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CG dinucleotide clustering is a species-specific property of the genome

Overview of attention for article published in Nucleic Acids Research, October 2007
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

Mentioned by

twitter
1 tweeter
patent
1 patent

Readers on

mendeley
88 Mendeley
citeulike
4 CiteULike
connotea
6 Connotea
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Title
CG dinucleotide clustering is a species-specific property of the genome
Published in
Nucleic Acids Research, October 2007
DOI 10.1093/nar/gkm489
Pubmed ID
Authors

Jacob L. Glass, Reid F. Thompson, Batbayar Khulan, Maria E. Figueroa, Emmanuel N. Olivier, Erin J. Oakley, Gary Van Zant, Eric E. Bouhassira, Ari Melnick, Aaron Golden, Melissa J. Fazzari, John M. Greally

Abstract

Cytosines at cytosine-guanine (CG) dinucleotides are the near-exclusive target of DNA methyltransferases in mammalian genomes. Spontaneous deamination of methylcytosine to thymine makes methylated cytosines unusually susceptible to mutation and consequent depletion. The loci where CG dinucleotides remain relatively enriched, presumably due to their unmethylated status during the germ cell cycle, have been referred to as CpG islands. Currently, CpG islands are solely defined by base compositional criteria, allowing annotation of any sequenced genome. Using a novel bioinformatic approach, we show that CG clusters can be identified as an inherent property of genomic sequence without imposing a base compositional a priori assumption. We also show that the CG clusters co-localize in the human genome with hypomethylated loci and annotated transcription start sites to a greater extent than annotations produced by prior CpG island definitions. Moreover, this new approach allows CG clusters to be identified in a species-specific manner, revealing a degree of orthologous conservation that is not revealed by current base compositional approaches. Finally, our approach is able to identify methylating genomes (such as Takifugu rubripes) that lack CG clustering entirely, in which it is inappropriate to annotate CpG islands or CG clusters.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
United Kingdom 2 2%
Brazil 1 1%
France 1 1%
Australia 1 1%
Netherlands 1 1%
Germany 1 1%
Canada 1 1%
Belgium 1 1%
Other 3 3%
Unknown 74 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 30%
Student > Ph. D. Student 15 17%
Professor 15 17%
Professor > Associate Professor 9 10%
Student > Master 8 9%
Other 12 14%
Unknown 3 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 52 59%
Biochemistry, Genetics and Molecular Biology 12 14%
Medicine and Dentistry 5 6%
Computer Science 4 5%
Engineering 3 3%
Other 5 6%
Unknown 7 8%

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 18 December 2019.
All research outputs
#4,449,160
of 15,257,867 outputs
Outputs from Nucleic Acids Research
#8,684
of 22,520 outputs
Outputs of similar age
#44,554
of 157,131 outputs
Outputs of similar age from Nucleic Acids Research
#62
of 228 outputs
Altmetric has tracked 15,257,867 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 22,520 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 60% 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 157,131 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 70% of its contemporaries.
We're also able to compare this research output to 228 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 69% of its contemporaries.