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Mendeley readers
Attention Score in Context
| Title |
Model SNP development for complex genomes based on hexaploid oat using high-throughput 454 sequencing technology
|
|---|---|
| Published in |
BMC Genomics, January 2011
|
| DOI | 10.1186/1471-2164-12-77 |
| Pubmed ID | |
| Authors |
Rebekah E Oliver, Gerard R Lazo, Joseph D Lutz, Marc J Rubenfield, Nicholas A Tinker, Joseph M Anderson, Nicole H Wisniewski Morehead, Dinesh Adhikary, Eric N Jellen, P Jeffrey Maughan, Gina L Brown Guedira, Shiaoman Chao, Aaron D Beattie, Martin L Carson, Howard W Rines, Donald E Obert, J Michael Bonman, Eric W Jackson |
| Abstract |
Genetic markers are pivotal to modern genomics research; however, discovery and genotyping of molecular markers in oat has been hindered by the size and complexity of the genome, and by a scarcity of sequence data. The purpose of this study was to generate oat expressed sequence tag (EST) information, develop a bioinformatics pipeline for SNP discovery, and establish a method for rapid, cost-effective, and straightforward genotyping of SNP markers in complex polyploid genomes such as oat. |
Mendeley readers
The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 2 | 1% |
| Netherlands | 1 | <1% |
| Pakistan | 1 | <1% |
| Norway | 1 | <1% |
| Brazil | 1 | <1% |
| Sweden | 1 | <1% |
| Canada | 1 | <1% |
| Slovenia | 1 | <1% |
| Argentina | 1 | <1% |
| Other | 2 | 1% |
| Unknown | 127 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 37 | 27% |
| Student > Ph. D. Student | 27 | 19% |
| Student > Master | 13 | 9% |
| Professor > Associate Professor | 9 | 6% |
| Student > Doctoral Student | 8 | 6% |
| Other | 23 | 17% |
| Unknown | 22 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 94 | 68% |
| Biochemistry, Genetics and Molecular Biology | 9 | 6% |
| Environmental Science | 3 | 2% |
| Computer Science | 2 | 1% |
| Medicine and Dentistry | 2 | 1% |
| Other | 3 | 2% |
| Unknown | 26 | 19% |
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 30 January 2011.
All research outputs
#20,143,522
of 22,649,029 outputs
Outputs from BMC Genomics
#9,234
of 10,605 outputs
Outputs of similar age
#171,766
of 182,527 outputs
Outputs of similar age from BMC Genomics
#70
of 77 outputs
Altmetric has tracked 22,649,029 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 10,605 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% 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 182,527 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 77 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.