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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.

Show MeSH
EC numbers connected with the architecture {SSF56112, SSF47889} by the ERP method.
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fig4: EC numbers connected with the architecture {SSF56112, SSF47889} by the ERP method.

Mentions: Enzyme Reaction Ranking. After determining the domain architecture for a nonannotated enzyme, various related enzyme reactions can be retrieved from the universe data set (Figure 4). The IntensityECi (5) is calculated based on the ratio of the mean value to the number of entries associated with ECi to evaluate the strength of the relationship between the reaction ECi and the determined domain architecture. An IntensityECi value of less than 1 indicates that the expression of ECi is greater than the mean value; thus, low values are preferred. The intensity values corresponding to reactions of the domain architecture {SSF56112, SSF57889} are calculated as follows:(5)(6)


Enzyme reaction annotation using cloud techniques.

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

EC numbers connected with the architecture {SSF56112, SSF47889} by the ERP method.
© Copyright Policy
Related In: Results  -  Collection

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

fig4: EC numbers connected with the architecture {SSF56112, SSF47889} by the ERP method.
Mentions: Enzyme Reaction Ranking. After determining the domain architecture for a nonannotated enzyme, various related enzyme reactions can be retrieved from the universe data set (Figure 4). The IntensityECi (5) is calculated based on the ratio of the mean value to the number of entries associated with ECi to evaluate the strength of the relationship between the reaction ECi and the determined domain architecture. An IntensityECi value of less than 1 indicates that the expression of ECi is greater than the mean value; thus, low values are preferred. The intensity values corresponding to reactions of the domain architecture {SSF56112, SSF57889} are calculated as follows:(5)(6)

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