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X Demographics
Mendeley readers
Attention Score in Context
| Title |
kClust: fast and sensitive clustering of large protein sequence databases
|
|---|---|
| Published in |
BMC Bioinformatics, August 2013
|
| DOI | 10.1186/1471-2105-14-248 |
| Pubmed ID | |
| Authors |
Maria Hauser, Christian E Mayer, Johannes Söding |
| Abstract |
Fueled by rapid progress in high-throughput sequencing, the size of public sequence databases doubles every two years. Searching the ever larger and more redundant databases is getting increasingly inefficient. Clustering can help to organize sequences into homologous and functionally similar groups and can improve the speed, sensitivity, and readability of homology searches. However, because the clustering time is quadratic in the number of sequences, standard sequence search methods are becoming impracticable. |
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 % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 178 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 4 | 2% |
| United Kingdom | 4 | 2% |
| France | 3 | 2% |
| Brazil | 3 | 2% |
| United States | 2 | 1% |
| Cuba | 1 | <1% |
| Sweden | 1 | <1% |
| Israel | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Other | 4 | 2% |
| Unknown | 154 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 50 | 28% |
| Researcher | 40 | 22% |
| Student > Master | 25 | 14% |
| Student > Bachelor | 21 | 12% |
| Professor > Associate Professor | 11 | 6% |
| Other | 18 | 10% |
| Unknown | 13 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 79 | 44% |
| Computer Science | 33 | 19% |
| Biochemistry, Genetics and Molecular Biology | 30 | 17% |
| Chemistry | 5 | 3% |
| Mathematics | 3 | 2% |
| Other | 14 | 8% |
| Unknown | 14 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 29 July 2021.
All research outputs
#3,928,240
of 24,172,513 outputs
Outputs from BMC Bioinformatics
#1,373
of 7,508 outputs
Outputs of similar age
#32,412
of 200,587 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 70 outputs
Altmetric has tracked 24,172,513 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,508 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 81% 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 200,587 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 70 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 72% of its contemporaries.