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On the necessity of dissecting sequence similarity scores into segment-specific contributions for inferring protein homology, function prediction and annotation

Overview of attention for article published in BMC Bioinformatics, June 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
8 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
32 Mendeley
citeulike
1 CiteULike

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Summary Twitter Dimensions citations
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Title
On the necessity of dissecting sequence similarity scores into segment-specific contributions for inferring protein homology, function prediction and annotation
Published in
BMC Bioinformatics, June 2014
DOI 10.1186/1471-2105-15-166
Pubmed ID
24890864
Authors

Wing-Cheong Wong, Sebastian Maurer-Stroh, Birgit Eisenhaber, Frank Eisenhaber

Abstract

Protein sequence similarities to any types of non-globular segments (coiled coils, low complexity regions, transmembrane regions, long loops, etc. where either positional sequence conservation is the result of a very simple, physically induced pattern or rather integral sequence properties are critical) are pertinent sources for mistaken homologies. Regretfully, these considerations regularly escape attention in large-scale annotation studies since, often, there is no substitute to manual handling of these cases. Quantitative criteria are required to suppress events of function annotation transfer as a result of false homology assignments.

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Twitter Demographics

The data shown below were collected from the profiles of 8 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Germany 1 3%
Australia 1 3%
Unknown 29 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 31%
Student > Bachelor 4 13%
Student > Master 4 13%
Student > Ph. D. Student 4 13%
Professor 4 13%
Other 5 16%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 38%
Biochemistry, Genetics and Molecular Biology 9 28%
Computer Science 6 19%
Engineering 2 6%
Unknown 3 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 06 June 2014.
All research outputs
#4,061,279
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#1,789
of 5,420 outputs
Outputs of similar age
#49,158
of 189,788 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 14 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 66% 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 189,788 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 14 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 71% of its contemporaries.

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