↓ Skip to main content

Leading the way: finding genes for neurologic disease in dogs using genome-wide mRNA sequencing

Overview of attention for article published in BMC Genomic Data, July 2012
Altmetric Badge

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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

blogs
1 blog
twitter
6 X users

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
25 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
Leading the way: finding genes for neurologic disease in dogs using genome-wide mRNA sequencing
Published in
BMC Genomic Data, July 2012
DOI 10.1186/1471-2156-13-56
Pubmed ID
Authors

Elaine A Ostrander, Holly C Beale

Abstract

Because of dogs' unique population structure, human-like disease biology, and advantageous genomic features, the canine system has risen dramatically in popularity as a tool for discovering disease alleles that have been difficult to find by studying human families or populations. To date, disease studies in dogs have primarily employed either linkage analysis, leveraging the typically large family size, or genome-wide association, which requires only modest-sized case and control groups in dogs. Both have been successful but, like most techniques, each requires a specific combination of time and money, and there are inherent problems associated with each. Here we review the first report of mRNA-Seq in the dog, a study that provides insights into the potential value of applying high-throughput sequencing to the study of genetic diseases in dogs. Forman and colleagues apply high-throughput sequencing to a single case of canine neonatal cerebellar cortical degeneration. This implementation of whole genome mRNA sequencing, the first reported in dog, is additionally unusual due to the analysis: the data was used not to examine transcript levels or annotate genes, but as a form of target capture that revealed the sequence of transcripts of genes associated with ataxia in humans. This approach entails risks. It would fail if, for example, the relevant transcripts were not sufficiently expressed for genotyping or were not associated with ataxia in humans. But here it pays off handsomely, identifying a single frameshift mutation that segregates with the disease. This work sets the stage for similar studies that take advantage of recent advances in genomics while exploiting the historical background of dog breeds to identify disease-causing mutations.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 20%
Other 4 16%
Student > Bachelor 3 12%
Student > Ph. D. Student 3 12%
Student > Doctoral Student 2 8%
Other 6 24%
Unknown 2 8%
Readers by discipline Count As %
Veterinary Science and Veterinary Medicine 7 28%
Agricultural and Biological Sciences 6 24%
Biochemistry, Genetics and Molecular Biology 5 20%
Arts and Humanities 1 4%
Computer Science 1 4%
Other 3 12%
Unknown 2 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 31 July 2012.
All research outputs
#3,193,889
of 25,373,627 outputs
Outputs from BMC Genomic Data
#88
of 1,204 outputs
Outputs of similar age
#20,430
of 177,987 outputs
Outputs of similar age from BMC Genomic Data
#3
of 17 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 92% 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 177,987 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.