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A Global Coexpression Network Approach for Connecting Genes to Specialized Metabolic Pathways in Plants

Overview of attention for article published in Plant Cell, April 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#12 of 8,101)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

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383 Mendeley
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Article details
Title
A Global Coexpression Network Approach for Connecting Genes to Specialized Metabolic Pathways in Plants
Published in
Plant Cell, April 2017
DOI 10.1105/tpc.17.00009
Pubmed ID
Authors
Abstract

Plants produce diverse specialized metabolites (SMs), but the genes responsible for their production and regulation remain largely unknown, hindering efforts to tap plant pharmacopeia. Given that genes comprising SM pathways exhibit environmentally dependent co-regulation, we hypothesized that genes within a SM pathway would form tight associations (modules) with each other in co-expression networks, facilitating their identification. To evaluate this hypothesis, we used 10 global co-expression datasets, each a meta-analysis of hundreds to thousands of experiments, across eight plant species to identify hundreds of co-expressed gene modules per dataset. In support of our hypothesis, 15.3-52.6% of modules contained two or more known SM biosynthetic genes, and module genes were enriched in SM functions. Moreover, modules recovered many experimentally validated SM pathways, including all six known to form biosynthetic gene clusters (BGCs). In contrast, bioinformatically predicted BGCs (i.e. those lacking an associated metabolite) were no more co-expressed than the null distribution for neighboring genes. These results suggest that most predicted plant BGCs are not genuine SM pathways and argue that BGCs are not a hallmark of plant specialized metabolism. We submit that global gene co-expression is a rich, largely untapped resource for discovering the genetic basis and architecture of plant natural products.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 35 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 383 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Colombia 1 <1%
Chile 1 <1%
Unknown 381 99%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 88 23%
Researcher 66 17%
Student > Master 44 11%
Student > Bachelor 26 7%
Student > Postgraduate 19 5%
Other 57 15%
Unknown 83 22%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 166 43%
Biochemistry, Genetics and Molecular Biology 84 22%
Computer Science 8 2%
Environmental Science 6 2%
Chemistry 4 1%
Other 16 4%
Unknown 99 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 157. 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 January 2023.
All research outputs
#331,950
of 34,640,721 outputs
Outputs from Plant Cell
#12
of 8,101 outputs
Outputs of similar age
#5,513
of 355,331 outputs
Outputs of similar age from Plant Cell
#1
of 64 outputs
Altmetric has tracked 34,640,721 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,101 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.8. This one has done particularly well, scoring higher than 99% 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 355,331 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 64 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.