↓ Skip to main content

ATGme: Open-source web application for rare codon identification and custom DNA sequence optimization

Overview of attention for article published in BMC Bioinformatics, September 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

twitter
4 X users
patent
2 patents
facebook
1 Facebook page

Citations

dimensions_citation
35 Dimensions

Readers on

mendeley
98 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
ATGme: Open-source web application for rare codon identification and custom DNA sequence optimization
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0743-5
Pubmed ID
Authors

Edward Daniel, Goodluck U. Onwukwe, Rik K. Wierenga, Susan E. Quaggin, Seppo J. Vainio, Mirja Krause

Abstract

Codon usage plays a crucial role when recombinant proteins are expressed in different organisms. This is especially the case if the codon usage frequency of the organism of origin and the target host organism differ significantly, for example when a human gene is expressed in E. coli. Therefore, to enable or enhance efficient gene expression it is of great importance to identify rare codons in any given DNA sequence and subsequently mutate these to codons which are more frequently used in the expression host. We describe an open-source web-based application, ATGme, which can in a first step identify rare and highly rare codons from most organisms, and secondly gives the user the possibility to optimize the sequence. This application provides a simple user-friendly interface utilizing three optimization strategies: 1. one-click optimization, 2. bulk optimization (by codon-type), 3. individualized custom (codon-by-codon) optimization. ATGme is an open-source application which is freely available at: http://atgme.org.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users 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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Unknown 97 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 16%
Student > Master 16 16%
Researcher 14 14%
Student > Bachelor 9 9%
Other 5 5%
Other 11 11%
Unknown 27 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 23%
Agricultural and Biological Sciences 23 23%
Computer Science 5 5%
Immunology and Microbiology 3 3%
Chemical Engineering 3 3%
Other 11 11%
Unknown 30 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 25 November 2021.
All research outputs
#5,202,169
of 24,532,617 outputs
Outputs from BMC Bioinformatics
#1,901
of 7,551 outputs
Outputs of similar age
#64,307
of 279,548 outputs
Outputs of similar age from BMC Bioinformatics
#42
of 148 outputs
Altmetric has tracked 24,532,617 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,551 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 73% 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 279,548 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 75% of its contemporaries.
We're also able to compare this research output to 148 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.