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Enzyme reaction annotation using cloud techniques.

Huang CC, Lin CY, Chang CW, Tang CY - Biomed Res Int (2013)

Bottom Line: The deluge of information which arose from high-throughput techniques in the postgenomic era has improved our understanding of biological data, although it presents obstacles in the data-processing stage.The high computational capacity provided by cloud computing has resulted in an exponential growth in the volume of incoming data.Our tool is designed as a single execution file; thus, it could be applied to any cloud platform in which multiple queries are supported.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Sciences, National Tsing Hua University, Hsinchu 300, Taiwan.

ABSTRACT
An understanding of the activities of enzymes could help to elucidate the metabolic pathways of thousands of chemical reactions that are catalyzed by enzymes in living systems. Sophisticated applications such as drug design and metabolic reconstruction could be developed using accurate enzyme reaction annotation. Because accurate enzyme reaction annotation methods create potential for enhanced production capacity in these applications, they have received greater attention in the global market. We propose the enzyme reaction prediction (ERP) method as a novel tool to deduce enzyme reactions from domain architecture. We used several frequency relationships between architectures and reactions to enhance the annotation rates for single and multiple catalyzed reactions. The deluge of information which arose from high-throughput techniques in the postgenomic era has improved our understanding of biological data, although it presents obstacles in the data-processing stage. The high computational capacity provided by cloud computing has resulted in an exponential growth in the volume of incoming data. Cloud services also relieve the requirement for large-scale memory space required by this approach to analyze enzyme kinetic data. Our tool is designed as a single execution file; thus, it could be applied to any cloud platform in which multiple queries are supported.

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The average execution time of the ERP method for 100 simulations.
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fig6: The average execution time of the ERP method for 100 simulations.

Mentions: In the model-building phase, we implemented the AR method in a server equipped with 12 CPUs (4 cores, 3 packages) and 128 GB of memory, and the server used for the ERP method was equipped with 2 CPUs (2 cores, 1 package) and 8 GB of memory. The average model-building time was over 1 hour for the AR method and 15 minutes for the ERP method. The reasons may be that the AR method needed to produce frequent item sets and many redundant rules was generated. Furthermore, estimates of the prediction time for a batch of query domain architectures are shown in Figure 6. The vertical axis indicates the execution time in seconds, and the horizontal axis marks the number of entries in a batch query.


Enzyme reaction annotation using cloud techniques.

Huang CC, Lin CY, Chang CW, Tang CY - Biomed Res Int (2013)

The average execution time of the ERP method for 100 simulations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814047&req=5

fig6: The average execution time of the ERP method for 100 simulations.
Mentions: In the model-building phase, we implemented the AR method in a server equipped with 12 CPUs (4 cores, 3 packages) and 128 GB of memory, and the server used for the ERP method was equipped with 2 CPUs (2 cores, 1 package) and 8 GB of memory. The average model-building time was over 1 hour for the AR method and 15 minutes for the ERP method. The reasons may be that the AR method needed to produce frequent item sets and many redundant rules was generated. Furthermore, estimates of the prediction time for a batch of query domain architectures are shown in Figure 6. The vertical axis indicates the execution time in seconds, and the horizontal axis marks the number of entries in a batch query.

Bottom Line: The deluge of information which arose from high-throughput techniques in the postgenomic era has improved our understanding of biological data, although it presents obstacles in the data-processing stage.The high computational capacity provided by cloud computing has resulted in an exponential growth in the volume of incoming data.Our tool is designed as a single execution file; thus, it could be applied to any cloud platform in which multiple queries are supported.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Sciences, National Tsing Hua University, Hsinchu 300, Taiwan.

ABSTRACT
An understanding of the activities of enzymes could help to elucidate the metabolic pathways of thousands of chemical reactions that are catalyzed by enzymes in living systems. Sophisticated applications such as drug design and metabolic reconstruction could be developed using accurate enzyme reaction annotation. Because accurate enzyme reaction annotation methods create potential for enhanced production capacity in these applications, they have received greater attention in the global market. We propose the enzyme reaction prediction (ERP) method as a novel tool to deduce enzyme reactions from domain architecture. We used several frequency relationships between architectures and reactions to enhance the annotation rates for single and multiple catalyzed reactions. The deluge of information which arose from high-throughput techniques in the postgenomic era has improved our understanding of biological data, although it presents obstacles in the data-processing stage. The high computational capacity provided by cloud computing has resulted in an exponential growth in the volume of incoming data. Cloud services also relieve the requirement for large-scale memory space required by this approach to analyze enzyme kinetic data. Our tool is designed as a single execution file; thus, it could be applied to any cloud platform in which multiple queries are supported.

Show MeSH