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CoExpNetViz: Comparative Co-Expression Networks Construction and Visualization Tool

Overview of attention for article published in Frontiers in Plant Science, January 2016
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  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

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6 X users

Citations

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90 Dimensions

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151 Mendeley
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Title
CoExpNetViz: Comparative Co-Expression Networks Construction and Visualization Tool
Published in
Frontiers in Plant Science, January 2016
DOI 10.3389/fpls.2015.01194
Pubmed ID
Authors

Oren Tzfadia, Tim Diels, Sam De Meyer, Klaas Vandepoele, Asaph Aharoni, Yves Van de Peer

Abstract

Comparative transcriptomics is a common approach in functional gene discovery efforts. It allows for finding conserved co-expression patterns between orthologous genes in closely related plant species, suggesting that these genes potentially share similar function and regulation. Several efficient co-expression-based tools have been commonly used in plant research but most of these pipelines are limited to data from model systems, which greatly limit their utility. Moreover, in addition, none of the existing pipelines allow plant researchers to make use of their own unpublished gene expression data for performing a comparative co-expression analysis and generate multi-species co-expression networks. We introduce CoExpNetViz, a computational tool that uses a set of query or "bait" genes as an input (chosen by the user) and a minimum of one pre-processed gene expression dataset. The CoExpNetViz algorithm proceeds in three main steps; (i) for every bait gene submitted, co-expression values are calculated using mutual information and Pearson correlation coefficients, (ii) non-bait (or target) genes are grouped based on cross-species orthology, and (iii) output files are generated and results can be visualized as network graphs in Cytoscape. The CoExpNetViz tool is freely available both as a PHP web server (link: http://bioinformatics.psb.ugent.be/webtools/coexpr/) (implemented in C++) and as a Cytoscape plugin (implemented in Java). Both versions of the CoExpNetViz tool support LINUX and Windows platforms.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 1%
United States 1 <1%
France 1 <1%
Germany 1 <1%
Unknown 146 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 25%
Researcher 35 23%
Student > Master 16 11%
Student > Postgraduate 8 5%
Student > Bachelor 8 5%
Other 26 17%
Unknown 20 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 73 48%
Biochemistry, Genetics and Molecular Biology 32 21%
Computer Science 7 5%
Engineering 5 3%
Medicine and Dentistry 3 2%
Other 7 5%
Unknown 24 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 19 January 2016.
All research outputs
#6,215,018
of 22,835,198 outputs
Outputs from Frontiers in Plant Science
#3,374
of 20,148 outputs
Outputs of similar age
#99,951
of 393,343 outputs
Outputs of similar age from Frontiers in Plant Science
#46
of 458 outputs
Altmetric has tracked 22,835,198 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 20,148 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 83% of its peers.
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 393,343 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 458 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.