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A systematic framework to derive N-glycan biosynthesis process and the automated construction of glycosylation networks

Overview of attention for article published in BMC Bioinformatics, July 2016
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Title
A systematic framework to derive N-glycan biosynthesis process and the automated construction of glycosylation networks
Published in
BMC Bioinformatics, July 2016
DOI 10.1186/s12859-016-1094-6
Pubmed ID
Authors

Wenpin Hou, Yushan Qiu, Nobuyuki Hashimoto, Wai-Ki Ching, Kiyoko F. Aoki-Kinoshita

Abstract

Abnormalities in glycan biosynthesis have been conclusively related to various diseases, whereas the complexity of the glycosylation process has impeded the quantitative analysis of biochemical experimental data for the identification of glycoforms contributing to disease. To overcome this limitation, the automatic construction of glycosylation reaction networks in silico is a critical step. In this paper, a framework K2014 is developed to automatically construct N-glycosylation networks in MATLAB with the involvement of the 27 most-known enzyme reaction rules of 22 enzymes, as an extension of previous model KB2005. A toolbox named Glycosylation Network Analysis Toolbox (GNAT) is applied to define network properties systematically, including linkages, stereochemical specificity and reaction conditions of enzymes. Our network shows a strong ability to predict a wider range of glycans produced by the enzymes encountered in the Golgi Apparatus in human cell expression systems. Our results demonstrate a better understanding of the underlying glycosylation process and the potential of systems glycobiology tools for analyzing conventional biochemical or mass spectrometry-based experimental data quantitatively in a more realistic and practical way.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 27 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 18%
Student > Bachelor 4 14%
Researcher 4 14%
Student > Master 3 11%
Student > Postgraduate 2 7%
Other 4 14%
Unknown 6 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 18%
Biochemistry, Genetics and Molecular Biology 4 14%
Chemical Engineering 3 11%
Engineering 3 11%
Chemistry 2 7%
Other 5 18%
Unknown 6 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 July 2016.
All research outputs
#20,336,031
of 22,881,154 outputs
Outputs from BMC Bioinformatics
#6,872
of 7,298 outputs
Outputs of similar age
#319,937
of 365,439 outputs
Outputs of similar age from BMC Bioinformatics
#87
of 99 outputs
Altmetric has tracked 22,881,154 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,298 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.