Title |
SNPchiMp: a database to disentangle the SNPchip jungle in bovine livestock
|
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Published in |
BMC Genomics, February 2014
|
DOI | 10.1186/1471-2164-15-123 |
Pubmed ID | |
Authors |
Ezequiel Luis Nicolazzi, Matteo Picciolini, Francesco Strozzi, Robert David Schnabel, Cindy Lawley, Ali Pirani, Fiona Brew, Alessandra Stella |
Abstract |
Currently, six commercial whole-genome SNP chips are available for cattle genotyping, produced by two different genotyping platforms. Technical issues need to be addressed to combine data that originates from the different platforms, or different versions of the same array generated by the manufacturer. For example: i) genome coordinates for SNPs may refer to different genome assemblies; ii) reference genome sequences are updated over time changing the positions, or even removing sequences which contain SNPs; iii) not all commercial SNP ID's are searchable within public databases; iv) SNPs can be coded using different formats and referencing different strands (e.g. A/B or A/C/T/G alleles, referencing forward/reverse, top/bottom or plus/minus strand); v) Due to new information being discovered, higher density chips do not necessarily include all the SNPs present in the lower density chips; and, vi) SNP IDs may not be consistent across chips and platforms. Most researchers and breed associations manage SNP data in real-time and thus require tools to standardise data in a user-friendly manner. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 60% |
Canada | 1 | 20% |
Unknown | 1 | 20% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 80% |
Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 1 | 1% |
Brazil | 1 | 1% |
United Kingdom | 1 | 1% |
New Zealand | 1 | 1% |
Argentina | 1 | 1% |
Unknown | 85 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 22 | 24% |
Student > Ph. D. Student | 18 | 20% |
Student > Master | 11 | 12% |
Student > Bachelor | 7 | 8% |
Student > Doctoral Student | 5 | 6% |
Other | 13 | 14% |
Unknown | 14 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 47 | 52% |
Biochemistry, Genetics and Molecular Biology | 10 | 11% |
Computer Science | 5 | 6% |
Veterinary Science and Veterinary Medicine | 3 | 3% |
Medicine and Dentistry | 3 | 3% |
Other | 6 | 7% |
Unknown | 16 | 18% |