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Exploratory analysis of genomic segmentations with Segtools

Overview of attention for article published in BMC Bioinformatics, October 2011
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

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34 X users
googleplus
1 Google+ user

Citations

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22 Dimensions

Readers on

mendeley
79 Mendeley
citeulike
8 CiteULike
Title
Exploratory analysis of genomic segmentations with Segtools
Published in
BMC Bioinformatics, October 2011
DOI 10.1186/1471-2105-12-415
Pubmed ID
Authors

Orion J Buske, Michael M Hoffman, Nadia Ponts, Karine G Le Roch, William Stafford Noble

Abstract

As genome-wide experiments and annotations become more prevalent, researchers increasingly require tools to help interpret data at this scale. Many functional genomics experiments involve partitioning the genome into labeled segments, such that segments sharing the same label exhibit one or more biochemical or functional traits. For example, a collection of ChlP-seq experiments yields a compendium of peaks, each labeled with one or more associated DNA-binding proteins. Similarly, manually or automatically generated annotations of functional genomic elements, including cis-regulatory modules and protein-coding or RNA genes, can also be summarized as genomic segmentations.

X Demographics

X Demographics

The data shown below were collected from the profiles of 34 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 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 11%
Canada 3 4%
Turkey 1 1%
Spain 1 1%
Russia 1 1%
Unknown 64 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 34%
Student > Ph. D. Student 15 19%
Student > Bachelor 8 10%
Student > Doctoral Student 6 8%
Student > Master 6 8%
Other 13 16%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 44 56%
Biochemistry, Genetics and Molecular Biology 10 13%
Computer Science 10 13%
Engineering 3 4%
Mathematics 2 3%
Other 5 6%
Unknown 5 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 02 May 2023.
All research outputs
#1,849,766
of 24,703,227 outputs
Outputs from BMC Bioinformatics
#384
of 7,575 outputs
Outputs of similar age
#8,900
of 144,673 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 103 outputs
Altmetric has tracked 24,703,227 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,575 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 94% 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 144,673 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 93% of its contemporaries.
We're also able to compare this research output to 103 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 93% of its contemporaries.