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Coupling SIMD and SIMT architectures to boost performance of a phylogeny-aware alignment kernel

Overview of attention for article published in BMC Bioinformatics, August 2012
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

twitter
3 tweeters

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
31 Mendeley
citeulike
2 CiteULike
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Title
Coupling SIMD and SIMT architectures to boost performance of a phylogeny-aware alignment kernel
Published in
BMC Bioinformatics, August 2012
DOI 10.1186/1471-2105-13-196
Pubmed ID
Authors

Nikolaos Alachiotis, Simon A Berger, Alexandros Stamatakis

Abstract

Aligning short DNA reads to a reference sequence alignment is a prerequisite for detecting their biological origin and analyzing them in a phylogenetic context. With the PaPaRa tool we introduced a dedicated dynamic programming algorithm for simultaneously aligning short reads to reference alignments and corresponding evolutionary reference trees. The algorithm aligns short reads to phylogenetic profiles that correspond to the branches of such a reference tree. The algorithm needs to perform an immense number of pairwise alignments. Therefore, we explore vector intrinsics and GPUs to accelerate the PaPaRa alignment kernel.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 6%
Sweden 2 6%
United Kingdom 1 3%
Unknown 26 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 32%
Researcher 8 26%
Student > Master 3 10%
Student > Postgraduate 2 6%
Professor 2 6%
Other 5 16%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 39%
Computer Science 9 29%
Biochemistry, Genetics and Molecular Biology 5 16%
Mathematics 1 3%
Environmental Science 1 3%
Other 1 3%
Unknown 2 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 11 August 2012.
All research outputs
#6,707,262
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#2,504
of 4,576 outputs
Outputs of similar age
#58,004
of 123,744 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 30 outputs
Altmetric has tracked 12,373,386 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 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 43rd percentile – i.e., 43% 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 123,744 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 30 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 70% of its contemporaries.