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P-value based visualization of codon usage data

Overview of attention for article published in Algorithms for Molecular Biology, June 2006
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1 Q&A thread

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20 Mendeley
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Title
P-value based visualization of codon usage data
Published in
Algorithms for Molecular Biology, June 2006
DOI 10.1186/1748-7188-1-10
Pubmed ID
Authors

Peter Meinicke, Thomas Brodag, Wolfgang Florian Fricke, Stephan Waack

Abstract

Two important and not yet solved problems in bacterial genome research are the identification of horizontally transferred genes and the prediction of gene expression levels. Both problems can be addressed by multivariate analysis of codon usage data. In particular dimensionality reduction methods for visualization of multivariate data have shown to be effective tools for codon usage analysis. We here propose a multidimensional scaling approach using a novel similarity measure for codon usage tables. Our probabilistic similarity measure is based on P-values derived from the well-known chi-square test for comparison of two distributions. Experimental results on four microbial genomes indicate that the new method is well-suited for the analysis of horizontal gene transfer and translational selection. As compared with the widely-used correspondence analysis, our method did not suffer from outlier sensitivity and showed a better clustering of putative alien genes in most cases.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 10%
Germany 1 5%
Canada 1 5%
Unknown 16 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 40%
Professor > Associate Professor 4 20%
Professor 2 10%
Student > Ph. D. Student 2 10%
Student > Doctoral Student 1 5%
Other 2 10%
Unknown 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 45%
Computer Science 4 20%
Engineering 2 10%
Mathematics 1 5%
Chemical Engineering 1 5%
Other 2 10%
Unknown 1 5%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 20 August 2012.
All research outputs
#12,863,576
of 22,684,168 outputs
Outputs from Algorithms for Molecular Biology
#87
of 264 outputs
Outputs of similar age
#54,071
of 64,083 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 1 outputs
Altmetric has tracked 22,684,168 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one has gotten more attention than average, scoring higher than 65% of its peers.
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We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them