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Pitfalls of the most commonly used models of context dependent substitution

Overview of attention for article published in Biology Direct, December 2008
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Title
Pitfalls of the most commonly used models of context dependent substitution
Published in
Biology Direct, December 2008
DOI 10.1186/1745-6150-3-52
Pubmed ID
Authors

Helen Lindsay, Von Bing Yap, Hua Ying, Gavin A Huttley

Abstract

Neighboring nucleotides exert a striking influence on mutation, with the hypermutability of CpG dinucleotides in many genomes being an exemplar. Among the approaches employed to measure the relative importance of sequence neighbors on molecular evolution have been continuous-time Markov process models for substitutions that treat sequences as a series of independent tuples. The most widely used examples are the codon substitution models. We evaluated the suitability of derivatives of the nucleotide frequency weighted (hereafter NF) and tuple frequency weighted (hereafter TF) models for measuring sequence context dependent substitution. Critical properties we address are their relationships to an independent nucleotide process and the robustness of parameter estimation to changes in sequence composition. We then consider the impact on inference concerning dinucleotide substitution processes from application of these two forms to intron sequence alignments from primates.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 4%
United States 1 4%
Switzerland 1 4%
Unknown 21 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 25%
Student > Ph. D. Student 5 21%
Student > Master 3 13%
Professor 3 13%
Lecturer 2 8%
Other 4 17%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 38%
Biochemistry, Genetics and Molecular Biology 4 17%
Computer Science 3 13%
Engineering 2 8%
Mathematics 1 4%
Other 2 8%
Unknown 3 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 14 November 2017.
All research outputs
#14,585,778
of 25,066,230 outputs
Outputs from Biology Direct
#314
of 532 outputs
Outputs of similar age
#149,827
of 182,255 outputs
Outputs of similar age from Biology Direct
#15
of 18 outputs
Altmetric has tracked 25,066,230 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 532 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one is in the 40th percentile – i.e., 40% 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 182,255 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.