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A new and updated resource for codon usage tables

Overview of attention for article published in BMC Bioinformatics, September 2017
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

blogs
1 blog
twitter
8 tweeters
wikipedia
4 Wikipedia pages

Citations

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150 Dimensions

Readers on

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294 Mendeley
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Title
A new and updated resource for codon usage tables
Published in
BMC Bioinformatics, September 2017
DOI 10.1186/s12859-017-1793-7
Pubmed ID
Authors

John Athey, Aikaterini Alexaki, Ekaterina Osipova, Alexandre Rostovtsev, Luis V. Santana-Quintero, Upendra Katneni, Vahan Simonyan, Chava Kimchi-Sarfaty

Abstract

Due to the degeneracy of the genetic code, most amino acids can be encoded by multiple synonymous codons. Synonymous codons naturally occur with different frequencies in different organisms. The choice of codons may affect protein expression, structure, and function. Recombinant gene technologies commonly take advantage of the former effect by implementing a technique termed codon optimization, in which codons are replaced with synonymous ones in order to increase protein expression. This technique relies on the accurate knowledge of codon usage frequencies. Accurately quantifying codon usage bias for different organisms is useful not only for codon optimization, but also for evolutionary and translation studies: phylogenetic relations of organisms, and host-pathogen co-evolution relationships, may be explored through their codon usage similarities. Furthermore, codon usage has been shown to affect protein structure and function through interfering with translation kinetics, and cotranslational protein folding. Despite the obvious need for accurate codon usage tables, currently available resources are either limited in scope, encompassing only organisms from specific domains of life, or greatly outdated. Taking advantage of the exponential growth of GenBank and the creation of NCBI's RefSeq database, we have developed a new database, the High-performance Integrated Virtual Environment-Codon Usage Tables (HIVE-CUTs), to present and analyse codon usage tables for every organism with publicly available sequencing data. Compared to existing databases, this new database is more comprehensive, addresses concerns that limited the accuracy of earlier databases, and provides several new functionalities, such as the ability to view and compare codon usage between individual organisms and across taxonomical clades, through graphical representation or through commonly used indices. In addition, it is being routinely updated to keep up with the continuous flow of new data in GenBank and RefSeq. Given the impact of codon usage bias on recombinant gene technologies, this database will facilitate effective development and review of recombinant drug products and will be instrumental in a wide area of biological research. The database is available at hive.biochemistry.gwu.edu/review/codon .

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters 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 294 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 294 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 48 16%
Student > Bachelor 47 16%
Student > Ph. D. Student 46 16%
Researcher 43 15%
Student > Doctoral Student 19 6%
Other 43 15%
Unknown 48 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 114 39%
Agricultural and Biological Sciences 49 17%
Engineering 12 4%
Chemistry 11 4%
Computer Science 10 3%
Other 39 13%
Unknown 59 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 December 2020.
All research outputs
#1,660,880
of 19,835,362 outputs
Outputs from BMC Bioinformatics
#450
of 6,664 outputs
Outputs of similar age
#36,380
of 285,687 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 27 outputs
Altmetric has tracked 19,835,362 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,664 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. 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 285,687 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 87% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.