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Gene-pseudogene evolution: a probabilistic approach

Overview of attention for article published in BMC Genomics, October 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)

Mentioned by

twitter
2 tweeters

Citations

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3 Dimensions

Readers on

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39 Mendeley
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Title
Gene-pseudogene evolution: a probabilistic approach
Published in
BMC Genomics, October 2015
DOI 10.1186/1471-2164-16-s10-s12
Pubmed ID
Authors

Owais Mahmudi, Bengt Sennblad, Lars Arvestad, Katja Nowick, Jens Lagergren

Abstract

Over the last decade, methods have been developed for the reconstruction of gene trees that take into account the species tree. Many of these methods have been based on the probabilistic duplication-loss model, which describes how a gene-tree evolves over a species-tree with respect to duplication and losses, as well as extension of this model, e.g., the DLRS (Duplication, Loss, Rate and Sequence evolution) model that also includes sequence evolution under relaxed molecular clock. A disjoint, almost as recent, and very important line of research has been focused on non protein-coding, but yet, functional DNA. For instance, DNA sequences being pseudogenes in the sense that they are not translated, may still be transcribed and the thereby produced RNA may be functional.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 1 3%
Germany 1 3%
Unknown 37 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 21%
Student > Bachelor 6 15%
Student > Ph. D. Student 5 13%
Student > Master 5 13%
Student > Doctoral Student 3 8%
Other 8 21%
Unknown 4 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 36%
Agricultural and Biological Sciences 13 33%
Computer Science 3 8%
Medicine and Dentistry 3 8%
Physics and Astronomy 1 3%
Other 0 0%
Unknown 5 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 October 2015.
All research outputs
#2,697,902
of 6,252,498 outputs
Outputs from BMC Genomics
#2,929
of 5,067 outputs
Outputs of similar age
#91,576
of 193,544 outputs
Outputs of similar age from BMC Genomics
#247
of 358 outputs
Altmetric has tracked 6,252,498 research outputs across all sources so far. This one has received more attention than most of these and is in the 56th percentile.
So far Altmetric has tracked 5,067 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 41st percentile – i.e., 41% 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 193,544 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 51% of its contemporaries.
We're also able to compare this research output to 358 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.