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Characterizing exons and introns by regularity of nucleotide strings

Overview of attention for article published in Biology Direct, February 2016
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  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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3 patents

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Title
Characterizing exons and introns by regularity of nucleotide strings
Published in
Biology Direct, February 2016
DOI 10.1186/s13062-016-0108-7
Pubmed ID
Authors

Tonya Woods, Thanawadee Preeprem, Kichun Lee, Woojin Chang, Brani Vidakovic

Abstract

Translation of nucleotides into a numeric form has been approached in many ways and has allowed researchers to investigate the properties of protein-coding sequences and noncoding sequences. Typically, more pronounced long-range correlations and increased regularity were found in intron-containing genes and in non-transcribed regulatory DNA sequences, compared to cDNA sequences or intron-less genes. The regularity is assessed by spectral tools defined on numerical translates. In most popular approaches of numerical translation the resulting spectra depend on the assignment of numerical values to nucleotides. Our contribution is to propose and illustrate a spectra which remains invariant to the translation rules used in traditional approaches. We outline a methodology for representing sequences of DNA nucleotides as numeric matrices in order to analytically investigate important structural characteristics of DNA. This representation allows us to compute the 2-dimensional wavelet transformation and assess regularity characteristics of the sequence via the slope of the wavelet spectra. In addition to computing a global slope measure for a sequence, we can apply our methodology for overlapping sections of nucleotides to obtain an "evolutionary slope." To illustrate our methodology, we analyzed 376 gene sequences from the first chromosome of the honeybee. For the genes analyzed, we find that introns are significantly more regular (lead to more negative spectral slopes) than exons, which agrees with the results from the literature where regularity is measured on "DNA walks". However, unlike DNA walks where the nucleotides are assigned numerical values depending on nucleotide characteristics (purine-pyrimidine, weak-strong hydrogen bonds, keto-amino, etc.) or other spatial assignments, the proposed spectral tool is invariant to the assignment of nucleotides. Thus, ambiguity in numerical translation of nucleotides is eliminated. This article was reviewed by Dr. Vladimir Kuznetsov, Professor Marek Kimmel and Dr. Natsuhiro Ichinose (nominated by Professor Masanori Arita).

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

Geographical breakdown

Country Count As %
Mexico 1 6%
Unknown 17 94%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 17%
Student > Master 3 17%
Professor > Associate Professor 2 11%
Student > Ph. D. Student 2 11%
Researcher 2 11%
Other 2 11%
Unknown 4 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 28%
Agricultural and Biological Sciences 4 22%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Mathematics 1 6%
Computer Science 1 6%
Other 0 0%
Unknown 6 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 28 September 2023.
All research outputs
#8,031,675
of 25,563,770 outputs
Outputs from Biology Direct
#254
of 538 outputs
Outputs of similar age
#121,741
of 408,722 outputs
Outputs of similar age from Biology Direct
#4
of 10 outputs
Altmetric has tracked 25,563,770 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 538 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one has gotten more attention than average, scoring higher than 51% 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 408,722 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.