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MatrixCatch - a novel tool for the recognition of composite regulatory elements in promoters

Overview of attention for article published in BMC Bioinformatics, August 2013
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Mentioned by

twitter
3 tweeters
facebook
1 Facebook page

Citations

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19 Dimensions

Readers on

mendeley
29 Mendeley
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Title
MatrixCatch - a novel tool for the recognition of composite regulatory elements in promoters
Published in
BMC Bioinformatics, August 2013
DOI 10.1186/1471-2105-14-241
Pubmed ID
Authors

Igor V Deyneko, Alexander E Kel, Olga V Kel-Margoulis, Elena V Deineko, Edgar Wingender, Siegfried Weiss

Abstract

Accurate recognition of regulatory elements in promoters is an essential prerequisite for understanding the mechanisms of gene regulation at the level of transcription. Composite regulatory elements represent a particular type of such transcriptional regulatory elements consisting of pairs of individual DNA motifs. In contrast to the present approach, most available recognition techniques are based purely on statistical evaluation of the occurrence of single motifs. Such methods are limited in application, since the accuracy of recognition is greatly dependent on the size and quality of the sequence dataset. Methods that exploit available knowledge and have broad applicability are evidently needed.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Iran, Islamic Republic of 1 3%
Spain 1 3%
Russia 1 3%
Sri Lanka 1 3%
Unknown 25 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 31%
Student > Ph. D. Student 8 28%
Student > Master 5 17%
Student > Bachelor 2 7%
Lecturer > Senior Lecturer 1 3%
Other 2 7%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 48%
Biochemistry, Genetics and Molecular Biology 6 21%
Computer Science 3 10%
Mathematics 1 3%
Psychology 1 3%
Other 0 0%
Unknown 4 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 29 October 2013.
All research outputs
#13,075,132
of 21,347,688 outputs
Outputs from BMC Bioinformatics
#4,270
of 6,923 outputs
Outputs of similar age
#94,178
of 176,934 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 8 outputs
Altmetric has tracked 21,347,688 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,923 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 35th percentile – i.e., 35% 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 176,934 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.