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NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks.

Baker EJ, Lin GN, Liu H, Kosuri R - Source Code Biol Med (2007)

Bottom Line: We also find that genome-scale sizes of the stored data are easily adaptable to logistical networks.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, School of Engineering and Computer Science, Baylor University, Waco, TX, USA. Erich_Baker@baylor.edu.

ABSTRACT

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.

No MeSH data available.


High level schema of NFU-enabled FASTA. The burden of database maintenance and distribution within the IBP network is handled by the server using the LoRS upload tool and associated XND files to catalog distributed database location and replicate. Following a request for execution, the server retrieves the query file from the client and uploads the query file and modified FASTA executable onto NFU-enabled storage depots where the appropriate database chunks reside. The FASTA results are download directly from the network, modified if necessary, and returned to the client.
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Figure 1: High level schema of NFU-enabled FASTA. The burden of database maintenance and distribution within the IBP network is handled by the server using the LoRS upload tool and associated XND files to catalog distributed database location and replicate. Following a request for execution, the server retrieves the query file from the client and uploads the query file and modified FASTA executable onto NFU-enabled storage depots where the appropriate database chunks reside. The FASTA results are download directly from the network, modified if necessary, and returned to the client.

Mentions: The overall server architecture consists of a DB Uploading Server, XNDServer, and Execution Uploading Server; a high-level schema is described in Figure 1. The DB Uploading Server and XNDServer are adapted from the IBP-BLAST system as previously reported [18]. Briefly, the DB Uploading Server partitions the original FASTA-formatted databases into smaller 'chunks,' which are uploaded into the logistical network through the LoRs upload tool (Figures 2, 3). This operation returns XND files (xml-formatted reference files), indexed references to uploaded files which are managed by the XNDServer (Figure 3). The Execution Uploading Server obtains the database chunk network location reference from the XNDServer and uploads the query file and FASTA executable file to the locations where the data resides for FASTA execution (Figure 4). Results of all individual chunk executions are returned to the server by the depot where they are merged to produce complete results for each query. Ultimately, these are downloaded by client side services to be displayed to the user.


NFU-Enabled FASTA: moving bioinformatics applications onto wide area networks.

Baker EJ, Lin GN, Liu H, Kosuri R - Source Code Biol Med (2007)

High level schema of NFU-enabled FASTA. The burden of database maintenance and distribution within the IBP network is handled by the server using the LoRS upload tool and associated XND files to catalog distributed database location and replicate. Following a request for execution, the server retrieves the query file from the client and uploads the query file and modified FASTA executable onto NFU-enabled storage depots where the appropriate database chunks reside. The FASTA results are download directly from the network, modified if necessary, and returned to the client.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2211279&req=5

Figure 1: High level schema of NFU-enabled FASTA. The burden of database maintenance and distribution within the IBP network is handled by the server using the LoRS upload tool and associated XND files to catalog distributed database location and replicate. Following a request for execution, the server retrieves the query file from the client and uploads the query file and modified FASTA executable onto NFU-enabled storage depots where the appropriate database chunks reside. The FASTA results are download directly from the network, modified if necessary, and returned to the client.
Mentions: The overall server architecture consists of a DB Uploading Server, XNDServer, and Execution Uploading Server; a high-level schema is described in Figure 1. The DB Uploading Server and XNDServer are adapted from the IBP-BLAST system as previously reported [18]. Briefly, the DB Uploading Server partitions the original FASTA-formatted databases into smaller 'chunks,' which are uploaded into the logistical network through the LoRs upload tool (Figures 2, 3). This operation returns XND files (xml-formatted reference files), indexed references to uploaded files which are managed by the XNDServer (Figure 3). The Execution Uploading Server obtains the database chunk network location reference from the XNDServer and uploads the query file and FASTA executable file to the locations where the data resides for FASTA execution (Figure 4). Results of all individual chunk executions are returned to the server by the depot where they are merged to produce complete results for each query. Ultimately, these are downloaded by client side services to be displayed to the user.

Bottom Line: We also find that genome-scale sizes of the stored data are easily adaptable to logistical networks.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, School of Engineering and Computer Science, Baylor University, Waco, TX, USA. Erich_Baker@baylor.edu.

ABSTRACT

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.

No MeSH data available.