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Phylogenetic patterns of emergence of new genes support a model of frequent de novo evolution

Overview of attention for article published in BMC Genomics, January 2013
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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 (97th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

blogs
3 blogs
twitter
14 tweeters
wikipedia
3 Wikipedia pages
googleplus
3 Google+ users

Citations

dimensions_citation
201 Dimensions

Readers on

mendeley
242 Mendeley
citeulike
1 CiteULike
Summary Blogs Twitter Wikipedia Google+ Dimensions citations
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Title
Phylogenetic patterns of emergence of new genes support a model of frequent de novo evolution
Published in
BMC Genomics, January 2013
DOI 10.1186/1471-2164-14-117
Pubmed ID
23433480
Authors

Rafik Neme, Diethard Tautz

Abstract

New gene emergence is so far assumed to be mostly driven by duplication and divergence of existing genes. The possibility that entirely new genes could emerge out of the non-coding genomic background was long thought to be almost negligible. With the increasing availability of fully sequenced genomes across broad scales of phylogeny, it has become possible to systematically study the origin of new genes over time and thus revisit this question.

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

Geographical breakdown

Country Count As %
United States 5 2%
United Kingdom 3 1%
Brazil 2 <1%
Spain 2 <1%
Italy 1 <1%
Australia 1 <1%
France 1 <1%
Ireland 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 224 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 69 29%
Researcher 46 19%
Student > Master 30 12%
Student > Bachelor 26 11%
Professor > Associate Professor 12 5%
Other 37 15%
Unknown 22 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 132 55%
Biochemistry, Genetics and Molecular Biology 62 26%
Computer Science 6 2%
Medicine and Dentistry 4 2%
Chemistry 3 1%
Other 11 5%
Unknown 24 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 38. 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 December 2021.
All research outputs
#908,064
of 22,691,736 outputs
Outputs from BMC Genomics
#130
of 10,617 outputs
Outputs of similar age
#8,019
of 280,671 outputs
Outputs of similar age from BMC Genomics
#4
of 374 outputs
Altmetric has tracked 22,691,736 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 10,617 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 98% 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 280,671 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 97% of its contemporaries.
We're also able to compare this research output to 374 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.

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