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High-throughput sequence alignment using Graphics Processing Units

Overview of attention for article published in BMC Bioinformatics, December 2007
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)

Mentioned by

twitter
6 tweeters
patent
27 patents
facebook
1 Facebook page
wikipedia
4 Wikipedia pages

Citations

dimensions_citation
218 Dimensions

Readers on

mendeley
365 Mendeley
citeulike
34 CiteULike
connotea
3 Connotea
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Title
High-throughput sequence alignment using Graphics Processing Units
Published in
BMC Bioinformatics, December 2007
DOI 10.1186/1471-2105-8-474
Pubmed ID
Authors

Michael C Schatz, Cole Trapnell, Arthur L Delcher, Amitabh Varshney

Abstract

The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 365 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 28 8%
Brazil 8 2%
France 5 1%
Canada 5 1%
United Kingdom 5 1%
Germany 4 1%
Italy 3 <1%
Hong Kong 2 <1%
Sweden 2 <1%
Other 18 5%
Unknown 285 78%

Demographic breakdown

Readers by professional status Count As %
Researcher 107 29%
Student > Ph. D. Student 79 22%
Student > Master 50 14%
Professor > Associate Professor 28 8%
Professor 23 6%
Other 59 16%
Unknown 19 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 158 43%
Computer Science 104 28%
Biochemistry, Genetics and Molecular Biology 27 7%
Engineering 16 4%
Medicine and Dentistry 11 3%
Other 25 7%
Unknown 24 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 June 2021.
All research outputs
#2,885,846
of 21,435,803 outputs
Outputs from BMC Bioinformatics
#1,079
of 6,961 outputs
Outputs of similar age
#14,309
of 106,264 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 21,435,803 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,961 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 done well, scoring higher than 84% 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 106,264 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 86% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them