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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Ultra-fast sequence clustering from similarity networks with SiLiX
|
|---|---|
| Published in |
BMC Bioinformatics, April 2011
|
| DOI | 10.1186/1471-2105-12-116 |
| Pubmed ID | |
| Authors |
Vincent Miele, Simon Penel, Laurent Duret |
| Abstract |
The number of gene sequences that are available for comparative genomics approaches is increasing extremely quickly. A current challenge is to be able to handle this huge amount of sequences in order to build families of homologous sequences in a reasonable time. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 1 | 33% |
| Spain | 1 | 33% |
| United Kingdom | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 281 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 1% |
| France | 4 | 1% |
| United Kingdom | 3 | 1% |
| Sweden | 2 | <1% |
| Germany | 2 | <1% |
| China | 2 | <1% |
| Belgium | 2 | <1% |
| Switzerland | 1 | <1% |
| Greece | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 259 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 75 | 27% |
| Student > Ph. D. Student | 73 | 26% |
| Student > Master | 33 | 12% |
| Student > Bachelor | 23 | 8% |
| Professor > Associate Professor | 14 | 5% |
| Other | 38 | 14% |
| Unknown | 25 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 151 | 54% |
| Biochemistry, Genetics and Molecular Biology | 46 | 16% |
| Computer Science | 25 | 9% |
| Chemistry | 6 | 2% |
| Environmental Science | 5 | 2% |
| Other | 15 | 5% |
| Unknown | 33 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 22 June 2022.
All research outputs
#6,122,657
of 22,714,025 outputs
Outputs from BMC Bioinformatics
#2,323
of 7,260 outputs
Outputs of similar age
#33,724
of 109,148 outputs
Outputs of similar age from BMC Bioinformatics
#21
of 64 outputs
Altmetric has tracked 22,714,025 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,260 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 67% 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 109,148 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 68% of its contemporaries.
We're also able to compare this research output to 64 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 65% of its contemporaries.