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SeqAn An efficient, generic C++ library for sequence analysis

Overview of attention for article published in BMC Bioinformatics, January 2008
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Mentioned by

twitter
2 tweeters
googleplus
1 Google+ user

Citations

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256 Dimensions

Readers on

mendeley
303 Mendeley
citeulike
26 CiteULike
connotea
5 Connotea
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Title
SeqAn An efficient, generic C++ library for sequence analysis
Published in
BMC Bioinformatics, January 2008
DOI 10.1186/1471-2105-9-11
Pubmed ID
Authors

Andreas Döring, David Weese, Tobias Rausch, Knut Reinert

Abstract

The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome 1 would not have been possible without advanced assembly algorithms. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there is a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 303 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 8 3%
United States 8 3%
Brazil 3 <1%
Sweden 2 <1%
United Kingdom 2 <1%
Pakistan 1 <1%
Netherlands 1 <1%
Canada 1 <1%
France 1 <1%
Other 2 <1%
Unknown 274 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 86 28%
Researcher 76 25%
Student > Master 34 11%
Student > Bachelor 26 9%
Student > Doctoral Student 15 5%
Other 48 16%
Unknown 18 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 143 47%
Computer Science 65 21%
Biochemistry, Genetics and Molecular Biology 42 14%
Medicine and Dentistry 9 3%
Immunology and Microbiology 4 1%
Other 17 6%
Unknown 23 8%

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 02 April 2020.
All research outputs
#12,863,576
of 22,684,168 outputs
Outputs from BMC Bioinformatics
#3,779
of 7,252 outputs
Outputs of similar age
#127,024
of 156,197 outputs
Outputs of similar age from BMC Bioinformatics
#26
of 37 outputs
Altmetric has tracked 22,684,168 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,252 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
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 156,197 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
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 is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.