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FSH: fast spaced seed hashing exploiting adjacent hashes

Overview of attention for article published in Algorithms for Molecular Biology, March 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#30 of 196)
  • Good Attention Score compared to outputs of the same age (73rd percentile)

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13 tweeters

Citations

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5 Dimensions

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9 Mendeley
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Title
FSH: fast spaced seed hashing exploiting adjacent hashes
Published in
Algorithms for Molecular Biology, March 2018
DOI 10.1186/s13015-018-0125-4
Pubmed ID
Authors

Samuele Girotto, Matteo Comin, Cinzia Pizzi

Abstract

Patterns with wildcards in specified positions, namelyspaced seeds, are increasingly used instead ofk-mers in many bioinformatics applications that require indexing, querying and rapid similarity search, as they can provide better sensitivity. Many of these applications require to compute the hashing of each position in the input sequences with respect to the given spaced seed, or to multiple spaced seeds. While the hashing ofk-mers can be rapidly computed by exploiting the large overlap between consecutivek-mers, spaced seeds hashing is usually computed from scratch for each position in the input sequence, thus resulting in slower processing. The method proposed in this paper, fast spaced-seed hashing (FSH), exploits the similarity of the hash values of spaced seeds computed at adjacent positions in the input sequence. In our experiments we compute the hash for each positions of metagenomics reads from several datasets, with respect to different spaced seeds. We also propose a generalized version of the algorithm for the simultaneous computation of multiple spaced seeds hashing. In the experiments, our algorithm can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.6[Formula: see text] to 5.3[Formula: see text], depending on the structure of the spaced seed. Spaced seed hashing is a routine task for several bioinformatics application. FSH allows to perform this task efficiently and raise the question of whether other hashing can be exploited to further improve the speed up. This has the potential of major impact in the field, making spaced seed applications not only accurate, but also faster and more efficient. The software FSH is freely available for academic use at: https://bitbucket.org/samu661/fsh/overview.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 33%
Student > Ph. D. Student 1 11%
Student > Bachelor 1 11%
Student > Master 1 11%
Student > Doctoral Student 1 11%
Other 1 11%
Unknown 1 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 44%
Computer Science 1 11%
Biochemistry, Genetics and Molecular Biology 1 11%
Engineering 1 11%
Unknown 2 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 23 May 2018.
All research outputs
#2,302,595
of 12,978,017 outputs
Outputs from Algorithms for Molecular Biology
#30
of 196 outputs
Outputs of similar age
#72,366
of 269,581 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 1 outputs
Altmetric has tracked 12,978,017 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 196 research outputs from this source. They receive a mean Attention Score of 2.9. 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 269,581 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 73% 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