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MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing

Overview of attention for article published in BMC Bioinformatics, June 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 (94th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
19 X users
patent
1 patent
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
296 Dimensions

Readers on

mendeley
158 Mendeley
citeulike
2 CiteULike
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Title
MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing
Published in
BMC Bioinformatics, June 2014
DOI 10.1186/1471-2105-15-224
Pubmed ID
Authors

Krishna R Kalari, Asha A Nair, Jaysheel D Bhavsar, Daniel R O’Brien, Jaime I Davila, Matthew A Bockol, Jinfu Nie, Xiaojia Tang, Saurabh Baheti, Jay B Doughty, Sumit Middha, Hugues Sicotte, Aubrey E Thompson, Yan W Asmann, Jean-Pierre A Kocher

Abstract

Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome.

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 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 158 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 2%
United States 3 2%
Netherlands 1 <1%
Italy 1 <1%
Sweden 1 <1%
Colombia 1 <1%
United Kingdom 1 <1%
Czechia 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 144 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 44 28%
Student > Ph. D. Student 39 25%
Student > Master 14 9%
Professor > Associate Professor 10 6%
Student > Bachelor 7 4%
Other 21 13%
Unknown 23 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 62 39%
Biochemistry, Genetics and Molecular Biology 28 18%
Computer Science 10 6%
Medicine and Dentistry 10 6%
Engineering 7 4%
Other 16 10%
Unknown 25 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 October 2023.
All research outputs
#1,355,251
of 25,706,302 outputs
Outputs from BMC Bioinformatics
#150
of 7,735 outputs
Outputs of similar age
#13,061
of 243,283 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 154 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,735 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 98% 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 243,283 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.