↓ Skip to main content

Detection of Regional Variation in Selection Intensity within Protein-Coding Genes Using DNA Sequence Polymorphism and Divergence

Overview of attention for article published in Molecular Biology and Evolution, July 2017
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

news
2 news outlets
twitter
14 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
22 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Detection of Regional Variation in Selection Intensity within Protein-Coding Genes Using DNA Sequence Polymorphism and Divergence
Published in
Molecular Biology and Evolution, July 2017
DOI 10.1093/molbev/msx213
Pubmed ID
Authors

Zi-Ming Zhao, Michael C. Campbell, Ning Li, Daniel S.W. Lee, Zhang, Jeffrey P. Townsend

Abstract

Numerous approaches have been developed to infer natural selection based on the comparison of polymorphism within species and divergence between species. These methods are especially powerful for the detection of uniform selection operating across a gene. However, empirical analyses have demonstrated that regions of protein-coding genes exhibiting clusters of amino acid substitutions are subject to different levels of selection relative to other regions of the same gene. To quantify this heterogeneity of selection within coding sequences, we developed Model Averaged Site Selection via Poisson Random Field (MASS-PRF). MASS-PRF identifies an ensemble of intragenic clustering models for polymorphic and divergent sites. This ensemble of models is used within the Poisson Random Field (PRF) framework to estimate selection intensity on a site-by-site basis. Using simulations, we demonstrate that MASS-PRF has high power to detect clusters of amino acid variants in small genic regions and to reliably estimate the probability of a variant occurring at each nucleotide site in sequence data. We applied MASS-PRF to human gene polymorphism derived from the 1000 Genomes Project and divergence data from the common chimpanzee. Based on this analysis, we discovered striking regional variation in selection intensity, indicative of positive or negative selection, in well-defined domains of genes that have previously been associated with neurological processing, immunity, and reproduction. We suggest that amino acid-altering substitutions within these regions likely are or have been selectively advantageous in the human lineage, playing important roles in protein function.

Twitter Demographics

The data shown below were collected from the profiles of 14 tweeters 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 32%
Student > Ph. D. Student 4 18%
Student > Bachelor 2 9%
Other 2 9%
Unspecified 2 9%
Other 4 18%
Unknown 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 55%
Biochemistry, Genetics and Molecular Biology 4 18%
Unspecified 3 14%
Environmental Science 1 5%
Medicine and Dentistry 1 5%
Other 0 0%
Unknown 1 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 06 December 2018.
All research outputs
#640,123
of 12,902,716 outputs
Outputs from Molecular Biology and Evolution
#407
of 3,486 outputs
Outputs of similar age
#24,289
of 264,033 outputs
Outputs of similar age from Molecular Biology and Evolution
#15
of 78 outputs
Altmetric has tracked 12,902,716 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,486 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.0. This one has done well, scoring higher than 88% 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 264,033 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 78 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.