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PLAST: parallel local alignment search tool for database comparison

Overview of attention for article published in BMC Bioinformatics, October 2009
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Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
57 Dimensions

Readers on

mendeley
95 Mendeley
citeulike
11 CiteULike
connotea
2 Connotea
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Title
PLAST: parallel local alignment search tool for database comparison
Published in
BMC Bioinformatics, October 2009
DOI 10.1186/1471-2105-10-329
Pubmed ID
Authors

Hoa Van Nguyen, Dominique Lavenier

Abstract

Sequence similarity searching is an important and challenging task in molecular biology and next-generation sequencing should further strengthen the need for faster algorithms to process such vast amounts of data. At the same time, the internal architecture of current microprocessors is tending towards more parallelism, leading to the use of chips with two, four and more cores integrated on the same die. The main purpose of this work was to design an effective algorithm to fit with the parallel capabilities of modern microprocessors.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 3 3%
Brazil 3 3%
Belgium 2 2%
Netherlands 1 1%
Italy 1 1%
Korea, Republic of 1 1%
Germany 1 1%
Canada 1 1%
Sweden 1 1%
Other 2 2%
Unknown 79 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 33 35%
Student > Ph. D. Student 18 19%
Student > Master 13 14%
Student > Bachelor 6 6%
Professor > Associate Professor 4 4%
Other 14 15%
Unknown 7 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 36 38%
Computer Science 20 21%
Biochemistry, Genetics and Molecular Biology 17 18%
Medicine and Dentistry 6 6%
Social Sciences 2 2%
Other 5 5%
Unknown 9 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 20 May 2014.
All research outputs
#7,452,489
of 22,783,848 outputs
Outputs from BMC Bioinformatics
#3,021
of 7,279 outputs
Outputs of similar age
#33,293
of 93,395 outputs
Outputs of similar age from BMC Bioinformatics
#27
of 59 outputs
Altmetric has tracked 22,783,848 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,279 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 50% 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 93,395 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.