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A tool for mapping Single Nucleotide Polymorphisms using Graphics Processing Units

Overview of attention for article published in BMC Bioinformatics, January 2014
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Title
A tool for mapping Single Nucleotide Polymorphisms using Graphics Processing Units
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-s1-s10
Pubmed ID
Authors

Andrea Manconi, Alessandro Orro, Emanuele Manca, Giuliano Armano, Luciano Milanesi

Abstract

Single Nucleotide Polymorphism (SNP) genotyping analysis is very susceptible to SNPs chromosomal position errors. As it is known, SNPs mapping data are provided along the SNP arrays without any necessary information to assess in advance their accuracy. Moreover, these mapping data are related to a given build of a genome and need to be updated when a new build is available. As a consequence, researchers often plan to remap SNPs with the aim to obtain more up-to-date SNPs chromosomal positions. In this work, we present G-SNPM a GPU (Graphics Processing Unit) based tool to map SNPs on a genome.

X Demographics

X Demographics

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

Geographical breakdown

Country Count As %
Spain 1 5%
Russia 1 5%
Poland 1 5%
Unknown 17 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 30%
Student > Master 5 25%
Student > Ph. D. Student 4 20%
Other 2 10%
Professor > Associate Professor 1 5%
Other 0 0%
Unknown 2 10%
Readers by discipline Count As %
Computer Science 7 35%
Agricultural and Biological Sciences 6 30%
Biochemistry, Genetics and Molecular Biology 2 10%
Environmental Science 1 5%
Medicine and Dentistry 1 5%
Other 1 5%
Unknown 2 10%
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 29 January 2014.
All research outputs
#15,291,764
of 22,741,406 outputs
Outputs from BMC Bioinformatics
#5,370
of 7,267 outputs
Outputs of similar age
#189,564
of 304,957 outputs
Outputs of similar age from BMC Bioinformatics
#71
of 100 outputs
Altmetric has tracked 22,741,406 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,267 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 304,957 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.