed collaborators access to a shared storage resource capable of storing the large volumes of data equated with modern life science. In addition, it provides a computation framework that removes the burden of computation from the client and places it within the network. |
format | Text |
id | pubmed-2211279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-22112792008-01-19 NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks Baker, Erich J Lin, Guan N Liu, Huadong Kosuri, Ravi Source Code Biol Med Methodology BACKGROUND: Advances in Internet technologies have allowed life science researchers to reach beyond the lab-centric research paradigm to create distributed collaborations. Of the existing technologies that support distributed collaborations, there are currently none that simultaneously support data storage and computation as a shared network resource, enabling computational burden to be wholly removed from participating clients. Software using computation-enable logistical networking components of the Internet Backplane Protocol provides a suitable means to accomplish these tasks. Here, we demonstrate software that enables this approach by distributing both the FASTA algorithm and appropriate data sets within the framework of a wide area network. RESULTS: For large datasets, computation-enabled logistical networks provide a significant reduction in FASTA algorithm running time over local and non-distributed logistical networking frameworks. We also find that genome-scale sizes of the stored data are easily adaptable to logistical networks. CONCLUSION: Network function unit-enabled Internet Backplane Protocol effectively distributes FASTA algorithm computation over large data sets stored within the scaleable network. In situations where computation is subject to parallel solution over very large data sets, this approach provides a means to allow distributed collaborators access to a shared storage resource capable of storing the large volumes of data equated with modern life science. In addition, it provides a computation framework that removes the burden of computation from the client and places it within the network. BioMed Central 2007-11-26 /pmc/articles/PMC2211279/ /pubmed/18039379 http://dx.doi.org/10.1186/1751-0473-2-8 Text en Copyright © 2007 Baker et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Baker, Erich J Lin, Guan N Liu, Huadong Kosuri, Ravi NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks |
title | NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks |
title_full | NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks |
title_fullStr | NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks |
title_full_unstemmed | NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks |
title_short | NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks |
title_sort | nfu-enabled fasta: moving bioinformatics applications onto wide area networks |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2211279/ https://www.ncbi.nlm.nih.gov/pubmed/18039379 http://dx.doi.org/10.1186/1751-0473-2-8 |
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