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miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression

Overview of attention for article published in BMC Bioinformatics, January 2009
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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 (89th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter
patent
1 patent
facebook
1 Facebook page

Citations

dimensions_citation
139 Dimensions

Readers on

mendeley
265 Mendeley
citeulike
17 CiteULike
connotea
3 Connotea
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Title
miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression
Published in
BMC Bioinformatics, January 2009
DOI 10.1186/1471-2105-10-328
Pubmed ID
Authors

Wei-Chi Wang, Feng-Mao Lin, Wen-Chi Chang, Kuan-Yu Lin, Hsien-Da Huang, Na-Sheng Lin

Abstract

MicroRNAs (miRNAs), small non-coding RNAs of 19 to 25 nt, play important roles in gene regulation in both animals and plants. In the last few years, the oligonucleotide microarray is one high-throughput and robust method for detecting miRNA expression. However, the approach is restricted to detecting the expression of known miRNAs. Second-generation sequencing is an inexpensive and high-throughput sequencing method. This new method is a promising tool with high sensitivity and specificity and can be used to measure the abundance of small-RNA sequences in a sample. Hence, the expression profiling of miRNAs can involve use of sequencing rather than an oligonucleotide array. Additionally, this method can be adopted to discover novel miRNAs.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 265 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 4%
Germany 6 2%
Italy 4 2%
United Kingdom 3 1%
China 3 1%
Brazil 2 <1%
Uruguay 2 <1%
Sweden 2 <1%
Canada 2 <1%
Other 10 4%
Unknown 221 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 79 30%
Student > Ph. D. Student 73 28%
Student > Master 30 11%
Professor > Associate Professor 20 8%
Professor 14 5%
Other 37 14%
Unknown 12 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 163 62%
Biochemistry, Genetics and Molecular Biology 41 15%
Computer Science 22 8%
Medicine and Dentistry 7 3%
Engineering 6 2%
Other 10 4%
Unknown 16 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 26 February 2018.
All research outputs
#1,248,073
of 12,565,531 outputs
Outputs from BMC Bioinformatics
#503
of 4,677 outputs
Outputs of similar age
#9,648
of 90,580 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 12,565,531 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,677 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 89% 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 90,580 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 89% 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