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Transcriptomic analysis of Chinese bayberry (Myrica rubra) fruit development and ripening using RNA-Seq

Overview of attention for article published in BMC Genomics, January 2012
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Title
Transcriptomic analysis of Chinese bayberry (Myrica rubra) fruit development and ripening using RNA-Seq
Published in
BMC Genomics, January 2012
DOI 10.1186/1471-2164-13-19
Pubmed ID
Authors

Chao Feng, Ming Chen, Chang-jie Xu, Lin Bai, Xue-ren Yin, Xian Li, Andrew C Allan, Ian B Ferguson, Kun-song Chen

Abstract

Chinese bayberry (Myrica rubra Sieb. and Zucc.) is an important subtropical fruit crop and an ideal species for fruit quality research due to the rapid and substantial changes that occur during development and ripening, including changes in fruit color and taste. However, research at the molecular level is limited by a lack of sequence data. The present study was designed to obtain transcript sequence data and examine gene expression in bayberry developing fruit based on RNA-Seq and bioinformatic analysis, to provide a foundation for understanding the molecular mechanisms controlling fruit quality changes during ripening.

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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 145 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Chile 2 1%
Italy 1 <1%
Uruguay 1 <1%
Portugal 1 <1%
Slovakia 1 <1%
Brazil 1 <1%
Mexico 1 <1%
Canada 1 <1%
Other 0 0%
Unknown 134 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 42 29%
Researcher 22 15%
Student > Master 21 14%
Student > Doctoral Student 7 5%
Professor 7 5%
Other 30 21%
Unknown 16 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 94 65%
Biochemistry, Genetics and Molecular Biology 23 16%
Engineering 3 2%
Environmental Science 2 1%
Computer Science 2 1%
Other 5 3%
Unknown 16 11%
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 15 January 2012.
All research outputs
#15,688,569
of 23,313,051 outputs
Outputs from BMC Genomics
#6,754
of 10,742 outputs
Outputs of similar age
#164,488
of 245,796 outputs
Outputs of similar age from BMC Genomics
#169
of 287 outputs
Altmetric has tracked 23,313,051 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 10,742 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 245,796 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 287 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.