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SNPchiMp v.3: integrating and standardizing single nucleotide polymorphism data for livestock species

Overview of attention for article published in BMC Genomics, April 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Title
SNPchiMp v.3: integrating and standardizing single nucleotide polymorphism data for livestock species
Published in
BMC Genomics, April 2015
DOI 10.1186/s12864-015-1497-1
Pubmed ID
Authors

Ezequiel L Nicolazzi, Andrea Caprera, Nelson Nazzicari, Paolo Cozzi, Francesco Strozzi, Cindy Lawley, Ali Pirani, Chandrasen Soans, Fiona Brew, Hossein Jorjani, Gary Evans, Barry Simpson, Gwenola Tosser-Klopp, Rudiger Brauning, John L Williams, Alessandra Stella

Abstract

In recent years, the use of genomic information in livestock species for genetic improvement, association studies and many other fields has become routine. In order to accommodate different market requirements in terms of genotyping cost, manufacturers of single nucleotide polymorphism (SNP) arrays, private companies and international consortia have developed a large number of arrays with different content and different SNP density. The number of currently available SNP arrays differs among species: ranging from one for goats to more than ten for cattle, and the number of arrays available is increasing rapidly. However, there is limited or no effort to standardize and integrate array- specific (e.g. SNP IDs, allele coding) and species-specific (i.e. past and current assemblies) SNP information. Here we present SNPchiMp v.3, a solution to these issues for the six major livestock species (cow, pig, horse, sheep, goat and chicken). Original data was collected directly from SNP array producers and specific international genome consortia, and stored in a MySQL database. The database was then linked to an open-access web tool and to public databases. SNPchiMp v.3 ensures fast access to the database (retrieving within/across SNP array data) and the possibility of annotating SNP array data in a user-friendly fashion. This platform allows easy integration and standardization, and it is aimed at both industry and research. It also enables users to easily link the information available from the array producer with data in public databases, without the need of additional bioinformatics tools or pipelines. In recognition of the open-access use of Ensembl resources, SNPchiMp v.3 was officially credited as an Ensembl E!mpowered tool. Availability at http://bioinformatics.tecnoparco.org/SNPchimp.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 22%
Student > Ph. D. Student 13 18%
Student > Master 7 10%
Student > Doctoral Student 7 10%
Student > Bachelor 7 10%
Other 12 16%
Unknown 11 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 45%
Biochemistry, Genetics and Molecular Biology 9 12%
Veterinary Science and Veterinary Medicine 6 8%
Computer Science 3 4%
Medicine and Dentistry 2 3%
Other 6 8%
Unknown 14 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 27 April 2015.
All research outputs
#5,720,424
of 23,881,329 outputs
Outputs from BMC Genomics
#2,196
of 10,793 outputs
Outputs of similar age
#64,236
of 266,320 outputs
Outputs of similar age from BMC Genomics
#64
of 259 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,793 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 79% of its peers.
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 266,320 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 259 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.