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iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties

Overview of attention for article published in PLOS ONE, October 2012
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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 (84th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog
twitter
1 X user
facebook
1 Facebook page

Citations

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175 Dimensions

Readers on

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48 Mendeley
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Title
iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0047843
Pubmed ID
Authors

Wei Chen, Hao Lin, Peng-Mian Feng, Chen Ding, Yong-Chun Zuo, Kuo-Chen Chou

Abstract

Nucleosome positioning has important roles in key cellular processes. Although intensive efforts have been made in this area, the rules defining nucleosome positioning is still elusive and debated. In this study, we carried out a systematic comparison among the profiles of twelve DNA physicochemical features between the nucleosomal and linker sequences in the Saccharomyces cerevisiae genome. We found that nucleosomal sequences have some position-specific physicochemical features, which can be used for in-depth studying nucleosomes. Meanwhile, a new predictor, called iNuc-PhysChem, was developed for identification of nucleosomal sequences by incorporating these physicochemical properties into a 1788-D (dimensional) feature vector, which was further reduced to a 884-D vector via the IFS (incremental feature selection) procedure to optimize the feature set. It was observed by a cross-validation test on a benchmark dataset that the overall success rate achieved by iNuc-PhysChem was over 96% in identifying nucleosomal or linker sequences. As a web-server, iNuc-PhysChem is freely accessible to the public at http://lin.uestc.edu.cn/server/iNuc-PhysChem. For the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented just for the integrity in developing the predictor. Meanwhile, for those who prefer to run predictions in their own computers, the predictor's code can be easily downloaded from the web-server. It is anticipated that iNuc-PhysChem may become a useful high throughput tool for both basic research and drug design.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 2 4%
Unknown 46 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 31%
Researcher 12 25%
Student > Master 5 10%
Professor 3 6%
Student > Doctoral Student 2 4%
Other 5 10%
Unknown 6 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 31%
Biochemistry, Genetics and Molecular Biology 10 21%
Computer Science 7 15%
Engineering 2 4%
Chemistry 2 4%
Other 4 8%
Unknown 8 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 27 September 2017.
All research outputs
#3,691,311
of 22,684,168 outputs
Outputs from PLOS ONE
#45,650
of 193,651 outputs
Outputs of similar age
#27,669
of 183,629 outputs
Outputs of similar age from PLOS ONE
#879
of 4,819 outputs
Altmetric has tracked 22,684,168 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 193,651 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one has done well, scoring higher than 76% 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 183,629 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 84% of its contemporaries.
We're also able to compare this research output to 4,819 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.