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A novel approach identifies the first transcriptome networks in bats: a new genetic model for vocal communication

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

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
27 tweeters

Citations

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15 Dimensions

Readers on

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44 Mendeley
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Title
A novel approach identifies the first transcriptome networks in bats: a new genetic model for vocal communication
Published in
BMC Genomics, October 2015
DOI 10.1186/s12864-015-2068-1
Pubmed ID
Authors

Pedro Rodenas-Cuadrado, Xiaowei Sylvia Chen, Lutz Wiegrebe, Uwe Firzlaff, Sonja C. Vernes

Abstract

Bats are able to employ an astonishingly complex vocal repertoire for navigating their environment and conveying social information. A handful of species also show evidence for vocal learning, an extremely rare ability shared only with humans and few other animals. However, despite their potential for the study of vocal communication, bats remain severely understudied at a molecular level. To address this fundamental gap we performed the first transcriptome profiling and genetic interrogation of molecular networks in the brain of a highly vocal bat species, Phyllostomus discolor. Gene network analysis typically needs large sample sizes for correct clustering, this can be prohibitive where samples are limited, such as in this study. To overcome this, we developed a novel bioinformatics methodology for identifying robust co-expression gene networks using few samples (N=6). Using this approach, we identified tissue-specific functional gene networks from the bat PAG, a brain region fundamental for mammalian vocalisation. The most highly connected network identified represented a cluster of genes involved in glutamatergic synaptic transmission. Glutamatergic receptors play a significant role in vocalisation from the PAG, suggesting that this gene network may be mechanistically important for vocal-motor control in mammals. We have developed an innovative approach to cluster co-expressing gene networks and show that it is highly effective in detecting robust functional gene networks with limited sample sizes. Moreover, this work represents the first gene network analysis performed in a bat brain and establishes bats as a novel, tractable model system for understanding the genetics of vocal mammalian communication.

Twitter Demographics

The data shown below were collected from the profiles of 27 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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Germany 1 2%
Norway 1 2%
Unknown 41 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 39%
Researcher 6 14%
Student > Master 5 11%
Student > Bachelor 3 7%
Student > Doctoral Student 3 7%
Other 5 11%
Unknown 5 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 39%
Neuroscience 9 20%
Biochemistry, Genetics and Molecular Biology 6 14%
Linguistics 2 5%
Environmental Science 2 5%
Other 2 5%
Unknown 6 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 21. 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 29 September 2016.
All research outputs
#1,073,430
of 16,675,445 outputs
Outputs from BMC Genomics
#284
of 9,102 outputs
Outputs of similar age
#24,101
of 287,453 outputs
Outputs of similar age from BMC Genomics
#32
of 996 outputs
Altmetric has tracked 16,675,445 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,102 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 96% 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 287,453 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 91% of its contemporaries.
We're also able to compare this research output to 996 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 96% of its contemporaries.