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Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce

Overview of attention for article published in BMC Molecular and Cell Biology, November 2013
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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1 blog
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1 X user

Citations

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12 Dimensions

Readers on

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30 Mendeley
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Title
Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce
Published in
BMC Molecular and Cell Biology, November 2013
DOI 10.1186/1472-6807-13-s1-s3
Pubmed ID
Authors

Boyu Zhang, Daniel T Yehdego, Kyle L Johnson, Ming-Ying Leung, Michela Taufer

Abstract

Ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Their secondary structures are crucial for the RNA functionality, and the prediction of the secondary structures is widely studied. Our previous research shows that cutting long sequences into shorter chunks, predicting secondary structures of the chunks independently using thermodynamic methods, and reconstructing the entire secondary structure from the predicted chunk structures can yield better accuracy than predicting the secondary structure using the RNA sequence as a whole. The chunking, prediction, and reconstruction processes can use different methods and parameters, some of which produce more accurate predictions than others. In this paper, we study the prediction accuracy and efficiency of three different chunking methods using seven popular secondary structure prediction programs that apply to two datasets of RNA with known secondary structures, which include both pseudoknotted and non-pseudoknotted sequences, as well as a family of viral genome RNAs whose structures have not been predicted before. Our modularized MapReduce framework based on Hadoop allows us to study the problem in a parallel and robust environment.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Portugal 1 3%
France 1 3%
Unknown 27 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 27%
Student > Master 5 17%
Student > Ph. D. Student 5 17%
Student > Bachelor 4 13%
Professor > Associate Professor 2 7%
Other 2 7%
Unknown 4 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 23%
Agricultural and Biological Sciences 7 23%
Computer Science 4 13%
Engineering 3 10%
Immunology and Microbiology 2 7%
Other 3 10%
Unknown 4 13%
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 26 February 2014.
All research outputs
#4,239,196
of 25,374,647 outputs
Outputs from BMC Molecular and Cell Biology
#83
of 1,233 outputs
Outputs of similar age
#37,805
of 229,118 outputs
Outputs of similar age from BMC Molecular and Cell Biology
#3
of 25 outputs
Altmetric has tracked 25,374,647 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 1,233 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 93% 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 229,118 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 83% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.