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A structure filter for the Eukaryotic Linear Motif Resource

Overview of attention for article published in BMC Bioinformatics, October 2009
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
32 Mendeley
citeulike
1 CiteULike
connotea
2 Connotea
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Title
A structure filter for the Eukaryotic Linear Motif Resource
Published in
BMC Bioinformatics, October 2009
DOI 10.1186/1471-2105-10-351
Pubmed ID
Authors

Allegra Via, Cathryn M Gould, Christine Gemünd, Toby J Gibson, Manuela Helmer-Citterich

Abstract

Many proteins are highly modular, being assembled from globular domains and segments of natively disordered polypeptides. Linear motifs, short sequence modules functioning independently of protein tertiary structure, are most abundant in natively disordered polypeptides but are also found in accessible parts of globular domains, such as exposed loops. The prediction of novel occurrences of known linear motifs attempts the difficult task of distinguishing functional matches from stochastically occurring non-functional matches. Although functionality can only be confirmed experimentally, confidence in a putative motif is increased if a motif exhibits attributes associated with functional instances such as occurrence in the correct taxonomic range, cellular compartment, conservation in homologues and accessibility to interacting partners. Several tools now use these attributes to classify putative motifs based on confidence of functionality.

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 3%
Germany 1 3%
Australia 1 3%
Brazil 1 3%
Unknown 28 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 31%
Researcher 9 28%
Student > Bachelor 4 13%
Student > Master 4 13%
Professor > Associate Professor 2 6%
Other 2 6%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 66%
Computer Science 4 13%
Biochemistry, Genetics and Molecular Biology 3 9%
Chemical Engineering 1 3%
Immunology and Microbiology 1 3%
Other 1 3%
Unknown 1 3%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 09 September 2011.
All research outputs
#815,291
of 3,616,992 outputs
Outputs from BMC Bioinformatics
#791
of 2,288 outputs
Outputs of similar age
#23,670
of 93,262 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 137 outputs
Altmetric has tracked 3,616,992 research outputs across all sources so far. This one has received more attention than most of these and is in the 62nd percentile.
So far Altmetric has tracked 2,288 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 58% 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 93,262 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 137 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 64% of its contemporaries.