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Mendeley readers
Attention Score in Context
| Title |
PLAN: a web platform for automating high-throughput BLAST searches and for managing and mining results
|
|---|---|
| Published in |
BMC Bioinformatics, February 2007
|
| DOI | 10.1186/1471-2105-8-53 |
| Pubmed ID | |
| Authors |
Ji He, Xinbin Dai, Xuechun Zhao |
| Abstract |
BLAST searches are widely used for sequence alignment. The search results are commonly adopted for various functional and comparative genomics tasks such as annotating unknown sequences, investigating gene models and comparing two sequence sets. Advances in sequencing technologies pose challenges for high-throughput analysis of large-scale sequence data. A number of programs and hardware solutions exist for efficient BLAST searching, but there is a lack of generic software solutions for mining and personalized management of the results. Systematically reviewing the results and identifying information of interest remains tedious and time-consuming. |
Mendeley readers
The data shown below were compiled from readership statistics for 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| Germany | 2 | 2% |
| Mexico | 2 | 2% |
| Norway | 2 | 2% |
| Italy | 1 | 1% |
| Brazil | 1 | 1% |
| Portugal | 1 | 1% |
| France | 1 | 1% |
| Sweden | 1 | 1% |
| Other | 2 | 2% |
| Unknown | 77 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 39 | 42% |
| Student > Ph. D. Student | 21 | 23% |
| Professor > Associate Professor | 7 | 8% |
| Student > Master | 7 | 8% |
| Professor | 5 | 5% |
| Other | 10 | 11% |
| Unknown | 4 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 59 | 63% |
| Biochemistry, Genetics and Molecular Biology | 8 | 9% |
| Computer Science | 5 | 5% |
| Medicine and Dentistry | 4 | 4% |
| Engineering | 3 | 3% |
| Other | 10 | 11% |
| Unknown | 4 | 4% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 02 November 2011.
All research outputs
#5,500,307
of 22,649,029 outputs
Outputs from BMC Bioinformatics
#1,994
of 7,234 outputs
Outputs of similar age
#32,588
of 160,465 outputs
Outputs of similar age from BMC Bioinformatics
#12
of 37 outputs
Altmetric has tracked 22,649,029 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,234 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 71% 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 160,465 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 79% of its contemporaries.
We're also able to compare this research output to 37 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 62% of its contemporaries.