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Null allele, allelic dropouts or rare sex detection in clonal organisms: simulations and application to real data sets of pathogenic microbes

Overview of attention for article published in Parasites & Vectors, July 2014
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Title
Null allele, allelic dropouts or rare sex detection in clonal organisms: simulations and application to real data sets of pathogenic microbes
Published in
Parasites & Vectors, July 2014
DOI 10.1186/1756-3305-7-331
Pubmed ID
Authors

Modou Séré, Jacques Kaboré, Vincent Jamonneau, Adrien Marie Gaston Belem, Francisco J Ayala, Thierry De Meeûs

Abstract

Pathogens and their vectors are organisms whose ecology is often only accessible through population genetics tools based on spatio-temporal variability of molecular markers. However, molecular tools may present technical difficulties due to the masking of some alleles (allelic dropouts and / or null alleles), which tends to bias the estimation of heterozygosity and thus the inferences concerning the breeding system of the organism under study. This is especially critical in clonal organisms in which deviation from panmixia, as measured by Wright's FIS, can, in principle, be used to infer both the extent of clonality and structure in a given population. In particular, null alleles and allelic dropouts are locus specific and likely produce high variance of Wright's FIS across loci, as rare sex is expected to do. In this paper we propose a tool enabling to discriminate between consequences of these technical problems and those of rare sex.

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The data shown below were collected from the profile of 1 X user 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 3%
Unknown 35 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 22%
Student > Ph. D. Student 7 19%
Student > Master 5 14%
Student > Bachelor 3 8%
Student > Postgraduate 2 6%
Other 3 8%
Unknown 8 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 47%
Biochemistry, Genetics and Molecular Biology 6 17%
Business, Management and Accounting 1 3%
Immunology and Microbiology 1 3%
Sports and Recreations 1 3%
Other 1 3%
Unknown 9 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 17 July 2014.
All research outputs
#22,759,452
of 25,374,647 outputs
Outputs from Parasites & Vectors
#5,346
of 5,987 outputs
Outputs of similar age
#207,726
of 241,651 outputs
Outputs of similar age from Parasites & Vectors
#85
of 99 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,987 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.