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Endogenous murine leukemia retroviral variation across wild European and inbred strains of house mouse

Overview of attention for article published in BMC Genomics, August 2015
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Title
Endogenous murine leukemia retroviral variation across wild European and inbred strains of house mouse
Published in
BMC Genomics, August 2015
DOI 10.1186/s12864-015-1766-z
Pubmed ID
Authors

Stefanie Hartmann, Natascha Hasenkamp, Jens Mayer, Johan Michaux, Serge Morand, Camila J. Mazzoni, Alfred L. Roca, Alex D. Greenwood

Abstract

Endogenous murine leukemia retroviruses (MLVs) are high copy number proviral elements difficult to comprehensively characterize using standard low throughput sequencing approaches. However, high throughput approaches generate data that is challenging to process, interpret and present. Next generation sequencing (NGS) data was generated for MLVs from two wild caught Mus musculus domesticus (from mainland France and Corsica) and for inbred laboratory mouse strains C3H, LP/J and SJL. Sequence reads were grouped using a novel sequence clustering approach as applied to retroviral sequences. A Markov cluster algorithm was employed, and the sequence reads were queried for matches to specific xenotropic (Xmv), polytropic (Pmv) and modified polytropic (Mpmv) viral reference sequences. Various MLV subtypes were more widespread than expected among the mice, which may be due to the higher coverage of NGS, or to the presence of similar sequence across many different proviral loci. The results did not correlate with variation in the major MLV receptor Xpr1, which can restrict exogenous MLVs, suggesting that endogenous MLV distribution may reflect gene flow more than past resistance to infection.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 5%
Unknown 18 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 21%
Researcher 3 16%
Student > Master 2 11%
Student > Doctoral Student 1 5%
Student > Bachelor 1 5%
Other 4 21%
Unknown 4 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 32%
Immunology and Microbiology 3 16%
Biochemistry, Genetics and Molecular Biology 2 11%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Computer Science 1 5%
Other 1 5%
Unknown 5 26%
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 03 May 2016.
All research outputs
#13,953,851
of 22,824,164 outputs
Outputs from BMC Genomics
#5,348
of 10,654 outputs
Outputs of similar age
#132,772
of 266,186 outputs
Outputs of similar age from BMC Genomics
#141
of 257 outputs
Altmetric has tracked 22,824,164 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 10,654 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 266,186 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 257 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.