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Small RNA and transcriptome deep sequencing proffers insight into floral gene regulation in Rosa cultivars

Overview of attention for article published in BMC Genomics, November 2012
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2 X users

Citations

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84 Mendeley
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Title
Small RNA and transcriptome deep sequencing proffers insight into floral gene regulation in Rosa cultivars
Published in
BMC Genomics, November 2012
DOI 10.1186/1471-2164-13-657
Pubmed ID
Authors

Jungeun Kim, June Hyun Park, Chan Ju Lim, Jae Yun Lim, Jee-Youn Ryu, Bong-Woo Lee, Jae-Pil Choi, Woong Bom Kim, Ha Yeon Lee, Yourim Choi, Donghyun Kim, Cheol-Goo Hur, Sukweon Kim, Yoo-Sun Noh, Chanseok Shin, Suk-Yoon Kwon

Abstract

Roses (Rosa sp.), which belong to the family Rosaceae, are the most economically important ornamental plants--making up 30% of the floriculture market. However, given high demand for roses, rose breeding programs are limited in molecular resources which can greatly enhance and speed breeding efforts. A better understanding of important genes that contribute to important floral development and desired phenotypes will lead to improved rose cultivars. For this study, we analyzed rose miRNAs and the rose flower transcriptome in order to generate a database to expound upon current knowledge regarding regulation of important floral characteristics. A rose genetic database will enable comprehensive analysis of gene expression and regulation via miRNA among different Rosa cultivars.

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X Demographics

The data shown below were collected from the profiles of 2 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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 1%
Netherlands 1 1%
France 1 1%
Brazil 1 1%
Unknown 80 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 27%
Researcher 21 25%
Student > Master 7 8%
Student > Postgraduate 6 7%
Professor > Associate Professor 6 7%
Other 11 13%
Unknown 10 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 68%
Biochemistry, Genetics and Molecular Biology 9 11%
Chemistry 2 2%
Nursing and Health Professions 1 1%
Computer Science 1 1%
Other 3 4%
Unknown 11 13%
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 23 November 2012.
All research outputs
#15,492,327
of 23,023,224 outputs
Outputs from BMC Genomics
#6,724
of 10,699 outputs
Outputs of similar age
#179,994
of 277,612 outputs
Outputs of similar age from BMC Genomics
#253
of 381 outputs
Altmetric has tracked 23,023,224 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,699 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 28th percentile – i.e., 28% 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 277,612 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 381 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.