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CLAST: CUDA implemented large-scale alignment search tool

Overview of attention for article published in BMC Bioinformatics, December 2014
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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19 X users
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1 Facebook page
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1 Google+ user

Citations

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Title
CLAST: CUDA implemented large-scale alignment search tool
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0406-y
Pubmed ID
Authors

Masahiro Yano, Hiroshi Mori, Yutaka Akiyama, Takuji Yamada, Ken Kurokawa

Abstract

BackgroundMetagenomics is a powerful methodology to study microbial communities, but it is highly dependent on nucleotide sequence similarity searching against sequence databases. Metagenomic analyses with next-generation sequencing technologies produce enormous numbers of reads from microbial communities, and many reads are derived from microbes whose genomes have not yet been sequenced, limiting the usefulness of existing sequence similarity search tools. Therefore, there is a clear need for a sequence similarity search tool that can rapidly detect weak similarity in large datasets.ResultsWe developed a tool, which we named CLAST (CUDA implemented large-scale alignment search tool), that enables analyses of millions of reads and thousands of reference genome sequences, and runs on NVIDIA Fermi architecture graphics processing units. CLAST has four main advantages over existing alignment tools. First, CLAST was capable of identifying sequence similarities ~80.8 times faster than BLAST and 9.6 times faster than BLAT. Second, CLAST executes global alignment as the default (local alignment is also an option), enabling CLAST to assign reads to taxonomic and functional groups based on evolutionarily distant nucleotide sequences with high accuracy. Third, CLAST does not need a preprocessed sequence database like Burrows¿Wheeler Transform-based tools, and this enables CLAST to incorporate large, frequently updated sequence databases. Fourth, CLAST requires <2 GB of main memory, making it possible to run CLAST on a standard desktop computer or server node.ConclusionsCLAST achieved very high speed (similar to the Burrows¿Wheeler Transform-based Bowtie 2 for long reads) and sensitivity (equal to BLAST, BLAT, and FR-HIT) without the need for extensive database preprocessing or a specialized computing platform. Our results demonstrate that CLAST has the potential to be one of the most powerful and realistic approaches to analyze the massive amount of sequence data from next-generation sequencing technologies.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
Brazil 2 4%
United States 2 4%
Japan 1 2%
United Kingdom 1 2%
Unknown 49 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 21%
Researcher 12 21%
Student > Bachelor 8 14%
Student > Master 8 14%
Other 4 7%
Other 10 18%
Unknown 3 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 44%
Biochemistry, Genetics and Molecular Biology 9 16%
Computer Science 9 16%
Engineering 4 7%
Immunology and Microbiology 2 4%
Other 4 7%
Unknown 4 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 06 June 2019.
All research outputs
#2,630,629
of 22,774,233 outputs
Outputs from BMC Bioinformatics
#838
of 7,276 outputs
Outputs of similar age
#39,036
of 361,188 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 135 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,276 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 88% 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 361,188 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 89% of its contemporaries.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.