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X Demographics
Mendeley readers
Attention Score in Context
| Title |
ELM: enhanced lowest common ancestor based method for detecting a pathogenic virus from a large sequence dataset
|
|---|---|
| Published in |
BMC Bioinformatics, July 2014
|
| DOI | 10.1186/1471-2105-15-254 |
| Pubmed ID | |
| Authors |
Keisuke Ueno, Akihiro Ishii, Kimihito Ito |
| Abstract |
Emerging viral diseases, most of which are caused by the transmission of viruses from animals to humans, pose a threat to public health. Discovering pathogenic viruses through surveillance is the key to preparedness for this potential threat. Next generation sequencing (NGS) helps us to identify viruses without the design of a specific PCR primer. The major task in NGS data analysis is taxonomic identification for vast numbers of sequences. However, taxonomic identification via a BLAST search against all the known sequences is a computational bottleneck. |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 22% |
| Sweden | 1 | 11% |
| United Kingdom | 1 | 11% |
| Germany | 1 | 11% |
| France | 1 | 11% |
| Norway | 1 | 11% |
| Australia | 1 | 11% |
| Unknown | 1 | 11% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 6 | 67% |
| Members of the public | 3 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| Sweden | 1 | 2% |
| Netherlands | 1 | 2% |
| Japan | 1 | 2% |
| Estonia | 1 | 2% |
| Unknown | 43 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 31% |
| Student > Ph. D. Student | 11 | 22% |
| Student > Master | 6 | 12% |
| Professor | 4 | 8% |
| Other | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 6 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 43% |
| Biochemistry, Genetics and Molecular Biology | 5 | 10% |
| Computer Science | 5 | 10% |
| Immunology and Microbiology | 3 | 6% |
| Business, Management and Accounting | 1 | 2% |
| Other | 3 | 6% |
| Unknown | 11 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 01 August 2014.
All research outputs
#7,536,099
of 25,870,940 outputs
Outputs from BMC Bioinformatics
#2,682
of 7,760 outputs
Outputs of similar age
#66,028
of 240,947 outputs
Outputs of similar age from BMC Bioinformatics
#56
of 132 outputs
Altmetric has tracked 25,870,940 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,760 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has gotten more attention than average, scoring higher than 64% 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 240,947 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.