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Computational identification of rare codons of Escherichia coli based on codon pairs preference

Overview of attention for article published in BMC Bioinformatics, January 2010
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Title
Computational identification of rare codons of Escherichia coli based on codon pairs preference
Published in
BMC Bioinformatics, January 2010
DOI 10.1186/1471-2105-11-61
Pubmed ID
Authors

Xianming Wu, Songfeng Wu, Dong Li, Jiyang Zhang, Lin Hou, Jie Ma, Wanlin Liu, Daming Ren, Yunping Zhu, Fuchu He

Abstract

Codon bias is believed to play an important role in the control of gene expression. In Escherichia coli, some rare codons, which can limit the expression level of exogenous protein, have been defined by gene engineering operations. Previous studies have confirmed the existence of codon pair's preference in many genomes, but the underlying cause of this bias has not been well established. Here we focus on the patterns of rarely-used synonymous codons. A novel method was introduced to identify the rare codons merely by codon pair bias in Escherichia coli.

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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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Italy 1 2%
Brazil 1 2%
Unknown 50 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 30%
Researcher 15 28%
Student > Doctoral Student 3 6%
Professor > Associate Professor 3 6%
Student > Master 3 6%
Other 8 15%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 55%
Biochemistry, Genetics and Molecular Biology 11 21%
Computer Science 3 6%
Immunology and Microbiology 1 2%
Medicine and Dentistry 1 2%
Other 2 4%
Unknown 6 11%
Attention Score in Context

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 01 February 2013.
All research outputs
#18,327,422
of 22,694,633 outputs
Outputs from BMC Bioinformatics
#6,289
of 7,254 outputs
Outputs of similar age
#150,546
of 164,519 outputs
Outputs of similar age from BMC Bioinformatics
#49
of 61 outputs
Altmetric has tracked 22,694,633 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.