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Introducing difference recurrence relations for faster semi-global alignment of long sequences

Overview of attention for article published in BMC Bioinformatics, February 2018
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  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#34 of 7,738)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

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Title
Introducing difference recurrence relations for faster semi-global alignment of long sequences
Published in
BMC Bioinformatics, February 2018
DOI 10.1186/s12859-018-2014-8
Pubmed ID
Authors

Hajime Suzuki, Masahiro Kasahara

Abstract

The read length of single-molecule DNA sequencers is reaching 1 Mb. Popular alignment software tools widely used for analyzing such long reads often take advantage of single-instruction multiple-data (SIMD) operations to accelerate calculation of dynamic programming (DP) matrices in the Smith-Waterman-Gotoh (SWG) algorithm with a fixed alignment start position at the origin. Nonetheless, 16-bit or 32-bit integers are necessary for storing the values in a DP matrix when sequences to be aligned are long; this situation hampers the use of the full SIMD width of modern processors. We proposed a faster semi-global alignment algorithm, "difference recurrence relations," that runs more rapidly than the state-of-the-art algorithm by a factor of 2.1. Instead of calculating and storing all the values in a DP matrix directly, our algorithm computes and stores mainly the differences between the values of adjacent cells in the matrix. Although the SWG algorithm and our algorithm can output exactly the same result, our algorithm mainly involves 8-bit integer operations, enabling us to exploit the full width of SIMD operations (e.g., 32) on modern processors. We also developed a library, libgaba, so that developers can easily integrate our algorithm into alignment programs. Our novel algorithm and optimized library implementation will facilitate accelerating nucleotide long-read analysis algorithms that use pairwise alignment stages. The library is implemented in the C programming language and available at https://github.com/ocxtal/libgaba .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 84 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 20%
Student > Bachelor 15 18%
Researcher 15 18%
Student > Master 7 8%
Other 5 6%
Other 8 10%
Unknown 17 20%
Readers by discipline Count As %
Computer Science 24 29%
Biochemistry, Genetics and Molecular Biology 22 26%
Agricultural and Biological Sciences 9 11%
Engineering 4 5%
Social Sciences 2 2%
Other 5 6%
Unknown 18 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 64. 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 September 2020.
All research outputs
#677,776
of 25,728,855 outputs
Outputs from BMC Bioinformatics
#34
of 7,738 outputs
Outputs of similar age
#15,266
of 345,223 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 93 outputs
Altmetric has tracked 25,728,855 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,738 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 99% 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 345,223 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 93 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.