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Inferring Binding Energies from Selected Binding Sites

Overview of attention for article published in PLoS Computational Biology, December 2009
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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 (91st percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

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2 blogs
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1 research highlight platform

Citations

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192 Dimensions

Readers on

mendeley
271 Mendeley
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18 CiteULike
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Title
Inferring Binding Energies from Selected Binding Sites
Published in
PLoS Computational Biology, December 2009
DOI 10.1371/journal.pcbi.1000590
Pubmed ID
Authors

Yue Zhao, David Granas, Gary D. Stormo

Abstract

We employ a biophysical model that accounts for the non-linear relationship between binding energy and the statistics of selected binding sites. The model includes the chemical potential of the transcription factor, non-specific binding affinity of the protein for DNA, as well as sequence-specific parameters that may include non-independent contributions of bases to the interaction. We obtain maximum likelihood estimates for all of the parameters and compare the results to standard probabilistic methods of parameter estimation. On simulated data, where the true energy model is known and samples are generated with a variety of parameter values, we show that our method returns much more accurate estimates of the true parameters and much better predictions of the selected binding site distributions. We also introduce a new high-throughput SELEX (HT-SELEX) procedure to determine the binding specificity of a transcription factor in which the initial randomized library and the selected sites are sequenced with next generation methods that return hundreds of thousands of sites. We show that after a single round of selection our method can estimate binding parameters that give very good fits to the selected site distributions, much better than standard motif identification algorithms.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 271 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 5%
China 3 1%
Switzerland 2 <1%
Germany 2 <1%
Argentina 2 <1%
France 1 <1%
Norway 1 <1%
Hong Kong 1 <1%
United Kingdom 1 <1%
Other 6 2%
Unknown 239 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 78 29%
Researcher 69 25%
Student > Master 25 9%
Professor > Associate Professor 22 8%
Student > Bachelor 16 6%
Other 34 13%
Unknown 27 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 132 49%
Biochemistry, Genetics and Molecular Biology 60 22%
Computer Science 23 8%
Engineering 9 3%
Physics and Astronomy 7 3%
Other 13 5%
Unknown 27 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 August 2018.
All research outputs
#3,073,668
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#2,732
of 8,964 outputs
Outputs of similar age
#14,934
of 177,035 outputs
Outputs of similar age from PLoS Computational Biology
#11
of 58 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 69% 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 177,035 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 91% of its contemporaries.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.