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The Duplexing of the Genetic Code and Sequence-Dependent DNA Geometry

Overview of attention for article published in Bulletin of Mathematical Biology, August 2018
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Title
The Duplexing of the Genetic Code and Sequence-Dependent DNA Geometry
Published in
Bulletin of Mathematical Biology, August 2018
DOI 10.1007/s11538-018-0486-3
Pubmed ID
Authors

Alex Kasman

Abstract

It is well known that sequences of bases in DNA are translated into sequences of amino acids in cells via the genetic code. More recently, it has been discovered that the sequence of DNA bases also influences the geometry and deformability of the DNA. These two correspondences represent a naturally arising example of duplexed codes, providing two different ways of interpreting the same DNA sequence. This paper will set up the notation and basic results necessary to mathematically investigate the relationship between these two natural DNA codes. It then undertakes two very different such investigations: one graphical approach based only on expected values and another analytic approach incorporating the deformability of the DNA molecule and approximating the mutual information of the two codes. Special emphasis is paid to whether there is evidence that pressure to maximize the duplexing efficiency influenced the evolution of the genetic code. Disappointingly, the results fail to support the hypothesis that the genetic code was influenced in this way. In fact, applying both methods to samples of realistic alternative genetic codes shows that the duplexing of the genetic code found in nature is just slightly less efficient than average. The implications of this negative result are considered in the final section of the paper.

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Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 25%
Researcher 1 25%
Unknown 2 50%
Readers by discipline Count As %
Mathematics 1 25%
Agricultural and Biological Sciences 1 25%
Unknown 2 50%
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 09 October 2018.
All research outputs
#15,544,609
of 23,102,082 outputs
Outputs from Bulletin of Mathematical Biology
#728
of 1,105 outputs
Outputs of similar age
#210,137
of 331,120 outputs
Outputs of similar age from Bulletin of Mathematical Biology
#33
of 49 outputs
Altmetric has tracked 23,102,082 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,105 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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 331,120 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one is in the 2nd percentile – i.e., 2% of its contemporaries scored the same or lower than it.