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Transcript mapping based on dRNA-seq data

Overview of attention for article published in BMC Bioinformatics, January 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 (86th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

blogs
1 blog
twitter
4 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
42 Mendeley
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Title
Transcript mapping based on dRNA-seq data
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-122
Pubmed ID
Authors

Thorsten Bischler, Matthias Kopf, Björn Voß

Abstract

RNA-seq and its variant differential RNA-seq (dRNA-seq) are today routine methods for transcriptome analysis in bacteria. While expression profiling and transcriptional start site prediction are standard tasks today, the problem of identifying transcriptional units in a genome-wide fashion is still not solved for prokaryotic systems.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Poland 1 2%
Germany 1 2%
Brazil 1 2%
Unknown 38 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 29%
Researcher 8 19%
Student > Master 6 14%
Student > Postgraduate 3 7%
Professor 3 7%
Other 7 17%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 50%
Biochemistry, Genetics and Molecular Biology 8 19%
Computer Science 3 7%
Medicine and Dentistry 2 5%
Immunology and Microbiology 1 2%
Other 2 5%
Unknown 5 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 19 May 2014.
All research outputs
#1,807,739
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#741
of 5,420 outputs
Outputs of similar age
#25,844
of 190,103 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 12 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 86% 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 190,103 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 86% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.