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Benchmarking and refining probability-based models for nucleosome-DNA interaction

Overview of attention for article published in BMC Bioinformatics, March 2017
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1 tweeter

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Title
Benchmarking and refining probability-based models for nucleosome-DNA interaction
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1569-0
Pubmed ID
Authors

Marco Tompitak, Gerard T. Barkema, Helmut Schiessel

Abstract

In investigations of nucleosome positioning preferences, a model that assigns an affinity to a given sequence is necessary to make predictions. One important class of models, which treats a nucleosome sequence as a Markov chain, has been applied with success when informed with experimentally measured nucleosomal sequence preferences. We find that we can also use such models as a fast approximative scheme for computationally expensive biophysical models, vastly increasing their reach. Employing these models in this way also allows us to benchmark them for the first time. Doing so for the approximative in silico models indirectly tells us about the accuracy we can expect of them when applied to real data. We find that models presented in the literature should perform well, but this performance depends on factors such as the order of the Markov model, the preprocessing of the probability distributions on which the model is based, and the size and quality of the sequence ensemble from which those distributions are calculated.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 41%
Student > Ph. D. Student 4 24%
Student > Bachelor 2 12%
Other 1 6%
Professor > Associate Professor 1 6%
Other 0 0%
Unknown 2 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 18%
Agricultural and Biological Sciences 3 18%
Physics and Astronomy 3 18%
Engineering 2 12%
Economics, Econometrics and Finance 1 6%
Other 2 12%
Unknown 3 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 10 March 2017.
All research outputs
#6,994,752
of 9,176,636 outputs
Outputs from BMC Bioinformatics
#3,245
of 3,909 outputs
Outputs of similar age
#185,483
of 253,540 outputs
Outputs of similar age from BMC Bioinformatics
#78
of 104 outputs
Altmetric has tracked 9,176,636 research outputs across all sources so far. This one is in the 13th percentile – i.e., 13% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,909 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
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We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.