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High throughput transcriptome analysis of lipid metabolism in Syrian hamster liver in absence of an annotated genome

Overview of attention for article published in BMC Genomics, April 2013
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3 X users

Citations

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8 Dimensions

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26 Mendeley
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Title
High throughput transcriptome analysis of lipid metabolism in Syrian hamster liver in absence of an annotated genome
Published in
BMC Genomics, April 2013
DOI 10.1186/1471-2164-14-237
Pubmed ID
Authors

Roland Schmucki, Marco Berrera, Erich Küng, Serene Lee, Wolfgang E Thasler, Sabine Grüner, Martin Ebeling, Ulrich Certa

Abstract

Whole transcriptome analyses are an essential tool for understanding disease mechanisms. Approaches based on next-generation sequencing provide fast and affordable data but rely on the availability of annotated genomes. However, there are many areas in biomedical research that require non-standard animal models for which genome information is not available. This includes the Syrian hamster Mesocricetus auratus as an important model for dyslipidaemia because it mirrors many aspects of human disease and pharmacological responses. We show that complementary use of two independent next generation sequencing technologies combined with mapping to multiple genome databases allows unambiguous transcript annotation and quantitative transcript imaging. We refer to this approach as "triple match sequencing" (TMS).

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The data shown below were collected from the profiles of 3 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 4%
Germany 1 4%
Unknown 24 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 27%
Student > Ph. D. Student 5 19%
Student > Master 3 12%
Other 2 8%
Student > Postgraduate 2 8%
Other 4 15%
Unknown 3 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 58%
Biochemistry, Genetics and Molecular Biology 4 15%
Computer Science 2 8%
Environmental Science 1 4%
Psychology 1 4%
Other 0 0%
Unknown 3 12%
Attention Score in Context

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 14 April 2013.
All research outputs
#13,886,991
of 22,705,019 outputs
Outputs from BMC Genomics
#5,328
of 10,624 outputs
Outputs of similar age
#111,108
of 199,476 outputs
Outputs of similar age from BMC Genomics
#61
of 122 outputs
Altmetric has tracked 22,705,019 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,624 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 46th percentile – i.e., 46% 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 199,476 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 122 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.