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An integrated network visualization framework towards metabolic engineering applications

Overview of attention for article published in BMC Bioinformatics, December 2014
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Title
An integrated network visualization framework towards metabolic engineering applications
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0420-0
Pubmed ID
Authors

Alberto Noronha, Paulo Vilaça, Miguel Rocha

Abstract

BackgroundOver the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential.ResultsIn this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks.ConclusionsThe framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 1 2%
United Kingdom 1 2%
Singapore 1 2%
Belgium 1 2%
United States 1 2%
Unknown 51 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 21%
Student > Master 11 20%
Researcher 10 18%
Professor 5 9%
Student > Doctoral Student 4 7%
Other 11 20%
Unknown 3 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 34%
Computer Science 14 25%
Biochemistry, Genetics and Molecular Biology 9 16%
Chemical Engineering 4 7%
Engineering 4 7%
Other 2 4%
Unknown 4 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 06 January 2015.
All research outputs
#16,016,780
of 25,784,004 outputs
Outputs from BMC Bioinformatics
#4,790
of 7,746 outputs
Outputs of similar age
#196,571
of 361,858 outputs
Outputs of similar age from BMC Bioinformatics
#77
of 151 outputs
Altmetric has tracked 25,784,004 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,746 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 361,858 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 151 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.