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Correlation analysis of the transcriptome of growing leaves with mature leaf parameters in a maize RIL population

Overview of attention for article published in Genome Biology (Online Edition), September 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)

Mentioned by

news
8 news outlets
twitter
13 tweeters

Citations

dimensions_citation
37 Dimensions

Readers on

mendeley
98 Mendeley
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Title
Correlation analysis of the transcriptome of growing leaves with mature leaf parameters in a maize RIL population
Published in
Genome Biology (Online Edition), September 2015
DOI 10.1186/s13059-015-0735-9
Pubmed ID
Authors

Joke Baute, Dorota Herman, Frederik Coppens, Jolien De Block, Bram Slabbinck, Matteo Dell’Acqua, Mario Enrico Pè, Steven Maere, Hilde Nelissen, Dirk Inzé

Abstract

To sustain the global requirements for food and renewable resources, unraveling the molecular networks underlying plant growth is becoming pivotal. Although several approaches to identify genes and networks involved in final organ size have been proven successful, our understanding remains fragmentary. Here, we assessed variation in 103 lines of the Zea mays B73xH99 RIL population for a set of final leaf size and whole shoot traits at the seedling stage, complemented with measurements capturing growth dynamics, and cellular measurements. Most traits correlated well with the size of the division zone, implying that the molecular basis of final leaf size is already defined in dividing cells of growing leaves. Therefore, we searched for association between the transcriptional variation in dividing cells of the growing leaf and final leaf size and seedling biomass, allowing us to identify genes and processes correlated with the specific traits. A number of these genes have a known function in leaf development. Additionally, we illustrated that two independent mechanisms contribute to final leaf size, maximal growth rate and the duration of growth. Untangling complex traits such as leaf size by applying in-depth phenotyping allows us to define the relative contributions of the components and their mutual associations, facilitating dissection of the biological processes and regulatory networks underneath.

Twitter Demographics

The data shown below were collected from the profiles of 13 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 2 2%
France 1 1%
Sweden 1 1%
Norway 1 1%
Slovakia 1 1%
United States 1 1%
Unknown 91 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 21%
Researcher 21 21%
Student > Master 15 15%
Student > Doctoral Student 7 7%
Student > Bachelor 5 5%
Other 15 15%
Unknown 14 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 58 59%
Biochemistry, Genetics and Molecular Biology 15 15%
Computer Science 3 3%
Unspecified 1 1%
Medicine and Dentistry 1 1%
Other 2 2%
Unknown 18 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 68. 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 29 September 2018.
All research outputs
#338,124
of 16,060,840 outputs
Outputs from Genome Biology (Online Edition)
#283
of 3,427 outputs
Outputs of similar age
#6,630
of 242,920 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 16,060,840 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,427 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.9. This one has done particularly well, scoring higher than 91% 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 242,920 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 97% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them