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FSuite: exploiting inbreeding in dense SNP chip and exome data

Overview of attention for article published in Bioinformatics, March 2014
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Mentioned by

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3 X users
googleplus
1 Google+ user

Citations

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30 Dimensions

Readers on

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58 Mendeley
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Title
FSuite: exploiting inbreeding in dense SNP chip and exome data
Published in
Bioinformatics, March 2014
DOI 10.1093/bioinformatics/btu149
Pubmed ID
Authors

Steven Gazal, Mourad Sahbatou, Marie-Claude Babron, Emmanuelle Génin, Anne-Louise Leutenegger

Abstract

FSuite is a user-friendly pipeline developed for exploiting inbreeding information derived from human genomic data. It can make use of single nucleotide polymorphism chip or exome data. Compared with other software, the advantage of FSuite is that it provides a complete suite of scripts to describe and use the inbreeding information. It includes a module to detect inbred individuals and estimate their inbreeding coefficient, a module to describe the proportion of different mating types in the population and the individual probability to be offspring of different mating types that can be useful for population genetic studies. It also allows the identification of shared regions of homozygosity between affected individuals (homozygosity mapping) that can be used to identify rare recessive mutations involved in monogenic or multifactorial diseases.

X Demographics

X Demographics

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

Geographical breakdown

Country Count As %
France 1 2%
Brazil 1 2%
Belgium 1 2%
Japan 1 2%
United States 1 2%
Unknown 53 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 31%
Researcher 12 21%
Student > Master 8 14%
Lecturer 3 5%
Student > Postgraduate 3 5%
Other 6 10%
Unknown 8 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 40%
Biochemistry, Genetics and Molecular Biology 12 21%
Computer Science 4 7%
Linguistics 1 2%
Mathematics 1 2%
Other 2 3%
Unknown 15 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 30 June 2014.
All research outputs
#14,915,133
of 25,374,917 outputs
Outputs from Bioinformatics
#8,880
of 12,809 outputs
Outputs of similar age
#117,962
of 235,803 outputs
Outputs of similar age from Bioinformatics
#107
of 162 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,809 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 28th percentile – i.e., 28% 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 235,803 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 162 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.