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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Faster Smith-Waterman database searches with inter-sequence SIMD parallelisation
|
|---|---|
| Published in |
BMC Bioinformatics, June 2011
|
| DOI | 10.1186/1471-2105-12-221 |
| Pubmed ID | |
| Authors |
Torbjørn Rognes |
| Abstract |
The Smith-Waterman algorithm for local sequence alignment is more sensitive than heuristic methods for database searching, but also more time-consuming. The fastest approach to parallelisation with SIMD technology has previously been described by Farrar in 2007. The aim of this study was to explore whether further speed could be gained by other approaches to parallelisation. |
X Demographics
The data shown below were collected from the profiles of 7 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 | 14% |
| Denmark | 1 | 14% |
| Japan | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 1% |
| Switzerland | 2 | 1% |
| France | 2 | 1% |
| Spain | 2 | 1% |
| Korea, Republic of | 1 | <1% |
| Sweden | 1 | <1% |
| India | 1 | <1% |
| Malaysia | 1 | <1% |
| China | 1 | <1% |
| Other | 3 | 2% |
| Unknown | 149 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 43 | 26% |
| Researcher | 30 | 18% |
| Student > Master | 20 | 12% |
| Professor > Associate Professor | 11 | 7% |
| Other | 10 | 6% |
| Other | 34 | 21% |
| Unknown | 17 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 49 | 30% |
| Agricultural and Biological Sciences | 47 | 28% |
| Biochemistry, Genetics and Molecular Biology | 24 | 15% |
| Engineering | 12 | 7% |
| Psychology | 2 | 1% |
| Other | 10 | 6% |
| Unknown | 21 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 07 November 2023.
All research outputs
#2,069,567
of 25,655,374 outputs
Outputs from BMC Bioinformatics
#445
of 7,734 outputs
Outputs of similar age
#8,930
of 122,698 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 99 outputs
Altmetric has tracked 25,655,374 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,734 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 particularly well, scoring higher than 94% 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 122,698 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.