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Quantifying similarity between motifs

Overview of attention for article published in Genome Biology (Online Edition), January 2007
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)

Mentioned by

2 patents
1 Wikipedia page


1261 Dimensions

Readers on

959 Mendeley
22 CiteULike
3 Connotea
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Quantifying similarity between motifs
Published in
Genome Biology (Online Edition), January 2007
DOI 10.1186/gb-2007-8-2-r24
Pubmed ID

Shobhit Gupta, John A Stamatoyannopoulos, Timothy L Bailey, William Noble


A common question within the context of de novo motif discovery is whether a newly discovered, putative motif resembles any previously discovered motif in an existing database. To answer this question, we define a statistical measure of motif-motif similarity, and we describe an algorithm, called Tomtom, for searching a database of motifs with a given query motif. Experimental simulations demonstrate the accuracy of Tomtom's E values and its effectiveness in finding similar motifs.

Mendeley readers

The data shown below were compiled from readership statistics for 959 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 18 2%
Germany 5 <1%
Italy 5 <1%
France 3 <1%
China 3 <1%
United Kingdom 3 <1%
Australia 2 <1%
Netherlands 2 <1%
Czechia 2 <1%
Other 15 2%
Unknown 901 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 292 30%
Researcher 168 18%
Student > Master 131 14%
Student > Bachelor 95 10%
Student > Doctoral Student 42 4%
Other 140 15%
Unknown 91 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 417 43%
Biochemistry, Genetics and Molecular Biology 260 27%
Computer Science 63 7%
Medicine and Dentistry 21 2%
Engineering 16 2%
Other 72 8%
Unknown 110 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 25 April 2017.
All research outputs
of 17,365,229 outputs
Outputs from Genome Biology (Online Edition)
of 3,593 outputs
Outputs of similar age
of 340,775 outputs
Outputs of similar age from Genome Biology (Online Edition)
of 6 outputs
Altmetric has tracked 17,365,229 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,593 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
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 340,775 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 78% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.