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Pydna: a simulation and documentation tool for DNA assembly strategies using python

Overview of attention for article published in BMC Bioinformatics, May 2015
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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 (88th percentile)

Mentioned by

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21 X users
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

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23 Dimensions

Readers on

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111 Mendeley
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Title
Pydna: a simulation and documentation tool for DNA assembly strategies using python
Published in
BMC Bioinformatics, May 2015
DOI 10.1186/s12859-015-0544-x
Pubmed ID
Authors

Filipa Pereira, Flávio Azevedo, Ângela Carvalho, Gabriela F Ribeiro, Mark W Budde, Björn Johansson

Abstract

Recent advances in synthetic biology have provided tools to efficiently construct complex DNA molecules which are an important part of many molecular biology and biotechnology projects. The planning of such constructs has traditionally been done manually using a DNA sequence editor which becomes error-prone as scale and complexity of the construction increase. A human-readable formal description of cloning and assembly strategies, which also allows for automatic computer simulation and verification, would therefore be a valuable tool. We have developed pydna, an extensible, free and open source Python library for simulating basic molecular biology DNA unit operations such as restriction digestion, ligation, PCR, primer design, Gibson assembly and homologous recombination. A cloning strategy expressed as a pydna script provides a description that is complete, unambiguous and stable. Execution of the script automatically yields the sequence of the final molecule(s) and that of any intermediate constructs. Pydna has been designed to be understandable for biologists with limited programming skills by providing interfaces that are semantically similar to the description of molecular biology unit operations found in literature. Pydna simplifies both the planning and sharing of cloning strategies and is especially useful for complex or combinatorial DNA molecule construction. An important difference compared to existing tools with similar goals is the use of Python instead of a specifically constructed language, providing a simulation environment that is more flexible and extensible by the user.

X Demographics

X Demographics

The data shown below were collected from the profiles of 21 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 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Netherlands 1 <1%
Sweden 1 <1%
United Kingdom 1 <1%
Belgium 1 <1%
United States 1 <1%
Unknown 105 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 29%
Student > Ph. D. Student 22 20%
Student > Master 17 15%
Student > Bachelor 12 11%
Student > Doctoral Student 4 4%
Other 11 10%
Unknown 13 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 39%
Biochemistry, Genetics and Molecular Biology 24 22%
Computer Science 8 7%
Engineering 7 6%
Chemistry 4 4%
Other 9 8%
Unknown 16 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 2015.
All research outputs
#2,673,634
of 24,473,185 outputs
Outputs from BMC Bioinformatics
#786
of 7,539 outputs
Outputs of similar age
#33,956
of 268,876 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 133 outputs
Altmetric has tracked 24,473,185 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,539 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 done well, scoring higher than 89% 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 268,876 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 133 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.