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miRPlant: an integrated tool for identification of plant miRNA from RNA sequencing data

Overview of attention for article published in BMC Bioinformatics, August 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

dimensions_citation
81 Dimensions

Readers on

mendeley
134 Mendeley
citeulike
1 CiteULike
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Title
miRPlant: an integrated tool for identification of plant miRNA from RNA sequencing data
Published in
BMC Bioinformatics, August 2014
DOI 10.1186/1471-2105-15-275
Pubmed ID
Authors

Jiyuan An, John Lai, Atul Sajjanhar, Melanie L Lehman, Colleen C Nelson

Abstract

Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep's probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 1%
Germany 1 <1%
Brazil 1 <1%
Norway 1 <1%
United Kingdom 1 <1%
United States 1 <1%
Unknown 127 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 20%
Student > Master 25 19%
Researcher 24 18%
Student > Bachelor 13 10%
Student > Doctoral Student 8 6%
Other 27 20%
Unknown 10 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 75 56%
Biochemistry, Genetics and Molecular Biology 24 18%
Computer Science 17 13%
Arts and Humanities 1 <1%
Engineering 1 <1%
Other 0 0%
Unknown 16 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 12 September 2014.
All research outputs
#1,818,466
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#798
of 4,588 outputs
Outputs of similar age
#30,921
of 197,156 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 19 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,588 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 82% 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 197,156 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.