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Mendeley readers
Attention Score in Context
| Title |
snpTree - a web-server to identify and construct SNP trees from whole genome sequence data
|
|---|---|
| Published in |
BMC Genomics, December 2012
|
| DOI | 10.1186/1471-2164-13-s7-s6 |
| Pubmed ID | |
| Authors |
Pimlapas Leekitcharoenphon, Rolf S Kaas, Martin Christen Frølund Thomsen, Carsten Friis, Simon Rasmussen, Frank M Aarestrup |
| Abstract |
The advances and decreasing economical cost of whole genome sequencing (WGS), will soon make this technology available for routine infectious disease epidemiology. In epidemiological studies, outbreak isolates have very little diversity and require extensive genomic analysis to differentiate and classify isolates. One of the successfully and broadly used methods is analysis of single nucletide polymorphisms (SNPs). Currently, there are different tools and methods to identify SNPs including various options and cut-off values. Furthermore, all current methods require bioinformatic skills. Thus, we lack a standard and simple automatic tool to determine SNPs and construct phylogenetic tree from WGS data. |
Mendeley readers
The data shown below were compiled from readership statistics for 170 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 6 | 4% |
| United States | 3 | 2% |
| United Kingdom | 2 | 1% |
| Netherlands | 1 | <1% |
| India | 1 | <1% |
| Sweden | 1 | <1% |
| Australia | 1 | <1% |
| China | 1 | <1% |
| Brazil | 1 | <1% |
| Other | 2 | 1% |
| Unknown | 151 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 41 | 24% |
| Researcher | 39 | 23% |
| Student > Master | 17 | 10% |
| Student > Bachelor | 12 | 7% |
| Student > Doctoral Student | 8 | 5% |
| Other | 28 | 16% |
| Unknown | 25 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 85 | 50% |
| Biochemistry, Genetics and Molecular Biology | 24 | 14% |
| Medicine and Dentistry | 7 | 4% |
| Computer Science | 6 | 4% |
| Immunology and Microbiology | 6 | 4% |
| Other | 11 | 6% |
| Unknown | 31 | 18% |
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 31 May 2014.
All research outputs
#17,285,668
of 25,373,627 outputs
Outputs from BMC Genomics
#7,120
of 11,244 outputs
Outputs of similar age
#191,618
of 286,287 outputs
Outputs of similar age from BMC Genomics
#126
of 202 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 27th percentile – i.e., 27% 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 286,287 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 202 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.